Survey of malaria indicators in Caprivi

Region, Namibia,

using cell phone data entry —

Preliminary report, 16 May 2011

Polly Helmut (1), Naemi Heita (2), Mayamba Valks (1), Michael Charles (3), Yusuf

Ibrahim (4), Bong Duke (5), Jenny Cervinskas (6), Karen Bramhill (7), Robert

Ondrusek (3), Jason Peat (8), Mac Otten (9)

1. Namibia Red Cross, Caprivi Region, Namibia

2. Namibia Red Cross, Windhoek, Namibia

3. International Federation of the Red Cross, South Africa

4. Ministry of Health, Kenya, and Datadyne consultant, Nairobi, Kenya

5. Consultant, International Federation of the Red Cross, Nigeria

6. Consultant, International Federation of the Red Cross, Ottawa, Canada

7. International Federation of the Red Cross, Nairobi, Kenya

8. International Federation of the Red Cross, Geneva, Switzerland

9. Consultant, International Federation of the Red Cross, Atlanta, USA

Executive Summary

Background. The Namibia Red Cross sponsored a pre-project baseline survey just

after the long rainy season among a population of 47,932 that lived in the Caprivi

Region during 8-12 May 2011 to examine important malaria indicators using several

innovative methods.

Methods: The survey used probability-proportional-to-estimated-size (PPES) sampling

of primary sampling units (PSUs) and PPES to select one segment from unequal-sized

segments. Households were chosen from the segment using simple random sampling.

The sample included 30 PSUs, 10 households per PSU, and 1014 persons. Cell phones

(Nokia classic 2300) were used by Namibia Red Cross volunteers to conduct the

household interviews and enter survey data using EpiSurveyor software in real-time.

Results: Based on a ratio 2.13 persons sleeping under an ITN found in the survey, the

percentage of persons with access to an ITN was 52%. An estimated 38% (95%

confidence interval [CI] 30-46%) of persons of all ages slept under an ITN during the

night before the survey and 86% of the ITNs were used the previous night. Of children

<5 years old with fever in the two weeks before the survey, 71% received an ACT and

61% of children with fever received an ACT within 24 hours. Forty percent of persons

were protected by IRS; 80% of persons lived in households that had a least one ITN or

had IRS with the past 12 months. A bulletin with preliminary survey results was

produced within 24 hours of the last interview and a preliminary report was produced

within 72 hours. The local costs including the cell phones was $36.454 USD.

Conclusion: The number of ITNs available to households needs to be doubled to

achieve universal coverage (all persons sleeping under an ITN). Prompt and effective

treatment seems to be high. The innovative survey methodologies provided valuable

health and malaria data rapidly and at low cost.

Background

The government of Namibia (GON) and partners are striving to reduce the number of

cases and deaths from malaria by 50% by 2010 and by 75% by 2015 in line with

Millennium Development Goals, World Health Assembly, Roll Back Malaria partners,

and GON goals. The Ministry of Health in Namibia is now stressing the importance of

universal coverage of persons of all ages (100% of persons using an ITN) in endemic

areas as advocated by the World Health Organization (WHO) to achieve the disease-

reduction goals. The two most important indicators of universal coverage with ITNs

are: 1) the percentage of persons that had access to ITNs in the household (assuming

that 1 ITN covers two persons), and 2) the percentage of all persons using ITNs the

previous night. For treatment, all persons with malaria are supposed to receive an

appropriate treatment within 24 hours, especially children <5 years old, the highest risk

group for malaria-related mortality. In late 2009, WHO advocated parasite-based

testing of all suspected malaria cases (including the use of rapid diagnostic tests—

RDTs), even in high-burden African countries.

Malaria continues to be a major public health problem in Namibia. The disease was the

leading cause of illness and death from 1999 to 2002 and still remains one of the top

five diseases of public health concern in the country. Malaria is endemic in Caprivi,

Kavango, Kunene, Ohangwena, Omusati, Oshana, Oshikoto, and part of Otjozondjupa

and Omaheke regions, where 65 percent of the Namibian population live and are at risk

of malaria1. The prevalence of malaria is highest between September to December.

Preliminary MOH data shows that malaria cases reported from health facilities in the

Caprivi Region declined from approximately 4000 cases in 2006 to 300 cases in 2010.

1 Namibia Demographic and Health Survey 2006/2007

This survey was carried out in Caprivi region2, in the northeast of Namibia. Divided

into the Kongola, Linyanti, Sibanda, Katima Mulilo Urban and Rural, and Kabbe

constituencies3, the region covers a total area of 14.528 km and accounts for 1.8% of

the total land area of Namibia. It shares borders with four countries, being Angola and

Zambia in the north, Botswana in the south, and Zimbabwe to the east. The

administrative centre of the region is Katima Mulilo, the only town in the region. Six

settlement areas serve as local administrative centers. These are Bukoalo,

Chinchimani, Linyanti, Mafuta, Ngoma, and Omega III. Eastern Caprivi is subject to

seasonal flooding and the Kabbe constituency comprises the eastern flood plains of the

region, and is subject to seasonal flooding. There is an annual flood season with

numerous flood-prone villages. As of early March 2011, more than 1000 people had

been “relocated permanently” from 32 flood-prone villages as the region prepares to

meet a Zambezi river recently swollen to record-size for this time of the year as a result

of torrential rains upstream (ref: www.irinnews.org/report.aspx?reportID=91770,

accessed March 3, 2011). It was expected that the swelling of the river would

continue. The floods force people to move to higher ground and settlements in camps

are established for those affected. The migration is usually temporary but there are

families for whom the move may be permanent. As of May , it is estimated that 90%

of the area of Kabbeh was flooded, as many as 7,000 people had been affected by the

floods, and a total of 20 temporary camps and two permanent resettlements camps

2 Namibia is divided into 13 regions, and each one is further subdivided into electoral constituencies. The latest Namibian Census of Population and Housing was carried out in 2001. The next census will be in September 2011.

3 A constituency refers to a group or area from which voters in an election are drawn. The number and size of each constituency varies with the size and population of each region. There are a total of 102 constituencies in the country, and overall, the country is divided into 4002 enumeration areas (EAs) (Population Census 2001).

(Choi in Linyanti and Izwe in Kongola) had been established (Namibia Red Cross,

personal communications).

Insecticide-treated bed nets (ITNs) are distributed in Caprivi through the routine health

services. A pregnant woman is eligible to receive one free bednet during antenatal care

services, and children under age five years that attend a health facility are also eligible

to receive a free LLIN. The Ministry of Health and Social Services (MOHSS) also

distributes LLINs in the camps through a combination of targeted distribution and

house-to-house distribution to vulnerable groups (pregnant women, children under age

five years, and elderly women aged 60 years and over). There has only been one

region-wide mass campaign in Caprivi, with LLINs distributed in 2008 as part of the

Zambezi River Basin Initiative. Since 2009, the Namibia Red Cross (NRC) has been

distributing LLINs in the camps. In addition to the MOHSS and the NRC, the NGO

SMA (Social Marketing Associates) has been distributing LLINs in villages in some

constituencies.

This malaria indicator survey will serve as a baseline for the Namibia Red Cross-

supported project Communities Fighting Malaria that aims to improve the health

and well-being and improve malaria control for persons in four of the five

constituencies where the Namibia Red Cross works--Kabbe, Katima Mulilo Rural,

Kongola and Linyanti. In this project, the NRC, in collaboration with the village

heads, has recruited 13 community volunteers who will serve as the project’s

supervisors in the villages. Each supervisor has the responsibility of supervising

an average of about 10 community-based volunteers. A total of 126 volunteers

will be trained by mid-2011 by these supervisors in each constituency. In turn,

each community-based RC volunteer is responsible for about 100 households. In

addition to the baseline survey, the NRC will carry out a mid-term evaluation and

a final evaluation. While the MST survey does not collect data on all the key

indicators of the Communities Fighting Malaria project, it does collect data on

many of the core indicators related to bednet ownership and usage, and to the

treatment of fever in under-five year olds and the diagnosis of malaria. The

results of the baseline survey can be used to adjust the program, if necessary, and

to advocate for needed changes to support the provision of LLINs and the

provision of services for treating fever in children under age five years, to

communities within the sample constituencies.

In 2001, Caprivi Region had a population of 79,826 people (with a household

population of 78,785 and 16,839 households), representing 4.4% of Namibia’s total

(Population Census 2001). 28% of the population is urban and 72% rural. The average

household size in Caprivi region is 4.7 persons compared to 5.1 persons nationally, and

well below the average rural household size of 5.7. Female-headed households

account for 49% of households, and 46% of households have orphans or children under

the age of 18. The average number of children per women is 3.8 and 13% of the

population is under five years. More than 95% of the populations speak one of several

dialects of what is commonly lumped together as Caprivian. The main dialects are

Sifwe, Subiya, Totela, and Yeyi. The 2006-07 Namibia Demographic and Health

Survey showed that Caprivi was one of four regions in Namibia4 that have the highest

proportion of the population in the lowest wealth quintile of the wealth index and only

a smallest proportion in the highest quintile.

4 The other three are Kavango, Ohangwena and Omusati.

The International Federation of the Red Cross, with the support of DataDyne, WHO,

American Red Cross, and CDC epidemiologists at the Global Immunization Division

have been working on a “management survey” concept that uses cell phones for data

entry for surveys for several years, primarily on the technical manual and field-testing

the concept. This concept includes use of cell phones and freely-available software to

conduct health surveys rapidly, simply, at low cost, with minimal external technical

assistance, and avoiding the main potential bias of the Expanded Programme on

Immunization (EPI) cluster survey method (initial selection of the first household

using a random direction from the center of the PSU). This survey was the second

field test of the management survey concept—the first was successfully completed in

January 2011 in Malindi, Kenya. This survey had the following innovations: 1) 30

clusters (like the EPI cluster survey)1, 2) total of 300 households in the sample (10

households per cluster), 3) use of DataDyne’s Episurveyor web-based tool to

collaboratively design model malaria questionnaires (including responses and skip

patterns) that can be easily adapted to local surveys and translated into local languages,

4) use of cell phones to enter data during the interview, 5) daily upload of data to an

internet-based database, 6) daily data cleaning of uploaded data, 7) daily feedback of

data quality issues to interviewers and supervisors based on previous day’s data

quality, 8) daily analysis of uploaded data, 9) completion of preliminary results bulletin

within 24 hours of the last interview, and 10) completion of a preliminary report within

72 hours.

Methods

The survey was conducted during 8-12 May 2011. May is part of the rainy season in

northeast Namibia. The sampling frame was a list of communal lands, settlements, and

mixed areas, (n=132 communities) from the 2001 census and ten camps. The total

number of households in the sampling frame was 7010. The 2001 census estimation of

population in the area was 47.932. Thirty primary sampling units (PSUs) were initially

selected using probability proportional to estimated size. Four PSUs (3 in Kabbe

constituency and 1 in Katima Rural) were found to be inaccessible due to flooding and

were replaced with 4 other randomly selected PSUs.

Using maps of each PSU that were prepared in 2010 in preparation for the 2011

population census and obtained from the Namibia Bureau of Statistics, the selected

PSUs in the settlements and communal lands were mapped and divided into 2-10

segments using natural boundaries. Once a segment was selected by PPES, all

households were listed or mapped and 10 households were chosen by simple random

sampling, with an additional 5 households selected in case members of selected

households could not be reached. In the camps, 10 households were chosen by simple

random sampling, with an additional 5 households selected in case a replacement

needed to be made. Data was collected on all persons sleeping in the household

(“sharing a common cooking pot”) the previous night. The design resulted in an equal

probability survey.

Three questionnaires were developed online using the Episurveyor web-based

questionnaire design tool (www.episurveyor.org)—household, person roster, and bed

net roster. Questions were modeled after RBM’s Malaria Indicator Survey (2005).2

Principal component analysis was used to create the wealth quintiles index for each

household. Analysis was performed in STATA version 11 (College Station, Texas,

USA), taking into account the design of the survey. “Access” to an ITN was defined

as the population that could have been covered by ITNs present in the households at

the time of the survey. The ratio of persons that could have been covered per ITN was

calculated from the net roster data (2.13 persons/ITN). The crude estimate of access

was the total number of ITNs in the households times 2.13 persons/ITN divided by

total population. The ratio of persons to ITN (2.13) for this calculation was similar to

the ratio of 2.0 used by WHO in its latest World Malaria Report.3

We estimated the number of ITNs needed for universal access, the number of ITNs

currently in the whole survey domain, and the gap to be filled. The number of ITNs

needed for universal access was calculated by dividing the sampling frame population

by the average number of persons sleeping under ITNs during the survey (2.13). The

number of ITNs currently present was estimated by multiplying the number of ITNs

found in households multiplied by the survey weight (47.27).

The indicator about protection by ITNs or IRS assumed that any ITN (even just one) in

the household protected all inhabitants. In households with many people, a single ITN

may provide only partial protection from malaria. Therefore, this indicator may over-

estimate protection.

There was significant "heaping" of responses of the age of nets in months at 12 and 36

months. We counted "12" to be in the 12-23 month category and "36" to be in the 36-

47 month category.

The questionnaires on the phone were in Silosi, the local language. One interviewer (a

person from the bushman tribe) did not speak Silosi and his questionnaire was in

English.

Survey operations. The survey operations were led by the Namibia Red Cross.

Training was provided for the 13 interviewers and three field supervisors during five

days (2-6 May 2011). Survey field work took 5 days (8-12 May 2011). Nokia-brand

cell phones--Nokia 2730 ($80 USD, no keyboard, no GPS)--were used to run

DataDyne’s freely-available cell-phone-based EpiSurveyor software

(http://www.datadyne.org/ and http://www.episurveyor.org/). Survey data were

immediately entered into the cell-phone database during the interview for the

household questionnaire. Immediately before administering the person roster and the

net roster questionnaires, a paper line list of persons that slept in the household the

previous night was created by asking about each person who slept in the household the

previous night so that the person line number/identifier was available during questions

in the person roster, and for questions in the net roster about who slept under each net.

Uploading of data on the cell-phone to the internet-based database using EpiSurveyor

software required a 2G/GPRS cell-network connection. The field supervisors

uploaded data at the end of the day upon their return to Katima Mulilo. Interviewers

(and cell-phone data entry persons) were the Namibian Red Cross volunteers who

serve as supervisors in the constituencies included in the Communities Fighting

Malaria project. The team supervisors were Namibian Red Cross supervisors that

were involved in other NRC projects.

Since the data were sent from the mobile phones each evening to an internet database,

the local consultant and out-of-country consultants were able to perform data cleaning

each evening. A Namibian analyst could not be located, so an out-of-country

consultant analyzed the data each night. A survey-results bulletin was produced within

24 hours of the last interview using an Excel-based, 4-page “survey results bulletin”

tool. The preliminary survey results (pages on four Excel worksheets were combined

into a single PDF) were distributed to interviewers at an end-of-survey debriefing

meeting within 15 hours of the last interview on 13 May 2011. A preliminary report

was distributed to stakeholders within 72 hours on 16 May 2011.

Report tables and graphs. The tables and graphs of the survey-results bulletin act as

the tables and graphs for this preliminary report5.

Results

Table 1 on the survey-results bulletin shows key descriptive information about the

survey. The number of persons in the sample frame was 47.932. Data were collected

on 1014 persons of all ages and 168 children <5 years old in 293 households. Ninety-

seven percent of the nets were permitted by the residents to be observed. Eighty-two

percent of the ITNs were reported hung the night prior to the survey. There were

enough ITNs to cover 42% of the sleeping places and enough hanging ITNs to cover

34% of the sleeping places. The main ITN indicators are shown in the figure on page

one of the survey-results bulletin. The percentage of households with at least one ITN

was 59% (95% confidence interval [CI], 51-67%). The average number of persons

5 The survey-results bulletin is a companion document to the survey report, and contains information, tables and graphs that show the survey findings.

sleeping under an ITN was 2.13. The percentage of all persons with access to an ITN

was 52%. The percentage of persons that slept under an ITN last night was 38% (CI

30-46%) for all persons and was 42% (CI 28-55%) for children <5 years old. In

households that owned at least one ITN, the percentage of children <5 years old that

slept under an ITN increased to 65%. The percentage of children with fever in the

previous two weeks that had a finger or heel stick for blood was 31%. The percentage

of children with fever that received treatment with an ACT was 71% and the

percentage that received an ACT within 24 hours of onset of fever was 61%. The

estimated number of ITNs needed to provide access to all persons (based on 2.13

persons per ITN) in the entire sampling frame was 22.503. The number of ITNs

currently in all households in the survey frame (those sampled and not sampled) was

11.676, 52% (n=22.503) of that needed to achieve universal access. The number of

ITNs needed to fill the gap was 10.828. The percentage of ITNs that were 36 months

and older was 36%. Therefore, an additional 4203 ITNs need to be replaced

immediately, bringing the total ITNs needed for replacement in 2010 to 15.031 (10.828

+ 4203).

Table 2 shows key ITN indicator point estimates, confidence intervals, and data by

wealth quintile. The ownership of ITNs was nearly the same across wealth quintiles,

but ITN use was higher in the wealthiest quintile. Table 3 shows the age of ITNs—

50% of the ITNs were <12 months old, 30% were 3 years old, and 36% were ≥36

months old. Table 4 shows that recent home visits and visits to clinics where malaria

was discussed were infrequent (<10%). Table 5 shows additional ITN and IRS

information. The greatest source of information about nets was from radio (50%) and

health center staff (32%). Table 6 shows additional information about treatment,

including the rare use of chloroquine, quinine, and sulfadoxine-pyramethamine for

treatment. The percentage of children <5 years old with fever in the previous two

weeks was 64%. Table 7 shows the width of the 95% confidence interval and the

design effect for 4 key variables from household summary data. The confidence

interval was ±9% for the percentage of persons using an ITN, ±8% for household

ownership of at least one ITN, and ±13% for children using an ITN. The confidence

interval was ±10 for percentage of children receiving an ACT. The design effect was

3.0 or less except for ITN use in all ages (7.4). The graph on page 4 shows the age

distribution of ITN use. Those 5-14 years old had lower use than other age groups.

The supplemental analyses on page 4 showed that 95% of nets were ITNs, 87% of nets

were LLINs, and 28% of ITNs had 3 or 4 persons sleeping under them. Eighty-six

percent of ITNs were used the previous night.

Cost. The total local cost of the survey was $36.454 USD. Training accounted for

32%, survey operations accounted for 59% (personnel 29%, transport 30%), phones

and accessories for 8%, and other for 2%.

Discussion

ITN access was approximately half (52%) of that needed to achieve universal

coverage. The most important indicator of ITN coverage (ITN use in persons of all

ages) was 38%. A high percentage of ITNs were used the night before the survey

(86%) so that there is only a small gap in ITN use given ownership. Therefore, the

primary ITN gap is ownership and access, not ITN use. The age of ITNs may also be

a significant issue since 36% of the ITNs were reported to be 3 years old or older. The

last mass distribution of LLINs throughout all communities in Caprivi took place in

2008 during the Zambezi Basin Initiative. The percentage of children <5 years old

with fever in the previous two weeks was very high (64%) compared to 30% found in

most other African countries (however, the percentage was also higher than average in

Malindi, Kenya in January 2011 [40%] in the first pilot survey). The percent of

children with fever may have been higher in the survey because, in this area, the local

term for fever, “mena”, can also mean a runny nose without fever. The percentage of

children <5 years old with fever that were treated with ACT (71%) and treated with

ACT within 24 hours (61%) was high indicating that access to treatment is reasonably

good. Forty percent of persons lived in households that received IRS within the

previous 12 months. IRS is a primary malaria control tool in most low-incidence

countries in southern Africa. The percentage of persons that were at least partially

protected by ITNs or IRS was reasonably high (80%) however this indicator may over-

estimate protection because one ITN in a household was assumed in the calculation of

this indictor to protect all persons. The burden of malaria is being reduced in the

area--300 malaria cases were reported in 2010 in Caprivi Region—a rate of 4 malaria

cases per 1000 persons, which is near the level that can be considered for elimination.

The survey findings for the ITN indicators were similar to those in the 2008 DHS in

Caprivi Region—55% of households owned an ITN, the average number of ITNs per

household was 1.0, and 41% of children <5 years old slept under an ITN the night

before the survey.

The survey analysis provided several innovative analyzes of ITNs: 1) estimated

number of ITNs in households in the survey area and the gap needed for universal

coverage, 2) the “access” of the population to ITNs, 3) the average number of persons

using each ITN, 4) the percentage of ITNs that were used last night, 5) and the age of

ITNs.

This survey provided an excellent estimate of the number of LLINs that the project

needs to obtain to achieve universal access: 15.031. An advantage of this survey is

that it provides a reasonably accurate estimate of the total number of existing ITNs, the

gap that needs to be filled, and the number of ITNs that are ≥36 months old that also

need immediate replacement.

The confidence interval of ≤10% for 3 of 4 key indicators shows that the sample size

and number of clusters were sufficient for most management decisions. The EPI

cluster survey target precision is ±10%. The design effect was ≤3.0 for 3 of 4 the main

indicators.

The local field costs, including the cell phones, of the survey were $36.454 USD. The

local costs in the Malindi, Kenya survey in January 2011 were $22.795 USD. We

were unable to locate a Namibian-based analyst to provide all the analytic functions for

this survey. Daily uploading of data to an internet database allowed an external analyst

to complete most of the analytic tasks within 24 hours and send the results to local

survey leaders to discuss with surveyors and stakeholders.

Conducting a health survey with precision of ±10% at a cost of $20-40.000 USD is

likely to be attractive to non-governmental organizations (NGOs) and ministries of

health (MOHs). Many current malaria surveys are costing from $300.000 to $1.2

million USD, although the most expensive of the current surveys include parasite

testing. More frequent surveys of ITN ownership and use would provide timely data

on the large disparity being found in many countries between the number of LLINs

distributed and the number of LLINs still in households 6-12 months after the mass

distributions.

This survey and analysis had several limitations. First, the very high percentage of

children with fever receiving an ACT (71%) triggers questions about validity.

However, the prompt recognition of symptoms and treatment with antimalarial drugs

has been emphasized by the MOHSS in the past few years, therefore, access may truly

be high. Second, the education and experience level of interviewers was substantially

less than those of other more-expensive surveys such as national malaria indicator

surveys and Demographic and Health Surveys. Some interviewers had difficulty with

properly completing the net roster that required the transfer of multiple line numbers

from the person roster to the net roster for each net. Modified recording procedures

and possibly criterion for selection of interviewers may be needed. Third, the sample

size of 300 households was too limited to provide estimates on pregnant women (3.5%

of the population) and would be too small to allow high precision for extensively

disaggregated analyses (for example, ITN use in children by wealth quintile for just

females). The management survey tools and methods are not limited to a single

survey domain of 30 clusters and 300 households. NGOs and MOHs can use any

sample size and number of clusters that they feel is appropriate.

In conclusion, this survey successfully met most of the objectives of the management

survey concept—that is, collection of valuable health data with rapid analysis and

feedback at low cost. Enabling local analysis of data still remains to be done, as well

as demonstration that the complete design-to-feedback-of-results cycle can be done in

multiple countries with minimal external technical support while maintaining adequate

data quality.

Annex 1. Survey questionnaires (household, person, and net)

A) HOUSEHOLD QUESTIONNAIRE

No. Variable Response Scale1 MANAGEMENT SURVEY

2 Consent obtained? Yes No (Skip to Q.36)

3 CLUSTER and HOUSEHOLD questions follow next 4 Cluster number

5 Household number

6 Name of head of household

7 Household in a rural or urban area? (Urban defined as a town with >=5000 persons)

Rural Urban

8 How many kilometers is your household from the nearest government, NGO, or mission health facility or hospital? (98=do not know). If less than 1 km, put "1".

9 BEDNET questions follow next 10 Number of people of all ages who slept in this household

last night? (do NOT include usual members of this household that slept somewhere else last night)

11 Last night, how many sleeping spaces were there (both inside and outside if someone slept outside)? (Sleeping space defined as a place where people sleep that could be covered by a single net).

12 Has anyone visited this household in the last 6 months to talk about malaria or ITNs?

Yes No Do not know

13 Has anyone in this household talked with people at the clinic or hospital about malaria or ITNs in the last 6 months?

Yes No Do not know

14 What is your greatest source of information on the use of bednets or ITNs?

Radio Health centre staff Community based volunteer Community leader Neighbor Relative Other No information

15 IRS questions follow next

16 At any time in the past 12 months, has anyone sprayed the interior walls of your dwelling against mosquitoes?

Yes No Do not know

17 HOUSEHOLD ASSET questions follow next 18 Does your household have electricity? Yes

No

19 Radio? Yes No

20 Television? Yes No

21 Refrigerator? Yes No

22 Electric iron? Yes No

23 Electric fan? Yes No

24 Bicycle? Yes No

25 Motorcycle or scooter? Yes No

26 Car or truck? Yes No

27 Donkey, horse, or camel? Yes No

28 Canoe, boat, or ship? Yes No

29 Phone? Yes No

30 Domestic worker (unrelated to head of household)? Yes No

31 Do members of this household work on agricultural land belonging to themselves or their family?

Yes No

32 What is the principal household source of drinking water? Piped water into residence Protected well in residence Unprotected well in residence Open well in yard

Protected well in yard Unprotected public well Protected public well Tap in yard Tanker truck Bottled water Public tap Rain water Surface water (e.g., river, lake) Spring

33 What is the principal type of toilet/sanitary facility used by members of your household?

Own flush toilet Shared flush toilet Own pit latrine Own improved pit latrine Shared pit latrine Bush or field Other

34 What is the principal type of flooring in your house (interviewer may choose to observe)?

Dirt or sand Dung /wood / palm/ bamboo Cement including vinyl Cement including parquet Carpeted Other

35 What is the principal type of cooking fuel in your house? Wood or dung Kerosene Charcoal Biogas Electricity LPG gas

36 This portion of the interview is complete. Close this questionnaire by clicking the option "Finish for now" on the next screen. If consent was NOT obtained, proceed to the next household. If consent was obtained, please proceed to the 'Roster of Persons' questionnaire.

B) PERSON ROSTER AND TREATMENT/TESTING OF CHILDREN

No. Variable Response Scale1 ROSTER OF PERSONS. Ask about the persons who slept here last night, including other persons

who may not be members of your family--domestic servants, friends or temporary visitors staying last night. (Start with the head of the household. If the head of household did not sleep here last night, start with the oldest person). Do NOT include usual members of the household on this list if they DID NOT sleep here last night.

2 Cluster number (same as in Aggregate questionnaire)

3 Household number (same as in Aggregate questionnaire)

4 Name of the person 5 Line Number of the person in the household (Obtain this

from Paper Person Roster, column 2)6 Gender Male

Female

7 Age in YEARS—Mark zero(0) if less than 12 months old.  (Estimate if they do not know, especially for adults) (IF ≥5 years skip to Q.14)

8 Did the child <5 years old have a fever in the last two weeks? Yes No (skip to Q. 15) Do not know (skip to Q. 15)

9 Did the child with fever receive ANY antimalarial medicine for the fever?

Yes No (skip to Q. 13) Do not know

10 Did the child with fever receive ACT for the treatment of fever?

Yes No (skip to Q. 12) Do not know

11 Did the child with fever receive ACT within 24 hours of onset of the fever?

Yes No Do not know

12 If the child with fever received some antimalarial but not ACT, what was the other antimalarial medicine?

Chloroquine SP_Fansidar- Quinine- Other- Do not know

13 Did the child with fever receive a finger or heel stick for blood for testing for malaria?

Yes (skip to Q. 15) No (skip to Q. 15) Do not know (skip to Q. 15)

14 Pregnant. If this person is female from 15-49 years old, is this woman pregnant?

Yes No or do not know (skip to Q.15)

15 IF there is another person who slept here last night click “Add New Record” on the next screen. IF there are NO MORE people, close this questionnaire by clicking the option” Finish for now” on the next screen. Then, proceed to the “Net Roster” questionnaire.

C) NET ROSTER

No. Variable Response Scale1 ROSTER OF NETS. I would like to ask you about each mosquito bednet that you have in the

household (includes all nets that were owned and present in the household last night—Interviewer must enter a new record for each net)

2 Cluster number (same as in Aggregate questionnaire)

3 Household number (same as in Aggregate questionnaire)

4 What net are you collecting information about? If the first net PUT number 1, if the second net PUT number 2, etc. (use consecutive numbers)

5 SURVEYOR ONLY: Ask if you can see this net. Did you observe the net?

Yes No

6 Was this net hung last night? (Look for evidence of hanging and observe or ask if the net was hanging)

Yes No Do not know

7 How many months ago did your household obtain the mosquito net? (RECORD IN MONTHS. Put "36" for 3 yrs, "48" for 4 yrs, and "60" for >=5yrs. 98=NOT SURE)

8 LLIN is a factory treated net that does not require any further treatment.Pretreated is a net that has been pretreated, but is not an LLIN (long-lasting insecticidal net) and requires further treatment after 6-12 months.

9 From where did you obtain this net? Mass campaign Market Health facility Pharmacy Other

10 Brand of the net? (Observe or ask for the brand of mosquito net. If the brand is unknown, and you cannot observe the net, show pictures of typical net types/brands to respondent)

LLIN1 Permanent (skip to Q.15) LLIN2 Olyset (skip to Q.15) LLIN3 Dawa (skip to Q.15)Pre-treated or treated net Other Do not know brand

11 When you got the net, was it already factory-treated with an insecticide to kill or repel mosquitos?

Yes No Not sure

12 Since you got the mosquito net, was it ever soaked or dipped in a liquid to repel mosquitoes or bugs?

Yes No Not sure

13 How many months ago was the net last soaked or dipped in a liquid to repel mosquitoes or bugs? (RECORD IN MONTHS. IF< 1 MONTH AGO, PUT 0 months, PUT "36"

for 3 y, "48" for 4 y, and "60" for >=5y. 98=NOT SURE)14 SURVEYOR ONLY: Classify this net as an ITN or not ITN

(an ITN is a long-lasting insecticidal net, new treated net in the last 12 months, or re-treated in the last 12 months)

ITN Not ITN Not sure

15 Did anyone sleep under this mosquito net last night? Yes No (skip to Q.21) Not sure

16 Line number of the first person that slept under this net.(Get this from the paper job aid “Person Roster”)

17 Line number of the second person that slept under this net.(Get this from the paper job aid “Person Roster”)

18 Line number of the third person that slept under this net.(Get this from the paper job aid “Person Roster”)

19 Line number of the fourth person that slept under this net.((Get this from the paper job aid “Person Roster”)

20 Line number of the fifth person that slept under this net. (Get this from the paper job aid “Person Roster”)

21 IF there is another bednet in the household click “Add New Record” on the next screen. IF there are NO MORE bednets, close this questionnaire by clicking "Finish for now". Proceed to the next household.

References

25

1 World Health Organization. The EPI coverage survey.

www.who.int/immunization_monitoring/routine/EPI_coverage_survey.pdf.

Accessed 30 January 2011.2 Roll Back Malaria. Malaria Indicator Survey: Basic Documentation for Survey

Design and Implementation.

http://www.rbm.who.int/partnership/wg/wg_monitoring/docs/mis2005/cc1.pdf.

Accessed 30 January 2011.3 World Health Organization. World Malaria Report 2010. Geneva, Switzerland. 2006-07 Namibian DHS.