NATIONAL IODINE SURVEY REPORT GHANA 2015 · 2019. 8. 29. · National Iodine Survey Report, Ghana 2015 5 Iodine is among the important micronutrients essential for proper growth and

June 2017

NATIONAL IODINE SURVEY REPORT GHANA 2015

June 2017

NATIONAL IODINE SURVEY REPORT GHANA 2015

2 National Iodine Survey Report, Ghana 2015

Contents

Abbreviations ............................................................................................................................4Foreword ..................................................................................................................................5Acknowledgement………………………… ................................................................................7

EXECUTIVE SUMMARY .........................................................................................................8Background ..............................................................................................................................8Objectives of the survey ...........................................................................................................9Survey Design ..........................................................................................................................9Main Results ...........................................................................................................................10Summary Discussion Points ..................................................................................................12Recommendations .................................................................................................................16

INTRODUCTION ...................................................................................................................17Background ............................................................................................................................17Rationale ................................................................................................................................19Objectives of the survey .........................................................................................................20

METHODOLOGY ...................................................................................................................21Overview of Study Organisation and Procedures ..................................................................21Survey Population ..................................................................................................................21Sample Size Determination and Sampling Procedure ...........................................................21Household and WIFA Unique Identifiers ................................................................................24Overview of Survey Planning and Implementation Phases ....................................................24The Questionnaire – Overview of Questionnaire Modules .....................................................26Recruitment and Training of Listing Teams and Enumerators ................................................28Survey Field Work Preparation ...............................................................................................31Survey Field Work ..................................................................................................................31Laboratory analysis of iodine in salt and urine samples ........................................................35Limitations and Constraints ...................................................................................................35

RESULTS ...............................................................................................................................36Survey Sample Characteristics ..............................................................................................36Household salt iodine .............................................................................................................40Awareness and Knowledge of Iodine Deficiency and Iodised Salt ........................................46Iodised salt purchasing practices and preferences................................................................52Multiple variable regression analyses for household salt iodine ............................................56Iodine status ...........................................................................................................................60Estimated salt intake from household salt and key processed foods ....................................62Multiple variable regression analyses for urinary iodine concentration ..................................69Edible Oil Use .........................................................................................................................73


DISCUSSION .........................................................................................................................74Survey sample characteristics ...............................................................................................74Household access to iodised and adequately iodised salt ....................................................75Respondent awareness and knowledge of iodine deficiency and iodised salt ......................80Iodised salt purchasing practices and salt characteristics ....................................................81Iodine Nutrition among WIFA .................................................................................................82Estimated Intake of Salt from Household Salt and Processed Foods among WIFA ..............84Factors associated with Iodine Status ...................................................................................85

RECOMMENDATIONS ..........................................................................................................88

APPENDICES ........................................................................................................................90Appendix 1: MPI indicator definitions ....................................................................................90Appendix 2: Survey Questionnaire .........................................................................................95Appendix 3: Data management and analysis document .....................................................115Appendix 4: Laboratory Quality Assurance – Process and Outcomes ................................130Appendix 5: GPS mapping of PSUs included in the survey ................................................131Appendix 6: Distribution of salt iodine level for samples with under 15ppm iodine ............133Appendix 7: Reported food frequency intake for bouillon, tomato paste & instant noodles 135Appendix 8: Edible Oil Use ...................................................................................................139


EA Enumeration Area

GAIN Global Alliance for Improved Nutrition

GDHS Ghana Demographic Health Survey

GHS Ghana Health Service

GSS Ghana Statistical Service

HH Household

IDD Iodine Deficiency Disorders

MPI Multi-dimensional Poverty Index

NMIMR Noguchi Memorial Institute for Medical Research

PSU Primary Sampling Unit

RTK Rapid Test Kit (for assessing salt iodine)

SAC School Age Children

SSC Statistical Services Centre

(University of Reading, United Kingdom)

UIC Urinary Iodine Concentration

UNICEF United Nations Children’s Fund

USI Universal Salt Iodisation

WIFA Women In Fertile Age

Abbreviations


Iodine is among the important micronutrients essential for proper growth and development. Indeed Iodine deficiency is said to be the single greatest cause of preventable mental impairment globally and also causes many other adverse effects on development and productivity. Specific consequences of iodine deficiency may include poor pregnancy outcomes, poor school performance, poor eye-hand coordination, deaf-mutism, and cretinism. At the population level, the resultant lower productivity could affect many people with devastating effects to the national economy.

In 2011, it was estimated that, in Ghana, about 500,000 children could be at risk of permanent brain damage due to Iodine Deficiency Disorders (IDD). Improving iodine intake in deficient populations can improve IQ by 13 percent points and the economic gains of improving iodine nutrition could amount to 433 million Ghana Cedis over a ten-year period.

The recommended control measure proven to effectively and inexpensively prevent IDD is by iodizing all salt for human and animal consumption known as Universal Salt Iodization (USI). Salt iodisation has been the major approach in the country to ensure adequate intake of iodine. Indeed Ghana has been implementing salt iodization following a survey conducted in 1992 that established that 33% of districts had significant iodine deficiency disorders and required urgent action. This is backed by the Food and Drugs Law Amendment Act (Act 523) passed in December 1996, and subsequently by the Public Health Act 851 (2012) making provision for the mandatory fortification of all salt for human and animal consumption.

Available data from MICS and DHS indicates that household consumption of any iodised salt has increased over time, however, only a small proportion are using adequately iodised salt.

Since 2008, national efforts to achieve USI have been given a lot of financial and technical push with special focus on strengthening communication to raise awareness about IDD and iodised salt whilst intensifying enforcement to improve salt quality. The need to assess progress forms the rationale behind a comprehensive survey that has generated very interesting results.

This survey report, which used the recommended methods for assessing iodine nutrition suggests the iodine status of women in fertile age (WIFA) appears to be adequate an indication of a major nutrition achievement for the country. The unanswered question though is could there possibly be other sources of dietary iodine besides iodised salt found in households that together are resulting in sufficient iodine intake in the population?

These findings are expected to shape the program direction of the national IDD control efforts in the coming years and will inform government, salt producers, and food manufacturers, and

Foreword


other key stakeholders as they implement measures for ensuring that iodine nutrition stays within the optimal levels. This is of utmost importance, as their collective actions will contribute to making all children in Ghana and indeed the whole population healthy and protected against iodine deficiency disorders and its serious consequences.

Dr. Anthony Nsiah-AsareThe Director GeneralGhana Health Service


The conduct of the Ghana Iodine Survey was a truly collaborative effort between the Ghana Health Service (GHS), UNICEF, the Global Alliance for Improved Nutrition (GAIN) and other key stakeholders and we appreciate their contribution and support to the various stages of survey. The survey was conducted by the Nutrition Department of the Ghana Health Service led by Ms Esi Foriwa Amoaful and the national coordinating task team comprising Ms Lillian Selenje and Ms Theodocia Ofosu-Appeah both of UNICEF Ghana, Ms Gifty Ampah, Ms Josephine Asante and Dr Patrick Aboagye all of GHS with technical assistance from GAIN consultant Dr Jacqueline Knowles. We are particularly grateful for their invaluable contribution to the data tools development, training, coordinating fieldwork and data analysis and report writing.

We wish to thank the Ghana Statistical Service (GSS) team for the work of sampling and demarcation of enumeration Areas. We are indeed grateful to all the data collectors from Ghana Health Service and the Ghana Statistical Service. The hard work of national, regional and district level staff of GHS who participated in the field-work is greatly appreciated.

We will like to acknowledge the work of Dave Mills and Cathy Garlick both of the Statistical Services Centre University of Reading for hosting the data and for analysis of the data.

We greatly appreciate support from the Department of Nutrition and Food Science of the University of Ghana for analysing the salt and urine samples. The GAIN/UNICEF Partnership and Bill and Melinda Gates Foundation funded the survey including all fieldwork and data analysis and the support is highly appreciate. We also thank the Canadian Government for the funds through UNICEF facilitated for the preparation of the final report.

Acknowledgement


BackgroundIodine deficiency is the single greatest cause of preventable mental impairment globally that also causes many other adverse effects on growth and development due to inadequate thyroid hormone production1. Many of these iodine deficiency disorders (IDD) result from the effects of iodine deficiency on foetal brain development during early pregnancy2. Iodine deficiency can be effectively and inexpensively prevented by iodising all salt for human and animal consumption (known as Universal Salt Iodization, USI)3. Internationally, the main indicator for having achieved USI is that 90% or more of households nationally have access to salt with at least 15ppm iodine and iodine deficiency is defined as a population median urinary iodine UIC <100 µg/L among school age children or non-pregnant women in fertile age4.

In Ghana, a 1992 nationwide survey demonstrated that IDD existed in all areas, however was particularly severe in the Upper East and Upper West regions5,6. Based on the public health importance of these findings, the first standard for iodisation of household salt in Ghana was adopted in 19967. Salt iodisation standards were reviewed in 2006 and the current standard (part of the Public Health Act 851, 2012) mandates that salt should contain at least 50ppm iodine during production to achieve a minimum of 25 ppm iodine at the retail and 15 ppm at household levels8. Subsequent to the law mandating salt iodisation, household use of salt with at least some iodine gradually increased from 27.1% in 19989 to 55.0% in 200610 and to 100% in 201111, although for many samples the level of iodine was sufficiently low (8ppm of less) to have possibly resulted from naturally occurring iodine12.

A national survey in 2009-1013 that used quantitative salt iodine assessment reported that 47.8% of households were using adequately iodised salt and 16.5% of households were using salt with no added iodine (<5ppm). The use of salt with some added iodine (>5ppm) was lowest in the Southern zones (76.5% of households) and highest in the Northern zone (91%). However population iodine status among school age children (SAC) and women in fertile age (WIFA) was better in the South (255 µg/l SAC and 180 µg/l WIFA) than the North, where the population remained iodine deficient (as defined by a population median UIC <100 µg/l): median 79µg/l SAC and 85 µg/l WIFA. This raised questions about potential other sources of dietary iodine in the South, either through the use of iodised salt in food industry products, through other dietary sources and/or through the influence of ground water iodine levels (known to be higher in the South than the North)14.

1. http://www.who.int/nutrition/topics/idd/en/2. Delange F. 2001. Iodine deficiency as a cause of brain damage. Postgrad Med J; 77:217–220 Editorial.3. Iodine and Health. Eliminating iodine deficiency disorders safely through salt iodization. 1994. A statement by the World Health Organization4. WHO 2007. Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers. WHO, UNICEF, ICCIDD 5. http://www.tulane.edu/~internut/Countries/Ghana/ghanaiodine.html [accessed March 2016]6. http://www.modernghana.com/news/216727/1/iodine-deficiency-disorders-rate-high-in-upper-wes.html7. The Food and Drugs Law Amendment Act (Act 523) December 19968. Ghana Standards Board GS 154 3rd edition9. Ghana Statistical Service (GSS) and Macro International Inc. (MI). 1999. Ghana Demographic and Health Survey 1998. Calverton, Maryland: GSS and MI.10. Multiple Indicator Cluster Survey 2006. Republic of Ghana Ministry of Health and Statistical Service Ghana, MEASURE DHS/Macro International Inc. Calverton, Maryland, USA and UNICEF.11. Ghana MICS Final Report. Ghana Statistical Service 2011. Table Nu11 page 55 (quantitative – titration – analysis method)12. Proceedings of the national workshop on iodine deficiency disorders in Ghana 1994. Edited by E. ASIBEY-BERKO, R. ORRACA-TETTEH. The iodine naturally present in salt samples collected from salt traders in the 12 districts had a mean iodine content of 7.9ppm13. Unpublished survey report Ghana Health Services 201014. Groundwater Quality: Ghana. British Geological Survey, WaterAid. https://www.bgs.ac.uk/home.html

Executive Summary


The Government of Ghana, represented by Ministry of Health, Ghana Health Service, and Ministry of Trade and Industry with support from UNICEF, GAIN and other partners, recently engaged in a process to assess, adjust and revitalise the national programme for elimination of iodine deficiency through USI. The resulting new national strategy, USI III15, recommends focusing efforts to improve access to adequately iodised salt within the salt producing areas of the country, where there are multiple small scale salt producers. It is well documented that quality assurance and enforcement of adequately iodised salt production by small-scale producers is a challenge to achieving USI. Non-iodised salt is most likely to leak into the surrounding market place, or be obtained directly by local households, without having undergone iodisation.

The 2015 Ghana Iodine Deficiency Survey was, therefore, designed as a nationwide cross-sectional survey with the main aim being to provide information on the percentage of households using adequately iodised salt and about iodine status among women in fertile age. The survey design also allowed for representative data to be obtained for the USI III focus area (small scale salt producing areas in the south) that could then be used as a baseline to monitor implementation of the strategy.

Objectives of the survey The specific objectives of the survey were to obtain data for the following indicators, representative for the national level and for four strata: North, Middle, South (salt producing) and South (non-salt producing) zones; to provide information to guide policy and programme and to provide baseline data for the targeted activities of the revised USI strategy:1. To estimate the proportion of households with iodised and adequately iodised salt;2. To estimate the awareness and knowledge of the population regarding iodine deficiency

and iodised salt;3. To estimate the proportion of household salt purchased in recommended packaging and

clearly labelled as iodised;4. To examine whether knowledge about IDD and iodised salt, and/or socio-demographic

related factors were associated with the level of iodine in household salt;5. To estimate the proportion of households regularly consuming bouillon-type condiments,

instant noodles and tomato paste/concentrate (as potential sources of iodised salt);6. To estimate the prevalence of iodine deficiency among women in fertile age (WIFA);7. To examine iodine status among WIFA by categorisation of the level of iodine in household

salt, frequency of consumption of target salt-containing foods, and by socio-demographic factors.

Survey DesignThe survey was conducted as stratified, cross-sectional cluster survey. The total target sample was 2,112 household interviews. The survey tool included modules to determine: household multi-dimensional poverty index (MPI) score16, knowledge and awareness of iodine deficiency and iodised salt, practices related to household salt purchasing and use, and frequency of consumption of key processed foods thought to contribute to salt intake across consumer groups. The survey sample size was calculated with the expectation of collecting approximately

15. Government of Ghana 2016. Universal Salt Iodisation Strategy III and Action Plan 2016-2020 16. Alkire S, Santos ME 2013. Acute Multidimensional Poverty - A New Index for Developing Countries http://www.ophi.org.uk/wp-content/uploads/ophi-wp-591.pdf


2,000 salt samples for quantitative analysis of iodine by titration, and 1,900 urine samples from WIFA (with a small proportion of these from pregnant WIFA) for analysis of iodine content based on the Sandell-Kolthoff reaction. The sample was taken from a total of 128 primary sampling units (PSUs), 32 selected based on proportional to population size within each of the four strata. Sixteen households were selected using systematic random selection from each PSU, without replacement.

All WIFA in all households were asked to consent to provide a urine sample and were asked about their pregnancy status. The final dataset was weighted to account for the probability of household selection. The survey was conducted during January and February 2015 by field teams from Ghana Health Services (GHS). The process was supervised and quality controlled by senior GHS staff and the performance of laboratory analysis of both salt and urinary iodine was assured through an external quality assurance process. The Ghana Health Service research ethical review committee approved the study.

Main ResultsThe 2015 Ghana Iodine Survey found the following key findings:

Household coverage iodised salt1. Nationally, 29.3% of households were using adequately iodised salt, a decrease from

2010. 2. Household coverage for salt with any iodine was 61.9%. 3. A higher percent of households in the North (37.6%) and South non-salt producing (48.6%)

strata were found to be using adequately iodised salt than in the Mid (18.6%) and South salt-producing (19.3%) strata.

4. Approximately one third of households in the North and South-non-salt-producing areas were accessing salt with over 40ppm iodine and the mean iodine content of household salt with any added iodine in these two areas was 40-45ppm.

5. An analysis of salt iodine content by brand found median iodine content of 73ppm for one leading brand, reportedly used by 312 households.

6. Households with high MPI (more deprived) were significantly less likely to use adequately iodised salt than households with low MPI (21.5% and 36.2%).

7. There was a tendency towards higher household coverage with adequately iodised salt in urban areas when compared with rural areas, however the difference was not significant.

Knowledge and awareness of iodine deficiency and iodised salt8. Awareness of iodised salt (81.7%) was higher than awareness of iodine deficiency (40.2%)

nationally.9. Having heard of iodine deficiency or iodised salt were significantly associated with

household use of adequately iodised salt.10. Nationally, only a third of respondents reported looking for iodised salt at the point of

purchase. Respondents who reported looking for iodised salt at the point of purchase were significantly more likely to use adequately iodised salt in their household (65.3% of households compared with 14.3% of households who did not report looking for iodised salt at purchase).

11. Households using salt bought in a sealed package (about a third of all households) was found to be the factor most significantly associated with salt iodine content across all


strata (59.0% of these households were using adequately iodised compared with 15.0% of households buying loose salt).

Factors most strongly associated with household salt iodine12. Multiple variable regression analyses indicated that salt iodine content was most significantly

associated with strata, the respondent looking for iodised salt at the point of purchase, and by salt grain type (p < 0.001 for all).

Iodine status 13. Iodine status had improved substantially since the 2009-10 survey, the national median

UIC among non-pregnant WIFA was 201.6µg/L (range for optimal intake among school-age children 100-299µg/L17).

14. The population of WIFA in the North were no longer iodine deficient, with a median UIC of 166.9µg/l.

15. The sample size (n = 103) for analysed urines from pregnant women was too low to draw definite conclusions, however the median of 183. 5µg/L for this group also indicated adequate iodine intake (a median UIC above 150µg/L indicates adequacy18).

Estimated salt intake from household salt and key processed foods16. The approximate national median salt intake (for household salt only) for WIFA was

estimated to be 5.0g. The estimated median varied from 4.7g in the North to 5.2g in the South-non-salt-producing strata. Intake among WIFA in rural areas appeared to be lower (4.6g) than among WIFA in urban areas (5.1g).

17. Consumption of bouillon was frequent and widespread throughout Ghana. At the national level, over 80% of non-pregnant WIFA respondents reported to consume bouillon at least once a week, with approximately half (48.8%) reporting to consume it at least 6 times a week. 58.9% of WIFA in the North reported consuming bouillon at least 11 times a week.

18. Tomato paste was consumed with a frequency similar to that for bouillon throughout Ghana, although consumption by strata was different. Over 80% of non-pregnant WIFA respondents said that they consumed tomato paste at least once a week, with 43.0% reporting to consume it at least 6 times a week.

19. WIFA in urban areas were more likely to have consumed tomato paste during the previous week than WIFA in rural areas (86.3% and 76.1% of WIFA respectively). WIFA in low MPI (non-deprived) households were more likely to have consumed tomato paste during the previous week (86.3%) than WIFA from high MPI households (78.4%).

20. Reported consumption of instant noodles by WIFA was relatively low with 80.0% of non-pregnant WIFA reporting no consumption of this product in the previous week.

21. The estimated median and mean total salt intake from combined consumption of bouillon, tomato paste and instant noodles during the previous week were 5.4g and 7.5g respectively.

22. There was a notably higher intake of salt from these products among WIFA in the North (median 9.3g, mean 10.1g).

23. No association was found between the combined level of salt intake from these foods and median UIC for non-pregnant WIFA.

24. The national estimate of combined median daily intakes for WIFA of salt from household

17. Zimmermann M., et al.2013 Thyroglobulin Is a Sensitive Measure of Both Deficient and Excess Iodine Intakes in Children and Indicates No Adverse Effects on Thyroid Function in the UIC Range of 100–299 g/L: A UNICEF/ICCIDD Study Group Report. J Clin Endocrinol Metab, March 2013, 98(3)18. WHO 2007. Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers. WHO, UNICEF, ICCIDD


salt and salt from the three processed foods was 5.8g salt/day. This varied from 5.6g/day in the Mid stratum to 6.0g/day in the North stratum. It should be noted that these are crude approximations and further data analysis and additional studies would be needed to refine the outcomes.

Factors most strongly associated with urinary iodine concentration (non-pregnant WIFA)25. Multiple variable regression analyses indicated that strata and salt iodine content were the

factors most strongly associated with UIC.26. Other factors that showed a positive association with UIC were urban residence, and

increased intake of tomato paste.

Summary Discussion PointsOverall, the data from the 2015 national iodine survey in Ghana showed that in all the four survey strata, the population of non-pregnant WIFA had adequate iodine nutrition, despite the relatively low household access to adequately iodised salt. Importantly, the limited data available for pregnant women suggest adequacy among this population group also. These findings represent a major public nutrition achievement for Ghana. This significant improvement in iodine nutrition notwithstanding, the unanswered question is the possible sources of iodine in the Ghanaian diets given the relatively low household access to adequately iodised salt reported in this survey. This has important policy implication and programmatic importance.

Information reported for consumption of key foods and condiments expected to be contributing to dietary salt intake indicate that iodisation of all salt used to produce bouillon and tomato paste (i.e. USI) would contribute significantly to dietary iodine intake. However the survey data do not provide strong evidence that salt used in bouillon production was iodised at the time of the survey.

Household Coverage Iodised Salt The main concerns related to the survey results were that: • Less than a third of all households nationally were using adequately iodised salt

(>15ppm).• HalfofallhouseholdsintheSouth-salt-producingareaandnearlyhalfofhouseholdsinthe

Mid area were using non-iodised salt (<5ppm). • Thequalitycontrolofsaltiodisationappearsweak,withonly8.6%ofthenationalsample

of households using salt with iodine in the WHO recommended range of 15-40ppm for household salt. Both high and low salt iodine levels were common.

Lowering the national standards and providing a range of acceptable iodine content for edible salt at production is strongly recommended, as well as regulating that change to make sure it is followed. Availability (and/or affordability) of salt with different characteristics and of packaged salt appear to be different by strata, with indication that there is: • Arelativelyheterogeneoussupplyofsaltofvariedgraintypeandahigherpercentoflarge,

coarse grain salt in the North, with around 40% of all salt packaged.• ArelativelyhighsupplyoffinegrainsaltintheMidarea,withasurprisinglylowproportion

(about a quarter) of all salt types combined being packaged.


• A fairly homogenous supply of finer grain salt to the South-non-salt-producing areas,around 46% of which was packaged.

• ApredominanceofcoarsergrainsaltintheSouth-salt-producingareas,withthemajorityof small coarse grain type. Just over a third (37%) of all salt in this area was packaged.

Whether household salt reflects available market supply and/or affordability, is not possible to determine from the survey data alone.

Caution about the validity of comparisons with previous surveys with different sample design should be considered, however it appears that national level household coverage of adequately iodised salt has decreased from the findings of the 2009-10 survey (47.8% of households19) but has the same level of coverage as the quantitative salt iodine results from the 2011 MICS data: 29.3%20.

These trends towards reduced household coverage of adequately iodised salt indicate possible decreased Government attention to regulatory monitoring and enforcement of iodised salt production and distribution. It could also be partially related to a decrease in supply, and increase in cost, of potassium iodate during the period prior to the survey.

Knowledge and AwarenessAwareness of iodised salt, nationally and within each stratum, was high, possibly reflecting the range of communication activities, which have been a core component of the national strategy to achieve optimal iodine nutrition to date. The lower awareness of iodine deficiency, especially among respondents in the North and in deprived households, could have been at least partly due to differing recognition/understanding of the phrase “iodine deficiency” and how it was translated into local languages.

The 2014 GDHS included questions to determine knowledge of iodised salt and its perceived benefits. The level of awareness of iodised salt (86.9% of women questioned) was comparable to results from this 2015 survey (81.7%).

The source of information about iodised salt was found to vary significantly by strata, however the specific source did not appear to affect the level of awareness of iodised salt among the respective respondent populations.

Iodised salt purchasing practices and salt characteristicsData on iodised salt purchasing practices, together with salt grain type, provide sources of highly useful information for strategy development. Some of the strongest positive single variable associations with household access to adequately iodised salt were seen where households had: • Obtainedsaltinasealedpackage• Obtainedsaltwithabrandnameand/oraniodinelabelorlogo(wherepackaged)• Lookedforiodisedsaltatthepointofpurchase.

However, only approximately a third of the survey sample reported to obtain salt in a sealed package and the same proportion reported to look for iodised salt at the point of purchase,

19. Unpublished survey report Ghana Health Services 201020. Ghana MICS Final Report. Ghana Statistical Service 2011


facts which require further investigation and strategic follow up to improve packaging and labelling practices. These indicators were particularly low in the Mid stratum, in rural areas and among deprived households. In addition to the above, if the iodisation quality of all edible salt was assured, then packaging and purchasing practices would no longer be associated with household salt iodine level and access to adequately iodised salt would be more equitable.

The multiple variable regression indicated a high level of overlap between packaging, logo/label and brand with grain type, with grain type the only factor remaining strongly significant.

Iodine Nutrition among WIFAThe median UIC of 201.6µg/L indicates that optimal iodine status had been achieved among the population of WIFA in Ghana, at the national level and in all strata included for this survey, including the Northern region where historically iodine deficiency has been endemic. In addition, the limited data available for pregnant women suggest adequacy among this population group, which is a major public nutrition achievement for Ghana. Further validation of iodine status among pregnant women in different regions of the country, including representative assessment in the Northern region, would help to confirm that deficiency no longer exists in any population group in the country.

Bearing in mind the constraints to direct comparison of results from surveys based on different sample frames and design, it would still be considered valid to state that iodine status has improved among WIFA in all areas of the country since the 2009-2010 survey. A particular achievement is moving from iodine deficient (85 µg/L in 2010) to adequate (168 µg/L in 2015) status in the North.

The fact that these improvements in status have been observed despite the low household coverage with iodised salt, indicates that other sources of dietary iodine, additional to iodised household salt, may now be available to a large proportion of WIFA. Further studies will be required to better inform future policy and programming in relation to this.

In the South-non-salt-producing stratum, the median UIC (317.4µg/L) indicates iodine intake above requirement21. The tolerable upper intake level for iodine intake among adults is, however, considerably higher than this22, indicating that the median UIC observed among WIFA in the South-non-salt-producing areas of Ghana does not indicate a cause for serious concern about iodine excess at this point. The situation should be monitored periodically, to check that it does not increase further in this region or in other regions.

At the national level, the median UIC of 313.0µg/L for WIFA in households using salt with iodine >40ppm reinforces the recommendation in this report to reduce the standards for iodine in edible salt and establish an upper limit for salt iodine level at production, in line with that proposed by ECOWAS.

Estimated Intake of Salt from Household Salt and Processed Foods among WIFAEstimates for intake of household salt were very approximate and were only made for the intake of household salt, therefore they do not account for any other sources of salt in the diet.

21. Zimmermann M., et al.2013 Thyroglobulin Is a Sensitive Measure of Both Deficient and Excess Iodine Intakes in Children and Indicates No Adverse Effects on Thyroid Function in the UIC Range of 100–299 g/L: A UNICEF/ICCIDD Study Group Report. J Clin Endocrinol Metab, March 2013, 98(3) (whether the same upper cut off applies for WIFA is under investigation) 22. European Commission/Scientific Committee on Food recommends no more than 600µg/day while the US Institute of Medicine recommends a tolerable maximum of 1100 µg/day


Despite the relatively crude assessment methodology, the median calculated intake estimates were fairly similar across all sub-groups with a range from 4.6 to 5.2g/pers/day, equivalent to 1.8 to 2.1g sodium/pers/day. The following need to be considered in the interpretation of these intake estimates:• Thesedataareforhouseholdsaltanddonotaccountforothersourcesofdietarysaltsuch

as processed foods and condiments (see below) or foods prepared outside the home.• Itisthoughtthatonlyapproximatelyaquarterofcookingsaltactuallyentersthediet,after

salt losses from cooking water etc. are considered.23

Estimates for intake of salt from processed foods: bouillon, tomato paste and instant noodles; were again approximations. The methodology was the same across the country, which therefore, allows for reasonable comparison of intakes between different sub-groups. Overall, the intake of salt from the three processed food products included in the survey was particularly high among WIFA in the North (the stratum with the most deprived households in the survey).

The relatively high consumption of processed food/condiment (non-household) sources of salt, particularly in the North of the country, provides an evidence base to advocate for the enforcement of legislation on the use of quality-assured iodised salt in the food industry. This would provide greater equity in access to iodised salt and its benefits, regardless of dietary preference for household or food industry sources of salt. The inclusion of food industry salt in USI enforcement and advocacy could be implemented in tandem with salt reduction efforts; adjusting the level of salt iodine to account for eventual reduced overall salt intake.

The results for median UIC by estimated salt intake from processed foods and from the multiple regression analysis of expected UIC with increasing consumption of processed foods, suggest that salt used in the products assessed in the survey may not be iodised, or is not consistently iodised. The only product showing some linear relationship between increased intake and increasing expected UIC was tomato paste. This may be a result of communication activities directed towards all key players in the salt trade chain, including food industry. Industry practices should be investigated to see whether tomato paste companies were already using iodised salt in production prior to the survey, to better interpret this finding.

Factors associated with Iodine Status The multiple variable regression analysis helps to determine which of the factors included in the survey were most highly associated with expected UIC levels when other factors were held constant. The two variables found to be highly significantly associated with UIC among WIFA after multiple regression analysis were strata and household salt iodine content. These associations are expected due to: a) the known differences in ground water iodine by strata, i.e. the rock type found in the Northern region and parts of the Mid region are associated with low iodine24; and b) the expected effect on iodine status of increasing dietary iodine intake through salt iodisation.

The very strong association between household access to salt with at least 15ppm iodine and iodine status indicates that, where salt iodisation is being well-implemented, it is a major factor behind the improvement in population iodine status. At the same time, the fact that

23. James WPT, Ralph A, Sanchez-Castillo CP. 1987. The dominance of salt in manufactured food in the salt intake of affluent societies. The Lancet , Volume 329 , Issue 8530 , 426 - 42924. Groundwater Quality: Ghana. British Geological Survey, WaterAid. https://www.bgs.ac.uk/home.html


adequate iodine status was found among WIFA in non-deprived households not accessing adequately iodised salt, indicates that in some areas there are other dietary sources of iodine than household salt, especially in the South. These cannot be directly determined from the survey data, however, likely additional sources of iodine are:• NaturalsourcesbasedonhighergroundwateriodineintheSouth• Increased access to environmental iodine (generated fromdifferent human activities in

urban environments25)• Potentiallyfromtheintakeofprocessedfoodsandcondiments,orfoodspreparedaway

from home, that are made with iodised salt26

• Naturally occurring iodine in thewider range of food products that are generallymoreaccessible to urban, non-deprived, households27, and/or to households consuming higher amounts of marine fish products. However, the dietary diversity for WIFA in this survey was not significantly associated with UIC (data not shown), which suggests that access to a wider range of food products alone is not closely related to dietary sources of iodine.

The findings of this survey provide firstly, a reason to celebrate the public nutrition success in achieving optimal iodine status among WIFA across all the major regions of Ghana, and secondly, a reinforcement of the important sub-national differences in household access to adequately iodised salt, in iodine status among WIFA, and in salt-supply related factors associated with both.

Assuming Ghana is close to achieving optimal iodine nutrition among all groups, which appears to be the case, the goal should now change to sustaining this achievement and ensuring that iodine intake does not increase above the optimal range for any group. Enforcing the quality of iodisation of all edible salt, including coarse grain household salt and food industry salt, will be a first step to ensuring equity of access to iodine across population groups. In parallel with this, it is important for regulations to establish the appropriate salt iodine level to sustain optimal, rather than more than optimal, iodine intake

Recommendations based on the findings of this 2015 survey are as follows:1. In order to benefit the entire population appropriately, continue plans to implement the USI

strategy III and in particular the first three key strategies related to improvement of salt industry practices and enforcement.

2. It is strongly recommended to lower the national salt iodine standard and establish a range of acceptable potassium iodate/iodine values for all domestically-produced and imported salt. This would provide easily understood and enforceable guidance across the salt industry and prevent both the very high iodine levels found in many iodised salt samples, and the relatively high median UIC observed in the South-non-salt-producing areas.

3. Add to objective 1 of the USI strategy III to specifically focus on: investigation of current food industry practices, advocacy for change where required, monitoring and enforcement of the use of appropriately iodised salt by the food industry, and standardised packaging labelling to reflect this.

4. Investigate options to implement more continual review of iodine status in order to protect against iodine excess, while ensuring that the status among all population groups, especially in the North and Mid regions is maintained or improved.

25. Water Quality Fact Sheet. Iodine. British Geological Survey, WaterAid. https://www.bgs.ac.uk/home.html26. Spohrer R, Larson M, Maurin C, Laillou A, Capanzana M, Garrett GS. 2013. The growing importance of staple foods and condiments used as ingredients in the food industry and implications for large-scale food fortification programs in Southeast Asia. Food and Nutrition Bulletin, vol. 34, no. 2 (supplement)27. Reardon T, Tschirley D, Dolislager M, Snyder J, Hu C, White S. 2014. Urbanization, diet change, and transformation of food supply chains in Asia. East Lansing, MI: Global Center for Food Systems Innovation. http://www.perhepi.org/wp-content/uploads/2014/08/3.-Urbanization-diet-change-and-transformations-of-food-supply-change-in-Asia_MSU-GCFSI-Reardon-et-al.pdf


The Ghana 2015 Iodine Deficiency Survey was designed as a nationwide cross-sectional survey with the main aims being to provide information: on the percentage of households using adequately iodised salt and about iodine status among women in fertile age. The survey also collected data on indicators related to awareness and knowledge of iodine deficiency and iodised salt, on the frequency of consumption of key foods contributing to (potentially iodised) salt intake, and on indicators related to health, education and living standards that were used to assess household risk of poverty through creation of a multidimensional poverty index.

BackgroundIodine deficiency disorders (IDD) are the leading cause of preventable mental retardation and impaired psychomotor development in young children28 which can occur in the absence of clinical manifestations of the deficiency, such as cretinism and goitre. A range of intellectual, motor and hearing deficits associated with iodine deficiency result mainly from the effects of deficiency on foetal brain development during early pregnancy29,30. Universal salt iodisation (USI) was adopted by the World Summit for Children (1990) as a safe, cost-effective and sustainable strategy to ensure sufficient consumption of iodine by all individuals31. In 1992/3, the Ministry of Health and the University of Ghana conducted a national survey, which included four towns or villages from each of the country’s ten regions and found the national goitre rate among women and children was 20%, with large regional variation. Although the survey showed that IDD was endemic in all the areas surveyed, the most severe cases were found in the Upper East and Upper West regions, with up to 57% goitre prevalence32,33. The mean national urinary iodine concentration (UIC) among school-aged children was 77 µg/L (range 28 – 183 µg/L). The median UIC was not reported which meant that the adequacy of population iodine status could not be determined. A median urinary iodine UIC <100 µg/L is used to define iodine deficiency among a population of school age children or non-pregnant women in fertile age34. Salt iodisation was recommended as the main preventative strategy in response to the findings of this survey.

The Food and Drugs Law Amendment Act (Act 523) was passed by the Ghanaian parliament in December 1996, making provision for the mandatory fortification with potassium iodate of all refined and unrefined salt for human and animal consumption. The law was however repealed and its provisions covered in the Public Health Act 851 (2012). The Ghana Standard for Salt (2006)35 mandates that salt should contain at least 50ppm iodine during production to achieve a minimum of 25ppm iodine at retail and 15ppm at household level. Internationally, the main indicator for having achieved USI is that 90% or more of households nationally have access to salt with at least 15ppm iodine36.

28. http://www.who.int/nutrition/topics/idd/en/29. Delange F. 2001. Iodine deficiency as a cause of brain damage. Postgrad Med J; 77:217–220 Editorial.30. Iodine and Health. Eliminating iodine deficiency disorders safely through salt iodization. 1994. A statement by the World Health Organization.31. “World Summit for Children -Mid-Decade Goal: Iodine Deficiency Disorders (IDD)”, UNICEF-WHO Joint Committee on Health policy, Special session, Jan 1994.32. http://www.ghanansem.org/index.php?option=com_content&task=view&id=22&Itemid=63#Health_7633. Nutritional Status of Children in Ghana. WISHH Ghana Annual Conference 2008. Gloria E. Otoo Department of Nutrition & Food Science. University of Ghana34. WHO 2007. Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers. WHO, UNICEF, ICCIDD35. Ghana Standards Board GS 154 3rd edition36. WHO 2007. Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers. WHO, UNICEF, ICCIDD Adequately iodised salt at household level (within the range of 15-45 ppm)

Introduction


Subsequent to the law mandating salt iodisation, household use of salt with at least some added iodine gradually increased from 27.1% in 199837 to 41.0% in 200338, and 55.0% in 200639. These data were based on a field method called the rapid test kit which has recently been shown to give acceptable results for the assessment of salt with some added iodine, however cannot be used to reliably determine whether the level of iodine in salt is adequate (>15ppm)40. Nonetheless, the above surveys presented the following results for household coverage with adequately iodised salt: 28.3% in 2003 and 35.1% in 2006.

Since 2008, national efforts to achieve USI have been supported with funding and technical assistance through a Bill and Melinda Gates Foundation-funded GAIN-UNICEF Universal Salt Iodization (USI) Partnership Project (2008-2016). The Partnership Project was established to contribute towards the elimination of iodine deficiency through salt iodisation in 13 countries, including Ghana. Partnership Project activities were developed and implemented in collaboration with the national IDD elimination programme and complement support provided by the Government, the salt industry and other agencies.

During a Partnership-supported workshop, government, industry and agency partners agreed that a realistic goal for salt iodisation in Ghana was to reach at least 55 percent of the population with adequately iodised salt by 2015. Achievement of this goal would mean that over 5.5 million additional people in Ghana would be protected from iodine deficiency through dietary iodine intake from household salt.

A national survey, supported partially by the Partnership, in 2009-1041 reported that 47.8% of households were using adequately iodised salt, 35.6% were using salt with 5-14.9ppm iodine and 16.5% of households were using salt with no added iodine (<5ppm). The use of non-iodised salt (<5ppm) was highest in the Southern zones (23.5% of households), compared with 9% in the Northern zone and 18.4% in the Mid zone. Population iodine status (median UIC among school age children (SAC) and women in fertile age (WIFA)) was, however, better in the South (255 µg/L SAC and 180 µg/L WIFA) than the Mid (166 µg/L SAC and 139 µg/L WIFA) and the North, where the population remained iodine deficient (median 79µg/L SAC and 85 µg/L WIFA).

This apparent mismatch in household iodised salt use and population iodine status raised questions about potential other sources of dietary iodine in the South, either through the use of iodised salt in food industry products, through other dietary sources and/or through the influence of ground water iodine levels (known to be higher in the South than the North)42.A MICS survey conducted in 2011 found that 29.3% of households were using adequately iodised salt nationally (quantitative lab-based method), with all households (100%) consuming salt with some iodine (>0ppm), although for many samples the level of iodine was sufficiently low to have possibly resulted from naturally occurring iodine43,44.

37. Ghana Statistical Service (GSS) and Macro International Inc. (MI). 1999. Ghana Demographic and Health Survey 1998. Calverton, Maryland: GSS and MI.38. Ghana Statistical Service (GSS), Noguchi Memorial Institute for Medical Research (NMIMR), and ORC Macro. 2004. Ghana Demographic and Health Survey 2003.Calverton, Maryland: GSS, NMIMR, and ORC Macro.39. Multiple Indicator Cluster Survey 2006. Republic of Ghana Ministry of Health and Statistical Service Ghana, MEASURE DHS/Macro International Inc. Calverton, Maryland, USA and UNICEF. 40. Gorstein J, et al. 2016. Performance of rapid test kits to assess household coverage of iodized salt. Public Health Nutrition 10.1017/S136898001600093841. Report on the Combined Survey on the Food Fortification Project and Prevalence of Iodine Deficiency in Ghana. GHS/GAIN/UNICEF 2010 (re-analysis of the data presented in the report)42. Groundwater Quality: Ghana. British Geological Survey, WaterAid. https://www.bgs.ac.uk/home.html43. Proceedings of the national workshop on iodine deficiency disorders in Ghana 1994. Edited by E. ASIBEY-BERKO, R. ORRACA-TETTEH. The iodine naturally present in salt samples collected from salt traders in the 12 districts had a mean iodine content of 7.9ppm44. Ghana MICS Final Report. Ghana Statistical Service 2011. Table Nu11 page 55


During 2013, the Government (Ministry of Health, Ghana Health Service, and Ministry of Industry) with support from the Partnership Project and other partners, engaged in a process to assess, adjust and revitalise the national programme for elimination of iodine deficiency through USI. The new national strategy, USI III45, recommendations focus on modernisation of production methods and improvement of quality assurance of iodised salt production within the major salt producing areas of Ghana.

In addition to this strategic focus for household salt, national and international attention has shifted to investigate other significant dietary sources of salt to ensure that, as dietary patterns change, all sources of edible salt are iodised. It is known that bouillon consumption in Ghana is relatively high and since this seasoning is approximately 50% salt, it has the potential to be an important source of dietary iodine if produced using quality iodised salt.

RationaleWith support from the Partnership Project since 2008, the national programme has strengthened communication to raise awareness about IDD and iodised salt, worked with regulatory agencies and salt producers to improve quality assurance and control, and reviewed and revised the strategy for achieving USI. A national iodine survey was considered timely in Ghana for the following reasons:• ToprovidebaselinedataforthenationalUSIIII2016-2020strategy,withparticularrelation

to the focus of the strategy, the following were proposed - Stratified data collection to represent household coverage with adequately iodised salt

and iodine status of women in fertile age (WIFA)46 among salt-producing and non-salt producing areas of the country separately.

- Collection of information required to determine future programme direction: household iodised salt quality, iodine status at the sub-national level, qualitative data about salt characteristics and consumer practices and preferences, differences in access to adequately iodised salt and in iodine status in relation to population risk of poverty and nutritional insecurity.

- Assessment of the potential influence on iodine status of selected processed products known to contribute to salt intake across population groups (if salt used in their production is iodised).

• Tomeettheneedforendofprojectassessmentwithanemphasisonhighqualitydatato assess the above points as well as assessing any links between the use of adequately iodised salt and specific national programme inputs and improvements (information on knowledge about iodine deficiency and on salt-related factors found to be most associated with salt iodine content and population iodine status).

• In addition to obtain data on basic indicators of household health, education, livingstandards, and on food and nutrient intake of the target groups to assess issues related to risk of poverty and nutritional insecurity and dietary diversity to inform other fortification programmes.

• Informationwasalsocollectedonthetypeandbrandofedibleoilinthehouseholdtoallowan assessment of fortified or fortifiable oil coverage.

45. Government of Ghana 2016. Universal Salt Iodisation Strategy III and Action Plan 2016-202046. A more accessible group than school-age children in a household survey and also a group that can be used to indicate likely population level adequacy of iodine at the start of pregnancy


Objectives of the survey The general objectives of the current survey were to estimate the proportion of households in Ghana using adequately iodised salt and to estimate the iodine nutritional status among women in fertile age (WIFA) in Ghana and provide programme information about factors related to the level of iodine in household salt and to iodine status. The specific objectives of the survey were to obtain data for the following indicators that are representative at the national level and by four strata: North, Middle, South (salt producing) and South (non-salt producing) zones; to provide a baseline for the targeted activities of the revised USI strategy:8. To estimate the proportion of households with iodised and adequately iodised salt;9. To estimate the awareness and knowledge of the population regarding iodine deficiency

and iodised salt;10. To estimate the proportion of household salt purchased in recommended packaging and

clearly labelled as iodised;11. To examine whether knowledge about IDD and iodised salt, and/or socio-demographic

related factors were associated with the level of iodine in household salt;12. To estimate the proportion of households regularly consuming bouillon-type condiments,

instant noodles and tomato paste/concentrate (as potential sources of iodised salt);13. To estimate the prevalence of iodine deficiency among women in fertile age (WIFA);14. To examine iodine status among WIFA by categorisation of the level of iodine in household

salt, frequency of consumption of target salt-containing foods, and by socio-demographic factors.

In addition, questions were included on the use of fortifiable edible oil, as an efficient use of resources to compile coverage data for two main fortifiable products (salt and oil) in the same survey and be able to link these to household factors, such as poverty risk.


Overview of Study Organisation and ProceduresThe survey was designed, planned, conducted and data were managed, presented and interpreted by the Ghana Health Service (GHS) in collaboration with: the Ghana Statistical Services (GSS); the Global Alliance for Improved Nutrition (GAIN); the Statistical Service Centre (SSC), Reading University; and UNICEF.

For field data collection, six-field enumeration teams were formed each comprising of 4 members: 3 interviewers and a supervisor. Each supervisor was responsible for their team of interviewers and worked closely with the central level team of coordinators at the GHS head office who were responsible for overall supervision of fieldwork teams.

The field and supervisory teams were comprised of personnel from the Ghana Health Service and the Ghana Statistical Service.

Survey PopulationThe survey population was drawn from persons residing in households in Ghana.

Inclusion Criteria of participants• Eligibleparticipantswhogaveconsentforinterview.• Eligiblewomeninfertileagewhogaveconsentforcollectionofaurinesample.

Exclusion Criteria of participants• Eligibleparticipantswhohavedeclinedconsent.• Householdswherenobodywaspresentatthetimeofthesurvey.

Sample Size Determination and Sampling Procedure

Sample size determinationThe sample size required for each stratum was based on: the estimated national proportion of households using adequately iodised salt from the 2009-10 survey (48%) which gives a more conservative (larger) sample size than using the coverage of 30% from the 2011 MICS.

For salt, the following parameters were used to estimate the sample size according to the sample size calculation below: A desired precision for household coverage of adequately iodised salt of approximately 8% per strata and less than 5% nationally, with a 95% confidence interval, and an assumed design effect of 3 (within each strata), and with an estimated response rate for household consent to participate in the survey and sample collection of 86%.

Methodology


n = (Z)2*(p/100)*(1-(p/100))/(d/100)2Adjustment for cluster sampling = (n * Deff)/(C/100)

Sample size calculation

n = (1.96)2*(48/100)*(1-(48/100))/(8/100)2 = 150Adjusted target sample size per stratum = (150 * 3)/0.86 = 523 householdsAt the national level (4 strata) = 2,092 households

Calculated target sample size for salt sample collection

Where,n= required sample size P= An estimated proportion of households using adequately iodised salt at the stratum level (assumed as 48%)q=1-pDEff: Design Effect (assumed as 3 within each stratum)d=permissible margin of error (set as 8% at the stratum level, which would reduce to < 5% for the national sample)Z = score at 95 percent level of significance (1.96)C= Expected response rate (86%, which includes expected proportion per sampling unit (1 in the case of a household)

This was rounded up to 2,112 households to allow for an acceptable number of households per Primary Sampling Unit (PSU) (16 households) with good distribution throughout each stratum (32 PSUs per stratum).

For urine sample collection for determination of urinary iodine concentration (UIC), the standard calculation of sample size does not fully apply since iodine deficiency is determined based on a population median UIC not by prevalence. However since it is necessary to achieve a level of 100 µg/L or more in at least 50% of samples to achieve population adequacy (median above 100 µg/L), the equations above were used based on 50% prevalence of samples with 100 µg/L iodine, which would also give the most conservative (largest) sample size.

The following additional parameters were used to estimate the sample size: a desired precision around the median urinary iodine concentration of approximately 10% per strata and less than 6% nationally with a 95% confidence interval, and an assumed design effect of 2.5 (within each strata) and an estimated response rate for household consent to participate in the survey combined with consent by a WIFA to provide a urine sample of 76%. The expected proportion of WIFA per household was estimated to be 1.2, based on information provided by Ghana Statistical Services from the national census.

n = (1.96)2*(50/100)*(1-(50/100))/(8/100)2 = 150Adjusted target sample size (for design effect and proportion per HH) per stratum = (150 * 2.5)/(0.76 *1.2) = 411 householdsAt the national level (4 strata) = minimum of 1,645 households

Target sample size for urine collection


Sampling procedureThe survey sample was stratified by four zones: North, Mid, South (salt-producing) and South (non-salt producing); which allowed for some level of comparison with results from previous surveys, which were stratified by North, Mid and Southern zones. Despite the fact that the small scale salt-producing areas of Ghana have a relatively low proportion of the population (about 8%), they are believed to be a major source of non- and inadequately-iodised salt and, as such are the focus of the new USI strategy. Therefore, it was decided to separate the Southern zone into salt and non-salt producing areas in order to provide programmatically useful information about the situation in the salt-producing areas of the country. Having data specifically representative of these areas will provide a baseline from which to monitor and eventually assess the impact of implementation of this revised USI strategy III.

The sample frame was determined based on the population of the districts in the selected zones using the 2010 Population and Housing Census data. In the final selection, the number of enumeration areas (EA) selected varied slightly from the plan. There were 33 PSUs from the Mid and South-salt-producing, 34 PSUs from the North and 32 PSUs from the South-non-salt-producing areas.

Based on the four stratified zones a multi-stage sampling process was used to identify households to include in the survey, as follows:

Overview of the sample design

WIFA – Woman in fertile age. PSU – Primary Sampling Unit. HH - Household

Indicator Sample size # HH/ PSU# PSUs (clusters)

Strata

Household use

of iodised and

adequately

iodised salt

Iodine status

WIFA

4

4

132

2,112 HH

Expect approx.

1,800 samples

2,112 HH

Expect approx.

1,930 samples

16 HH

16 HH

All consenting

WIFA from every

household

# PSU/stratum

33 PSUs

(528 HHs)

per stratum

To simplify the survey protocol, maximise the number of urine samples collected, and provide an opportunity for a nationally representative sample of urinary iodine from pregnant women to assess iodine status among this group; the target number of households for urine collection was rounded up to include collection of urine samples from all consenting WIFA (non-pregnant and pregnant) present in the 2,112 households proposed for collection of information and salt samples.

The table below summarises the proposed number of households per PSU and number of PSU per stratum.


47. For further information on the sampling please see the study protocol

1. For the first stage, 33 enumeration areas (EA) were selected within each of the stratum using PPS (probability proportional to size) sampling methodology from the census listing of EAs and respective population sizes within each of the selected zones. Making a total of 132 EAs nationally.

2. In the second stage, where needed (EAs with a large number of households) the EA was segmented and a random selection of one segment made, which was determined to be the selected Primary Sampling Units (PSU).

3. In the third stage, conducted by listing teams that went to the field prior to the survey field work, all households in each PSU were roughly mapped and listed, then 16 households were selected using systematic random sampling from the listing of all households in the selected segment47. The location and the name of the head of the selected households were recorded to aid the survey teams in finding the correct household for in-depth interview and collection of salt and urine samples. For this survey a Household was defined as one person or a group of people, not necessarily related eating from the same pot.

Household and WIFA Unique IdentifiersEach PSU was assigned a unique identifier from G001 through to G132. Households (HH) in each PSU were numbered from 01 through to 16. Thus the combination of PSU.ID and HH.ID was used to uniquely identify a household within the survey and was also used to identify the salt sample from that household.

For example, the following identification code corresponds to the 5th household of the 25th PSU: G02505

All women in fertile age (WIFA) within a selected survey household were requested to provide a urine sample and therefore each WIFA was given a unique ID. WIFA providing urine samples from one household were numbered from 01-06 and this number added to the household identification number.

For example, the urine sample from the second woman to provide a sample in the above household would have the unique ID: G0250502

The use of barcodes to assign these unique IDs to households and samples is explained in more detail later in this section.

Overview of Survey Planning and Implementation PhasesPhase 1- Preparatory Activities• Preparatoryactivitiespriortothefielddatacollectionincluded:developmentoftheprotocol

with input from multiple stakeholders, selection of the survey sample and identification of PSUs, and obtaining ethical approval.

• QuestionnaireDevelopment–questionnairesweredeveloped inbothpaperandmobiledata platform formats, reviewed and pre-tested in a non-study community then revised as needed.

• Developmentofotherfieldtools–formsfortrackingsamplescollectedandtransfertothelaboratory etc.


48. ODK Collect is used on Android devices and renders forms into a sequence of input prompts https://opendatakit.org/use/collect/ 49. XLSForm is a tool to simplify the creation of forms. Forms can be designed with Excel and XLSForm will convert them to XForms that can be used with ODK tools.

• Trainingandrecruitmentof interviewers–Afourandahalf-daytrainingwasconductedfor field interviewers and supervisors (nutritionists and other qualified field Interviewers/enumerators) to prepare them for all aspects of the field work. The training included one day conducting the pilot survey described below.

• Procurementoflaboratorymaterialsandotherlogisticrequirements.

Pilot Survey – The pilot included fieldwork covering validation of the questionnaires and practice of interview techniques, as well as the collection of salt and urine samples. It was followed by feedback sessions for the interviewers, supervisors and trainers to address any points in the questionnaire and interview techniques that needed revision or improvement prior to data collection.

Phase 2- Listing and Community PreparationIt became apparent during the pilot survey that the process of mapping and listing would be intensive and to conduct the listing as part of the survey field work would have placed a large physical burden as well as time constraints on the data collection teams, possibly reducing the quality of both the listing and data collection.

Therefore it was decided to form a separate listing team who were trained to ensure that households were correctly preselected and mapped prior to the survey field work, thus allowing the listing to be done effectively and for interviewers to focus more effectively on the interview process and eliciting correct information from respondents. The listing team also conducted an initial introduction of the survey to the community and the selected HHs, facilitating entry of survey teams to the community.

Although the late decision to conduct the listing separately resulted in a delay to the start of the field work, overall this decision improved the quality of both the listing and the field work

Phase 3: Field data Collection/ Survey ImplementationHouse to house data collection methods using individual household interviews techniques were employed to gather the data required in the questionnaire, to field test a sample of household cooking salt, to collect an additional salt sample for later laboratory analysis, and to collect a urine sample from all consenting WIFA in the household.

Questionnaires/ Instrument DevelopmentData collection instruments, including household enumeration forms, sample collection and transfer forms and consent forms were developed to facilitate interviews, enumeration and management of fieldwork. The main survey tool was designed using ODK Collect48 and implemented using smart phones; the SSC team produced the coding for the ODK system. The forms were initially designed in Microsoft Excel and converted using XLSForm49 to a form ready to be used with ODK Collect.


The Questionnaire – Overview of Questionnaire ModulesThe survey instrument was designed to collect data about general household characteristics, knowledge about iodine deficiency and iodised salt, assess the characteristics of the salt used, and to estimate consumption of salt-containing food products. As part of the household characteristics was a module to enable calculation of a household Multidimensional Poverty Index (MPI) score, an index of poverty developed by Oxford Poverty and Human Development Initiative50 and the United Nations Development Programme51. The MPI is a sensitive measure of acute poverty - defined as the ability to meet a minimum set of international standards related to the Millennium Development Goals and includes dimensions of health, education and living standards. See Appendix 1 for a detailed description of the MPI components and scoring.

Another module included in the survey was on the type, brand and source of fortifiable edible oil used in the household, in order to obtain information on the coverage of two fortifiable vehicles (salt and oil) and be able to relate this information with the MPI data within the same survey population. Results for this component are included in Appendix 7.

Sections of the main survey instrument (see Appendix 2) included collection of information on:1. Identification of household location, selection of the household respondent and consent i. The respondent for the survey was selected according to the following criteria (in order

of preference), designed to prioritise household members most likely to be knowledgeable about food preparation and salt purchase and use:

- Wife of the head of household or the head of household where they are female - A woman in fertile age present at the time of the visit (preferably 18 years or over) - Another adult member of the household present at the time of the visit ii. Information was collected about the respondent age, gender and level of education.2. Household and respondent characteristics i. This section included information that gave an overview of the household composition

(gender and age group breakdown)

3. Education i. In addition to information about the education of the respondent, additional information

was collected to determine whether any member of the household had over 5 years of schooling and whether any child of school age in the household was currently not attending school

4. Health i. A short birth history was taken to assess whether any child born to a household member

in the past 5 years had died. ii. Questions were asked about food security (access component), based on the 9 question

Household Food Insecurity Access Scale (HFIAS) module compiled by FHI360/FANTA52 to assess household anxiety about food security as well as actual indications that food was of insufficient quality or quantity.

iii. Dietary diversity – the main respondent was asked to detail what they had consumed in

50. Alkire S, Santos ME 2013. Acute Multidimensional Poverty - A New Index for Developing Countries http://www.ophi.org.uk/wp-content/uploads/ophi-wp-591.pdf 51. Human Development Report 2015. What is the Multidimensional Poverty Index? http://hdr.undp.org/en/content/what-multidimensional-poverty-index 52. Coates, Jennifer, Anne Swindale and Paula Bilinsky. 2007. Household Food Insecurity Access Scale (HFIAS) for Measurement of Household Food Access: Indicator Guide (v. 3). Washington, D.C.: FHI 360/FANTA.


53. Based on standard DHS and MICS modules for the components listed.

the past 24 hours, according to a structured interview guide, and responses were coded according to pre-assigned food group categories, to assess nutritional vulnerability.

5. Living standards53 i. Household assets and wealth-related factors - Questions were asked about access to

electricity, type of cooking fuel, floor material, and certain items that the household may or may not own

ii. Water and Sanitation – questions were asked about the sources of drinking water, toilet facilities and general hygiene in relation to disposal of stools of children under 2 and hand washing practices.

Information from sections 1 (respondent’s education level), 3, 4 (not including dietary diversity) and 5 were used to calculate the multi-dimensional poverty index (MPI) score for each household, Dietary diversity information was used as a further indicator of vulnerability.

6. Knowledge and awareness of iodine deficiency and iodised salt i. To determine whether the respondent had heard of iodine deficiency and knew of its

consequences and how to prevent it ii. To determine whether the respondent had heard of iodised salt, and the source of

information

7. Attitudes, behaviour and practices in relation to iodised salt i. Perceived use of iodised salt, packaging and labelling of the salt used in the household,

whether the respondent looked for iodised salt at the point of purchase, typical salt purchasing practices – frequency and amount

8. Consumption of other products likely to be contributing to salt (potentially iodised salt) intake

i. Frequency of consumption in the past week of: meals prepared with bouillon or tomato concentrate; instant noodles and vendor cooked foods. To determine possible additional (or at least potential) sources of iodised salt

9. Cooking oil use i. Type, brand and source of oil usually used in the household to assess access to fortified

and potentially fortifiable oils across the country and by household vulnerability (MPI score)

10. Salt sample i. Consent for field testing a small sample, collection and handling of an additional sample

for laboratory testing of iodine

11. Urine sample i. Consent for collection and handling of a sample for laboratory testing of iodine

All skips and data feasibility checks and alerts were coded into the ODK collect form to reduce data collection errors. Details of the system and the constraints identified are included in Appendix 3.


Recruitment and Training of Listing Teams and EnumeratorsListing teams were recruited based on prior experience in enumeration area mapping, segmentation, and listing of selected households. Enumerators were recruited to match with the required interviewing skills and the regional language needs of the survey. Enumerators with prior experience with interviews and other survey procedures were selected from the regions in the identified zones. A team of three coordinators and principal investigators supported the data collection teams, responsibilities are outlined in the Field Work section below.

Two different training sessions were held, one for the listing team and one for the team of interviewers, supervisors and coordinators.

Training of Listing TeamsThe aim of the listing exercise was to provide the interview teams with the correct list of randomly selected households for interviews. For effective and efficient conduct of fieldwork it was necessary to obtain a comprehensive household list from which the required sample of 16 households to be visited was drawn, with information on exact location and with head of household identification details.

A total of 43 people were recruited and trained to compile the household lists. The participants together with key resource people took part in the training sessions at the Windy Lodge from 12th to 14th December 2014. The participants were taken through the process of map reading, map interpretation, map alignment and map orientation. This was followed by a presentation on how to list structures in an Enumeration Area (EA) in a sequential order and the subsequent systematic random selection of 16 households to be included in the survey. The participants also undertook some simulation exercises and field practice. At the end of the training ten listing teams comprising 1 leader and 3 mapping assistants within each team, were selected and provided with listing forms.

A total of 8 out of the 10 teams were each assigned 13 EAs while the remaining 2 teams were allocated 14 EAs and these were listed using the listing forms provided over a period of 16 days.

Training of Interview TeamsTraining covered community entry, use of the mobile phone-based survey tool, review and uploading of data forms, maintenance of the phones, laboratory sample collection, handling and transport, as well as general interview techniques.

The training had the following objectives:1. Familiarise the field teams with the objectives of the survey and the methodology to be

used.2. Ensure supervisors and interviewers are able to apply the household identification

procedures.3. Ensure supervisors and interviewers have an in-depth knowledge and understanding of

each question in the survey questionnaire: knowing what is being asked, why it is being asked and how it should be asked. In addition, the correct translation of each question into the local language needed to be fully understood and appropriately applied.

4. Understand the roles and responsibilities of each member of the field team, and the chain of responsibilities to help in ensuring data quality.


The training sessions included the following activities:1. Discussion of community entry procedures to ensure that the required protocol was followed

in the field. This included entry into the PSU and the introduction of the survey to the respondents, for the latter in particular the use of the paragraph for obtaining consent.

2. Discussion of the roles and responsibilities of different members of the team.3. Reading, discussing and using the questionnaire under classroom conditions. Role-play

where interviewers took the place of interviewees and interviewers was organized and witnessed by members of the training team followed by discussions to improve the ability of the interviewers to carry out interviews (using first paper-based and then mobile-based versions of the questionnaire).

4. Using the household identification procedure on pre-prepared PSU and household listings.

5. Understanding the bar codes provided and when/how to use these6. Use of the mobile device including barcode scanning and backing up and uploading data

to the aggregate server.7. Understanding about how to test for iodine in salt using the field kit method, the type of salt

sample to collect and how to handle and label this8. Understanding of how to ask for consent for a urine sample, the collection, labelling and

handling techniques9. A field based practice (pilot) in which interviewers and supervisors had the opportunity

to practice the process of household selection, geo-referencing of households, interview, sample collection and writing of field notes as needed followed discussion and lessons drawn from the field visit.

10. Planning the survey field work, transport, logistics and supplies (for sample collection and mobile device and related data maintenance)

The training methods included a combination of classroom training, presentations, role play and other practice together with a day of field testing (pilot survey). The training was conducted in Kumasi for four and a half days during the week beginning 24th November 2014. All trainees were given a detailed training manual (with an additional manual for supervisors) and paper-based versions of the questionnaire to read beforehand. During the training, the questions and instructions on the questionnaire were discussed in detail.

Trainees were taken through demonstration sessions on the interviewing process with an analysis of each question, an example of how to ask it, then an opportunity for all trainees to practice reading the questionnaire aloud to another person several times. When all trainees were comfortable with reading the questions aloud there were supervised role-play sessions in which trainees practiced interviewing one another. During this process, trainees verified the meaning of questions, the flow of questions and responses, and noted any suggested corrections to the form.

Review and practice of the questionnaire was conducted first using the hard copy version and then using the mobile phone-based version afterwards. The training and role playing incorporated practice conducting the interviews in different languages, ensuring common agreement on how each question should be asked in a variety of local languages.

The training included mobile-phone based sessions on using: the ODK software, the file manager, GPS location, scanning barcodes, maintenance of the phone (keeping it charged,


etc.). Additional sessions were provided for the team supervisors highlighting their specific tasks such as reviewing and backing up the data from each interviewer’s phone via Bluetooth then uploading the completed forms to the aggregate server along with a supervisor checklist.

The phones were physically and electronically labelled so they could be uniquely identified and each interviewer was assigned his/her own phone with the interviewer ID number being the same as the phone ID number. Field staff were allocated to teams and each team was provided with one spare phone and different forms of charging devices.

The pilot survey gave enumerators the opportunity to practice interviewing household respondents, to use the GPS locator, the bar code scanner, the barcodes, and to request salt from households and urine samples from eligible women. At the end of the practice, enumerators went through the process of reviewing and editing completed questionnaires as well as labelling and managing samples, just as would be done in the survey fieldwork. The supervisors also used these questionnaires to practice data review, transfer and upload procedures. The resulting completed questionnaires were used to check that data entry parameters are correctly set.

Immediately after completion of the training, enumerators were put into teams to correspond with their strengths and language skills, with respect to the zones they were to visit. Fieldwork for data collection was delayed for two weeks due to the late but essential decision to conduct the survey listing separately to the field work. Because of the delay, a refresher training was held for team supervisors (conducted by the survey coordination and training team) and the supervisors in turn trained the data collection teams immediately prior to the start of field work.

Salt samples were stored in black plastic bags with a PSU ID barcoded label on the outside and urine samples were stored in barcode labelled boxes which were placed in a cool box with ice packs, then transferred to a refrigerator when available. Both types of samples were transferred to the analysis laboratory (at the Noguchi Memorial Institute for Medical Research (NMIMR) Laboratory of the Nutrition and Food Science Department, University of Ghana) at the earliest opportunity. Urine samples were stored in freezers at minus 12oC at the laboratory.

Field Work for ListingUsing the EA maps for the areas pre-selected by the Ghana Statistical Services. The team first divided the EA into segments where required, then randomly selected one segment which became the survey PSU. The PSU was reviewed to plan how to undertake the mapping and listing exercise.

The supervisors of the various team started the process of mapping by listing all the structures in a systematic order and giving identification numbers e.g. ID 009. The rest of the team members followed and entered every dwelling or structure between them and added the name of the household head to the list. To ensure that no dwelling was missed this was done in a strictly assigned order.

The completed listing forms were all brought to the head office of Ghana Statistical Services where the 16 households for the survey were selected using Systematic Sampling with Equal Probability of Household Selection Procedure per PSU. A listing report was generated for use by enumeration teams.


Survey Field Work Preparation

Ethical Consideration and ReviewThe survey protocol included instruction to ensure informed consent and maximize confidentiality before interviews and collection of samples. Participation in the survey was completely voluntary and based on the informed consent with no inducement of any form for participation, and all interviews were conducted in private. Interviewees were made aware that they could change their mind and stop the interview at any time.

Detailed survey proposal, protocols and survey tools were submitted for the approval by the Ghana Health Service research Ethical Review Committee before the commencement of all survey procedures.

Survey Field Work

Team compositionEach team was comprised of a supervisor, 3 interviewers and a driver. Survey Team composition and roles:• Interviewers: The team members conducted face–to-face individual interviews at the

household level, collected the salt and urine specimens. They also conducted the on the spot salt test using the rapid field test kit (RTK).

• The supervisors: Responsible for managing all fieldwork, all financial and logisticsadministration, and liaison with respondent communities and the regional or zonal coordinators. They also ensured appropriate sampling and quality data collection, functioned as data quality editors and were responsible for uploading all forms to the server whenever possible.

• Coordinators: A coordinating team was formed to support fieldwork by liaising withinterview teams and the laboratory with regard to transport and reception of samples, and provided a logistic backstopping function. The field coordinators were directly responsible to the principal investigator and oversaw overall data quality management, communication of any data-related quality issues identified through review of uploaded forms, training, and re-training, and follow up of the day to day activities of data collection and transport of salt and urine specimens.

• Principle Investigator: Responsible for general survey oversight and, specifically, fordeveloping the survey protocol, questionnaire, and work plan, obtaining ethical clearance, securing approvals for the budget, contracting, recruitment, training, and overall supervising survey staff, securing survey supplies, implementing the study, data management, and analysis and reporting. The survey design, planning, training and implementation as well as data management, analysis, laboratory quality assurance and survey reporting was coordinated by the Ghana Health Service and UNICEF and supported by GAIN and the Statistical Service Centre (SSC), Reading University.

Data CollectionPre-Survey Visit or Community Preparation – this was undertaken by the team supervisors and included discussions and meeting with key health and other local government officials and community leaders in the survey areas to (1) brief on objectives, procedures, survey schedules and necessary logistical arrangement and (2) review the list of sample households.


The fieldwork started in mid-January 2015 and was completed by mid-February 2015.

The survey was conducted using ODK Collect on smart phones; the SSC team produced the coding for the ODK system. The forms were designed in Microsoft Excel and converted using XLSForm to a format ready to be downloaded and used with ODK Collect. Completed questionnaires were uploaded to the same server at the end of each day of fieldwork, or as soon as a connection became available in the following days. Skips in the questionnaire were automatically coded into the ODK system and as many checks as possible were included.

In addition to the main ODK form, there were separate paper- and mobile-based summary forms for the interviewers and field supervisors designed to help monitor data collection. Each interviewer had a paper-based household checklist for each PSU where they recorded which households they had visited, which questionnaires were completed, how many refusals there had been and the number of urine and salt samples collected from each. This information was collated by the supervisors at the end of each day and an image of the completed paper-based checklist uploaded to the server.

During the fieldwork the SSC monitored the data uploads and sent thrice weekly progress reports back to the country team. These reports detailed the number of PSUs and households that had been completed and the number of salt and urine samples collected from each PSU. Being able to monitor progress in this way was very useful to identify and solve problems as they arose.

SSC staff produced Excel macros to convert the data into a format, which was shared with the survey coordination team for review, and a format ready to be imported into SPSS for checking and analysis.

BarcodesBarcodes were used to identify households in the survey and to label the salt and urine samples so that results from the lab analyses could be matched with the corresponding survey data. Barcodes at the household level comprised the PSU_ID and the HH_ID; these were used to identify each household and label the salt samples. The PSU_ID was a four- character code starting with “G” and followed by three digits, codes went from G001 through to G132; the HH_ID was a two-digit code between 01 and 16.

The barcodes were printed on sheets of labels ready to be used in the field. Each interviewer was provided with the labels for all households that he/she had been allocated. The ODK data collection system prompted the interviewer to scan the unique household code on the consent form at the start of the interview and to scan the label placed on the sealable bag for salt collection once they had placed the label onto the bag. The data collection form indicated that an error had occurred if this code on the salt sample did not match the household code used at the start of the interview. An example household barcode is shown below:

G00101

PSU=G001

HHID=01

G00101


For the urine samples the codes comprised the PSU_ID, the HH_ID plus a two-digit code to identify each woman within the household. Because it was not known in advance how many women there would be in each household, labels were produced for up to five women in each household together with some sheets of “spare” codes in case there were more than five women in any household. As it turned out these “spare” codes were not needed. For each woman, four labels were provided; one for the collection pot and others to label the individual sample containers for the lab (which in the end were not used due to a change in the field methodology). Because these labels were small (2.5 x 2.5 cm), it was not always easy to read the barcode with the phone the corresponding QR code which could be easily scanned, was also printed on the label. An example label is shown below:

The codes were produced using macros in Excel and the barcodes and QR codes generated using TBarCode Office54.

Data Monitoring and Quality AssuranceThe use of ODK collection forms with in-built skips and multiple internal cross-checks (e.g. for matching household and salt sample barcodes) together with on-going (more or less real time) monitoring of the data uploaded to the server, provided quality assurance that interviews were being conducted correctly and completely.

The main issues identified during data monitoring were the lower than expected number of urine samples – collected from only 60% of completed households; and high number of unoccupied households in a couple of the PSUs. One PSU had 5 unoccupied households and two PSUs had 4 unoccupied households. One of these was a conflict district, therefore people move around depending on the situation. Other listed households were in areas where families may have taken a break from farming over Christmas (and so were at home when the lists were drawn up) but had since returned to their farmlands. There were a few cases where the wrong enumerator code had been used but these were easily identified and corrected.

Data Management and AnalysisSPSS (version 22) was used to produce the results to populate the tables specified in the analysis plan of the survey protocol. The Complex Samples Module in SPSS was used to calculate confidence intervals (95%) of population estimates and percentages for the weighted data. A CSA (Complex Samples Analysis) plan file was developed, which includes the Strata, the PSU, the probability of selection for the PSU, the probability of selection for a household within the PSU, and the weights adjusted for non-response.

Multiple variable regression analyses were conducted using R version 3.1.3 software. Salt iodine content was assessed using a general linear model, with the weighting and variance estimation accounted for according to the appropriate survey design using the survey library within the R statistical analysis package55. A stepwise selection procedure was conducted using a p-value of 0.1 as the inclusion criteria.

54. TBarCode Office is an Add-In for Microsoft Word and Excel - http://www.tec-it.com/en/software/barcode-software/office/word-excel/Default.aspx55. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.; T. Lumley (2014) “survey: analysis of complex survey samples”. R package version 3.30

G0010301


Urinary iodine content was assessed on the logarithmic scale using a general linear model, with the weighting and variance estimation accounted for according to the appropriate survey design using the survey library within the R statistical analysis package. A stepwise selection procedure was conducted using a p-value of 0.1 as the inclusion criteria for the multiple variable model.

The coefficients and confidence intervals are presented to indicate the relative change expected on the original salt iodine scale (ppm) and urinary iodine scale (µg/L), e.g. for urinary iodine, an exponential beta of 1.1 indicates that urinary iodine is expected to be 1.1 times higher for that term relative to the reference category, holding all other variables constant. Wald p-values were presented for all variables included in the model.

Appendix 3 contains additional details on Data Management and Handling, including how MPI, Food Security and Diet Diversity scores were calculated; and how salt and processed food intakes were estimated.

Calculation of sample weightsThe total number of households in each stratum were:

1. South Salt Producing n = 4797082. South Non-salt Producing n = 14493523. Mid Non-salt Producing n = 28112284. North Non-salt Producing n = 1020740.

Using the known total number of households in each selected PSU, the probability of selection (P) of the PSU was calculated as: P = 33 * (hh / HH)

Where P = Probability of selecting a PSU hh = Total number of households in selected PSU HH = Total number of households in Stratum.

The probability of selecting a household (F) within each PSU was calculated as: F = x / hh

Where x = Number of households with completed interviews in the PSU

The design weight (DW) adjusted for non-response was then calculated as: DW = 1 / (P * F)

The design weight gives an estimate of the number of households in the entire population represented by a single household. These adjusted weights were then “normalised” so that:

∑ hhcomp * norm_weight = ∑ hhcompWhere HHcomp = number of completed households in the PSU norm_weight = normalised weights

These adjusted, normalised weights were used throughout the analyses.


Laboratory analysis of iodine in salt and urine samplesSalt and urine samples were received from the field by the NMIMR laboratory of the Nutrition and Food Science Department, University of Ghana. All samples were in bags barcode labelled with the PSU ID and each sample was individually barcode labelled with the appropriate sample ID. The laboratory used a barcode reader to enter the ID into an excel database and the result of the iodine analysis was entered alongside. All samples were analysed in duplicate.

Salt iodine was measured using the iodometric titration method and urinary iodine was determined using ammonium persulfate digestion with spectrophotometric detection of the Sandell-Kolthoff reaction56.

Quality Assurance of Laboratory AnalysesGAIN developed a contract with the UP State USI Coalition (Technical) in India (SGPGIMS)57 to function as an external quality assurance (EQA) laboratory for eight laboratories involved with a series of iodine surveys globally, including in Ghana. The function was to check the overall accuracy of analytical performance through periodic provision of salt and urine samples with pre-assessed (unknown to the survey lab) iodine content. The performance for both the salt and urinary iodine laboratory assessments during the time of the Ghana survey was rated as “Good”, providing confidence in the sample analysis results.

Further details of the laboratory quality assurance procedure and outcome are provided in Appendix 4.

Limitations and ConstraintsThere was a delay of approximately two weeks between enumerator training and the start of field work due to the decision that the mapping and listing exercise should be conducted by an expert team rather than by the survey teams at on arrival at each identified PSU. To alleviate the effect of this delay, refresher training was conducted immediately prior to the field-work, as described in the section on Training of Interview Teams.

The number of non-pregnant and pregnant WIFA in selected households was lower than that had been expected based on census data, and there was lower than expected consent to provide urine samples. The sample size for non-pregnant WIFA was still sufficient to obtain reliable median values by sub-group, however the sample for pregnant WIFA was too small to generate a reliable estimate of national status.

56. WHO 2007. Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers. WHO, UNICEF, ICCIDD57. Department of Endocrinology and Molecular Medicine – Biotechnology. Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226 014, India


Survey Sample CharacteristicsThe complete sample was 2112 households – 16 in each of 132 PSUs. The national response rates for: completed interviews, valid household salt iodine level and urinary iodine concentration results were 91.3%, 74.3% and 79.9% respectively, as shown in Table 1. These rates varied slightly by strata, the most notable difference was the lower rate for valid salt iodine level results in the North (66.0%). Overall, rural areas tended to have slightly higher response rates than urban areas for all three parameters.

At the national level the following findings related to response rates were reported:• 25householdsarenotinthedataset;presumablyunoccupied=1.2%• 122householdswereunoccupiedandrecordedassuch=5.8%• 36householdsrefusedconsent=1.7%• 13saltsampleswerecollectedbuthadinsufficientsalttoanalyseorweremis-labelled• 35urinesamplesdidnothavevalidiodineresultscorrespondingtoaWIFAID.Ofthese,

18 were received but were not on the lab hand-over form, 3 were recorded on the hand-over form but there was no sample in the bag, 10 samples could not be analysed, 2 were duplicated entries on the list, and 2 samples were unaccounted for. Of the 10 that could not be analysed 2 were because the tube had come unscrewed and was empty and for the other 8 there was indigestible suspended sediment present in the sample.

Tables 2 and 3 show a breakdown of the characteristics of the survey sample, nationally and by sub-group. The unweighted versus weighted numbers shows that the South-salt-producing area contributed the least to weighted national estimates while the Mid area accounted for nearly half of the national estimate. Respondents were mainly WIFA (62.4%), with 17.1% male respondents.

At the national level, a fairly even split in the percentage of households considered to be deprived and non-deprived was found. However in the North, 62.2% of households were categorised as deprived, significantly higher than in the Mid (45.6%) and South-non-salt-producing (37.9%) areas. The South-non-salt-producing area also had a significantly lower percentage of deprived households than the South-salt-producing area (56.2%). In addition, significantly more rural households were found to be deprived (57.8%) than urban households (42.0%).

Nationally, 50.5% of households were found to be food secure (access component), however almost one quarter of all households (21.2%) were categorised as severely food insecure (access). In terms of dietary diversity of the respondent, 63.1% of respondents were classified as having a diverse diet.

The highest levels of severe household food insecurity (35.3%) and lack of dietary diversity of the respondent (50.6%) were found in the North. While households in the Mid stratum had the lowest levels of severe household food insecurity (12.1%) and highest reported levels of respondent dietary diversity (71.6%). These estimates of food insecurity and dietary diversity in the Mid area were both significantly different to estimates for the North and for the South

Results


(combined) areas. There was little difference between findings for rural and urban households for these two variables.

Appendix 5 illustrates the distribution of selected PSUs across Ghana. The data are from GPS readings made by team supervisors and team members. There were certain locations where GPS readings were difficult to make so these maps may not include every PSU surveyed.

Surv

ey s

ampl

e ch

arac

teris

itic

Hous

ehol

ds

in s

urve

y sa

mpl

e (n

)

NATI

ONAL

North

non

-sal

tpr

oduc

ing

Mid

non

-sal

t pr

oduc

ing

Sout

h no

n-sa

lt pr

oduc

ing

Sout

h sa

lt pr

oduc

ing

Urba

n

Rura

l

2112

544

528

512

528

1360

752

1965

497

501

462

505

1249

716

1929

491

491

455

492

1224

705

1582

367

410

374

431

1000

582

91.3

%

90.3

%

93.0

%

88.9

%

93.2

%

90.0

%

93.8

%

74.3

%

66.0

%

77.1

%

72.7

%

81.6

%

73.3

%

76.1

%

79.9

%

82.1

%

77.4

%

78.3

%

81.2

%

77.2

%

84.7

%

122

46 16 37 23 80 42

1569

359

407

372

431

997

572

1714

497

425

345

447

1098

616

1302

388

310

263

341

799

503

103

30 34 13 26 73 30

18 6 6 3 3 13 5

1370

408

329

270

363

848

522

Resp

onse

ra

te

(inte

rvie

w)

(%)

Salt

sam

ples

colle

cted

(n

)

Salt

sam

ples

with

a

titra

tion

resu

lt (n

)

Com

plet

equ

estio

nnai

res

(n)

Hous

ehol

ds

with

no

salt

(n)

Resp

onse

ra

te s

alt

iodi

ne1 (%

)

Urin

e sa

mpl

es

colle

cted

- NP

W

IFA1 (n

)

Urin

e sa

mpl

es

with

iodi

ne

resu

lt W

IFA5

(n)

Resp

onse

ra

te fo

r urin

e sa

mpl

es fr

om

WIF

A6 (%)

Urin

e sa

mpl

es

colle

cted

- un-

know

n st

atus

4 (n

)

Urin

e sa

mpl

es

colle

cted

- PW

3 (n)

WIF

A in

HH

S at

tim

e of

sur

vey

(n)

Hous

ehol

ds

visi

ted

(n)

1 Sa

lt sa

mpl

e co

llect

ed A

ND w

ith v

alid

titra

tion

resu

lt co

mpa

red

with

the

num

ber o

f hou

seho

lds

in th

e or

igin

al s

urve

y sa

mpl

e2

Non-

preg

nant

(NP)

wom

en in

ferti

le a

ge (W

IFA)

- b

ased

on

wom

en’s

resp

onse

to w

heth

er th

ey w

ere

preg

nant

or n

ot3

Preg

nant

wom

en (P

W) -

bas

ed o

n w

omen

’s re

spon

se to

whe

ther

they

are

pre

gnan

t or n

ot4

Ther

e w

ere

18 w

omen

in th

e su

rvey

sam

ple

who

wer

e no

t sur

e of

thei

r pre

gnan

cy s

tatu

s an

d th

ese

wom

en w

ere

excl

uded

from

all

anal

yses

of u

rinar

y io

dine

dat

a5

Num

ber o

f urin

e sa

mpl

es w

ith a

val

id io

dine

mea

sure

men

t (pr

egna

nt (n

=98

) and

non

-pre

gnan

t (n=

1272

) WIF

A co

mbi

ned)

6 Ba

sed

on th

e nu

mbe

r of u

rine

sam

ples

col

lect

ed a

nd w

ith a

val

id io

dine

mea

sure

men

t com

pare

d w

ith W

IFA

pres

ent i

n th

e ho

useh

old

at th

e tim

e of

the

surv

ey.

It w

as n

ot p

ossi

ble

to d

eter

min

e ac

cura

te e

stim

ates

for t

he e

xpec

ted

num

ber o

f WIF

A (n

on-p

regn

ant a

nd p

regn

ant)

from

the

orig

inal

sur

vey

sam

ple

hous

ehol

ds.

Tab

le 1

. Ove

rvie

w o

f sur

vey

sam

ple

(n) a

nd r

esp

onse

rat

e (%

)


Table 2. Overview of survey sample household characteristics

Percent distribution of households by selected characteristics

1 The total percentage for sub groups doesn’t always add up to 100%, due to rounding and missing responses.2 Food secure (access)/insecure (access) is a component of the MPI score, however it is presented here with additional categorisation to indicate the degree of food

security (access) in the sample

Survey sample characteristic

Stra

taRe

side

nce

MPI

Food

sec

urity

(acc

ess)

2Re

spon

dent

Type

Diet

ary

dive

rsity

NATIONAL

North non-saltproducing

Mid non-saltproducing

South non-saltproducing

South salt producing

Urban

Rural

Low MPI score(not deprived)

High MPI score(deprived)

Secure

Mildly insecure

Moderatelyinsecure

Severely insecure

Diverse

Not diverse

Female 15-49years of age

Female > 49years of age

Male > 15 years of age

100.0%

25.5%

25.5%

23.6%

25.5%

63.5%

36.5%

48.8%

50.7%

43.3%

11.3%

19.1%

26.0%

61.8%

38.2%

62.4%

20.5%

17.1%

100.0%

17.7%

48.8%

25.2%

8.3%

65.1%

34.9%

52.5%

47.5%

50.5%

10.8%

17.6%

21.2%

63.1%

36.9%

61.4%

22.4%

16.2%

1929

491

491

455

492

1224

705

942

978

835

218

368

501

1192

737

1204

395

329

1929

342

941

485

161

1256

673

1009

913

971

208

338

407

1218

711

1185

431

312

Unweightedpercent 1

Weightedpercent

Unweighted

Number of Households

Weighted


Perc

ent d

istr

ibut

ion

of h

ouse

hold

s by

ove

rall

MPI

Natio

nal

Sout

h (c

ombi

ned)

Strata Residence

North

non

-sal

tpr

oduc

ing

Mid

non

-sal

tpr

oduc

ing

Sout

h no

n-sa

ltpr

oduc

ing

Sout

h sa

ltpr

oduc

ing

Urba

n

Rura

l

52.5

%(4

8.4,

56.

6)

37.8

%(3

0.4,

45.

9)

54.4

%(4

7.6,

61.

0)

62.1

%(5

4.3,

69.

3)

43.8

%(3

6.4,

51.

5)

57.6

%(5

1.5,

63.

4)

58.0

%(5

3.5,

62.

4)

42.2

%(3

4.8,

50.

1)

1920

488

491

452

489

941

1217

703

1929

491

491

455

492

947

1224

705

1922

489

491

453

489

942

1218

704

47.5

%(4

3.4,

51.

6)

62.2

%(5

4.1,

69.

6)

45.6

%(3

9.0,

52.

4)

37.9

%(3

0.7,

45.

7)

56.2

%(4

8.5,

63.

6)

42.4

%(3

6.6,

48.

5)

42.0

%(3

7.6,

46.

5)

57.8

%(4

9.9,

65.

2)

63.1

%(5

9.3,

66.

8)

49.4

%(4

1.4,

57.

4)

71.6

%(6

5.6,

76.

9)

54.3

%(4

6.0,

62.

4)

69.3

%(6

3.7,

74.

3)

58.1

%(5

1.6,

64.

3)

61.6

%(5

6.4,

66.

6)

66.0

%(5

9.8,

71.

7)

36.9

%(3

3.2,

40.

7)

50.6

%(4

2.6,

58.

6)

28.4

%(2

3.1,

34.

4)

45.7

%(3

7.6,

54.

0)

30.7

%(2

5.7,

36.

3)

41.9

%(3

5.7,

48.

4)

38.4

%(3

3.4,

43.

6)

34.0

%(2

8.3,

40.

2)

50.5

%(4

7.0,

53.

9)

27.4

%(2

1.6,

34.

0)

58.5

%(5

3.5,

63.

5)

57.3

%(4

9.4,

64.

8)

31.5

%(2

3.4,

41.

0)

50.9

%(4

4.7,

57.

0)

52.7

%(4

8.8,

56.

6)

46.3

%(3

8.8,

54.

1)

10.8

%(8

.4, 1

3.8)

14%

(10.

9, 1

7.9)

11.7

%(7

.4, 1

8.0)

5.9%

(3.9

, 8.7

)

13.5

%(9

.9, 1

8.1)

7.8%

(6.0

, 10.

0)

8.9%

(7.0

, 11.

7)

14.2

%(9

.1, 2

1.7)

17.6

%(1

5.6,

19.

8)

23.3

%(2

0.1,

26.

9)

17.7

%(1

4.4,

21.

6)

11.4

%(8

.2, 1

5.7)

23.4

%(2

0.1,

27.

0)

14.4

%(1

1.7,

17.

5)

18.3

%(1

5.8,

21.

1)

16.2

%(1

3.1,

19.

9)

21.2

%(1

8.6,

24.

0)

35.3

%(2

8.9,

42.

3)

12.1

%(9

.2, 1

5.7)

25.4

%(1

9.4,

32.

5)

31.6

%(2

3.6,

40.

9)

27.0

%(2

1.9,

32.

7)

20.1

%(1

7.1,

23.

4)

23.3

%(1

7.7,

29.

9)

Wei

ghte

dnu

mbe

rW

eigh

ted

num

ber

Wei

ghte

dnu

mbe

rM

oder

atel

y fo

od in

secu

reM

ildy

food

in

secu

reFo

od s

ecur

eDi

vers

e di

etSe

vere

ly

food

in

secu

re

Non-

dive

rse

diet

Low

MPI

(non

de

priv

ed H

H)Hi

gh M

PI

(dep

rived

HH)

Perc

ent d

istr

ibut

ion

of h

ouse

hold

s by

die

tary

div

ersi

ty o

f res

pond

ent

Perc

ent d

istr

ibut

ion

of h

ouse

hold

s by

food

sec

urity

(a

cces

s) c

ateg

orie

s

Tab

le 3

. Ove

rvie

w o

f sur

vey

sam

ple

hou

seho

ld c

hara

cter

istic

s b

y su

b-g

roup


Household salt iodine

TitrationTables 4 and 5 and Figure 1 present the findings for household salt iodine content. Nationally, 29.3% of households were found to be using adequately iodised (>15ppm iodine) salt at the time of the survey. A significantly higher percentage of households in the North (37.6%) and South-non-salt-producing (48.6%) areas were using adequately iodised salt than households in the Mid (18.6%) and South-salt-producing (19.3%) areas. Use of adequately iodised salt was higher among urban (31.4%) than rural (25.2%) households, however the 95% CI suggests that the difference was not significant. Households with higher MPI (more deprived) were significantly less likely to be using adequately iodised salt than households with a low MPI (21.5% compared with 36.2% of households).

Just over 60% of households nationally were using salt with some added iodine (>5ppm iodine), however this coverage decreased to less than half (48.7%) of households in the South-salt-producing areas. The percentage of households using inadequately iodised salt (5-14.9ppm) was fairly consistent in the North, Mid and South-salt-producing strata, at 30-38% of households. It was significantly lower (20.6%) among household sin the South-non-salt-producing area.

Approximately one third of households in the North and South-non-salt-producing areas were using salt with over 40ppm iodine. The mean salt iodine ppm for households using salt with any added iodine in the North was 39.6ppm, whereas the median was a lot lower at 16.0ppm, while the mean and median were more similar in households in the South-non-salt-producing area (mean 45.4ppm, median 37.2ppm). The mean and median salt iodine ppm were approximately the same for households using salt with some added iodine in the Mid and South-salt-producing areas (approximate mean 25ppm and median 9.3ppm).

Urban households using salt with any iodine had slightly higher respective mean and median (36.8ppm and 16.0ppm) salt iodine than rural households (28.1ppm and 10.6ppm), the differences did not appear to be significant. Households with a lower MPI (less deprived) did however have significantly higher respective mean and median salt iodine (40.0ppm and 22.7ppm) than households with a high MPI (26.0ppm and 9.3ppm).

A cut off of 5ppm for salt with some added iodine was proposed to differentiate low levels of naturally occurring iodine from added iodine. An additional analysis was conducted to examine the distribution of salt iodine for samples with <15ppm to see where peak iodine occurred and verify whether 5ppm was a reasonable cut off to use. See Appendix 6. Some previous research suggested that salt in Ghana had approximately 7.9ppm of naturally occurring iodine58. The peak ppm level of salt samples with <15ppm (potentially indicating naturally occurring trace iodine) was around 2-3ppm, suggesting that a cut of 5ppm was reasonable to use.

Rapid test KitResults from the field-based rapid test kit (RTK) findings for household salt iodine are shown in Table 6. Of the 63.9% of salt samples found to be positive for iodine using the RTK, 3.6% of samples only showed a colour change after using the re-check solution. This percent varied from 0.9% of iodine-positive salt samples in the South-non-salt-producing area to 12.4% of iodine-positive samples in the South-salt-producing area.

58. Proceedings of the national workshop on iodine deficiency disorders in Ghana 1994. Edited by E. Asibey-Berko, R. Orraca-Tetteh


The percent of households characterised as using salt with no added iodine by titration (<5ppm) and by rapid test kit (no colour change) was approximately the same (38.15 compared with 36.1% respectively), however the percent households determined to be using adequately iodised salt (>15ppm titration, deep colour change RTK) was significantly different (29.3% compared with 42.6% respectively). These findings were the same for all sub-groups.

The table below shows details for the sensitivity and specificity of the RTK findings for salt iodine content from this survey, when compared with the gold standard method of titration. Agreement between the methods is higher when no colour change by RTK was compared with a titration-assessed cut off of 5ppm is used to indicate any added iodine rather than a reading of 0ppm iodine (no iodine). Due to the over-estimation of adequately iodised salt, the RTK sensitivity and NPV for adequate salt iodine are high, whereas the PPV is low.

RTK results compared with titration results for any and adequate salt iodine

0ppm

63.7%

99.8%

63.6%

33.3%

63.6%

99.8%

0.2%

No iodine

Adequate iodine

Any added iodine

≥5ppm

63.7%

62.0%

83.2%

68.3%

77.6%

81.1%

71.4%

≥15ppm

42.4%

29.3%

85.6%

75.4%

78.4%

59.2%

92.5%

Cut off used to define salt iodine category

Coverage RTK

Coverage Titration

RTK Sensitivity

RTK Specificity

AR =

PPV =

NPV =

Ability to identify true positivies

Ability to identify true negatives

Agreement rate (accuracy)

Positive predictive value

Negative predictive value

42 National Iodine Survey Report, Ghana 2015Ta

ble

4. H

ouse

hold

sal

t io

din

e ra

nge

by

surv

ey c

hara

cter

istic

s, fo

r al

l hou

seho

lds

with

a c

omp

lete

inte

rvie

w

* as

terix

den

otes

est

imat

e w

as b

ased

on

less

than

25

unw

eigh

ted

resp

onse

s[

] den

otes

est

imat

e w

as b

ased

on

25 to

50

unw

eigh

ted

resp

onse

s1

The

deno

min

ator

for t

his

indi

cato

r is

all h

ouse

hold

s w

ith s

alt (

i.e. n

ot in

clud

ing

hous

ehol

ds w

ith n

o sa

lt, w

here

sal

t not

pro

vide

d fo

r tes

ting,

or w

here

resu

lts m

issi

ng).

2 Th

e de

nom

inat

or fo

r thi

s in

dica

tor i

s al

l hou

seho

lds

with

a te

sted

sal

t sam

ple

or w

ith n

o sa

lt in

the

hous

ehol

d. T

his

repr

esen

ts th

e pr

opor

tion

of h

ouse

hold

s w

ithou

t acc

ess

to io

dine

th

roug

h io

dise

d sa

lt (e

ither

due

to u

sing

non

iodi

sed

salt

or to

not

hav

ing

salt

in th

e HH

) and

is th

e ba

ses

for e

stim

ates

use

d in

pre

viou

s M

ICS.

NATI

ONAL

North

non

-sal

tpr

oduc

ing

Mid

non

-sal

tpr

oduc

ing

Sout

h no

n-sa

ltpr

oduc

ing

Sout

h sa

ltpr

oduc

ing

Urba

n

Rura

l

Low

MPI

sco

re

(not

dep

rived

)

High

MPI

sco

re(d

epriv

ed)

Survey Strata MPI ScoreUrban/Rural

38.1

%(3

3.8,

42.

7)

25.5

%(1

8.5,

33.

9)

43.5

%(3

6.4,

50.

9)

30.8

%(2

3.1,

39.

8)

51.3

%(4

3.3,

59.

2)

39.3

%(3

3.4,

45.

4)

36%

(30,

42.

5)

35.7

%(3

0.8,

40.

9)

40.8

%(3

5.3,

46.

4)

32.6

%(2

8.6,

36.

9)

36.9

%(2

9.6,

44.

9)

37.9

%(3

0.8,

45.

6)

20.6

%(1

5.7,

26.

6)

29.5

%(2

4.4,

35.

1)

29.3

%(2

5, 3

4)

38.8

%(3

1, 4

7.3)

28.1

%(2

3.6,

33)

37.7

%(3

2.7,

43.

1)

20.7

%(1

7, 2

4.9)

32.1

%(2

4, 4

1.5)

[12.

5%]

(8.2

, 18.

7)

33.1

%(2

3.8,

43.

9)

[10.

3%]

(7.3

, 14.

3)

22.3

%(1

7.5,

27.

9)

17.6

%(1

1.6,

25.

6)

26%

(21.

2, 3

1.4)

14.8

%(1

0.8,

19.

9)

8.6%

(7.1

, 10.

5)

* 6%(4

.2, 8

.6)

15.5

%(1

1.2,

21.

1)

9%(5

.9, 1

3.4)

9.2%

(7.3

, 11.

5)

7.6%

(5, 1

1.5)

10.2

%(8

.2, 1

2.6)

6.7%

(4.8

, 9.4

)

1929

342

941

485

161

1256

673

1009

913

Hous

ehol

ds

targ

eted

for s

alt

colle

ctio

n an

d te

stin

g (n

)

Perc

ent o

f ta

rget

ed

hous

ehol

ds

in w

hich

sal

t w

as te

sted

% H

ouse

hold

s w

ith n

o sa

lt or

no

n-io

dise

d sa

lt (<

5ppm

)2<

5ppm

5-14

.9 p

pm15

-39.

9 pp

m≥4

0 pp

m

Hous

ehol

ds

in w

hich

sal

t w

as te

sted

(n

)

% (9

5% C

I) Ho

useh

olds

with

sal

t iod

ine

with

in e

ach

iodi

ne ra

nge

1Ho

useh

olds

w

ith n

o sa

lt (n

)

113

33 32 40 8 77 36 58 54

1563

249

777

396

140

1021

541

820

739

81.0

%

73.0

%

82.6

%

81.5

%

87.4

%

81.3

%

80.4

%

81.2

%

81.0

%

42.3

%

34.1

%

45.8

%

37.2

%

53.8

%

43.5

%

40.0

%

40.0

%

44.8

%


Tab

le 5

. Hou

seho

ld s

alt

iod

ine

cate

gorie

s, m

edia

n an

d m

ean

iod

ine

pp

m v

alue

s b

y su

rvey

cha

ract

eris

tics

1 Th

e de

nom

inat

or fo

r thi

s in

dica

tor i

s al

l hou

seho

lds

with

sal

t (i.e

. not

incl

udin

g ho

useh

olds

with

no

salt,

whe

re s

alt n

ot p

rovi

ded

for t

estin

g, o

r whe

re re

sults

mis

sing

).2

Only

cal

cula

ted

for i

odis

ed s

alt (

salt

with

>=

5pp

m io

dine

)

% (9

5% C

I) Ho

useh

olds

with

sal

t iod

ine

with

in e

ach

iodi

ne ra

nge

1

NATI

ONAL

North

non

-sal

tpr

oduc

ing

Mid

non

-sal

tpr

oduc

ing

Sout

h no

n-sa

ltpr

oduc

ing

Sout

h sa

ltpr

oduc

ing

Urba

n

Rura

l

Low

MPI

sco

re

(not

dep

rived

)

High

MPI

sco

re(d

epriv

ed)


38.1

%(3

3.8,

42.

7)

25.5

%(1

8.5,

33.

9)

43.5

%(3

6.4,

50.

9)

30.8

%(2

3.1,

39.

8)

51.3

%(4

3.3,

59.

2)

39.3

%(3

3.4,

45.

4)

36%

(30,

42.

5)

35.7

%(3

0.8,

40.

9)

40.8

%(3

5.3,

46.

4)

61.9

%(5

7.3,

66.

2)

74.5

%(6

6.1,

81.

5)

56.5

%(4

9.1,

63.

6)

69.2

%(6

0.2,

76.

9)

48.7

%(4

0.8,

56.

7)

60.7

%(5

4.6,

66.

6)

64%

(57.

5, 7

0)

64.3

%(5

9.1,

69.

2)

59.2

%(5

3.6,

64.

7)

29.3

%(2

5.3,

33.

6)

37.6

%(2

8.8,

47.

3)

18.6

%(1

3.6,

24.

8)

48.6

%(3

8.3,

59)

19.3

%(1

4.6,

24.

9)

31.4

%(2

6.5,

36.

9)

25.2

%(1

7.7,

34.

5)

36.2

%(3

1, 4

1.7)

21.5

%(1

7.2,

26.

6)

13.3

(8.0

, 59.

8)

16.0

(8.0

, 70.

5)

9.3

(6.7

, 30.

6)

37.2

(12.

0, 7

0.5)

9.4

(6.7

, 32.

1)

16.0

(8.0

, 66.

5)

10.6

(6.7

, 51.

0)

22.7

(8.0

, 69.

2)

9.3

(6.7

, 40.

4)

33.7

(30.

0, 3

7.4)

39.6

(32.

2, 4

7.0)

25.1

(19.

5, 3

0.8)

45.4

(38.

1, 5

2.8)

25.0

(20.

9, 2

9.1)

36.8

(32.

2, 4

1.4)

28.1

(21.

3, 3

4.9)

40.0

(35.

1, 4

4.8)

26.0

(21.

9, 3

0.2)

No a

dded

io

dine

<

5ppm

Any

adde

d io

dine

≥5

ppm

Adeq

uate

iodi

ne

≥5pp

m

Hous

ehol

ds

whe

re s

alt h

ad

som

e io

dine

(≥

5ppm

) (n)

Hous

ehol

ds

in w

hich

sa

lt w

as

test

ed (n

)

Med

ian2

(2

5th

to 7

5th

perc

entil

e)

ppm

iodi

ne

Mea

n2

(95%

CI)

ppm

iodi

ne

1563

249

777

396

140

1021

541

820

739

967

186

439

274

68 620

346

527

437

44 National Iodine Survey Report, Ghana 2015Fi

gure

1. H

ouse

hold

use

of n

on-i

odis

ed, i

nad

equa

tely

iod

ised

and

ad

equa

tely

iod

ised

sal

t b

y su

rvey

cha

ract

eris

tics

100% 90%

80%

70%

60%

50%

40%

30%

20%

10% 0%

NATI

ONAL

North

no

n-sa

lt pr

oduc

ing

Mid

no

n-sa

lt pr

oduc

ing

Sout

h no

n-sa

lt pr

oduc

ing

Low

MPI

sc

ore

(not

de

priv

ed)

High

MPI

sc

ore

(dep

rived

)

Sout

h sa

lt pr

oduc

ing

Urba

nRu

ral

No a

dded

iodi

ne<

5ppm

Adeq

uate

iodi

ne>

=15

ppm

Inad

equa

te a

dded

iodi

ne 5

-14.

9ppm

29.3

%

37.6

%

48.6

%

18.6

%19

.3%

31.4

%25

.2%

21.5

%

36.2

%

32.6

%

36.9

%20

.6%

37.9

%29

.4%

29.3

%38

.8%

37.7

%

28.1

%

38.1

%

25.5

%30

.8%

43.5

%51

.3%

39.3

%36

.0%

40.8

%35

.7%


Tab

le 6

. Hou

seho

ld s

alt

by

pre

senc

e of

iod

ine

(sem

i-q

uant

itativ

e an

alys

is u

sing

the

MB

I rap

id t

est

kit

(RTK

))

% (9

5% C

I) Ho

useh

olds

with

sal

t RTK

col

our c

hang

e in

dica

ting

diffe

rent

sal

t iod

ine

cate

gorie

s

NATI

ONAL

North

non

-sal

tpr

oduc

ing

Mid

non

-sal

tpr

oduc

ing

Sout

h no

n-sa

ltpr

oduc

ing

Sout

h sa

ltpr

oduc

ing

Urba

n

Rura

l

Low

MPI

sco

re

(not

dep

rived

)

High

MPI

sco

re(d

epriv

ed)


36.1

%(3

1.7,

40.

7)

28.9

%(2

1.3,

38)

43.6

%(3

6.1,

51.

4)

26.1

%(1

9.9,

33.

4)

36.8

%(2

8.1,

46.

4)

35.6

%(2

9.7,

42.

1)

37%

(30.

9, 4

3.5)

31.1

%(2

6.2,

36.

5)

41.6

%(3

6.4,

47.

1)

21.3

%(1

8.4,

24.

6)

25.2

%(1

9.9,

31.

5)

20.5

%(1

5.9,

26)

16.1

%(1

1.3,

22.

4)

33.4

%(2

6.1,

41.

5)

18.1

%(1

4.6,

22.

1)

27.4

%(2

2.9,

32.

4)

19.7

%(1

6, 2

3.9)

23.2

%(1

9.4,

27.

4)

42.6

%(3

7.9,

47.

4)

45.8

%(3

6.8,

55.

1)

35.9

%(2

8.9,

43.

6)

57.8

%(4

7.5,

67.

5)

29.9

%(2

2.4,

38.

5)

46.3

%(4

0, 5

2.8)

35.6

%(2

8.7,

43.

2)

49.2

%(4

3.7,

54.

8)

35.2

%(2

9.9,

40.

9)

113

33 32 40 8 77 36 58 54

No c

olou

r ch

ange

(0

ppm

iod

ine)

% T

arge

ted

hous

ehol

ds

whe

re s

alt t

este

d

Hous

ehol

ds ta

rget

ed

(com

plet

e in

terv

iew

s)n

= 1

929

Hous

ehol

ds

whe

re s

alt t

este

dw

ith R

TK (n

)

Slig

ht c

olou

r cha

nge

(1-1

4 pp

m io

dine

)

Deep

col

our

chan

ge(>

=15

ppm

)

#Ho

useh

olds

with

no

salt

86.1

%

84.5

%

86.5

%

85.3

%

88.9

%

85.9

%

86.3

%

85.9

%

86.4

%

1660

289

815

414

143

1079

581

867

789


Awareness and Knowledge of Iodine Deficiency and Iodised Salt

Awareness and knowledgeAt the national level, respondents were twice as likely to have heard of iodised salt (81.7%) than to have heard of iodine deficiency (40.2%) – See Table 7. A similar trend was seen in all strata, although in the North and South-salt-producing areas, more than double the respondents were aware of iodised salt when compared with awareness of iodine deficiency, while in the South-non-salt-producing areas, awareness of iodine deficiency was higher than in other strata therefore the difference was less (52.9% had heard of iodine deficiency and 86.4% had heard of iodised salt). Awareness of iodine deficiency and iodised salt was slightly (but not significantly) higher among respondents from urban areas than those from rural areas. Respondents from non-deprived households (low MPI) had a significantly higher awareness of iodine deficiency (54.3%) and iodised salt (90.6%) when compared with respondents from deprived (high MPI) households (24.9% and 71.8% respectively).

The household salt of respondents who had heard of iodine deficiency was significantly more likely to be adequately iodised (36.9%) and less likely to be non-iodised (30.7%) than the household salt of respondents who had not heard of iodine deficiency (24.0% adequately iodised, 43.2% non-iodised). See Table 8.

Similarly, the household salt of respondents who had heard of iodised salt was significantly more likely to be adequately iodised (31.7%) and less likely to be non-iodised (36.1%) than the household salt of respondents who had not heard of iodised salt (18.1% adequately iodised, 47.5% non-iodised). See Table 8.

Figure 2 shows that among respondents who had heard of iodine deficiency, 81.2% nationally knew a correct benefit of iodine. The majority of respondents (64.4%) mentioned prevention of goitre as a benefit. Responses for other options (it was a multiple option question) were considered correct if they fitted into one of the following categories: Improves intelligence/ school performance, Improves pregnancy outcomes/reduces miscarriages and still births, Improves health in infants and children, Improves health in adults and adolescents, Improves child growth, Improves brain development, Prevents cretinism. Generally there were very low numbers of responses for any of these categories. The only other response with sufficient numbers to be considered reliable (mentioned by 19% of respondents) was “Improves health among adults and adolescents”. Almost 13% of respondents who said they had heard of iodine deficiency reported that they did not know of any specific benefit of iodine.

Among respondents who were aware of iodine deficiency, their knowledge of the benefits of iodine was similar across sub-groups. However, significant differences in knowledge were still found: between respondents from the South-non-salt-producing area (86.3% knew a correct consequence) and respondents from the North (74.2% knew a correct consequence); and between respondents from households with a low MPI compared with those from households with a high MPI (84.3% and 73.7% knew a correct consequence, respectively).

The majority of respondents (92.9%) with knowledge of iodine deficiency and who reported to know a preventative method, said that using iodised salt was the best method to prevent iodine deficiency (Figure 2), however it can be seen from Table 8 that of these respondents, over a quarter (29.6%) were using non-iodised salt in the household. Over 90% of these


respondents in all sub-groups said that using iodised salt was the best method to prevent iodine deficiency. The highest percentage was among respondents from high MPI (deprived) households (98.0%), however the sample size for this estimate was relatively small compared with that for households with low MPI.

Use of iodised saltRespondents who had heard of iodised salt were asked whether their cooking and table salt (where different salt was used) were iodised. Nationally, 59.7% responded that they believed their cooking salt was iodised while 37.8% believed their table salt was iodised. The highest percent of responses was in the South-non-salt-producing area (74.7% believed their cooking salt was iodised and 46.5% for table salt) and among households with low MPI (non-deprived) (68.7% believed their cooking salt was iodised and 42.7% for table salt). See Table 7.

Of respondents who reported using iodised salt for cooking in the household, 47.4% were using adequately iodised salt while 25.1% were using non-iodised salt. This was highly significantly different to the situation among households where the respondent did not think that cooking salt was iodised (8.0% were using adequately iodised salt and 53.1% were using non-iodised salt). See Table 8.

A question was included to ask those who had heard of iodised salt whether any household member had concerns about using iodised salt. Data are not presented because the number of positive responses (that someone had a concern about using iodised salt) was very small (2% of the total sample, unweighted n = 26). Fifteen of these responses referred to perceived negative effects of iodine on health, three were concerns about chemicals, three related to perceived changes to taste and two said it was more expensive.

Respondents who reported that they were not using iodised salt for cooking (n = 549) gave the following reasons (reasons given by <1% of the survey sample are not included): iodised salt was not readily available (29.6%), iodised salt was more expensive than non-iodised salt (23.8%), someone in the household preferred coarse salt (perceived to be non-iodised) (8.0%), that is wasn’t part of usual cultural habits to use iodised salt (8.5%), didn’t use any salt for cooking (6.4%), and 20.6% of respondents said they did not know why they did not use iodised salt for cooking.

Respondents who reported that they were not using iodised salt as table salt (n = 906) gave the following reasons (reasons given by <1% of the survey sample are not included): iodised salt was not readily available (13.6%), iodised salt was more expensive than non-iodised salt (9.4%), someone in the household preferred coarse salt (perceived to be non-iodised) (2.1%), that is wasn’t part of usual cultural habits to use iodised salt (7.8%), didn’t add any salt to prepared food for health reasons (32.8%) or for other reasons (21.9%), and 10.1% of respondents said they did not know why they did not use iodised table salt.


Table 7. Description of respondent awareness of and knowledge about iodine deficiency and iodised salt, along with perception of household iodised salt use

NATIONAL




South saltproducing

Urban

Rural

Low MPI score (not deprived)


MPI

Sco

reUr

ban/

Rura

l

40.2%(36.3, 44.1)

26.6%(22, 31.8)

40%(33.9, 46.3)

52.9%(44.1, 61.6)

31.8%(24.9, 39.6)

43.3%(38.2, 48.5)

34.4%(28.6, 40.6)

54.3%(49.1, 59.3)

24.9%(21.1, 29.1)

81.7%(78.8, 84.3)

75.8%(69.9, 80.9)

81.6%(76.6, 85.7)

86.4%(80.8, 90.5)

80.4%(74.1, 85.5)

84.3%(81.1, 87.1)

76.8%(71.0, 81.7)

90.6%(87.8, 92.9)

71.8%(67.8, 75.6)

59.7%(55.0, 64.2)

60.1%(51.8, 67.8)

52.6%(44.5, 60.6)

74.7%(66.9, 81.1)

52.3%(44.9, 59.7)

63.5%(57.9, 68.6)

52.0%(42.8, 61.0)

68.7%(63.8, 73.2)

47.0%(41.0, 53.2)

37.8%(34.1, 41.6)

25.5%(17.4, 35.7)

38.1%(32.8, 43.7)

46.5%(38.6, 54.6)

32.1%(26.7, 37.9)

39.8%(35.3, 44.6)

33.5%(27.3, 40.3)

42.7%(37.9, 47.5)

30.7%(26, 35.8)

Heard of iodine deficiency

Percent (95% CI)

All respondents All respondents

Only calculated for those whohad heard of iodised salt

Perceived use iodised salt for cooking

Iodised Salt awareness and perception of usePercent (95% CI)

Perceived use iodised salt for table salt

Heard ofiodised salt

1929

342

941

485

161

1256

673

1009

913

Number


Figu

re 2

. Per

cent

of r

esp

ond

ents

who

kne

w a

cor

rect

con

seq

uenc

e an

d p

erce

nt o

f res

pon

den

ts w

ho r

esp

ond

ed

that

usi

ng io

dis

ed s

alt

was

the

bes

t m

etho

d t

o p

reve

nt io

din

e d

efici

ency

.

Know

cor

rect

con

sequ

ence

of i

odin

e de

ficie

ncy1

B

elie

ve th

at u

sing

iodi

sed

salt

is th

e m

ost e

ffect

ive

met

hod

to p

reve

nt io

dine

defi

cien

cy2

100% 90%

80%

70%

60%

50%

40%

30%

20%

10% 0%

81.2

%

NATIONAL

North non-salt producing

Mid non-salt producing

South non-salt producing


Urban

Rural

Low MPI score

High MPI score

74.2

%

79.9

%86

.3%

76.9

%83

.2%

76.3

%

84.3

%

73.7

%

92.9

%91

.0%

94.7

%91

.0%

92.5

%93

.5%

91.4

%91

.2%

98.0

%

Kno

wle

dge

of c

orre

ct c

onse

quen

ces

was

onl

y as

ked

for

resp

onde

nts

who

had

hea

rd o

f iod

ine

defic

ienc

y (n

= 7

75)

Bes

t met

hod

to p

reve

nt io

dine

defi

cien

cy w

as o

nly

aske

d fo

r re

spon

dent

s w

ho s

aid

they

kne

w o

f a p

reve

ntat

ive

met

hod

(n =

469

)1

Det

erm

ined

as

resp

onde

nts

that

fitt

ed in

one

of t

he fo

llow

ing

cate

gorie

s: P

reve

nts

goitr

e, im

prov

es in

tellig

ence

/ sc

hool

per

form

ance

, im

prov

es p

regn

ancy

out

com

es/

redu

ces

mis

carr

iage

s an

d st

ill bi

rths

, im

prov

es h

ealth

in in

fant

s an

d ch

ildre

n, im

prov

es h

ealth

in a

dults

and

ado

lesc

ents

, im

prov

es c

hild

gro

wth

, im

prov

es b

rain

de

velo

pmen

t, pr

even

ts c

retin

ism

2 O

ther

resp

onse

s in

clud

ed: T

akin

g io

dine

sup

plem

ents

(2.6

% o

f res

pond

ents

nat

iona

lly) a

nd e

atin

g se

afoo

d/se

awee

d (1

.9%

of r

espo

nden

ts n

atio

nally

). 2.

5% o

f re

spon

dent

s w

ho s

aid

they

kne

w a

met

hod

then

sai

d th

at th

ey d

id n

ot k

now

a m

etho

d.


ble

8. S

alt

iod

ine

cate

gory

by

resp

ond

ent

awar

enes

s of

iod

ine

defi

cien

cy a

nd io

dis

ed s

alt

Num

ber

< 5

ppm

5-14

.9pp

m

≥ 15

ppm

HH salt iodine content

637

30.7

%(2

5.8,

36.

1)

32.3

%(2

7.5,

37.

6)

36.9

%(3

1.7,

42.

5)

Hear

d of

io

dine

de

ficie

ncy

Hadn

’t he

ard

of io

dine

de

ficie

ncy

Know

cor

rect

co

nseq

uenc

e of

iodi

ne

defic

ienc

y1

IDD

know

ledg

e (%

)IS

aw

aren

ess

and

perc

eptio

n of

use

(%)

Didn

’t kn

ow

corr

ect

cons

eque

nce

iodi

ne

defic

ienc

y

Thin

k us

ing

iodi

sed

salt

is

best

met

hod

to p

reve

nt

iodi

ne

defic

ienc

y

Didn

’t m

entio

n us

ing

iodi

sed

salt

as b

est

met

hod

to

prev

ent i

odin

e de

fienc

y

Perc

eive

d di

d no

t use

io

dise

d sa

lt fo

r coo

king

Perc

eive

d us

e io

dise

d sa

lt fo

r co

okin

g2

Hadn

’t he

ard

of

iodi

sed

salt

Hear

d of

io

dise

d sa

lt

925

43.2

%(3

7.9,

48.

7)

32.8

%(2

8.0,

38.

0)

24.0

%(1

9.8,

28.

8)

120

33.9

%(2

3.4,

46.

3)

36.4

%(2

6.4,

47.

6)

29.7

%(1

9.4,

42.

6)

358

29.6

%(2

3.9,

36.

0)

30.4

%(2

4.2,

37.

4)

40.0

%(3

3.8,

46.

6)

27

37.8

%(1

9.7,

59.

9)

20.6

%(7

.8, 4

4.3)

41.6

%(2

2.6,

63.

4)

1282

36.1

%(3

1.6,

40.

8)

32.2

%(2

8.0,

36.

7)

31.7

%(2

7.4,

36.

4)

281

47.5

%(4

0.2,

54.

8)

34.4

%(2

8.3,

41.

0)

18.1

%(1

2.6,

25.

4)

753

25.1

%(2

0.4,

30.

5)

27.6

%(2

2.7,

33.

1)

47.4

%(4

2.0,

52.

8)

464

53.1

%(4

6.9,

59.

2)

38.9

%(3

3.3,

44.

7)

8.0%

(5.6

, 11.

4)

517

30.0

%(2

5.0,

35.

5)

31.4

%(2

6.2,

37.

1)

38.6

%(3

3.6,

43.

9)

All r

espo

nden

tsAl

l res

pond

ents

Only

cal

cula

ted

for t

hose

who

ha

d he

ard

of io

dise

d sa

ltOn

ly c

alcu

late

d fo

r tho

se w

ho h

ad h

eard

of i

odin

e de

ficie

ny

1 D

eter

min

ed a

s re

spon

se th

at fi

tted

in o

ne o

f the

follo

win

g ca

tego

ries:

Pre

vent

s go

itre,

impr

oves

inte

lligen

ce/

scho

ol p

erfo

rmac

e, im

prov

es p

regn

ancy

out

com

es/r

educ

es m

is-

carr

iage

s an

d st

ill bi

rths

, im

prov

es h

ealth

in in

fant

s an

d ch

ildre

n, im

prov

es h

ealth

in a

dults

and

ado

lesc

ents

, im

prov

es b

rain

dev

elop

men

t, pr

even

ts c

reni

tism

2 T

he h

ouse

hold

sal

t sam

ple

requ

este

d fo

r an

alys

is o

f iod

ine

cont

ent w

as th

e sa

lt us

ed fo

r co

okin

g th

e pr

evio

us e

veni

ng m

eal,

or e

lse

gene

rally

use

d fo

r co

okin

g. T

here

fore

this

cr

oss

tabu

latio

n w

as o

nly

done

for

cook

ing

salt

and

not f

or ta

ble

salt


Source of information about iodised saltTable 9 shows that the most frequently reported source of information about iodised salt (multiple responses were possible) was the radio, with 60.3% of all respondents nationally mentioning this. The second most mentioned source was television (37.1% of respondents), with health facilities and salt retailers each mentioned by approximately 17% of respondents. The trend in responses varied by sub-group, except for retailers, where there was little variation in the percent of responses within each sub-group, ranging from 15-20%. By strata, health facilities were a more frequently reported source of information in the North (28.7%) than in any other strata (range 10.1 to 17.4%), whereas television and radio were less frequently reported in the North (22.0% and 33.2% respectively) than in any other strata (range from 42% to 47% for television and 55% to 68% for radio).

There was little difference in the frequency of reported sources of information between urban and rural areas or between low and high MPI households, except for television, which was more frequently mentioned by respondents from low MPI (non-deprived) households (44.1%) than by respondents from high MPI households (27.4%).

Table 9. Detailed description of where respondents had heard about iodised salt

NATIONAL




South saltproducing

Urban

Rural



MPI

Sco

reUr

ban/

Rura

l

16.7%(13.9, 20)

20.0%(15.6, 25.1)

15.9%(11.9, 20.9)

15.7%(9.8, 24.3)

18.4%(13.6, 24.4)

16.9%(13.2, 21.3)

16.3%(12.6, 20.9)

15.5%(12.2, 19.5)

18.3%(15.1, 22.1)

37.1%(32.5, 41.9)

22.0%(16.2, 29.2)

37.8%(29.9, 46.5)

41.9%(35, 49.3)

47%(39.6, 54.5)

40.2%(34.6, 46.1)

30.7%(23.6, 38.9)

44.1%(38.8, 49.5)

27.4%(22.6, 32.8)

60.3%(56.7, 63.9)

33.2%(25.9, 41.4)

66.3%(59.9, 72.1)

67.7%(61.9, 73)

55.4%(50.6, 60.1)

60.9%(56, 65.5)

59.3%(53, 65.3)

60.6%(56.1, 64.9)

60.2%(55.0, 65.1)

Where heard about iodised salt - multiple responses possible 1

Percent (95% CI)

1576

259

768

419

129

1059

516

915

656

NumberRespondents

who had heard of

iodised salt

Surv

ey/S

trata

Health Facility Retailer Television Radio

17.0%(14.5, 19.9)

28.7%(23.4, 34.7)

17.4%(13.2, 22.6)

[11.3%](8, 15.8)

[10.1%](7.4, 13.7)

16.6%(13.6, 20)

18.0%(13.3, 24.0)

15.9%(12.9, 19.4)

18.6%(15.1, 22.7)

1 Additional responses where numbers were too low to report were: newspaper or magazine (1.0% of respondents nationally), friends or family (6.5%) of responses nationally, but 17.0% of responses in the south salt producing area), school or college (6.5% of responses nationally) and ”other” sources” (4.3% of responses nationally)


Iodised salt purchasing practices and preferences

Salt packaging and labellingApproximately one third of survey respondents (33.9%) from households with salt at the time of the survey reported that their household salt was obtained in a sealed pack, with the remainder using loose salt in some type of unsealed packaging or container. See Table 10. The salt was observed by the interviewer to be in its original packaging in around 60% of households reporting to use salt obtained in a sealed pack (it is usual to put salt in another container after purchasing). Households in the Mid stratum were significantly less likely to obtain salt in a sealed pack (25.2%) than households in the North (40.2%) and in the South-non-salt-producing (46.3%) strata. Urban (39.0%) and less deprived (44.1%) households were significantly more likely to obtain salt in sealed packaging than in rural (24.5%) and deprived (22.6%) households respectively.

The same table (Table 10) shows that about one third of respondents (33.9%) who had heard of iodised salt and had salt in their household at the time of the survey, reported to look for iodised salt at the point of purchase. The South-non-salt-producing stratum had the highest percent of respondents reporting this, 51.5%, which was significantly higher than in the South-salt-producing (26.2%) and in the Mid (25.2%) strata. Again, responses were significantly higher in low MPI (41.7%) and urban (38.0%) households than in high MPI (22.9%) and rural (25.4%) households, respectively.

Of households reporting to use salt obtained in a sealed pack, three-quarters (77.4%) nationally used brand name salt. The Mid stratum had the highest reported percentage at 83.1%, however since this stratum had the lowest response for obtaining salt in a sealed pack, the denominator was relatively small compared to the other strata.

Among households reporting to have obtained salt in a sealed pack and where the respondent had heard of iodised salt, 81.9% nationally reported to use salt with an iodine label or logo. The South-non-salt-producing stratum reported a significantly higher percent for this response (87.7%) when compared with the North (65.9%) and the South-salt-producing (61.8%) strata. The Mid stratum response for using salt from a pack with an iodine label or logo was 87.7%, however the relatively small size of the unweighted denominator should be taken into account in interpretation of this estimate.

Of the 33.9% of respondents who reported to look for iodised salt at the point of purchase, 81.4% said they determined that the salt was iodised by the packet logo or label. The remainder mostly (15.7%) reported that they looked for a specific brand of salt that they’d heard was iodised, this response was more frequently reported in the North and in South-salt-producing areas where about a quarter of respondents said they based their purchase on having heard that a specific brand of salt was iodised.

Categorisation of household salt iodine level by these responses is shown in Table 11. For all positive responses: sealed pack, looked for iodised salt, used brand name salt, and used salt with an iodine logo or label; the percent of households using adequately iodised salt was significantly higher than the percent among households who reported a negative response for each question respectively. The difference in the percent households using adequately iodised salt was particularly significant when comparing salt reported to have been obtained


in a sealed pack (adequately iodised in 59.0% of these households) with salt obtained loose (adequately iodised in 15.1% of these households) and comparing salt from households where the respondent reported to look for iodised salt at the point of purchase (adequately iodised in 65.6% of these households) with salt from households where this was not reported (adequately iodised in 14.3% of these households).

Tab

le 1

0. H

ouse

hold

sal

t p

acka

ging

, lab

ellin

g an

d p

urch

asin

g p

ract

ices

by

surv

ey c

hara

cter

istic

s

* as

terix

den

otes

est

imat

es b

ased

on

less

than

25

unw

eigh

ted

resp

onse

s[

] den

otes

est

imat

es b

ased

on

25 to

50

unw

eigh

ted

resp

onse

s1

18 re

spon

dent

s (<

2% o

f tot

al) r

epor

ted

not t

o kn

ow w

heth

er s

alt w

as b

ough

t in

a se

aled

pac

kage

, the

se d

ata

are

not s

how

n2

64 o

f res

pond

ents

sai

d th

at s

omeo

ne e

lse

boug

ht th

e sa

lt, 3

5 sa

id th

ey d

idn’

t kno

w a

nd 8

sai

d th

ey’d

col

lect

ed th

e sa

lt fro

m th

e si

te o

f pro

duct

ion

(6 o

f the

se fr

om

the

sout

h sa

lt pr

oduc

ing

area

).3

15 re

spon

dent

s re

port

ed n

ot to

kno

w if

thei

r sa

lt w

as fr

om a

larg

er b

rand

or

not.

NATI

ONAL

North

non

-sal

tpr

oduc

ing

Mid

non

-sal

tpr

oduc

ing

Sout

h no

n-sa

ltpr

oduc

ing

Sout

h sa

ltpr

oduc

ing

Urba

n

Rura

l

Low

MPI

sco

re

(not

dep

rived

)

High

MPI

sco

re(d

epriv

ed)


33.9

%(2

9.7,

38.

3)

40.2

%(3

2.7,

48.

2)

25.2

%(1

9, 3

2.7)

46.3

%(3

8.2,

54.

6)

36.7

%(2

8.4,

45.

9)

39%

(33.

7, 4

4.5)

24.5

%(1

8, 3

2.3)

44.1

%(3

8.8,

49.

4)

22.6

%(1

8.9,

26.

8)

For a

ll ho

useh

olds

with

sal

t

Salt

pack

agin

g an

d pu

rcha

sed

prac

tice

- re

spon

dent

resp

onse

s

Look

ed fo

r io

dise

d sa

lt at

po

int o

f pu

rcha

se2

Perc

ent (

95%

CI)

Boug

ht s

alt i

n a

seal

ed p

acka

gePe

rcen

t (95

% C

I)

No, u

nsea

led

pack

aged

as

loos

e sa

lt

Repo

rted

to

use

bran

d na

me

salt3

Perc

ent (

95%

CI)

Repo

rted

to u

se s

alt

with

logo

or l

abel

ed

as io

dise

d Pe

rcen

t (95

% C

I)Nu

mbe

rNu

mbe

rNu

mbe

rNu

mbe

rYe

s

Hous

ehol

ds w

ith s

alt a

nd w

here

th

e re

spon

dent

had

hea

rd o

f io

dise

d sa

lt

Hous

ehol

ds re

porti

ng th

at

salt

boug

ht in

a s

eale

d pa

ckag

e

Hous

ehol

ds re

porti

ng th

at s

alt b

ough

t in

sea

led

pack

& re

spon

dent

had

he

ard

of io

dise

d sa

lt

64.7

%(6

0.2,

69.

0)

58.7

%(5

0.4,

66.

4)

72.8

%(6

5.2,

79.

3)

53.2

%(4

4.9,

61.

3)

62.7

%(5

3.6,

71.

0)

59.4

%(5

3.7,

64.

8)

74.6

%(6

7.1,

80.

9)

53.8

%(4

8.6,

58.

9)

76.9

%(7

2.5,

80.

7)

33.9

%(2

9.8,

38.

2)

36.3

%(2

9.8,

43.

3)

25.2

%(1

8.9,

32.

7)

51.5

%(4

3.5,

59.

3)

26.2

%(1

9.8,

33.

8)

38.0

%(3

2.9,

43.

4)

25.4

%(1

8.5,

33.

8)

41.7

%(3

6.7,

46.

8)

22.9

%(1

9.0,

27.

4)

77.4

%(7

2.6,

81.

5)

69.0

%(6

0.9,

76.

1)

83.1

%(7

3.5,

89.

7)

79.5

%(7

0.9,

86.

0)

65.0

%(5

4.0,

74.

7)

78.9

%(7

3.3,

83.

6)

72.9

%(6

2.8,

81.

1)

80.1

%(7

5.0,

84.

5)

71.2

%(6

3.1,

78.

0)

81.9

%(7

7.2,

85.

8)

65.9

%(5

6.6,

74.

1)

87.7

%(7

8.1,

93.

5)

89.0

%(8

1.7,

93.

6)

61.8

%(5

1.7,

71.

0)

82.9

%(7

7.7,

87.

1)

78.5

%(6

5.2,

87.

6)

86.0

%(8

1.0,

89.

8)

72.0

%(6

3.3,

79.

2)

1816

309

909

445

153

1179

637

951

859

1481

235

738

384

124

994

488

862

614

616

124

229

206

56 460

156

419

194

575

108

220

198

49 437

138

404

169


ble

11.

Hou

seho

ld s

alt

iod

ine

cate

gory

by

salt

pac

kagi

ng, l

abel

ling

and

res

pon

den

t re

por

ted

sal

t p

urch

asin

g p

ract

ices

* as

terix

den

otes

est

imat

es b

ased

on

less

than

25

unw

eigh

ted

resp

onse

s[

] den

otes

est

imat

es b

ased

on

25 to

50

unw

eigh

ted

resp

onse

s1

18 re

spon

dent

s (<

2% o

f tot

al) r

epor

ted

not t

o kn

ow w

heth

er s

alt w

as b

ough

t in

a se

aled

pac

kage

, the

se d

ata

are

not s

how

n2

Exc

lude

s: 6

4 re

spon

dent

s w

ho s

aid

that

som

eone

els

e bo

ught

the

salt,

35

said

they

did

n’t k

now

and

8 s

aid

they

’d c

olle

cted

the

salt

from

the

site

of p

rodu

ctio

n (6

from

the

sout

h sa

lt pr

oduc

ing

area

).3

15 re

spon

dent

s re

port

ed n

ot to

kno

w if

thei

r sa

lt w

as fr

om a

larg

er b

rand

or

not.

4 In

dica

tion

of th

e pe

rcen

t of r

espo

nses

ver

ified

by

the

inte

rvie

wer

thro

ugh

obse

rvat

ion.

In m

ost c

ases

, con

sum

ers

tran

sfer

the

salt

to a

n al

tern

ativ

e st

orag

e co

ntai

ner

afte

r op

enin

g.

HH salt iodine content

Num

ber (

HHs

with

sal

t)

Yes

506

17.7

%(1

4.2,

21.

8)

23.3

%(1

8.8,

28.

6)

59.0

%(5

3.3,

64.

6)

No 1043

48.0

%(4

2.5,

53.

6)

36.9

%(3

1.9,

42.

2)

15.1

%(1

1.6,

19.

4)

Yes

415

13.8

%(1

0.5,

18.

0)

20.6

%(1

6.0,

26.

2)

65.6

%(5

9.8,

70.

9)

No 808

48.2

%(4

2.4,

54.

1)

37.5

%(3

2.2,

43.

2)

14.3

%(1

1.1,

18.

1)

Yes

386

[12.

9%]

(9.5

, 17.

2)

21.6

%(1

6.4,

27.

9)

65.5

%(5

9.2,

71.

4)

Yes

385

[14.

4%]

(10.

8, 1

9.0)

20.2

%(1

4.9,

26.

8)

65.3

%(5

8.6,

71.

5)

No 40

[42.

6%]

(28.

5, 5

7.9)

[21.

7%]

(12.

4, 3

5.4)

[35.

7%]

(22.

8, 5

1.0)

No 81

[35.

9%]

(25.

5, 4

7.8)

[28.

0%]

(18.

5, 4

0.0)

[36.

1%]

(25.

9, 4

7.7)

313

[18.

3%]

21.2

%

60.5

%

< 5

ppm

5-14

.9pp

m

≥ 15

ppm

Salt

iodi

ne c

onte

nt b

y pa

ckag

ing

and

purc

hasi

ng p

ract

ice

For a

ll ho

useh

olds

with

sal

t io

dine

test

resu

ltsHH

s re

porti

ng o

n sa

lt pa

ckag

ing

and

with

sal

t iod

ine

test

resu

lts

HHs

with

sal

t and

re

spon

dent

had

hea

rd

of io

dise

d sa

lt &

salt

test

resu

lts

Repo

rted

to b

uy s

alt i

n a

seal

ed p

acka

ge1

Perc

ent (

95%

CI)

Repo

rted

to u

se b

rand

na

me

salt3

Perc

ent (

95%

CI)

Repo

rted

to u

se s

alt w

ith lo

go

or la

bele

d as

iodi

sed

Perc

ent (

95%

CI)

HHS

repo

rtin

g to

buy

sa

lt in

a s

eale

d pa

ck

AND

salt

obse

rved

to

be in

orig

inal

pac

k4

Perc

ent (

95%

CI)

Look

ed fo

r iod

ised

sal

t at

poi

nt o

f pur

chas

e2

Perc

ent (

95%

CI)

HHs

with

sal

t bou

ght i

n se

aled

pac

k &

resp

onde

nt

had

hear

d of

iodi

sed

salt

& sa

lt te

st re

sults


Household salt characteristicsNationally, 59.4% of households with salt iodine results were using salt with a fine, powdered, grain type; 24.6% were using coarse salt with a small grain size and 16.0% were using coarse salt with a large grain size. See Table 12.

Households in the South-salt-producing area were significantly less likely to use powdered grain salt (31.0%) when compared with households in the Mid (68.0%) and South-non-salt-producing (61.3%) areas, and significantly more likely to be using coarse salt with a small grain size (47.7%) than households in the North (20.9%), the Mid (21.7%) and South-non-salt-producing (24.3%) areas. In the North, households were more likely to use coarse salt with a large grain (33.4%) and this was significantly different to its use in the Mid (10.3% of households) and South-non-salt-producing (14.3% of households) areas.

There was little difference in salt grain type by urban and rural residence type. However households with a low MPI (less deprived) were found to be significantly more likely to use powdered salt (69.1%) and less likely to use coarse salt with either a small (18.8%) or large (12.2%) grain than households with a high MPI (48.7% powdered, 30.9% coarse small grain, 20.3% coarse large grain).

Table 13 shows that nearly a quarter of powdered salt (22.3%) was found to have no added iodine (<5ppm) while almost half (45.2%) was adequately iodised. Less than 10% of coarse grain salt was adequately iodised however almost half (46.6%) of the large coarse grain salt had some added iodine. This percent was lower for small coarse grain salt at 33.5% of samples.

Table 12. Household salt characteristics by survey characteristics (only for households where salt iodine analysed)

NATIONAL




South saltproducing

Urban

Rural



MPI

Sco

reUr

ban/

Rura

l

59.4%(52.2, 66.3)

45.7%(35.8, 56.0)

68.0%(53.8, 79.5)

61.3%(51.4, 70.4)

31.0%(22.8, 40.7)

62.5%(52.7, 71.4)

53.7%(42.6, 64.4)

69.1%(61.6, 75.6)

48.7%(40.9, 56.7)

24.6%(19.5, 30.5)

20.9%(16.8, 25.7)

21.7%(13.2, 33.5)

24.3%(17.4, 32.9)

47.7%(38.6, 57.0)

23.5%(17, 31.5)

26.6%(19.1, 35.7)

18.8%(14.1, 24.5)

30.9%(24.3, 38.5)

16.0%(12.0, 20.9)

33.4%(24.1, 44.2)

[10.3%](5.1, 19.9)

14.3%(8.0, 24.4)

21.3%(14.9, 29.5)

14%(9.7, 19.8)

19.7%(12.2, 30.4)

12.2%(8.6, 17.0)

20.3%(15.0, 27.0)

1514

229

739

374

136

863

388

748

664

Number

Surv

ey/S

trata

Powder Small GrainCoarse

Grain Type

Large GrainCoarse

* asterix denotes estimates based on less than 25 unweighted responses[ ] denotes estimates based on 25 to 50 unweighted responses


Table 13. Household salt iodine category by salt characteristics

* asterix denotes estimates based on less than 25 unweighted responses[ ] denotes estimates based on 25 to 50 unweighted responses

< 5ppm

5-14.9ppm

≥ 15ppm

384

66.5%(59.0, 73.1)

26.4%(20.5, 33.3)

7.1%(4.6, 10.8)

250

53.4%(44.0, 62.6)

42.5%(34.6, 50.7)

*

Number

HH s

alt i

odin

e co

nten

t

929

22.3%(35.8, 56.0)

68.0%(53.8, 79.5)

61.3%(51.4, 70.4)

Powder Small Grain Coarse

Grain Type

Large Grain Coarse

Multiple variable regression analyses for household salt iodine

Multiple variable regression analyses were conducted to determine which of the different survey variables was associated with household salt iodine after taking into account the effect of other variables – overall and after interaction with strata. The analyses for the final model shown in Table 14 were conducted using the three component MPI scores for education, health and living standards because this allowed for more refined analysis of factors associated with salt iodine content and the interaction with other variables and with strata-level interactions.

The relative salt iodine level indicates the absolute change in salt iodine associated with each sub-group – e.g. a value of 0.1 indicates that salt iodine is expected to be 10 times lower than the level of the reference sub-group, holding all other variables constant.

In single variable analyses (cross tabulation tables above), all the variables included in the multiple regression analysis were significantly associated with salt iodine level, except for urban/rural where there was no significant difference. However after accounting for the influence of other variables in the multiple variable regression, only strata, grain type and whether the respondent looked for iodised salt at the point of purchase were found to be significantly associated with household salt iodine content at the national level, i.e. consistently across all survey strata, (variable effect p <0.001 for all three variables). The strongest association was with salt grain type.

When interactions with strata were included in the model the association between salt iodine and grain type was found to have a different strength of association by strata (interaction with strata p <0.001). (See Table 14). Although fine, powdered, salt was associated with higher iodine content than coarse grain salt in all strata, the difference in salt iodine between the grain types is larger in the Mid region than in other strata and somewhat smaller in the North region (Figure 3).


In addition, the interaction with strata showed that salt brand and salt being obtained in sealed packs had different associations with iodine level depending on strata (p = 0.004 and p = 0.011 respectively). See Table 14.

For salt brand, the difference found by strata showed that, after accounting for the influence of other variables, the leading brands of salt in the North and Mid strata had much higher average iodine content than the leading brands in the two South strata. In the two South strata, salt from brands with lower market share had higher iodine content than salt from leading market brands and unbranded salt (Figure 4). However, the sample size for this low market share branded salt was too small to provide reliable data (n = 53 nationally).

Figure 5 illustrates the significant strata level difference found in the association between salt iodine and salt packaging. Unpackaged salt in the South-non-salt-producing areas had higher iodine than packaged salt, which was the opposite of the finding in the other 3 strata and overall nationally.


Table 14. Multiple variable regression analyses of factors associated with household salt iodine level

1 Superscript letter a indicates a significant difference to the reference category (first listed level) p < 0.052 Salt iodine content relative to reference (first listed level) as assessed by multiple regression model of log-transformed salt iodine (times different (mg/kg) to the reference level salt iodine). i.e. a relative level of 0.1 means the expected level is 10 times lower than the reference value

Intercept

Strata

Residence type

MPI Education

MPI Health

MPI Living

Standards

Heard of iodine

deficiency

Heard of iodised

salt

Salt obtained in

sealed pack

Salt package

had iodine logo

or label3

Salt Brand

Respondent

looked for

iodised salt at

point of purchase

Grain type

LevelVariable Number of HHs

Relative salt iodine level 1, 2

(95% CI)

3.67 (2.02, 6.66)

1.66 a (1.34, 2.07)

0.77 a (0.63, 0.95)

1.35 a (1.05, 1.75)

0.89 (0.72, 1.11)

0.98 (0.88, 1.09)

1.04 (0.89, 1.21)

1.12 (0.91, 1.38)

1.01(0.89, 1.15)

0.95 (0.81, 1.12)

1.07 (0.64, 1.80)

1.07 (0.64, 1.80)

1.22 (0.69, 2.16)

1.28 (0.68, 2.43)

1.63 (0.94, 2.84)

0.96 (0.49, 1.87)

1.67 a (1.36, 2.04)

1.08 (0.80, 1.45)

2.67 a (2.18, 3.27)

431

359

407

372

572

997

654

915

877

688

1401

167

976

593

291

1278

1009

550

84

396

1089

66

50

392

1061

1068

426

75

780

789





Rural

Urban

High (deprived)

Low

High (deprived)

Low

High (deprived)

Low

No

Yes

No

Yes

No

Yes

No

Yes

Missing/ Don’t know

No brand

Other brand

Leading market brand

Missing/Don’t know

No

Yes

Missing/Don’t know

Coarse

Powder

P value(Variable effect)

<0.001

0.300

0.740

0.639

0.276

0.909

0.526

0.799

0.773

0.199

<0.001

<0.001

P value(Interaction with strata)

0.413

0.154

0.566

0.703

0.677

0.192

0.011

0.564

0.004

0.863

<0.001


Figure 4. Interaction between salt brand and salt iodine content (ppm) by strata

Figure 3. Interaction between grain type and salt iodine content (ppm) by strata


Figure 5. Interaction between salt packaging and salt iodine content (ppm) by strata

Iodine status

The national median Urinary Iodine Concentration (UIC) of 201.6µg/L among women in fertile age (WIFA) was at the top end of the internationally recommended range for optimal iodine status for a population of WIFA59, however more recent research has shown that the optimal range for school-age children is 100-299.9 µg/L, and the same may apply to WIFA (ongoing research to determine this). See Table 15 and Figure 3.

For WIFA in the North, Mid and South-salt-producing strata, the median UIC indicates optimal iodine status. Whereas in the South-non-salt-producing stratum, the median UIC of 317.4µg/L) indicates iodine intake above requirement (>300µg/L).

In line with recommendation in the 2007 WHO Programme Guide, UIC data were also analysed to assess the extent of any population iodine deficiency by investigating the percent of the population with UIC <50µg/L (where at least 20% of the population have UIC <50µg/L, it can indicate severe deficiency). At the national level 5.2% of the population were found to have a UIC of <50µg/L (data not shown).

Although the median UIC was within or slightly above the optimal range for all sub-groups, it appears to be lower among WIFA from the North (168.3µg/L) and Mid (174.2µg/L) strata, WIFA in rural areas (169.0µg/L), and WIFA from more deprived (high MPI) households (177.6µg/L).

The median UIC among WIFA from households using salt with different iodine categories shows that even where the household were using salt with apparently no added iodine (<5ppm), WIFA had adequate iodine intake (median UIC 180.4 µg/L). Even so, there appears to be a trend towards increasing median UIC with increasing salt iodine content (see Table 15).

59. WHO 2007. Assessment of iodine deficiency disorders and monitoring their elimination: a guide for programme managers. WHO, UNICEF, ICCIDD


The final achieved sample size for urine samples from pregnant women (n = 102) was too small to report the median UIC as a reliable estimate and it should therefore be reported and used with caution. The national median UIC among pregnant women was 183.5µg/L, indicating likely adequacy of iodine intake among this group.

Un-weighted within stratum analyses with 95% confidence intervals (data not shown) along with single variable regression analysis indicated that UIC was significantly higher for WIFA:i. In South-non-salt-producing and South-salt-producing areas than in Mid and North areasii. From less deprived householdsiii.From households where household salt had >40ppm iodine.

Table 15. Urinary iodine concentration (UIC) among Women in Fertile Age (WIFA) by survey characteristics and level of iodine in the household salt

* asterix denotes estimates based on less than 25 unweighted responses[ ] denotes estimates based on 25 to 50 unweighted responses1 Weighted for sample weights and the number of urine samples expected if all eligible WIFA (all WIFA in survey HH) had a valid urinary iodine result

NATIONAL




South saltproducing

Urban

Rural



< 5ppm

5 - 14.9ppm

15 - 40ppm

≥ 40ppm

Salt not tested

Urine samples with result1 (n)

Median UIC g/l(25th and 75th

percentile)

Urine samples collected but not possible to

analyse (n)

201.6(119.4, 341.7)

168.3(87.2, 291.2)

174.2(116.0, 297.4)

317.4(187.8, 467.7)

218.5(125.4, 354.1)

220.8(137.7, 360.7)

169.0(92.4, 291.2)

219.6(142.2, 352.8)

177.6(93.3, 323.0)

180.4(110.1, 300.9)

173.9(105.6, 312.5)

[272.3](189.2, 399.4)

313.0(168.8, 458.6)

182.7(109.5, 313.9)

1214

261

571

273

109

758

456

610

596

378

387

71

216

161

36

7

23

5

1

24

12

20

15

14

4

5

11

1

MPI

Sco

reUr

ban

/ Rur

alHH

sal

t iod

ine

cont

ent

Surv

ey /

Stra

ta


Figure 6. Median urinary iodine concentration (UIC) µg/l among Women in Fertile Age (WIFA)

Estimated salt intake from household salt and key processed foods

Household saltThe approximate national median salt intake (for household salt only) for WIFA was estimated to be 5.0g, as shown in Table 16. The estimated median varied from 4.7g in the North to 5.2g in the South-non-salt-producing strata. Intake among WIFA in rural areas appeared to be lower (4.6g) than among WIFA in urban areas (5.1g).

Calculations used to determine salt intake estimates are shown in Appendix 3.

It should be noted that these are all considered to be crude approximations, since they are based only on recollected salt purchase and the number, age and sex of household members; not on any biochemical indicator of intake.


Table 16. Estimated daily salt intake (grams) for WIFA based on responses related to purchase quantity and frequency and use of FAO inter-HH distribution of consumption

NATIONAL




South saltproducing

Urban

Rural

Low MPI score

High MPI score

MPI

Sco

reUr

ban

/ Rur

al

1804

314

878

461

151

1175

629

937

864

5.0

4.7

4.9

5.2

5.0

5.1

4.6

5.1

4.9

2.6

2.5

2.8

2.6

2.6

2.7

2.5

2.8

2.5

8.6

8.0

8.2

10.3

9.0

8.6

8.6

9.0

8.6

# Households

Surv

ey/S

trata

Median 25th percentile

Estimated daily intake (g) of household salt WIFA

75th percentile

Estimates based on approximations (hence wide range of values) and intakes only relate to cooking/table salt at the household (not including processed food or vendor-cooked food salt).

Processed food consumption and related salt intake estimatesInformation were collected in the survey for three processed food types that were identified to be consumed across population groups and likely contributing to overall salt intake. Figure 7 shows that consumption of bouillon was frequent and widespread throughout Ghana. At the national level, over 80% of non-pregnant WIFA respondents reported to consume bouillon at least once a week, with approximately half (48.8%) reporting to consume it at least 6 times a week.

WIFA in the North consumed bouillon more frequently than WIFA in other strata, with 58.9% consuming it at least 11 times a week. Using a conservative estimate of a bouillon serving size of 1.25g, the median intake of bouillon per week (among WIFA consuming at least some bouillon) indicated a much higher consumption in the North (median 17.5g) compared to intake in the other areas (4.6 to 7.5g/week). See Table 17.

The differences in intake between urban and rural and low and high MPI households were relatively small.

Figure 8 shows that tomato paste was consumed with a frequency similar to that for bouillon throughout Ghana, although consumption by strata was different. Over 80% of non-pregnant


WIFA respondents said that they consumed tomato paste at least once a week, with 43.0% reporting to consume it at least 6 times a week.

WIFA in the North were less likely to consume tomato paste (30.1% reported never consuming it in the previous week) than WIFA in other strata. WIFA in the Mid stratum were most likely to have consumed tomato paste 6 times or more during the previous week (47.1% of WIFA). However among households where the respondent WIFA did consume tomato paste, the median intake was highest in the North (245.0g) and lowest in the South-non-salt-producing area (140.0g). See Table 17.

WIFA in urban areas were more likely to have consumed tomato paste during the previous week than WIFA in rural areas (86.3% and 76.1% of WIFA respectively). WIFA in low MPI households were more likely to have consumed tomato paste during the previous week (86.3%) than WIFA from high MPI households (78.4%). Reported consumption of instant noodles by WIFA was relatively low with 80.0% of non-pregnant WIFA reporting no consumption of this product in the previous week, detailed data shown in Appendix 7 only.

There was a tendency for WIFA in the South-non-salt-producing areas to consume instant noodles more than WIFA in the North (25.6% and 14.9% of WIFA consuming at least half a packet in the previous week, respectively). However overall there was little difference in consumption within sub-groups. See Table 17 for estimated median intake.

Appendix 7 shows the detailed frequency of consumption result tables along with associated median UIC for these 3 processed food products.

The median and mean of total salt intake from combined consumption of bouillon, tomato paste and instant noodles during the previous week is shown in Table 1860. The national median and mean salt intake from these products were 5.4g and 7.5g respectively. There was little difference within sub-groups except for a notably higher intake among WIFA in the North (median 9.3g, mean 10.1g), where mean intake was significantly higher than for WIFA in the Mid (median 5.1g, mean 6.9g) and in the South-non-salt-producing strata (median 3.8g, mean 6.3g).

No association was found between the combined level of salt intake from these foods and median UIC for non-pregnant WIFA (data not shown).

Dividing the median weekly salt intake from these processed foods by 7 to approximate median daily intake showed that estimates of household salt intake were much greater than intake from these foods. The national estimate of combined median intakes for WIFA was 5.8g salt/day. This varied from 5.6g/day in the Mid stratum to 6.0g/day in the North stratum. The relative proportion of these combined median salt intakes that was from processed foods (compared with household salt) was highest in the North (accounting for approximately 22% of the combined intake) and lowest in the South-non-salt-producing areas at approximately 9%). It should be noted that these are crude approximations and further data analysis and additional studies would be needed to refine the outcomes.

60 See Appendix 3 for the calculation method used


Figu

re 7

. Fre

que

ncy

of b

ouill

on c

onsu

mp

tion

dur

ing

the

wee

k p

rece

din

g th

e su

rvey

, rep

orte

d b

y no

n-p

regn

ant

WIF

A

resp

ond

ents

(unw

eigh

ted

n =

110

9)

0 tim

es1-

5 tim

es6-

10 t

imes

11-1

5 tim

es16

-21

times

Hig

h M

PI s

core

(dep

rived

)

Low

MP

I sco

re (n

ot d

epriv

ed)

Rur

al

Urb

an

Sou

th s

alt

pro

duc

ing

Sou

th n

on-s

alt

pro

duc

ing

Mid

non

-sal

t p

rod

ucin

g

Nor

th n

on-s

alt

pro

duc

ing

NAT

ION

AL

0.0%

10.0

%20

.0%

30.0

%40

.0%

50.0

%60

.0%

70.0

%80

.0%

90.0

%10

0.0%

66 National Iodine Survey Report, Ghana 2015Fi

gure

8. F

req

uenc

y of

bou

illon

con

sum

ptio

n d

urin

g th

e w

eek

pre

ced

ing

the

surv

ey, r

epor

ted

by

non-

pre

gnan

t W

IFA

re

spon

den

ts (u

nwei

ghte

d n

= 1

109)

0 tim

es1-

5 tim

es6-

10 t

imes

11-1

5 tim

es16

-21

times

Hig

h M

PI s

core

(dep

rived

)

Low

MP

I sco

re (n

ot d

epriv

ed)

Rur

al

Urb

an

Sou

th s

alt

pro

duc

ing

Sou

th n

on-s

alt

pro

duc

ing

Mid

non

-sal

t p

rod

ucin

g

Nor

th n

on-s

alt

pro

duc

ing

NAT

ION

AL

0.0%

10.0

%20

.0%

30.0

%40

.0%

50.0

%60

.0%

70.0

%80

.0%

90.0

%10

0.0%


Table 17. Estimated median intake (g) of bouillon, tomato paste and instant noodles during the week preceding the survey, reported by non-pregnant WIFA respondents

Calculation of median intake only included respondents whoconsumed the product. based on number of days consumed x typical number times consumed per day x average serving size for an adult (1.25g bouillon, 30g tomato paste, 85g packet instant noodles)

191.3(127.5, 340.0)

129.8(127.5, 321.9)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

191.3(127.5, 413.8)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

Instant noodles

NATIONAL




South saltproducing

Urban

Rural



MPI

Sco

reUr

ban

/ Rur

al

8.8(3.8, 17.5)

17.5(8.8, 17.5)

7.5(3.8, 15.0)

4.6(2.5, 8.8)

7.5(3.8, 11.3)

8.4(3.8, 17.5)

8.8(3.8, 17.5)

7.5(2.5, 17.5)

8.8(3.8, 17.5)

210.0(70.0, 280.0)

245.0(105.0, 401.0)

210.0(105.0, 401.0)

140.0(70.0, 245.0)

193.9(70.0, 305.9)

210.0(105.0, 315.0)

182.0(70.0, 250.9)

175.0%(70.0, 280.0)

210.0(105.0, 280.0)

1088

208

522

265

93

714

374

605

480

Number ofrespondents

Respondent = WIFA Median (25th, 75th percentile) intake (g) in the past week

Surv

ey S

trata

Bouillon Tomato paste


7.5(7.0, 7.9)

10.1(9.2, 10.9)

6.9(6.3, 7.5)

6.3(5.5, 7.1)

7.6(5.7, 9.4)

7.8(7.2, 8.3)

6.8(6.1, 7.5)

7.4(6.8, 8.1)

7.4(6.9, 8.0)

Table 18. Average salt intake from combined consumption of bouillon, tomato concentrate, and instant noodles over the past week; reported by non-pregnant WIFA respondents (unweighted n = 1109)

1 Bouillon, tomato paste and instant noodles; foods identified prior to the survey as contributing to salt intake across population groups2 Estimated salt intake per serving: bouillon 0.6g, tomato paste 0.3g, instnat noodles 3g

NATIONAL




South saltproducing

Urban

Rural



MPI

Sco

reUr

ban

/ Rur

al

5.4(2.1, 10.8)

9.3(6.1, 12.9)

5.1(1.8, 9.8)

3.8(1.5, 8.4)

5.4(1.9, 10.2)

5.7(2.4, 11.3)

5.4(1.9, 9.6)

5.1(1.8, 10.8)

6.3(2.7, 10.5)

1088

208

522

265

93

714

374

605

480

Number ofrespondents

Respondent = WIFAMedian

(25th, 75th percentiles)

Estimate for total salt intake (g) from key processed food 1,2

Mean (95% CI)

Surv

ey S

trata


Multiple variable regression analyses for urinary iodine concentration

Multiple variable regression analyses were conducted to determine which of the different survey variables were significantly associated with UIC among WIFA, both overall and after interactions with strata (identifying where the strength of associations differed significantly between strata). The outcome from the final model is shown in Table 19. As was done for the regression model for salt iodine, the three MPI component domains were included as separate variables instead of the overall MPI score.

After accounting for the effect of other variables in the multiple variable regression analysis, the main factors associated with higher expected UIC levels (from the multiple variable regression analysis) were:• Householdlocation: - WIFA in the Mid and North regions tended to have lower UIC than households in the

South-salt-producing area (p = 0.07 for both) when all other factors were held constant. - WIFA from the South-salt-producing areas tended to have higher UIC than WIFA in the

South-salt-producing areas. Although this was not a significant difference, the difference in UIC among WIFA in the South-non-salt-producing and UIC among WIFA in the Mid and North regions was even greater.

• Householdsaltiodinelevel: - WIFA from households using salt with at least 15ppm iodine had a significantly higher

relative UIC than WIFA from households using non-iodised salt (p < 0.001) - This trend was observed for all strata, however the effect of increasing household salt

iodine on expected UIC was greatest among WIFA in the North and lowest among WIFA in the Mid region. See Figure 9.

- Within each stratum, there was little difference in relative UIC among WIFA from households using non-iodised salt and those in households using inadequately iodised salt.

• Intakeofbouillon: - The association between bouillon intake and UIC was significant (p = 0.018) but showed

no clear trend. The expected UIC among WIFA consuming some but less than 20g bouillon in the past week was significantly higher than among WIFA who consumed no bouillon. However, the expected UIC among WIFA who consumed 20g bouillon or more in the past week was slightly lower than among WIFA who reported no consumption of bouillon.

• Intakeoftomatopaste - The relative UIC for WIFA increased significantly (variable effect p = 0.014) as the intake

of tomato paste increased from no intake to <200g/week and on to 200g or more/week.

Younger age (<25 years) was associated with slightly higher expected UIC levels, but this did not reach significance.

The association between UIC and MPI health was not significant overall, however it was significantly different by strata, see Table 19 and Figure 10. It can be seen that for South-non-salt-producing households the level of deprivation in the MPI health dimension makes no difference to the expected relative UIC among WIFA. Yet in the Mid and North strata, WIFA


from more deprived (high MPI health) households had lower expected UIC than WIFA from non-deprived (low MPI health) households. This was different to findings in the South-salt-producing area where WIFA from deprived (high MPI health) households had higher relative UIC than WIFA from non-deprived households (low MPI health).

An additional multiple variable regression analysis using linear data for salt iodine modelled specifically for a WIFA 30 years of age, from a deprived rural household, who consumed some bouillon in the previous week showed that the largest relative change in expected UIC with increasing household salt iodine continued up to around 20ppm, after which the expected relative change in UIC plateaued. (See Figure 11 – in this analysis, the percent change in UIC from the level in households with 0ppm iodine to the level in households with 20ppm iodine is approximately the same across all four strata).


Table 19. Multiple (variable) regression analyses of factors associated with UIC among the survey population of WIFA

Superscript letter a indicates a significant difference to the reference category (first listed level) p < 0.052 Urinary iodine concentration relative to reference (first listed level) as assessed by multiple regression model of log-transformed urinary iodine (times different the µg/L is to the reference level urinary iodine).

Intercept

Strata

MPI Education

MPI Health

MPI Living

Standards

Heard of iodine

deficiency

Salt iodine

content

Age

Intake of bouillon

Intake of tomato

paste

Intake of instant

noodles





High (deprived)

Low

High (deprived)

Low

High (deprived)

Low

No

Yes

<5

5-15

>15

<25

25-30

30-35

>35

0

<20

>20

0

<200

>200

Yes

<25

LevelVariable Number of HHs

P value(Interaction with strata)

338

378

298

258

572

700

765

502

1145

125

814

458

423

384

301

443

229

207

393

179

691

366

238

418

531

1055

217

0.844

0.049

0.119

0.428

0.019

0.251

0.636

0.264

0.518

Relative salt iodine level 1,2

(95% CI)

152.11 (119.83, 193.09)

0.80 (0.62, 1.03)

0.82 (0.67, 1.02)

1.20 (0.97, 1.48)

1.06 (0.94, 1.21)

1.08 (0.96, 1.21)

1.14 (0.96, 1.34)

1.00 (0.88, 1.13)

0.98 (0.85, 1.13)

1.38 a (1.19, 1.61)

0.88 a (0.77, 0.99)

0.85 a (0.73, 0.98)

0.88 a (0.78, 0.99)

1.17 a (1.01, 1.36)

0.99 (0.82, 1.21)

1.16 a (1.01, 1.33)

1.24 a (1.07, 1.44)

1.08 (0.93, 1.26)

P value(Variable effect)

<0.001

0.329

0.224

0.130

0.986

<0.001

0.090

0.018

0.014

0.332


Figure 10. Interaction between MPI health and urinary iodine content (µg/L) by strata

Figure 9. Interaction between household salt iodine category (ppm) and urinary iodine content (µg/L) by strata


Figure 11. Interaction between household salt iod/’’ine content (ppm) and urinary iodine con-tent (µg/L) by strata for a WIFA in a rural area, of average age and deprived in certain MPI components

Edible Oil UseTables showing the findings for household use of edible oil are shown in Appendix 8. The tables show the main type of oil used in the household (responses for up to two main types were allowed) and also the brand, packaging and source of this oil. This information may be useful to assess the expected reach and impact of any existing or future edible oil fortification intervention.


Overall, the data from the 2015 national iodine survey in Ghana showed that in all the four survey strata, the population of non-pregnant WIFA had adequate iodine nutrition, despite the relatively low household access to adequately iodised cooking/table salt. Importantly, the limited data available for pregnant women suggest adequacy among this population group also. These findings represent a major public nutrition achievement for Ghana. This significant improvement in iodine nutrition notwithstanding, the unanswered question is the possible sources of iodine in the Ghanaian diets given the relatively low household access to adequately iodised salt reported in this survey and indeed from other surveys. This has important policy implication and programmatic importance.

Information reported for consumption of key foods and condiments expected to be contributing to dietary salt intake indicate that the use of iodised salt to produce all bouillon and tomato paste (i.e. USI) would contribute significantly to dietary iodine intake. If USI was fully implemented in this way, it would provide a level of protection from iodine deficiency among populations in households without access to adequately iodised salt, reducing the long-standing inequity in access to dietary iodine from iodised salt.

Survey sample characteristicsThis survey used the MPI to assess vulnerability to acute poverty (termed as deprivation for the purpose of the report). Many of the components are, however, similar to those used to develop the composite indicator to assess wealth quintiles, as used in the 2014 Ghana Demographic Health Survey (GDHS)61. It is not possible to compare the actual percent of wealth-poor versus deprived households between the two surveys due to differences in survey design and construction of the wealth-related indices. However the finding of a higher percent of deprived households in rural areas and in the North in this survey are in line with 2014 GDHS trends for wealth quintiles, where only 105 of rural households and 6% of households in the Northern region were categorised as being in the top two wealth quintiles, compared with 71% of households in urban areas. The 2015 Ghana Millennium Development Goals (MDG) report62, which examined different indicators of poverty by urban/rural residency and by each of the 10 regions of Ghana, also reported similar findings; concluding that “There is deep poverty in rural areas and the three northern regions”. These subnational differences in poverty distribution have existed for a long time, as shown in the MDG report.

Food security (access component) was an integral part of the household Multidimensional Poverty Index (MPI), however the MPI calculation only utilised basic information from this assessment (food secure/insecure). Since the full 9 question HFIAS was included in the survey questionnaire, it was felt worthwhile to explore the results in more detail independently of the MPI score. This measure of food security is less comprehensive than the full FAO measure, and it only measures indicators of access (anxiety about access, food quantity and quality).

61. Ghana Statistical Service (GSS), Ghana Health Service (GHS), and ICF International. 2015. Ghana Demographic and Health Survey 2014. Rockville, Maryland, USA: GSS, GHS, and ICF International.62. National Development Planning Commission, Republic of Ghana; United Nations Ghana September 2015. Ghana Millennium Development Goals 2015 Report.

Discussion


The pattern of between-strata differences in indicators of food insecurity (access) were similar to those found for household deprivation and could indicate related causal factors for both (a significantly higher percent of households in the Mid and South-non-salt-producing areas were found to be food secure (access) than in the North and South-salt-producing).

Dietary diversity was not part of the MPI score, however it was included as a module to assess its relationship with access to adequately iodised salt. Also to enable its subsequent use for assessment of likely adequacy of intake of certain food groups by WIFA for other health and nutrition programme purposes. When the dietary diversity of the respondent was investigated, it was unexpected to see that a higher percent of respondents in the South-salt-producing area than in the South-non-salt-producing areas had diverse diets, despite households in the South-salt-producing being less deprived and more food insecure (see Table 3). Factors that might have influenced findings in these two areas are:

a) A higher percent of respondents in the South-non-salt-producing area were older women (23.5% over 49 years), when compared with the South-salt-producing areas (17.1% over 49 years), data not shown. Women of reproductive age may have more varied diets than older women.

b) Salt-producing areas have more access to fish, both in the diet and as a product to trade for other food items, which may alleviate some of the otherwise expected effects of household deprivation on diet diversity.

Household access to iodised and adequately iodised saltNationally, over 60% of households were accessing salt with some added iodine (>5ppm), however, there were large differences in coverage and quality of iodisation by strata. Of greatest concern is the fact that half of all households in the South-salt-producing area and nearly half of households in the Mid area were accessing salt with no added iodine. The quality control of salt iodisation appears weak, with only 8.6% of the national sample of households using salt with iodine levels in the WHO recommended range of 15-40ppm for household salt. Approximately one third of households in the North and South-non-salt-producing areas were accessing salt with over 40ppm iodine and the mean iodine content of household salt with any added iodine in these two areas was 40-45ppm. This indicates that population groups accessing salt with at least 15ppm iodine in these areas would likely be consuming more than adequate dietary iodine even if household salt was the main source of their dietary salt.

An analysis of salt iodine content by brand (data not shown) found a median iodine content of 73ppm for one leading brand, which was reportedly used by 312 (weighted n) of households. Approximately 70% of salt samples from this brand were found to have >40ppm iodine. Lowering the national standards and providing a range of acceptable iodine content for edible salt at production is strongly recommended, as well as regulating that change to make sure it is followed. A more typical salt iodine standard for production in a country with estimated salt intake of between with 5-10g would be an allowable range in the region of 30-60ppm potassium iodate (equivalent to approximately 18-35ppm iodine), as recommended by WHO63 and agreed by the Economic and Monetary Union of West African States64.

63. WHO. Guideline: fortification of food-grade salt with iodine for the prevention and control of iodine deficiency disorders. Geneva: World Health Organization; 201464. UEMOA 1000 : 2011 ICS 67 – agreed on standard of 30-60ppm at production


The North and South-non-salt-producing areas had similar, and highest, household coverage of both any and adequately iodised salt, however, the salt supply to the South-non-salt-producing areas appeared to be more homogenous in quality than that to the North. This can be seen by the fact that the mean and median iodine median ppm for salt with at least some iodine were similar in the South-non-salt-producing area but quite different from each other in the North (indicating a more skewed distribution of salt iodine level). Also by the fact that the North had a significantly higher percent of households using inadequately iodised salt (Table 4). Explanatory factors appear to be that:

• HouseholdsintheSouth-non-salt-producingareahadgreateraccesstosuppliesoffinergrain, well-packaged, salt; while salt supplies to the North appeared to be more varied and with a higher percent of large, coarse grain salt (Table 12, Figure 12), which tended to be more likely inadequately iodised than the fine powder salt (Table 13).

Whether household access was related to availability or affordability or both is not possible to determine without having conducted a parallel market-survey. However, the South-non-salt-producing area had a lower percentage of deprived households than the North (Table 3); a variable associated with access to quality-assured, packaged, adequately iodised salt.


Figu

re 1

2. F

igur

e co

mp

arin

g th

e ch

arac

teris

tics

of s

alt

pur

chas

ing

pra

ctic

es a

nd s

alt

grai

n ty

pe

that

wer

e fo

und

to

be

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ifica

ntly

ass

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ted

with

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t io

din

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y su

b-g

roup

NATI

ONAL

North

no

n-sa

lt pr

oduc

ing

Mid

no

n-sa

lt pr

oduc

ing

Sout

h no

n-sa

lt pr

oduc

ing

Low

MPI

sc

ore

(not

de

priv

ed)

High

MPI

sc

ore

(dep

rived

)

Sout

h sa

lt pr

oduc

ing

Urba

nRu

ral

80%

70%

60%

50%

40%

30%

20%

10% 0%

Bou

ght

salt

in a

sea

led

pac

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oked

for

iod

ised

sal

t a

poi

nt o

f pur

chas

eP

owd

ered

gra

inC

oars

e gr

ain


The characteristics of salt accessed by households in the North was more similar to salt accessed in the South-salt-producing areas, i.e. less likely than the other two strata to be fine powder grain, less likely to have reported using salt with a well-known brand name or with an iodine logo/label (Tables 10 and 12). However, in the North the percent of households accessing iodised and adequately iodised salt was significantly higher than in the South-salt-producing area. This finding was somewhat expected based on the known access to raw, non-iodised, salt in the South-salt-producing areas. Factors that support this and may explain the findings further are that a slightly higher percentage of respondents from households in the North (Figure 12):• Purchasedsaltinasealedpackage• Lookedforsaltatthepointofpurchase/obtainingit(bothfactorspositivelyassociatedwith

salt iodine level).

An additional factor may be that salt being transported to the Northern region is more likely to be subjected to regulatory monitoring checks en-route and producers therefore make more effort to add at least some iodine regardless of salt grain quality.

The population characteristics of households in the Mid and South-non-salt-producing areas were similar in terms of levels of poverty, food security, and awareness of iodine deficiency and iodised salt. In addition, the majority of households in both areas were accessing fine powder salt (Table 12, Figure 12). Given that all these factors were significantly associated with access to adequately iodised household salt in single variable analyses (and also in multiple variable analysis for grain type), it was surprising that household coverage with adequately iodised salt in the South-non-salt-producing area was significantly greater than in the Mid area.

Explanatory contributing factors could be that respondents from households in the Mid stratum (Figure 12) were:• Significantly less likely toreportpurchasingsalt inasealedpackage,meaningthatfine

powder salt may have been available in unpackaged form and thus was less likely to have undergone any regulatory quality control

• Less likely tohave lookedfor iodisedsaltat thepointofpurchase/obtainingthesalt,avariable significantly associated with salt iodine level in the multiple variable regression (Table 10).

Whether these differences relate only to available salt supply (i.e. the choice of available powdered salt – packaged versus unpackaged – is significantly different in the two areas) and/or potentially to pricing of packaged powdered salt versus loose powdered salt, is not possible to determine from the data from this survey alone.

Compared with experiences observed for other countries, 65,66 it was slightly surprising to find that there were no significant difference in coverage of iodised and adequately iodised salt between urban and rural households. Despite a trend towards higher salt iodine content and the use of fine, powdered salt grain type, in urban households, it appears that fine powdered salt and coarse grain salt were available and used by households in both urban and rural areas (Table 12).

65. Tran TD, Hetzel B Fisher J 2016. Access to iodized salt in 11 low- and lower-middle-income countries: 2000 and 2010 Bull World Health Organ 94 122–12966. Knowles J, et al. 2017 Updated household iodized salt coverage data from 10 national surveys, conducted in the period 2013–2015. Draft submitted to J Nutr.


The finding that household access to adequately iodised salt was significantly higher among less deprived (low MPI) than among deprived (high MPI) households (Table 5) is in line with previous studies that have shown similar inequity in access to adequately iodised salt by indicators of household wealth67,68. There was, less expectedly, no corresponding significant difference in household access to salt with some added iodine (all salt with >5ppm iodine). It appears that both deprived and non-deprived households have equivalent access to salt with some added iodine, however more deprived households access salt with poorer quality of iodisation, as also reflected in the significantly lower mean iodine level for all iodised salt in deprived households when compared with non-deprived households (Table 5).

Results for the percent of households accessing adequately iodised salt using the semi-quantitative RTK support the recent recommendations that this field method is only valid for assessing coverage of household salt with some added iodine and is not a reliable indicator of adequacy of salt iodine content (Table 6)69. In fact, the RTK over-estimated household coverage with adequately iodised salt in all strata and nationally. This finding makes it difficult to compare the main (titration-based) results for coverage with adequately iodised salt from this survey with results from most previous surveys, which were mainly conducted using RTK assessment of salt iodine.

Stratification in this survey was designed with the programmatic objective to obtain a representative baseline for the new national USI III strategy, focused on small scale salt-producing areas. This, together with the use of different sampling frames, means that sub-national results for different geographic areas cannot be compared directly with results from previous surveys.

While taking into account these cautions about the validity of comparisons with previous surveys, appropriately weighted national data should account for differences in stratification and be possible to compare, especially where there are 95% confidence intervals around the coverage estimates. These 2015 survey results (61.9% of households accessing salt with some added iodine) are in line with the results from the 2014 GDHS which found 65.5% of households were accessing salt with some iodine (RTK method). In terms of trends, it appears that national level household coverage of salt with some added iodine has decreased since 2009 (two surveys using titration analysis of salt iodine content: Partnership-supported survey in 2009-10, 83.5%; and MICS 2011, 100% – cut off used was >0ppm iodine and many samples had very low levels of iodine that may have been naturally occurring). For adequately iodised salt (where it is only possible to compare with previous surveys that used titration analysis of salt iodine), household coverage in the 2015 survey (29.3%) shows a decrease from the quantitative (titration-based) findings of the 2009-10 Partnership survey (47.8% of households) but the same level of coverage as the quantitative salt iodine results from the 2011 MICS data: 29.3%. It is not valid to compare data for adequately iodised salt coverage found in 2015 with results from the 2014 GDHS since these latter results were based on the RTK method.

These trends towards reduced household coverage of adequately iodised salt indicate possible decreased Government attention to regulatory monitoring and enforcement of iodised salt

67. Tran TD, Hetzel B Fisher J 2016. Access to iodized salt in 11 low- and lower-middle-income countries: 2000 and 2010 Bull World Health Organ 94 122–12968. https://data.unicef.org/topic/nutrition/iodine-deficiency/ [in the “read more” section]69. Gorstein J, et al. 2016. Performance of rapid test kits to assess household coverage of iodized salt. Public Health Nutrition 10.1017/S1368980016000938


production and distribution. This reflects the findings from the situation analysis conducted to formulate the basis of the new USI Strategy III. It could also be related to a decrease in supply, and increase in cost, of potassium iodate during the period prior to the survey. It would be expected that salt iodisation quality and household coverage levels will improve as the new strategy (approved in 2016) is implemented in full.

Respondent awareness and knowledge of iodine deficiency and iodised salt

Awareness of iodised salt, nationally and within each stratum, was high, possibly reflecting the range of communication activities, which have been a core component of the national strategy to achieve optimal iodine nutrition to date, including a more business-oriented approach supported by the USI Partnership Project. Awareness of iodine deficiency was much lower, especially among respondents in the North and in deprived households, which could be at least partly due to differing recognition/understanding of the phrase “iodine deficiency” and how it was translated into local languages. Despite the differences in the level of awareness of iodised salt and iodine deficiency, there was a positive association between awareness of both with household salt iodine content (Table 8). Given the high availability of non-iodised salt on the market, this association between awareness and household salt iodine reinforces the value of the USI Strategy III objective to: “strengthen communication on the benefits of consumption of iodised salt” at all levels of the supply chain. In terms of prompting behaviours to prevent iodine deficiency, an awareness of the value of iodised (versus non-iodised) salt and an understanding to look for iodised salt at the point of purchase would appear to be more important than knowing the term “iodine deficiency”.

The proposed outcome for the USI III strategic objective above is an increased percent of households using iodised salt, it may, therefore, be worthwhile investigating why only a third of the population who reported to be aware of iodised salt, said that they looked for it when they went to purchase/obtain salt (Table 10). Over half of the responses to the question about why iodised cooking salt was not used related to price and/or availability. It is likely therefore, that consumer choice was influenced by access (availability/affordability) rather than by perceived health benefits. This reinforces the need to strengthen industry-level awareness about, and enforcement of national regulations for iodised salt production, packaging and labelling.

The 2014 GDHS included questions to determine knowledge of iodised salt and its perceived benefits. The level of awareness of iodised salt (86.9% of women questioned) was comparable to results from this 2015 survey (81.7%). In terms of benefits, the most popular response in the 2014 survey (49.3% of women), was that iodised salt provided energy. This may reflect a generalised perceived benefit of an improved food product rather than a specific response in relation to knowledge of iodine. Interestingly, in the 2014 GDHS, only a third of women referred to prevention of goitre as a benefit of iodised salt, whereas in this 2015 survey, almost two thirds of respondents mentioned goitre as a consequence of iodine deficiency. It is possible that goitre is highly associated with iodine deficiency but that its prevention is less closely associated with iodised salt.

Recent communication activities have aimed at increasing knowledge about the consequences of iodine deficiency other than goitre, because: a) the invisible impact of iodine deficiency on brain development occurs at much lower levels of deficiency than are related with goitre, and


b) the occurrence of goitre has decreased as iodine status of the population has improved and may no longer be commonly seen in the population. Responses provided in this survey suggest that goitre remained the most known-about consequence of iodine deficiency with a low percent of respondents giving correct responses for other conditions. It may be that the messages about other consequences of deficiency/benefits of iodised salt had only changed in the few years preceding the survey and there had not been sufficient implementation time to show a meaningful impact. On a positive side, the survey results show a successful impact of interventions to raise knowledge about iodised salt being the best method to prevent iodine deficiency (Figure 2).

In terms of perception about using iodised salt in their own households, almost half of respondents who said they were not using iodised cooking salt in their household were incorrect, 39% were using salt with some added iodine and 8% were using adequately iodised salt (Table 8). This level of misconception may be due to a commonly held belief (52% of respondents of the 2014 GDHS) that only fine, powdered, salt is iodised, while in fact the 2015 survey found that 43% of large grain coarse salt had at least some added iodine (Table 13).

Data from this survey help dismiss concerns that a significant proportion of the population not using iodised salt were doing so due to concerns about its use. An extremely small percentage of respondents expressed such concerns within their household. In fact, the most common reasons given for not using iodised salt in the household were related to availability and cost. Both of these barriers would be removed if all edible salt was iodised. This implies that it would not be an effective use of funds to focus future communication messages on addressing what appears to be only a perceived prevalence of misconceptions about negative effects of iodised salt.

The source of information about iodised salt was found to vary significantly by strata, however the specific source did not appear to affect awareness of iodised salt among the respective respondent populations (Table 9). In terms of developing future communication strategies, it was interesting to note that differences in information sources were more influenced by geographic differences (strata-level) than by urban/rural residence type or by MPI score.

Iodised salt purchasing practices and salt characteristics

Data on iodised salt purchasing practices, together with the determination of salt grain type by the iodine analysis laboratory, provide sources of highly useful information for strategy development. Some of the strongest positive single variable associations with household access to adequately iodised salt (Table 11) were seen where households had: • Obtainedsaltinasealedpackage• Obtainedsaltwithabrandnameand/oraniodinelabelorlogo(wherepackaged)• Lookedforiodisedsaltatthepointofpurchase.

The fact that results for salt iodine content for samples observed at the household to be in their original packaging were almost the same as those for samples reported to have been obtained in sealed packaging indicates that reported purchasing practices from this survey were likely reliable.


The percent of households reporting to use salt obtained in sealed packaging and reporting to look for iodised salt at the point of purchase (factors associated with salt iodine level) were low nationally, and particularly low in: the Mid stratum, rural areas, and among deprived households (Table 10). This requires further investigation in order to design and implement strategies to improve these practices or, preferably, to improve the iodisation quality of all edible salt, so that purchasing practices would no longer be associated with household salt iodine level.

The fact that the association of salt iodine level with packaging, logo/label and brand was not significant after the multiple variable regression indicated a high level of overlap with grain type, which remained strongly significant. Overall it suggests that most salt purchased in a sealed pack, with a brand name, and with an iodine label or logo is of fine, powdered, grain type. The highly significant association between higher salt iodine content with fine, powdered, salt grain type is in agreement with the finding from the Partnership-supported fortification survey conducted in 2009-10.

As noted in the results, the p values for strata interactions in the multiple regression analysis suggests that the relationship with grain type differs by strata. There is no obvious explanation for the larger difference in iodine content between coarse (low iodine) and powdered (high iodine) found in the Mid stratum than in the other strata (Figure 3).

The fact that the category of “(combined) leading market brands” had significantly higher iodine content (by about 20ppm) in the Mid and North areas than the same category in the two Southern areas is most likely due to the reported preference for one leading brand in the North and Mid strata compared with higher use of the second leading brand in the two Southern strata70.

It is not possible from the survey data to hypothesise why unpackaged salt had higher iodine than packaged salt in the South-non-salt-producing areas (Figure 5), however it deserves further investigation.

Iodine Nutrition among WIFA

The median UIC of 201.6µg/L indicates that optimal iodine status had been achieved among the population of WIFA in Ghana, at the national level and in all strata included for this survey, including the Northern region where historically iodine deficiency has been endemic. In addition, the limited data available for pregnant women suggest adequacy among this population group, which is a major public nutrition achievement for Ghana. Further validation of iodine status among pregnant women in different regions of the country, including representative assessment in the Northern region, would help to confirm that deficiency no longer exists in any population group in the country.

Bearing in mind the constraints to direct comparison of results from surveys based on different sample frames and design, it would still be considered valid to state that iodine status has improved among WIFA in all areas of the country since 2010. A particular achievement is

70. Data on the use of salt by named brand types is not shown due to potential sensitives around this, however this information is available to GHS for programme-related investigation and use.


moving from iodine deficient (2010) to adequate (2015) status in the North. Due to a lack of information required to weight the data from the 2009-10 survey, it wasn’t possible to calculate a national estimate of median UIC from that survey, however the regional median UICs among WIFA in 2010 were substantially lower at: South (180 µg/L), Mid (139 µg/L) and the North (85 µg/L).

It appears that other sources of dietary iodine, additional to iodised household salt, may now be available to a large proportion of WIFA. This assumption is based on the fact that despite the low household coverage with iodised salt, the median UIC for WIFA has increased in all regions. Further studies will be required to better inform future policy and programming in relation to this.

In the South-non-salt-producing stratum, the median UIC (317.4µg/L) indicates iodine intake above requirement (upper cut off for the optimal range for school age children is a median UIC >300µg/L71, which may be possible to apply to WIFA. Studies to assess this are ongoing). The recommended dietary intake for iodine among non-pregnant/non-lactating adults is 150µg/day (WHO 2007), with a proposed optimal range of 150-250µg/day72. The tolerable upper intake level for iodine intake among adults is considerably higher than this, although it is not agreed on internationally: the European Commission/Scientific Committee on Food recommends no more than 600µg/day while the US Institute of Medicine recommends a tolerable maximum of 1100 µg/day. Taking either upper intake value, the median UIC observed among WIFA in the South-non-salt-producing areas of Ghana does not indicate a cause for serious concern about iodine excess at this point. However, the situation should be monitored periodically, to check that it does not increase further in this region or in other regions. The North of the country has, historically, experienced the most severe iodine deficiency due to the comparatively low ground water iodine73. The population in this region are, therefore, more likely than in other regions to have possible existing damage to the thyroid gland from low iodine, which would make them more susceptible to iodine-induced hyperthyroidism if dietary iodine increased substantially over a short period. The median UIC among WIFA in the North appeared to be optimal in 2015, however this should also be monitored periodically and interventions implemented if the median UIC should decrease to below 100µg/L or increase to close to 300µg/L.

At the national level, the median UIC of 313.0µg/L for WIFA in households using salt with iodine >40ppm indicates that overall iodine intake in this group was also above requirements (Table 15). This reinforces the recommendation made earlier to reduce the standards for iodine in edible salt and establish an upper limit for salt iodine level at production, in line with that proposed by ECOWAS. In this way, all population groups should obtain optimal levels of iodine from all sources of dietary salt combined.

71. Zimmermann M., et al.2013 Thyroglobulin Is a Sensitive Measure of Both Deficient and Excess Iodine Intakes in Children and Indicates No Adverse Effects on Thyroid Function in the UIC Range of 100–299 g/L: A UNICEF/ICCIDD Study Group Report. J Clin Endocrinol Metab, March 2013, 98(3)72. Zimmermann M. 2009. Iodine Deficiency. Endocrine reviews 30(4):376-40873. Groundwater Quality: Ghana. British Geological Survey, WaterAid. https://www.bgs.ac.uk/home.html


Estimated Intake of Salt from Household Salt and Processed Foods among WIFA

Estimates for intake of household salt were very approximate and were only made for the intake of household salt, therefore they do not account for any other sources of salt in the diet. Despite the relatively crude assessment methodology, the median calculated intake estimates were fairly similar across all sub-groups with a range from 4.6 to 5.2g/pers/day, equivalent to 1.8 to 2.1g sodium/pers/day. If this was the only source of dietary salt for most of the population then it would fit reasonable well with the 2012 WHO guidelines for sodium intake for adults (<2g/pers/day)74. The following need to be considered in the interpretation of these intake estimates:• Thesedataareforhouseholdsaltanddonotaccountforothersourcesofdietarysaltsuch

as processed foods and condiments (see below) or foods prepared outside the home.• Itisthoughtthatonlyapproximatelyaquarterofcookingsaltactuallyentersthediet,after

salt losses from cooking water etc. are considered.75

Estimates for intake of salt from processed foods: bouillon, tomato paste and instant noodles; were again approximations, based on reported frequency of weekly and daily consumption, together with estimates of adult serving sizes and of average salt content. The methodology was the same across the country which, therefore, allows for reasonable comparison of intakes between different sub-groups. Overall, the intake of salt from the three processed food products included in the survey was particularly high among WIFA in the North (the stratum with the most deprived households in the survey).

The approximation of salt intake from data on household salt use and frequency of consumption of the three food products above indicated that the combined salt intake was highest in the North, due to this relatively high contribution of salt from the processed food products. This can be seen in Figure 13.

Lowest relative salt intake from the three processed foods (as a proportion of the combined salt intake) was found among WIFA in the South-non-salt-producing areas. This contrasts with what may have been expected from the literature, where an increasing proportion of dietary salt intake from processed foods and condiments is usually associated with urbanisation and increased disposable income.46,76 This disparity may be due to the type of processed foods/condiments assessed in this survey, which were targeted because they were known to be used by a wide range of consumer groups. Salt intake from other processed foods such as bread and other bakery products, as well as foods prepared away from home, could still be expected to be higher among less deprived, more urbanised, households.

74. Guideline: Sodium intake for adults and children Geneva, World Health Organization (WHO), 2012.75. James WPT, Ralph A, Sanchez-Castillo CP. 1987. The dominance of salt in manufactured food in the salt intake of affluent societies. The Lancet , Volume 329 , Issue 8530 , 426 - 42976. Reardon T, Tschirley D, Dolislager M, Snyder J, Hu C, White S. 2014. Urbanization, diet change, and transformation of food supply chains in Asia. East Lansing, MI: Global Center for Food Systems Innovation. http://www.perhepi.org/wp-content/uploads/2014/08/3.-Urbanization-diet-change-and-transformations-of-food-supply-change-in-Asia_MSU-GCFSI-Reardon-et-al.pdf


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The relatively high consumption of processed food/condiment (non-household) sources of salt, particularly in the North of the country, provides an evidence base to advocate for the enforcement of legislation on the use of quality-assured iodised salt in the food industry. As mentioned above, this would provide greater equity in access to iodised salt and its benefits, regardless of dietary preference for household or food industry sources of salt. The use of iodised salt by food producers would also help to mitigate the impact of the current low household coverage of adequately iodised salt, although ideally the quality of household salt iodisation would be improved at the same time. The inclusion of food industry salt in USI enforcement and advocacy could be implemented in tandem with salt reduction efforts; adjusting the level of salt iodine to account for eventual reduced overall salt intake.

The results for median UIC by estimated salt intake from processed foods and from the multiple regression analysis of expected UIC with increasing consumption of processed foods assessed in the survey (Tables 18 and 19), suggest that salt used in these products may not be iodised, or is not consistently iodised. The only product showing some linear relationship between increased intake and expected UIC was tomato paste. This may be a result of communication activities directed towards all key players in the salt trade chain, including food industry. Industry practices should be investigated to see whether tomato paste companies were already using iodised salt in production prior to the survey, to better interpret this finding.

Factors associated with Iodine Status The multiple variable regression analysis helps to determine which of the factors included in the survey were most highly associated with expected UIC levels and, therefore, which were probably associated in single variable analyses due to overlapping influences. For example, the difference in UIC among WIFA from urban compared to rural areas was not significant when all other factors were held constant in the multiple variable regression. The same was true for the MPI component domains (which were each, individually, significantly associated with UIC – separate data analysis not shown here). This suggests that the single variable associations observed for residence type and for MPI may have been reflecting differences in these variables more strongly related to strata. For example, results for household MPI scores suggest that the highest percent of deprived households was found in the North region, which also has the lowest naturally occurring iodine.

The two variables found to be highly significantly associated with UIC among WIFA after multiple regression analysis were strata and household salt iodine content. These associations are expected due to: a) the known differences in ground water iodine by strata, i.e. the rock type found in the Northern region and parts of the Mid region are associated with low iodine77; and b) the expected effect on iodine status of increasing dietary iodine intake through salt iodisation. The very strong association between household access to salt with at least 15ppm iodine and iodine status indicates that, where salt iodisation is being well-implemented and it is a major factor behind the improvement in population iodine status. The example shown in Figure 11 illustrates a specific situation for the population of WIFA from deprived, rural households. It shows that in the North and Mid regions, the median UIC was less than optimal (100µg/L) when household salt had no added iodine. For WIFA in households with

77. Groundwater Quality: Ghana. British Geological Survey, WaterAid. https://www.bgs.ac.uk/home.html


these characteristics, optimal iodine status of the population was achieved in all strata when household salt iodine reached 20ppm.

The fact that, overall, adequate iodine status was found among WIFA in non-deprived households not accessing adequately iodised salt, especially in the South, indicates that there are other dietary sources of iodine than household salt. These cannot be directly determined from the survey data, however, likely additional sources of iodine are:• NaturalsourcesbasedonhighergroundwateriodineintheSouth• Increased access to environmental iodine (generated fromdifferent human activities in

urban environments78)• Potentiallyfromtheintakeofprocessedfoodsandcondiments,orfoodspreparedaway

from home, that are made with iodised salt79

• Naturally occurring iodine in thewider range of food products that are generallymoreaccessible to urban, non-deprived, households80, and/or to households consuming higher amounts of marine fish products. However, the dietary diversity for WIFA in this survey was not significantly associated with UIC (data not shown), which suggests that access to a wider range of food products alone is not closely related to dietary sources of iodine.

The above theory with regard to the use of iodised salt in processed foods and condiments is somewhat supported by the fact that the other two variables significantly associated with UIC after multiple variable regression were levels of tomato paste and bouillon intake. However, as mentioned above, the relationship between UIC and bouillon is inconsistent and inconclusive. Whereas for tomato paste, the increase in UIC with increasing reported intake makes it more plausible to hypothesise that the product may be produced using quality-assured iodised salt. Since both of these products were found to be notable sources of dietary salt across different population consumer groups, enforcement of the use of iodised salt in their production could provide a relatively equitable source of dietary iodine to complement that from household salt.

The findings of this survey provide firstly, a reason to celebrate the public nutrition success in achieving optimal iodine status among WIFA across all the major regions of Ghana, and secondly, a reinforcement of the important sub-national differences in household access to adequately iodised salt, in iodine status among WIFA, and in salt-supply related factors associated with both.

Assuming Ghana is close to achieving optimal iodine nutrition among all groups, which appears to be the case, the goal should now change to sustaining this and ensuring that iodine intake does not increase above the optimal range for any group. Enforcing the quality of iodisation of all edible salt, including coarse grain household salt and food industry salt, will be a first step to ensuring equity of access to iodine across population groups. In parallel with this, it is important for regulations to establish the appropriate salt iodine level to sustain optimal, rather than more than optimal, iodine intake.

78. Water Quality Fact Sheet. Iodine. British Geological Survey, WaterAid. https://www.bgs.ac.uk/home.html79. Spohrer R, Larson M, Maurin C, Laillou A, Capanzana M, Garrett GS. 2013. The growing importance of staple foods and condiments used as ingredients in the food industry and implications for large-scale food fortification programs in Southeast Asia. Food and Nutrition Bulletin, vol. 34, no. 2 (supplement)80. Reardon T, Tschirley D, Dolislager M, Snyder J, Hu C, White S. 2014. Urbanization, diet change, and transformation of food supply chains in Asia. East Lansing, MI: Global Center for Food Systems Innovation. http://www.perhepi.org/wp-content/uploads/2014/08/3.-Urbanization-diet-change-and-transformations-of-food-supply-change-in-Asia_MSU-GCFSI-Reardon-et-al.pdf


Recommendations based on the findings of this 2015 survey are as follows:1. In order to benefit the entire population appropriately, continue plans to implement the USI

strategy III and in particular the first three key strategies (Objective 1) related to improvement of salt industry practices and enforcement as the evidence points to the fact that salt supply was found to be the most important factor, and more important than consumer knowledge, in household access to adequately iodised salt.

2. It is strongly recommended to lower the national salt iodine standard and establish a range of acceptable potassium iodate/iodine values for all domestically produced and imported salt. This would provide easily understood and enforceable guidance across the salt industry and prevent both the very high iodine levels found in many iodised salt samples, and the relatively high median UIC observed in respondents from the South-non-salt-producing areas. A range of 30-60ppm potassium iodate at production and import is recommended by WHO and the Economic and Monetary Union of West African States and this should be applied and enforced.

3. Along side improving salt industry practices and enforcement as part of provisions in the objective 1 of the USI strategy III, actions should specifically focus on: investigation of current food industry practices, advocacy for change where required, monitoring and enforcement of the use of appropriately iodised salt by the food industry, and standardised packaging labelling to reflect this.

a. This survey shows that a significant proportion of dietary salt may be coming from commonly used processed foods and condiments, however it is not known whether any or all of these are produced using iodised salt or not. The expectation

is that if this recommendation is implemented in parallel with recommendations 1 and 2 it would result in more equitable access to safe levels of iodine through appropriately iodised salt for all population groups, regardless of their preferred main dietary salt source.

b. This should be done against the backdrop that enforcement of iodised salt use by the food industry is legislated for under the current Public Health Act 851 (2012), which refers to iodisation of all salt for human and animal consumption.

4. Investigate options to implement more continual review of iodine status in order to protect against iodine excess, while ensuring that the status among all population groups, especially in the North and Mid regions is maintained or improved.

a. The variety of possible iodine sources aside from household salt, i.e. processed food salt and apparently from non-salt sources (especially in the South); together with current low household access to adequately iodised salt, means that household salt

iodine content is no longer a reliable proxy for iodine status in Ghana (although it is still an important iodine source).

b. Political and consumer support for USI are essential for its success. If iodine intakes start to move further towards iodine excess in some regions, then the strategy may be difficult to defend and such support may be difficult to sustain.

c. It may be possible to use ante-natal clinics as a site for

Recommendations


monitoring iodine status among pregnant women, as recommended in the USI strategy III. However such a system should be designed with awareness of any bias towards women attending for antenatal care, which is high nationally (97%) but

varies by region, urban/rural residence and wealth quintile81. Some level of standardisation of the trimester in which to obtain urine samples from women should also be implemented since relative expected excretion varies during pregnancy.

d. Coordinate the public health goal of achieving optimal population iodine status with national salt reduction efforts so that salt iodine levels may be adjusted In the future

as needed (based on monitoring data) to account for decreased overall salt intake from all sources.

81. GDHS 2014


Note that these MPI scores are developed for definition of deprivation at the household level, based on GAIN adaptation of the OPHI index. This does not follow exactly the same rationale as the UNDP MPI index82 which has greater focus on the status of individual household members (and was developed after the time of the iodine surveys). Key differences between the iodine survey MPI scoring and the UNDP classification are noted below. All indicator definitions are listed, including for indicators that were not included, so that the basis for calculations and possible comparison with other survey data are clear.

HEALTH DIMENSION

1. Nutrition Indicator of deprivation: MUAC WRA < 210mm This was not included in the Ghana survey, therefore the health dimension part of the MPI was comprised of the household hunger and short birth history components only.

2. Household Hunger/Food security (access)The 9 question food security (access) module was used83. Indicator of deprivation: Mild, moderate or severe food insecurity (MPI code as deprived)

Household (HH) Hunger score should be possible to collect for all HHs regardless of HH composition. Where responses to HH Hunger score are missing (respondent refused to answer) then the MPI calculation for this HH was excluded.

HH hunger is not a component indicator in the UNDP index.The food security (access) indicators provide more detailed information than is required for the MPI score and it is suggested that additional analysis and use should be made of these.

3. Mortality Indicator of deprivation: At least 1 death of a live-born child, born to a current HH member

in the last 5 years

Where there have been no live births to current HH members in the past 5 years, the household was considered to be not deprived for this indicator.

Where there have been live births to current HH members in the past 5 years but the respondent refuses to answer questions in this module, then MPI calculation for this HH was excluded.

Appendix 1: MPI indicator definitions

82. UNDP’s Multidimensional Poverty Index: 2014 Specifications. A UNDP Human Development Report Office Occasional Paper.83. Coates, Jennifer, Anne Swindale and Paula Bilinsky. 2007. Household Food Insecurity Access Scale (HFIAS) for Measurement of Household Food Access: Indicator Guide (v. 3). Washington, D.C.: FHI 360/FANTA.


EDUCATION DIMENSION

Years of Schooling Indicator of deprivation: No HH member has completed 5 years of schooling

For Ghana (due to an error in the programmed skip) the definition used is: Indicator of deprivation: Respondent had less than 5 years schooling.

School attainment for the respondent should be available for all HHs regardless of HH composition. Where responses are missing (respondent refused to answer) then MPI calculation for this HH was excluded.

School Attendance Indicator of deprivation: A school age child in the HH (5 – 14 years) not currently attending

School

Where there is no child aged 5-14 years in the household, the HH is considered to be not-deprived in school attendance.

Where there is a child aged 5-14 years in the household but data on attendance are missing, the MPI calculation for this HH was excluded.

LIVING STANDARDS DIMENSION

Electricity Indicator of deprivation: No access to electricity in the household

Where a response to this question is missing, the MPI calculation for this HH was excluded.

Floor Indicator of deprivation: Unimproved house flooring

Defined as: anything other than finished flooring (vinyl, asphalt, tiles, concrete, cement, parquet or polished wood).Where a response to this question is missing, the MPI calculation for this HH was excluded.

Cooking Fuel Indicator of deprivation: Non-use of clean fuel

“Dirty” fuel defined as: Coal, lignite, charcoal, wood, straw, grass, animal dung, (& similar).Coal is considered as a “clean” fuel within the UNDP document.Where at least one of the responses was for a “dirty” fuel the HH is considered deprived for this indicator.Where a response to this question is missing, the MPI calculation for this HH was excluded.


AssetsAsset listings vary by country but not substantially and it is still possible to categorise responses according to UNDP category criteria.

Indicator of deprivation: No asset in category 1 and/or no asset from either category 2 or 3

Category 1 - Assets that allow access to information (TV, radio, telephone, etc)Category 2 - Assets that support mobility (e.g. bike, car, boat)Category 3 - Assets that support livelihood (e.g. refrigerator, livestock, etc.)

Where there is no response to this question it may not be clear if the HH has no assets (consider as deprived) or if the respondent refused to answer (would need to exclude calculation of MPI for this HH).

Sanitation Indicator of deprivation: No regular access to an improved sanitation facility and/or the

sanitation facility is shared

Improved sanitation facility includes: Flush or pour flush into: Piped sewer system/septic tank Pit latrine Somewhere elsePit latrine VIP Latrine With slab Composting toilet If any of the above types of facilities are shared they are considered as unimproved.

Unimproved includes: Flush or pour flush to unknown place Pit latrine without slab/open pit Bucket Hanging latrine Bush, field, or no facilities Public toilet (WC, KVIP, Pit, etc.)

Where a response to this question is missing, the MPI calculation for this HH was excluded.

Additional indicators to calculate for national programme interest not as a component of the MPI (presented as positive indicators): Sanitary disposal of children’s faeces Positive if any of the following responses: i. Child used toilet/latrine ii. Put into toilet or latrine iii. Buried


Unsanitary disposal i. Put / rinsed into drain or ditch ii. Thrown into garbage iii. Buried iv. Left in the open v. Other

Knowledge of five critical moments for hand washing Score of count 1-5 for the five following responses: i. Before eating ii. Before feeding a child iii. Before cooking/preparing food iv. After defecation/urination v. After cleaning a child that has defecated or changing a child’s nappy

Water Indicator of deprivation: Unsafe drinking water – no access to improved source of drinking

water and inadequate drinking water treatment

In other words: Access to improved drinking water is defined as: access to probably safe drinking water (water from an improved water source) OR adequate treatment of an unimproved source.

Improved source of drinking water includes: Piped water into dwelling/Tap Piped water into yard/plot/compound Public tap/Standpipe Tube-well/Borehole Protected dug well/spring Rainwater collection system Bottled water/sachet water

Unimproved source of drinking water includes: Unprotected dug well/spring Cart with small tank or drum Tanker-truck Surface water (river, dam, lake, irrigation channel, canal, stream Other

Where a response to this question is missing the MPI calculation for thisHH was excluded.

Adequate treatment includes: Boil Add bleach/chlorine tablet Use a water filter (gravel/ceramic/sand) Solar disinfection


Inadequate treatment includes: Strain it through a cloth only Let it stand and settle only Nothing Other

For Ghana – the second part on treatment was asked about for bottled water also and bottled water was only considered improved where treated appropriately. In some countries, the use of bottled water alone can be considered as improved.

Where a response to this question on treatment is missing and the response for water source was for a non-improved water source, the MPI calculation for this treatment indicator cannot be assessed and the HH was excluded.

If the response to the question on water source was an improved water source then it doesn’t matter if the second part was missed since the MPI score can be calculated as not deprived.

Treatment of households with missing observation or non-eligible populationsThere are two types of missing information: a. Indicator missing - Where an indicator was not proposed for collection (not included in the

questionnaire), e.g. MUAC, then assuming that there is information for other indicators in the same dimension, the weighting of these indicators is adjusted to make up 1/3. In the case of not collecting nutrition (MUAC) information for Ghana, it means the HH hunger score and short birth history increase their weighting for the overall MPI score from 1/9th to 1/6th each.

b. Missing observation- two causes i. Where the indicator is proposed for collection but cannot be completed in a

household due to no eligible population group in the household, e.g. short birth history in a household where no live births to current household members in the last 5 years. Then the household is considered as non-deprived for this indicator.

ii. The respondent does not wish to or cannot answer the question. In this case it is not known if the HH is deprived or not deprived for this indicator and

the household was excluded from MPI calculations.


Appendix 2: Survey Questionnaire

BackgroundThe Ghana Health Service, supported by GAIN, UNICEF and other agencies, have been implementing programmes to address micronutrient deficiency, such as vitamin A, iron and iodine deficiency disorders, in different target groups throughout the country. These programmes include supplementation and food fortification and have the goal of improving the micronutrient status of children and women of reproductive age, especially pregnant and lactating women, through increased availability and intake of micronutrient-fortified foods. One of these programmes is for the elimination of iodine deficiency disorders through the supply and use of iodised salt.

At the start of the project a baseline survey was conducted to determine the availability, access and intake of fortified foods by children between 2 and 5 years of age and women of reproductive age. These programmes have been in operation for some time and there is now the need to review progress made so that national strategies can be amended as necessary. Therefore we wish to collect information on knowledge, practices, availability and household consumption of fortified food products, in particular iodised salt and commercially processed vegetable oils.

Data collected from this survey will be used for the evaluation of the Salt Iodization programme; for follow-up and review of programmes and to compile data which will enable us to describe the current status of iodine deficiencies among women; and to verify the feasibility and expected impact of oil fortification with vitamin A to improve the vitamin A status of the population.

Study Procedures and ParticipationThis survey will be carried out by personnel of the Ghana Health Service and the Ghana Statistical Service who will visit selected households throughout the country. If you agree to take part in this survey, a member of your household will be asked a series of questions about the characteristics of your household, food intake in your household, the use of and knowledge about iodised salt and about the availability of commercially available vegetable oils. A small quantity of salt usually used in meal preparation for your household will be collected; one portion will be tested for iodine at the end of the interview while the other will be taken for laboratory analysis of iodine content.

Ghana 2014Iodine Survey Consent Form and Questionnaire


Any women between the ages of 15 and 49 years in the household may also be asked to give permission for a sample of urine to be taken to determine the iodine status among women of reproductive age in Ghana. Other than in the case where data need to be re-verified, you will be visited just once and any information you give will be treated as confidential. Participation is voluntary and you are free to refuse consent now or at any time during the interview process; there will be no adverse consequences for refusal. The sample(s) of urine given will only be used to carry out a test of iodine content; no other tests will be conducted.

Do you have any questions?

Household ConsentI confirm that the study has been explained to me and I understand the interview process. I have been informed that I or my partner/husband/child’s father are free to ask any questions we might have about this project and, for any further questions, Mrs Kate Quarshie and Ms Esi Amoaful Ghana Health Service, Accra may be contacted.

I understand that my participation, and that of any other member of the household, is voluntary and that the information given will remain confidential and will be available only to the investigating team and may be reviewed by the institutional ethics committee.

I understand that I may not have any direct benefit from the participation of my household in this survey, but my community as a whole would benefit.

Name of Respondent: ________________________________________

Date: ____________________________

Signature or thumb print:

Name of Interviewer: ________________________________________

Date: ____________________________

Signature of Interviewer:


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Appendix 3: Data Management and Analysis Document

84 Open Data Kit (ODK) is a free and open-source set of tools for mobile data collection – https://opendatakit.org85 ODK Collect is used on Android devices and renders forms into a sequence of input prompts - https://opendatakit.org/use/collect/ 86 XLSForm is a tool to simplify the creation of forms. Forms can be designed with Excel and XLSForm will convert them to XForms that can be used with ODK tools.

This brief report summarises the data management and basic analysis for the Ghana Iodine survey that was provided by the Statistical Services Centre, University of Reading, UK (SSC).

ODK84

The survey in Ghana was done using ODK Collect85 on smart phones; the SSC team produced the coding for the ODK system. The forms were designed in Microsoft Excel and converted using XLSForm86 to a form ready to be used with ODK Collect.

Once the ODK system was complete it was loaded to Form Hub type server from where it could be downloaded to individual phones. Completed questionnaires were uploaded to the same server at the end of each day of fieldwork.

Skips in the questionnaire were automatically coded into the ODK system and as many checks as possible were included. There were just two checks that it was not possible to include in the coding:1. In the section where women in fertile age were interviewed there was a question for the

interviewer on whether or not the woman being interviewed was the main respondent for the survey in that household. It was not possible to automatically check that only one women at most was identified as being the main respondent. Consequently in several households two or more women were identified as the main respondent. In all but one household all women identified as the main respondent had the same pregnancy status so this did not adversely affect the analysis.

2. In the urine collection section the age of each woman was recorded. However, it was not possible to automatically check these ages against the household composition data with age ranges collected earlier in the questionnaire. This was not a problem for the analysis as the important criteria here was to ensure the women were aged between 15 and 49 years and a check that the woman providing a urine sample was within this age range was included in the coding.

The only error identified in the ODK system was to do with the education questions. There were five questions on education in the questionnaire which were to be used to calculate the Education dimension of the MPI (Multi-dimensional Poverty Index). The respondent was asked:


• IN4:Haveyoueverattendedschoolorcollege?If no, skip to next section.• IN5:Areyoucurrentlyattendingschoolorcollege?• IN6:Doyouhave5ormoreyearsofeducation?

Later in the Schooling section the respondent was asked:• ED1:Isthereanyothermemberofthishouseholdwhohas5ormoreyearsofschooling?• ED2:IsthereanychildintheHHagedbetween5&14yearswhodoesnotattendschool?

ED2 was only asked if there were children aged between 5 and 14 years in the household and ED1 was supposed to be asked if the respondent had not had 5 or more years of education him/herself. Unfortunately ED2 was only asked if the response to IN6 was “No” and was not asked if the response to IN4 was “No”. This had implications in how the MPI was calculated, detailed in a later section of this report.

In addition to the main ODK form, there were separate paper-based summary forms for the interviewers and field supervisors designed to help monitor data collection. Each interviewer had a paper-based household checklist for each PSU where they recorded which households they had visited, which questionnaires were completed, how many refusals there had been and the number of urine and salt samples collected from each. This information was collated by the supervisors at the end of each day and an image of the completed paper-based checklist uploaded to the server. Teams appeared to be slow initially in uploading the supervisor forms with only a few being uploaded in the first few days of fieldwork. However this number soon picked up and by the end of the fieldwork there were between 19 and 24 supervisor uploads per team. These uploaded images weren’t always clear enough to read so we would not recommend capturing images of hand written forms in the future.

During the fieldwork the SSC monitored the data uploads and sent thrice weekly progress reports back to the country team. These reports detailed the number of PSUs and households that had been completed and uploaded, and the number of salt and urine samples collected from each PSU. Being able to monitor progress in this way was very useful to identify and solve problems as they arose.

Data were uploaded to an aggregate server during the fieldwork and downloaded at regular intervals in Excel format. SSC staff produced Excel macros to convert the data into a format to share with the survey coordination team for review, and a format ready to be imported into SPSS for checking and analysis.

BarcodesBarcodes were used to identify households in the survey and to label the salt and urine samples so that results from the lab analyses could be matched with the corresponding survey data. Barcodes at the household level comprised the PSU_ID and the HH_ID; these were used to identify each household and label the salt samples. The PSU_ID was a four character code starting with “G” and followed by three digits, codes went from G001 through to G132; the HH_ID was a two-digit code between 01 and 16.

The barcodes were printed on sheets of labels ready to be used in the field. Each interviewer was provided with the labels for all households that he/she had been allocated in a given PSU. The ODK system prompted the interviewer to scan the code at the start of the interview


and again at the stage of salt collection when one of the labels was placed on the salt sample collection bag. An example household barcode is shown below:

For the urine samples the codes comprised the PSU_ID, the HH_ID plus a two-digit code to identify each woman within the household. Because it was not known in advance how many women there would be in each household, labels were produced for up to five women in each household along with some sheets of “spare” codes in case there were more than five women in any household. As it turned out these “spare” codes were not needed. For each woman, four labels were provided with identical ID codes; one for the urine collection pot and others to label the individual sample containers for the lab. Because these labels were small (2.5 x 2.5 cm), it was not always easy to read the barcode with the phone the corresponding QR code was also printed on the label. All phones were set up with software for both barcode and QR code scanning. An example WIFA ID label is shown below:

The codes were produced using macros in Excel and the barcodes and QR codes generated using TBarCode Office87.

TrainingTraining of the field teams was done during the week beginning 24th November 2014. The training included sessions on using the phones, using ODK, scanning the barcodes, etc. Additional sessions were provided for the team supervisors highlighting their specific tasks such as backing up the data via Bluetooth and uploading the completed forms to the aggregate server. A field test was included as part of the training.

Prior to the training, a comprehensive training manual was produced along with a separate manual for the team supervisors.

During the training week the phones were prepared for the fieldwork with the necessary software: ODK Collect, an app for scanning the barcodes, and a file manager. The latest version of the ODK form for the server was downloaded to each phone. The phones were physically labelled so they could be uniquely identified and each interviewer was assigned his/her own phone. Field staff were allocated to teams and each team was provided with one spare phone.

G00101

PSU=G001HHID=01

87 TBarCode Office is an Add-In for Microsoft Word and Excel - http://www.tec-it.com/en/software/barcode-software/office/word-excel/Default.aspx

G0010301


Data ReportingThe fieldwork started in mid-January 2015 and was completed by mid-February 2015. Throughout the fieldwork supervisors were uploading completed forms on a daily basis and three times each week the data was downloaded and a progress report sent to the country team. The following table shows the progress of the fieldwork and data uploads:

Date # HHs # PSUs # Salt samples # Urine samples % Complete23-Jan 234 16 198 187 12%26-Jan 564 40 459 405 30%28-Jan 766 50 623 558 38%30-Jan 968 62 777 712 47%

Date # HHs # PSUs # Salt samples # Urine samples % Complete2-Feb 1232 80 970 890 61%4-Feb 1517 97 1177 1075 73%6-Feb 1687 107 1302 1182 81%9-Feb 1890 120 1446 1305 91%11-Feb 2071 131 1571 1416 99%13-Feb 2087 132 1582 1423 100%

Response RatesThe complete sample was 2112 households – 16 in each of 132 PSUs.• 25householdsarenotinthedataset;presumablyunoccupied=1.2%• 122householdsunoccupied&markedas999=5.8%• 36householdsrefusedconsent=1.7%• 1929householdscompletedquestionnaire=91.3%• 1582saltsamples=74.9%ofsampleor82%ofcompletedquestionnaires• Urinesamplesfrom1169households=55.4%ofsampleor60.6%ofcompleted

questionnaires

The main issues that cropped up during data monitoring were the lower than expected number of urine samples – collected from only 60% of completed households; and high number of unoccupied households in a few of the PSUs – G038 had 5 households unoccupied and PSUs G046 and G114 both had 4 households unoccupied. G114 is in one of the conflict districts so people are often moving around depending on the situation, also some of the listed households were in farming areas where families had taken a break over Christmas (and so were at home when the lists were drawn up) but had since returned to their farmlands.

There were a few cases where the wrong enumerator code had been used but these were easily identified and corrected.

Lab Analyses resultsSalt samples were collected from 1586 households and results obtained from 1577 of these samples. For the remaining samples the amount taken was not enough to analyse.


1423 urine samples were collected from 1169 households. The lab was able to analyse 1388 urine samples. Of the missing 35 samples, 18 were received but were not on the lab hand-over form, 3 were recorded on the hand-over form but there was no sample in the bag, there were 2 duplicates in the results list, 10 samples that could not be analysed, and 2 unaccounted for. Of the 10 that could not be analysed 2 were because the tube had come unscrewed and was empty and for the other 8 there was indigestible suspended sediment present in the sample.

AnalysisSPSS (version 22) was used to produce the results to populate the tables specified in the Analysis Plan. We used the Complex Samples Module in SPSS to calculate confidence intervals (95%) of population estimates and percentages for the weighted data. For this we developed a CSA (Complex Samples Analysis) plan file which includes the Strata, the PSU, the probability of selection for the PSU, the probability of selection for a household within the PSU, and the weights adjusted for non-response.

We then developed SPSS syntax to generate the tables creating a separate syntax file for each tab in the analysis plan.

With the exception of the Overview tab, the HH Characteristics tab and the Respondent Type tab all results produced were based on weighted data. For all tables of weighted results we also generated cross-tabulations and frequencies of un-weighted counts for each cell. When presenting results cells where the un-weighted count was less than 25 responses were replaced by an asterisk (*); cells where the un-weighted count was between 25 and 50 were presented in square brackets, e.g. [4%].

Where means are presented these were always calculated using the CSDESCRIPTIVES command in SPSS; this uses the CSA plan file to give the weighted mean together with 95% confidence intervals. Unfortunately this command does not produce medians. The RATIO STATISTICS command can be used with a constant denominator of 1 to produce medians with confidence intervals however this does not give correct results on weighted data; the weights are rounded to the nearest whole number, thus cases with a weight of less than 0.5 are omitted. It was therefore decided to only produce confidence intervals for medians at the strata level as cases are effectively self-weighted by strata. For national medians and medians for other categories we produced the 25th and 75th percentiles using the EXAMINE command on weighted data. Although, in common with the RATIO STATISTICS command, the EXAMINE command does round the weights, we found that, for every case lost for weights that rounded to zero, the command omitted a case with the highest weight. For example if we had 10 cases with weights: 0.1, 0.3, 0.5, 0.6, 0.9, 1.2, 1.3, 1.4, 1.7, 1.9; these would be rounded to 0, 0, 1, 1, 1, 1, 1, 1, 2, 2; the EXAMINE command would calculate the median based on the six cases in the middle whereas the RATIO STATISTICS command would use the top eight cases.

The regression analyses for salt and urinary iodine levels were done using R version 3.1.3

During the analysis the following indicators were calculated:•MPI(Multi-dimensionalPovertyIndex)•DietaryDiversityScore•HouseholdFoodInsecurity


MPI CalculationsThe MPI consists of three dimensions:• Education• Health• LivingStandards

Education DimensionThe Education dimension of the MPI comprised two indicators:• SchoolAttainment–ahouseholdissaidtobedeprivedinthisindicatorifnohousehold

member has received more than 5 years of formal schooling• SchoolAttendance–ahouseholdissaidtobedeprivedinthisindicatoristhereisachildin

the household aged between 5 years and 14 years who does not attend school; households with no children of school age are considered to be not deprived in this indicator

To calculate the School Attainment we used the following questions:• IN4:Haveyoueverattendedschoolorcollege?• IN6:Doyouhave5ormoreyearsofeducation?

As mentioned earlier in the ODK section of this report, there was an error in the ODK system which resulted in us having to base the School Attainment indicator on whether the respondent had received 5 or more years of education rather than any household member.

The following syntax generated this indicator: If (IN4=1 and IN6=1) School Attainment=0. If (IN4=1 and IN6=0) School Attainment=1. If (IN4=0) School Attainment=1.

For both IN4 and IN6 the codes used are 1=Yes, 0=No. For the indicator 1=Deprived, 0=Not deprived.To calculate the School Attendance indicator we used the following questions:• HH2d: Number of males aged 5 to 14 years in the household• HH3d: Number of females aged 5 to 14 years in the household• ED2: Is there a child in the HH aged between 5 and 14 years who does not attend

school?

The following syntax generated this indicator:

If (HH2d=0 and HH3d=0) SchoolAttendance=0. If ((HH2d>0 or HH3d>0) and ED2=0) SchoolAttendance=0. If ((HH2d>0 or HH3d>0) and ED2=1) SchoolAttendance=1.

Codes for ED2 are 1=Yes, 0=No. Thus a household is deprived if there are children of school age in the household and at least one of these children does not attend school.

The Education dimension is the average of these two indicators, thus:


COMPUTE mpiEducation = (SchoolAttainment + SchoolAttendance) / 2.

Health DimensionThe Health dimension included indicators for Household Hunger and for Mortality.

The Mortality indicator was based on whether or not a child that was born alive in the last 5 years to a current household member has since died. For this we use the questions in the Short Birth History section of the questionnaire:• SBH1: How many live births have there been to people currently living in this household in the

last 5 years?• SBH2: In what year was the child born?• SBH3: Where is the child now?• D1: Has a child died in the last 5 years?

Questions SBH2 and SBH3 were asked for each of the recent births mentioned in SBH1. The responses to SBH3 were coded as 1=Alive, 2=Dead; these options were not read to the respondents but the responses were interpreted appropriately by the enumerators. The ODK system was programmed to automatically calculate the answer to D1. If no children were born to household members in the last 5 years, then the household is considered to be not deprived in this indicator.

The following syntax was used to generate the Mortality indicator:

If (SBH1=0) Mortality=0.If (SBH1>0 and D1=0) Mortality=0.If (SBH1>0 and D1=1) Mortality=1.

The Household Hunger indicator is based on the following questions in the Food Security section of the questionnaire:• H1:Duringthepast30dayshaveyoubeenworriedthatyourhouseholdwouldnothaveenough

food?• H2:Duringthepast30days,forlackofresources(e.g.money),wereyouoranymemberof

your household unable to eat the kinds of foods that you usually like to consume?• H3:Duringthepast30days,forlackofresources,didyouoranymemberofyourhousehold

eat the same thing every day?• H4:Duringthepast30days,forlackofresources,didyouoranymemberofyourhousehold

eat foods that you preferred not to eat?• H5:Duringthepast30days,forlackoffood,didyouoranymemberofyourhouseholdeata

smaller meal than you felt you needed?• H6:Duringthepast30days,forlackoffood,haveyouoranyotherhouseholdmemberreduced

the number of meals usually consumed per day?• H7:Duringthepast30days,forlackoffood,haveyouoranymemberofyourhouseholdgone

to bed hungry?• H8:Duringthepast30days,wasthereevernofoodatallinyourhouseholdbecausetherewas

not the means to get more?• H9:Duringthepast30days,forlackofresources,didyouoranymemberofyourhousehold

go a whole day without eating anything?


Codes for these questions were 0=Never, 1=Rarely, 2=Sometimes, 3=Often. A household is considered deprived for this indicator if they have answered Sometimes or Often for any of the nine questions or have answered Rarely to any question other than H1. The following syntax was used to generate this indicator:

If ((H1=0 or H1=1) and H2=0 and H3=0 and H4=0 and H5=0 and H6=0 and H7=0 and H8=0 and H9=0) HouseholdHunger=0.If (H1>1 or H2>0 or H3>0 or H4>0 or H5>0 or H6>0 or H7>0 orH8>0 or H9>0) HouseholdHunger=1.

The Health dimension is the average of these two indicators, thus:

COMPUTE mpiHealth = (Mortality + HouseholdHunger) / 2.

Living Standards DimensionThe Living Standards dimension comprises six indicators:• Electricity: a household is deprived if there is no electricity in the home.• Sanitation: a household is deprived if there is not regular access to an improved sanitation

facility and/or the sanitation facility is shared with other households.• Drinking Water: a household is deprived if there is no access to an improved source of drinking

water and inadequate drinking water treatment.• Floor: a household is deprived if the floors of the dwelling are anything other than finished

floors (e.g. tiles, concrete, cement, polished wood).• Cooking Fuel: a household is deprived if “dirty” fuel is used for cooking; this is defined as coal,

lignite, charcoal, wood, straw, grass, animal dung or similar.• Assets: a household is deprived if they do not own any assets that allow access to information

(TV, Radio, Phone), or they do not own any asset supporting mobility or livelihood (Bike, Boat, Fridge, Car, Animal-drawn cart, Computer)

The Electricity indicator is based on the following question:• M1:Doesyourhouseholdhaveelectricity?

This question is coded as 1=Yes, 0=No, so the syntax for this indicator is:If (M1=1) Electricity=0.If (M1=0) Electricity=1.

The Sanitation indicator is based on the following questions:• W3: What kind of toilet facility do members of your household usually use?• W4: Do you share you main toilet facility with other households?

For W3 the codes used were:1. Flush to piped sewer system/septic tank2. Flush to pit latrine3. Flush to somewhere else4. Flush to unknown place/not sure/don’t know where5. VIP (Ventilated Improved Pit) Latrine


6. Pit latrine with slab7. Pit latrine without slab/open pit8. Composting toilet9. Bucket10. Hanging toilet/handing latrine11. No facilities (bush, field, etc.)12. Public toilet (WC, KVIP, pit, etc.)

Options 1, 2, 5, 6 and 8 are considered to be improved facilities. Thus the syntax for this indicator is:

If (W4=0 and (W3=1 or W3=2 or W3=5 or W3=6 or W3=8)) Sanitation=0.If (W3=12) Sanitation=1.If (W4=1 or W3=3 or W3=4 or W3=7 or W3=9 or W3=10 or W3=11) Sanitation=1.

The Drinking Water indicator is based on the following questions:• W1:Whatisyourmainsourceofdrinkingwater?• W2:Whatdoyouusuallydotothewatertomakeitsafertodrink?

For W1 the codes used were:1. Piped water into dwelling/tap2. Piped water into yard/plot/compound3. Public tap/standpipe5. Tube-well or Borehole7. Protected dug well/spring8. Rainwater collection system9. Bottled/sachet water10. Unprotected dug well/spring11. Cart with small tank12. Tanker truck13. Surface water (river/dam/lake/pond/etc.)14. Other

Options 1 to 8 are considered to be improved sources of drinking water so a household using any of these is not deprived in this indicator.

For W2 the codes used were:1. Boil it2. Add bleach/chlorine tablet3. Use a water filter4. Solar disinfectant5. Strain it through a cloth only6. Let it stand and settle only7. Nothing8. Other88. Don’t know


Options 1 to 4 are considered adequate treatments so a household using one of these treatments, regardless of the water source, is considered to be not deprived in this indicator. The question about water treatment was not asked for bottled water, however bottled water is considered to be “not improved” in this instance. The syntax for this indicator is:

If (W1=1 or W1=2 or W1=3 or W1=5 or W1=7 or W1=8) DrinkingWater=0.If ((W1=10 or W1=11 or W1=12 or W1=13 or W1=14) and (W2=1 or W2=2 or W2=3 or W2=4)) DrinkingWater=0.If (W1=9) DrinkingWater=1.If ((W1=10 or W1=11 or W1=12 or W1=13 or W1=14) and (W2=5 or W2=6 or W2=7 or W2=8 or W2=88)) DrinkingWater=1.

The Floor indicator is based on the following question:• M3: Record the main material of the floor of the dwelling from observation, if not possible then

ask the respondent.

Options for this question are:1. Natural floor (earth/sand/dung)2. Rudimentary floor (wood planks, palm/bamboo)3. Finished floor (parquet or polished wood, vinyl or asphalt)4. Other

Thus the syntax for this indicator is:

If (M3=3) Floor=0. If (M3=1 or M3=2 or M3=4) Floor=1.

The Cooking Fuel indicator is based on the following question:• M2:Whatfuelsdoesyourhouseholduseforcooking?

This question is a multiple response question so has been coded into 11 separate variables in the dataset all with codes 1=Yes, 0=No. Thus we have the following variables:• M2_1:Electricity• M2_2:Gas• M2_3:Biogas• M2_4:Kerosene• M2_5:Coal/lignite• M2_6:Charcoal• M2_7:Wood• M2_8:Straw/shrubs/grass/cropresidue• M2_9:Animaldung• M2_10:Solar• M2_11:Other


Clean fuels are defined as Electricity, Gas, Biogas, Kerosene and Solar; any household using one of the other types of fuel (including “Other”) is defined as deprived in this indicator. The syntax for this indicator is:

If (M2_5=0 and M2_6=0 and M2_7=0 and M2_8=0 and M2_9=0 and M2_77=0) CookingFuel=0.If (M2_5=1 or M2_6=1 or M2_7=1 or M2_8=1 or M2_9=1 or M2_77=1) CookingFuel=1.

The Assets indicator is based on the following question:• M3: Which of the following does your household own?

This question is also a multiple response question coded into the following 10 separate variables all coded as 1=Yes, 0=No:• M3_1: Radio• M3_2: Television• M3_3: Mobile or non-mobile phone• M3_4: Bicycle/tricycle• M3_5: Motorcycle, scooter• M3_6: Motorised boat• M3_7: Refrigerator• M3_8: Car, truck or tractor• M3_9: Animal-drawn cart• M3_10: Computer/laptop/tablet• M3_12: Sheep/goat• M3_13: Cattle• M3_14: Fishing net• M3_15: Canoe

For a household to be classed as “not deprived” in this indicator they would need to own at least one of the first three assets in the list (i.e. radio, television or phone) and at least one of the other items listed. The syntax for this asset is:

If (M3_1=0 and M3_2=0 and M3_3=0) Assets=1.If (M3_4=0 and M3_5=0 and M3_6=0 and M3_7=0 and M3_8=0 and M3_9=0 and M3_10=0 and M3_12=0 and M3_13=0 and M3_14=0 and M3_15=0) Assets=1.If ((M3_1=1 or M3_2=1 or M3_3=1) and (M3_4=1 or M3_5=1 or M3_6=1 or M3_7=1 or M3_8=1 or M3_9=1 or M3_10=1 or M3_12=1 or M3_13=1 or M3_14=1 or M3_15=0)) Assets=0.

The Living Standards dimension is calculated as the average of these 6 indicators, thus:

COMPUTE mpiLivingStandards= (Electricity + Sanitation + DrinkingWater + Floor + CookingFuel + Assets)/6.

Finally the overall MPI is calculated as the average of the three dimensions and categorised as LOW (<1/2) or HIGH (≥1/2):


COMPUTE MPI = (mpiEducation + mpiHealth + mpiLivingStandards)/3.If (MPI < 1/3) mpiCategory=1.If (MPI >= 1/3) mpiCategory=2.Variable labels mpiCategory “Multi-dimensional Poverty Index”.Value labels mpiCategory 1 “Low MPI (<1/3)” 2 “High MPI (>=1/3)”.

Dietary Diversity ScoreThe Dietary Diversity Score is based on response to the following question:• DD1:Sincethistimeyesterdaydidyouhaveanyofthefollowingtoeatordrink?

1. Plain water2. Sweetened or flavoured water, minerals, malt drinks, tea or infusion, coffee, liquor, beer,

light soup3. Any food made from grain such as millet, wheat, sorghum, rice, maize4. Any food made from fruits of vegetables that have yellow or orange flesh such as carrots,

pumpkin, squash, orange sweet potatoes, ripe mangoes, papaya5. Any dark green leafy vegetables (Kontomire, Gboma, cassava leaves, alefu, ayoyo,

Bokoboko, bean leaves)6. Any food made from roots or tubers such as white potatoes, white yams, cassava, onions,

beets, tiger nuts7. Any food made from lentils, beans, peas, nuts or seeds8. Any other fruits or vegetables (coconut, eggplant, tomatoes, peppers, avocado, lemon,

green mango, banana)9. Liver, kidney, heart or other organ meat10. Any meat such as beef, pork, goat, cat, dog, guinea fowl, grass cutter, rat, chicken, duck11. Fresh or dried fish, shellfish, or seafood, snails, insects, crabs12. Cheese, yoghurt or other milk products including powdered milk13. Eggs14. Sugary foods such as sugar cane, sweets, candies, chocolate, cakes or biscuits15. Any food prepared with red palm oil (e.g. palm nut soup)16. Any food made with other oil, fat or butter

This question is coded into 16 separate variables in the dataset each coded as 1=Yes, 0=No. The respondent is classified as having a diverse diet if they have eaten something from at least four of the following categories:• Milkormilkproducts(option12)• Grains,rootsortubers(option3or6)• VitaminArichfruitsandvegetablesorredpalmoil(option4or15)• Darkgreenleafyvegetables(option5)• Otherfruitsandvegetables(option8)• Pulses(option7)• Organmeat(option9)• Meatorfish(option10or11)• Eggs(option13)


Thus we compute the dietary diversity score as follows:

COMPUTE DietaryDiversity = 0.If (DD1_12=1) DietaryDiversity = DietaryDiversity+1.If (DD1_3=1 or DD1_6=1) DietaryDiversity = DietaryDiversity+1.If (DD1_4=1 or DD1_15=1) DietaryDiversity = DietaryDiversity+1.If (DD1_5=1) DietaryDiversity = DietaryDiversity+1.If (DD1_8=1) DietaryDiversity = DietaryDiversity+1.If (DD1_7=1) DietaryDiversity = DietaryDiversity+1.If (DD1_9=1) DietaryDiversity = DietaryDiversity+1.If (DD1_10=1 or DD1_11=1) DietaryDiversity = DietaryDiversity+1.If (DD1_13=1) DietaryDiversity = DietaryDiversity+1.Recode DietaryDiversity (0 thru 3=0) (4 thru highest=1) into DDScore.Variable labels DDScore “Dietary Diversity Score”.Value labels DDScore 0 “Not diverse diet” 1 “Diverse diet”.

Household Food InsecurityThis index is similar to the Household Hunger indicator of the Health dimension of the MPI in that it is based on the same nine questions from the Food Security section of the questionnaire (H1 to H9). “Not deprived” for the Household Hunger indicator is the same as being “Food Secure” for the Household Food Insecurity index. Households that are classed as “Deprived” for Household Hunger are classified as being “Mildly”, “Moderately” or “Severely food insecure” according to the individual responses to the nine questions. The following table illustrates the different categories for this index:


Thus we have the following syntax for calculating this index:

If ((H1=0 or H1=1) and H2=0 and H3=0 and H4=0 and H5=0 and H6=0 and H7=0 and H8=0 and H9=0) FoodSecurity=1.If ((H1=2 or H1=3 or H2=1 or H2=2 or H2=3 or H3=1 or H4=1) and not (H3=2 or H3=3) and not (H4=2 or H4=3) and H5=0 and H6=0 and H7=0 and H8=0 and H9=0) FoodSecurity=2.If ((H3=2 or H3=3 or H4=2 or H4=3 or H5=1 or H5=2 or H6=1 or H6=2) and not H5=3 and not H6=3 and H7=0 and H8=0 and H9=0) FoodSecurity=3.If (H5=3 or H6=3 or H7=1 or H7=2 or H7=3 or H8=1 or H8=2 or H8=3 or H9=1 or H9=2 or H9=3) FoodSecurity=4.Variable labels FoodSecurity “Household Food Insecurity”.Value labels FoodSecurity 1 “Food Secure” 2 “Mildly food insecure” 3 “Moderately food insecure” 4 “Severely food insecure”.

Estimates of Salt IntakeSalt intake was estimated based on household purchase quantity and frequency responses, converted to an average monthly value, then using the FAO Factors for Calculating Adult Food Consumption Equivalents (FACE) to estimate intake by WFIA within a household. An average FACE value of 0.75 was used for all WIFA in the estimates below.

Conversion of questionnaire responses to average frequency and quantity of purchase estimates

FACE Value from FAO

8

4

2.5

1

0.45

0.2

0.15

75

150

300

500

800

1500

2500

300

Purchase quantity estimate

Monthly purchase estimate

> 1 x per week

Weekly

2-3 x per month

1 x per month

1 x per 2-3 months

1 x per 4-6 months

1 x per > 6 month

< 100g

100-200g

200-400g

500g

600-1000g

1000-2000g

2000-3000g

> 3000g

Appr

ox fr

eque

ncy

edib

le s

alt p

urch

ase

for c

ooki

ng, t

able

, foo

d pr

eser

vatio

n (in

pas

t m

onth

)

Appr

ox q

uant

ity e

dibl

e sa

lt pu

rcha

se fo

r co

okin

g, ta

ble,

food

pr

eser

vatio

n (in

pas

t m

onth

)


Females

0.27

0.45

0.61

0.73

0.78

0.83

0.77

0.73

Males

0.27

0.45

0.61

0.73

0.86

0.96

1.02

1.00

Age (yr)

< 1

1-3

4-6

7-9

10-12

13-15

16-19

≥ 20

Annex Foof and Agriculture Organization (FAO)Factors for Calculating Adult Food Consumption Equivalents

A. Bouillon intake estimate # times consumed per week * 1.25gB. Salt intake from bouillon estimate # times consumed per week * 0.6gAssuming 50% salt content of bouillonC. Tomato paste intake estimate # times consumed per week * 30gD. Salt intake from tomato paste estimate # times consumed per week * 0.3gAssuming 1g/100g salt content of tomato concentrateE. Instant noodle intake estimate # packets consumed per week * 85gF. Salt intake from instant noodle estimate # packet consumed per week * 3gAssuming 3g salt/packet instant noodles TOTAL salt intake from processed foods (g) estimate for WIFA B + D + F

Monthly estimates of salt use per household (HH) were based on the process, using stated interview responses:

(1) Estimate monthly HH salt intake (g) = Monthly purchase frequency * purchase quantity estimate(2) Daily HH salt intake (g) = Estimate for monthly HH salt intake (g) (1)/30(3) Estimate daily salt intake of one HH member (g) = Daily HH intake estimate (2)/(Number HH members in each age group * relevant FACE value)(4) Estimate daily salt intake of WRA (g) = Estimate daily salt intake of HH member (g) (3) * 0.75 (assigned FACE value)

Estimates of Processed Food IntakeEstimates for median intake of the food product and for estimated salt intake form the product were calculated using the process and assumptions below.

Estimate of salt intake from key processed foods


Appendix 4: Laboratory Quality Assurance – Process and Outcomes

88 Department of Endocrinology and Molecular Medicine – Biotechnology. Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226 014, India

GAIN developed a contract with the UP State USI Coalition (Technical) (SGPGIMS)88 to function as an external quality assurance laboratory for eight laboratories involved with a series of iodine surveys globally. This included the Micronutrient Laboratory, Department of Nutrition and Food Science, University of Ghana, Legon, where the salt and urine samples were analysed for iodine.

The laboratory was provided with a series of internal quality assurance (IQA) and external quality assurance (EQA) samples for both salt and urine over a period of time before and during the duration of sample analysis. IQA and EQA samples for salt were prepared from vacuum refined powdered salt.

The function of the IQA was to check reagents and analysis performance on a routine basis through supply of three different pre-assessed known iodine value reference material [(a) iodized salt samples, (b) urine samples with known iodine content). Once values observed and recorded were all within acceptable limits of measurement i.e. coefficient of variation is <10%, the survey samples could be processed.

The function of the EQA was to check the overall accuracy of analytical performance. Salt and urine samples with pre-assessed iodine content were produced. The survey laboratories were not aware of the iodine value. Six salt samples and four urine samples (each set with a range of iodine values from very low to expected highest values) were analysed in duplicate in 3 runs (on different days) and values reported back to SGPGIMS in a given format.

The SGPGIMS provided a troubleshooting service to advise on possible reasons for any problems encountered with either IQA or EQA results.

The Micronutrient Laboratory, Department of Nutrition and Food Science, University of Ghana, Legon EQA-based performance was rated as: “Good” across all five rounds of salt EQA testing and “Good” for the two rounds of EQA it participated in for urine.

In addition, the Micronutrient Laboratory, Department of Nutrition and Food Science, University of Ghana, Legon, laboratory participates in the CDC EQUIP programme which is another EQA based laboratory testing programme for urinary iodine analysis. Status of participation within this programme can be requested from the micronutrient laboratory directly.


Appendix 5: GPS Mapping of PSUs included in the Survey

GPS map based on location data from team supervisor phones


GPS map based on location data from interviewer phones


Appendix 6: Distribution of Salt Iodine Level for Samples with under 15ppm Iodine

The distribution of iodine levels in salt with <15ppm iodine was looked at in more detail to make provide some idea of whether salt in this category was likely to be inadequately iodised or to have had no added iodine, i.e. containing no iodine or only natural, trace, levels of iodine.

The peak in distribution of salt iodine ppm for this category of samples was found to be 2-3ppm, suggesting that <5ppm is a reasonable cut off to use for added versus trace iodine.

Distribution of salt iodine levels (ppm) for all samples with <15ppm iodine

0 2 4 6 8 10 12 14

Fre

que

ncy

400

300

200

100

0

Salt iodine concentration (ppm)


0 pp

m

1-4.

9 pp

m

5 to

<10

ppm

10 to

<15

ppm

Tota

l

Freq

uenc

y

366

596

393

116

1472

Nat

iona

lS

alt

iod

ine

leve

l (tit

ratio

n fo

r sa

mp

les

<15

pp

m

iod

ine)

Perc

ent

24.9

40.5

26.7

7.9

100.

0

Freq

uenc

y

92 64 67 25 248

No

rth Pe

rcen

t

37.2

25.6

26.9

10.3

100.

0

Freq

uenc

y

164

338

240

54 797

Mid

Perc

ent

20.6

42.5

30.1

6.8

100.

0

Freq

uenc

y

90 122

52 30 293

So

uth-

non-

salt-

pro

duc

ing

Perc

ent

30.6

41.6

17.7

10.1

100.

0

Freq

uenc

y

20 72 35 7 134So

uth-

salt-

pro

duc

ing

Perc

ent

15.2

53.8

25.9

5.1

100.

0


Appendix 7: Reported food frequency intake for bouillon, tomato paste and instant noodlesThe distribution of iodine levels in salt with <15ppm iodine was looked at in more detail to make provide some idea of whether salt in this category was likely to be inadequately iodised or to have had no added iodine, i.e. containing no iodine or only natural, trace, levels of iodine.

The peak in distribution of salt iodine ppm for this category of samples was found to be 2-3ppm, suggesting that <5ppm is a reasonable cut off to use for added versus trace iodine.

136 National Iodine Survey Report, Ghana 2015B

ouill

on In

take

and

Ass

ocia

ted

Med

ian

UIC

for

Diff

eren

t Fr

eque

ncy

of In

take

Lev

els

NATI

ONAL

North

non

-sal

t pro

duci

ng

Mid

non

-sal

t pro

duci

ng

Sout

h no

n-sa

lt pr

oduc

ing

Sout

h sa

lt pr

oduc

ing

Urba

n

Rura

l

Low

MPI

sco

re (n

ot d

epriv

ed)

High

MPI

sco

re (d

epriv

ed)

Med

ian

UIC

(25t

h, 7

5th

perc

entil

e)

Resp

onde

nt =

WIF

ASurvey Strata MPI ScoreUrban/Rural

17.3

%(1

4.5,

20.

6)

*

20.2

%(1

5.6,

25.

9)

22.3

%(1

6.7,

29.

2)

[15%

](1

1.2,

19.

8)

16.7

%(1

3.5,

20.

4)

18.6

%(1

3.2,

25.

5)

18.3

%(1

4.7,

22.

6)

16.2

%(1

2.6,

20.

7)

202

(117

, 330

)

33.9

%(3

0.1,

37.

8)

[10.

8%]

(6.8

, 16.

7)

35.1

%(2

8.9,

41.

8)

47.8

%(4

0.1,

55.

7)

39.2

%(3

3.1,

45.

8)

35.6

%(3

0.7,

40.

7)

30.6

%(2

4.4,

37.

5)

38.0

%(3

2.5,

43.

8)

28.7

%(2

4.4,

33.

4)

217

(134

, 382

)

24.1

%(2

1, 2

7.4)

25.4

%(2

0.2,

31.

3)

24.1

%(1

8.9,

30.

3)

21.8

%(1

7.3,

27.

0)

27.3

%(2

2.2,

33.

1)

24.3

%(2

0.8,

28.

2)

23.6

%(1

8.1,

30.

0)

21.9

%(1

7.9,

26.

4)

26.7

%(2

2.5,

31.

4)

221

(130

, 362

)

8.8

(3.8

, 17.

5)

17.5

(8.8

, 17.

5)

7.5

(3.8

, 15.

0)

4.6

(2.5

, 8.8

)

7.5

(3.8

, 11.

3)

8.4

(3.8

, 17.

5)

8.8

(3.8

, 17.

5)

7.5

(2.7

, 17.

5)

8.8

(3.8

, 17.

5)

18.9

%(1

6.5,

21.

7)

37%

(30.

1, 4

4.5)

19%

(15.

2, 2

3.5)

*

[13.

3%]

(9.5

, 18.

4)

18.4

%(1

5.2,

22.

1)

20%

(15.

5, 2

5.5)

17.8

%(1

4.3,

21.

9)

20.5

%(1

6.4,

25.

2)

169

(103

, 279

)

5.8%

(4.6

, 7.3

)

21.9

%(1

6.7,

28.

1)

* * *

[5%

](3

.5, 7

.2)

[7.2

%]

(4.6

, 11.

2)

[4.1

%]

(2.8

, 6.1

)

7.9%

(5.8

, 10.

7)

162

(97,

226

)

1088

208

522

265

93 714

374

605

480

1045

Num

ber o

f re

spon

dent

s

Med

ian

(25t

h, 7

5th

perc

entil

e) in

take

bo

uillo

n (g

) in

the

past

wee

k1

Repo

rted

bou

illon

con

sum

ptio

n fo

r the

pre

viou

s w

eek

0 tim

es1-

5 tim

es6-

10 ti

mes

11-1

5 tim

es16

-21

times


Tom

ato

Pas

te In

take

and

Ass

ocia

ted

Med

ian

UIC

for

Diff

eren

t Fr

eque

ncy

of In

take

Lev

els

NATI

ONAL

North

non

-sal

t pro

duci

ng

Mid

non

-sal

t pro

duci

ng

Sout

h no

n-sa

lt pr

oduc

ing

Sout

h sa

lt pr

oduc

ing

Urba

n

Rura

l

Low

MPI

sco

re (n

ot d

epriv

ed)

High

MPI

sco

re (d

epriv

ed)

Med

ian

UIC

(25t

h, 7

5th

perc

entil

e)

Resp

onde

nt =

WIF

ASurvey Strata MPI ScoreUrban/Rural

17.2

%(1

4.5,

20.

3)

30.1

%(2

3.2,

38.

1)

[15.

2%]

(11.

1, 2

0.6)

[11.

6%]

(8.1

, 16.

3)

[14.

8%]

(11.

1, 1

9.5)

13.7

%(1

1, 1

6.9)

23.9

%(1

8.6,

30)

13.7

%(1

0.6,

17.

4)

21.6

%(1

7.5,

26.

4)

156

(84,

254

)

39.8

%(3

5.8,

43.

9)

26.1

%(2

0.7,

32.

3)

37.3

%(3

0.6,

44.

5)

54.6

%(4

6.5,

62.

4)

42.5

%(3

5.9,

49.

9)

40.7

%(3

5.8,

45.

7)

38.1

%(3

1, 4

5.9)

43.7

%(3

8.4,

49.

2)

34.7

%(3

0, 3

9.7)

215

(129

, 378

)

25.3

%(2

2.2,

28.

7)

23.8

%(1

9.7,

28.

6)

26.3

%(2

1, 3

2.3)

24.8

%(1

9.2,

31.

4)

24.7

%(2

0.3,

29.

7)

26.5

%(2

2.8,

30.

7)

23%

(17.

9, 2

9.1)

23.8

%(1

9.7,

28.

5)

27.3

%(2

2.8,

32.

4)

209

(133

, 353

)

210.

0(7

0.0,

280

.0)

245.

0(1

05.0

, 490

.0)

210

(105

.0, 4

01.0

)

140.

0(7

0.0,

245

.0)

139.

9(7

0.0,

305

.9)

210

(105

.0, 3

15.0

)

182.

0(7

0.0,

250

.9)

175.

0(7

0.0,

280

.0)

210.

0(1

05.0

, 280

.0)

15.3

%(1

2.6,

18.

4)

[12.

2%]

(7.9

, 18.

4)

20.8

%(1

6.2,

26.

4)

*

[12.

3%]

(8.3

, 18)

16.2

%(1

2.7,

20.

6)

13.4

%(1

0, 1

7.9)

16.7

%(1

2.9,

21.

3)

13.5

%(1

0, 1

8.1)

173

(118

, 308

)

2.4%

(1.7

, 3.4

)

* * * *

[2.9

%]

(1.9

, 4.3

)

* * *

[204

](1

16, 3

92)

1088

208

522

265

93 714

374

605

480

1048

Num

ber o

f re

spon

dent

s

Med

ian

(25t

h, 7

5th

perc

entil

e) in

take

bo

uillo

n (g

) in

the

past

wee

k1

Repo

rted

bou

illon

con

sum

ptio

n fo

r the

pre

viou

s w

eek

0 tim

es1-

5 tim

es6-

10 ti

mes

11-1

5 tim

es16

-21

times

* as

terix

den

otes

est

imat

es b

ased

on

less

than

25

unw

eigh

ted

resp

onse

s[

] den

otes

est

imat

es b

ased

on

25 to

50

unw

eigh

ted

resp

onse

s1

Calc

ulat

ion

of m

edia

n in

take

onl

y in

clud

ed re

spon

dent

s w

ho c

onsu

med

the

prod

uct.

Base

d on

num

ber o

f day

s co

nsum

ed x

typi

cal n

umbe

r tim

es c

onsu

med

per

day

x a

vera

ge s

ervi

ng s

ize

for a

n ad

ult

(30g

tom

ato

past

e)


Instant Noodle Intake and Associated Median UIC for Different Frequency of Intake Levels

NATIONAL




South saltproducing

Urban

Rural



Median UIC (25th, 75th percentile)

MPI

Sco

reUr

ban/

Rura

l

191.3(127.5, 340.0)

129.8(127.5, 321.9)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

191.3(127.5, 413.8)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

191.3(127.5, 340.0)

Median (25th, 75th percentile) intake instant

noodles (g) in the past week1

1088

208

522

265

93

714

374

605

480

1048

Number of respondents

Respondent = WIFA

Reported instant noodle consumtpion for the

Surv

ey/S

trata

12.2%(9.9, 15.0)

[9.1%](5.9, 13.8)

[12.5%](8.6, 17.8)

[14.5%](10.7, 19.3)

[11.3%](8.4, 15.1)

14.1%(11.2, 17.4)

[8.7%](5.4, 13.9)

13.3%(10.1, 17.3)

11%(8.1, 14.6)

237(161, 321)

0.5 to 2.5 packets

0 packets

80.0%(76.8, 82.9)

85.1%(79.2, 89.6)

80.6%(74.9, 85.3)

74.4%(68.2, 79.7)

81.4%(76.1, 85.7)

77.2%(73.4, 80.6)

85.4%(79.5, 89.9)

77.8%(73.7, 81.5)

82.7%(78.3, 86.4)

205(138, 356)

* asterix denotes estimates based on less than 25 unweighted responses[ ] denotes estimates based on 25 to 50 unweighted responses1 Calculation of median intake only included respondents who consumed the product. Based on number of days consumed x typical number times consumed per day x average serving size for an adult (85g packet instant noodles)A frequency of intake of over 2.5 packets per week was reported by 7.8% of the sample nationally


Appendix 8: Edible Oil Use

To use in assessment of which oils are fortifiable and the potential impact of fortifying these.

To u

se in

ass

essm

ent

of w

hich

oils

are

fort

ifiab

le a

nd t

he p

oten

tial i

mp

act

of fo

rtify

ing

thes

e.R

epo

rted

mai

n ty

pe

of

edib

le o

il us

ed in

the

ho

useh

old

Mai

n ty

pe o

f edi

ble

oil u

sed

by s

urve

y ar

ea, r

esid

ence

type

and

MPI

sco

re

NATI

ONAL

North

non

-sal

tpr

oduc

ing

Mid

non

-sal

tpr

oduc

ing

Sout

h no

n-sa

ltpr

oduc

ing

Sout

h sa

ltpr

oduc

ing

Urba

n

Rura

l

Low

MPI

sco

re

(not

dep

rived

)

High

MPI

sco

re(d

epriv

ed)


39.3

%(3

4.4,

44.

4)

20.9

%(1

4.6,

29.

2)

54.5

%(4

5.7,

63.

1)

21.4

%(1

3.9,

31.

5)

42.6

%(3

5.6,

49.

8)

34.6

%(2

8.7,

41.

1)

47.9

%(3

8.3,

57.

6)

33.7

%(2

8.2,

39.

7)

45.5

%(3

9.3,

51.

9)

39.3

%(3

4.3,

44.

5)

33.6

%(2

6.9,

41)

42.5

%(3

4, 5

1.4)

37.3

%(2

8, 4

7.5)

38.4

%(3

2, 4

5.3)

43.1

%(3

7, 4

9.5)

32.1

%(2

4.3,

41)

44.1

%(3

8.1,

50.

2)

33.9

%(2

8.4,

40)

27.9

%(2

3.2,

33.

2)

35.5

%(2

8.7,

43)

19.1

%(1

2.1,

28.

7)

37%

(27.

3, 4

7.9)

36.3

%(2

9.7,

43.

4)

28.8

%(2

2.8,

35.

7)

26.2

%(1

8.7,

35.

5)

28.5

%(2

2.7,

35)

27.5

%(2

2.5,

33)

29.8

%(2

5.8,

34.

1)

34%

(27.

7, 4

0.9)

32.5

%(2

5.7,

40.

2)

20.2

%(1

4.3,

27.

8)

33.4

%(2

6.1,

41.

6)

29.1

%(2

4, 3

4.8)

31.1

%(2

4.8,

38.

1)

31.4

%(2

6.6,

36.

7)

27.9

%(2

3.2,

33.

1)

4.4%

(3.1

, 6.3

)

*

[5.5

%]

(3.2

, 9.2

)

* *

[5.4

%]

(3.7

, 7.8

)

*

7.2%

(5.1

, 10.

1)

*

4.9%

(3.3

, 7.3

)

26.8

%(1

7.4,

39)

* * *

[2.6

%]

(1.5

, 4.7

)

9.1%

(4.8

, 16.

6)

[2.6

%]

(1.6

, 4.2

)

7.4%

(4.6

, 11.

6)

Num

ber

HHS

Refin

edpa

lmDo

n’t k

now

HHs

givi

ng

2 re

spon

ses

of m

ost

com

mon

ly

used

oil

type

sSo

ybea

nSh

ea

butte

rCo

conu

tRe

d pa

lm

1929

342

941

485

161

1256

673

1009

913

3.2%

(2.2

, 4.6

)

* *

[8%

](4

.7, 1

3.1)

* * * * *

1 Th

e ta

ble

does

n’t i

nclu

de d

etai

ls o

f oil

type

s w

here

less

than

50

hous

ehol

ds re

spon

ded

that

they

use

d th

ese

type

s: g

roun

dnit

49 H

Hs to

tal,

sunfl

ower

47

HHs

tota

l, ve

geta

ble

blen

d 33

HHs

tota

l, Ot

her t

ypes

of o

il 22

HHs

tota

l*

aste

rix d

enot

es e

stim

ates

bas

ed o

n le

ss th

an 2

5 un

wei

ghte

d re

spon

ses

[ ] d

enot

es e

stim

ates

bas

ed o

n 25

to 5

0 un

wei

ghte

d re

spon

ses


Reported source and packaging of main type of edible oil used in the household Us

ual p

acka

ging

Usua

l sou

rce

Coconut Red palm

Source and packagine of main type of edible oil used(One or two responses of oil type per HH were possible)1

Refined palm

Sheabutter

Soybean Don’t know

61

*

[53.3%]

*

*

*

*

*

*

[64.8%]

*

*

*

*

757

*

69.8%

[7.2%]

21.7%

*

*

*

*

66.1%

*

29.2%

*

*

672

11.9%

78.5%

8.0%

*

*

*

*

76.9%

11.3%

*

9.9%

*

*

94

41.3%

[26.7%]

[32%]

*

*

*

*

*

*

65.9%

[33.1%]

*

86

[38.8%]

[56.6%]

*

*

*

*

*

89.1%

*

*

*

*

*

539

15.9%

70.6%

8.8%

*

*

*

4.4%

58.7%

12.9%

[3.8%]

19.1%

*

[4.7%]

Supermarket

Open market

Street vendor

Made at home

Food aid

Other

Don’t know

Original bottle

Other bottle

Sealed brandedsachet

Unbranded (tied)plastic bag

Other

Don’t know

1 The table doesn’t include details of oil types where less than 50 households responded that they used these types: groundnit 49 HHs total, sun-flower 47 HHs total, vegetable blend 33 HHs total, Other types of oil 22 HHs total* asterix denotes estimates based on less than 25 unweighted responses[ ] denotes estimates based on 25 to 50 unweighted responses


Reported main brand of edible oil used in the household

1 Other brand includes named brands liste as responses (unweighted #): Obaapa (14), Gino (74), Oki (9), Kings (1), Imperial Chief (0), Mr Chef (0), Unoli (37), Viking (9), Nana (0), San Kofa (2), and other un-named brands* asterix denotes estimates based on less than 25 unweighted responses[ ] denotes estimates based on 25 to 50 unweighted responses

MPI

Sco

reUr

ban/

Rura

l

NumberHHs

Frytol Other brand1 Unbranded Don’t know

Main brand of eligible oil used(one or two responses per HH possible)

Surv

ey/S

trata

NATIONAL




South saltproducing

Urban

Rural



1929

342

941

485

161

1256

673

1009

913

57.3%(53.1, 61.4)

64.2%(55.8, 71.8)

50.5%(43.2, 57.8)

62.3%(56.2, 68.1)

67.8%(61.1, 73.8)

60.9%(55.8, 65.7)

50.7%(43.3, 58.1)

61.7%(57.2, 65.9)

52.6%(47.1, 58.1)

27.9%(24.2, 31.9)

25.5%(20.3, 31.5)

25.5%(19.7, 32.3)

35.9%(28.4, 44.2)

23.2%(17.8, 29.7)

28.8%(24.4, 33.6)

26.3%(20, 33.7)

28.8%(24.6, 33.3)

27%(22.4, 32.2)

39.2%(34.1, 44.4)

40.7%(33.7, 48)

49.9%(40.9, 58.9)

17.2%(10.3, 27.4)

39.4%(30.1, 49.4)

34.2%(28.7, 40.2)

48.3%(37.9, 58.9)

35.4%(30.1, 41.1)

43.2%(36.6, 50.1)

3.8%(2.6, 5.5)

*

*

*

*

[3.2%](2, 5)

[4.9%](2.7, 8.8)

[3.8%](2.1, 6.7)

[3.8%](2.6, 5.6)


Reported source and packaging of main brand of edible oil used in the household

1 Other brand includes named brands liste as responses (unweighted #): Obaapa (14), Gino (74), Oki (9), Kings (1), Imperial Chief (0), Mr Chef (0), Unoli (37), Viking (9), Nana (0), San Kofa (2), and other un-named brands* asterix denotes estimates based on less than 25 unweighted responses[ ] denotes estimates based on 25 to 50 unweighted responses

Usua

l pac

kagi

ngUs

ual s

ourc

e

Main brand of edible oil used(one or two responses per HH possible)

NUMBER

Supermarket

Open market

Street vendor

Made at home

Food aid

Other

Don’t know

Original bottle

Other bottle

Sealed brandedsachet

Unbranded (tied)plastic bag

Other

Don’t know

Frytol Other brand1 Unbranded Don’t know

1106

14.5%(11.2, 18.6)

76.8%(72.7, 80.5)

8.3%(6.2, 10.9)

0.1%(0, 0.7)

*

*

*

73.6%(69.2, 77.6)

11.8%(9.5, 14.5)

2.6%(1.8, 3.8)

11.6%(9, 14.9)

*

*

538

14%(9.8, 19.6)

64.8%(58, 71)

9.4%(6.6, 13.1)

10.4%(7.2, 14.9)

*

*

*

44.4%(37.5, 51.6)

33.9%(26.8, 41.9)

*

16.1%(11.9, 21.4)

[3.7%](2.2, 6.1)

*

755

1.7%(0.9, 3.2)

65.2%(57.3, 72.3)

10.2%(7.2, 14.4)

22.5%(16.1, 30.5)

*

*

*

4.6%(2.8, 7.5)

52.5%(45.7, 59.1)

*

38.6%(33.1, 44.5)

*

*

73

*

[55.3%](42.1, 67.8)

*

*

*

*

*

*

*

*

*

*

*


Households reporting to use two main types of edible oil

National number





Urban

Rural



574

20.2%

53.3%

17.1%

9.3%

63.6%

36.4%

55.5%

44.5%

% of households giving 2 responses for main oil type

MPI

Sc

ore

Urba

n /

Rura

lSu

rvey

/ St

rata


Notes


Notes


Notes

Documents

NATIONAL IODINE SURVEY REPORT GHANA 2015 · 2019. 8. 29. · National Iodine Survey Report, Ghana 2015 5 Iodine is among the important micronutrients essential for proper growth and