Embed Size (px)
Citation preview
UNDERSTANDING LIVESTOCK OWNERSHIP, DIETARY DIVERSITY AND ASF CONSUMPTION, AND THE ENVIRONMENTAL AND SPATIAL DETERMINANTS OF
NUTRITION STATUS IN CU5 IN RURAL SOUTHERN HAITI
By
LINDSEY A. LAYTNER
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2018
© 2018 Lindsey A. Laytner
To my parents, Kevin, as well as the mothers and children of Haiti
4
ACKNOWLEDGMENTS
I would like to thank my parents, Ron and Linda Laytner, for encouraging me to
think critically and creatively, and pursue a career in science. Dad, this dissertation is
for you. Additionally, I would like to extend a huge thanks to my brother, Lance Laytner,
as well as my immediate and extended family for all their love and support throughout
my academic career. A huge thank you to my friends and colleagues over the many
years for their love, friendship, and support.
I would like to express my sincere gratitude to my mentor and co-mentor, Sarah
McKune and Arie Havelaar, for believing in my abilities, shielding me from distractions,
providing guidance, and feedback along the way. I would like to also thank my other
committee members, Song Liang, Liang Mao, and Elizabeth Wood for their expertise,
collaboration, and support throughout the entire dissertation process. Lastly, many
thanks to Nancy Seraphin for introducing me to the St. Boniface Foundation and
UNICEF-Haiti team, and getting me access to this rich dataset for my dissertation
analyses. Without Nancy’s collaboration, this work would not have been possible.
I would also like to give a special thank you to Punam Amratia, Karoun
Bagamian, Amber Barnes, and Poulomy Chakraborty. You have been my science-soul
sisters, my mentors, and my dearest friends—I am so incredibly blessed to be in your
circles (I love you, ladies). Last (but never least), my incredible partner-in-crime, Kevin
Glassman—I don’t even have words to express my immense love and gratitude to you.
Thank you for grounding me, pushing me, staying up late, helping me, listening to me
for hours on end, holding me, making me laugh, and illuminating my life with the
brightest light during some of my darkest times. You have kept me on course, and have
5
given me so much hope, love, and support—I will never forget it, and I am forever
grateful.
I am forever grateful to every person I have met or worked with along the way.
This has been an emotional journey for me, with many trials and tribulations. There was
no clear path to the finish line, but the journey has taught me to keep running, even
when you can’t see it. Eventually, you will—so never give up.
6
TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 13
ABSTRACT ................................................................................................................... 15
CHAPTER
1 BACKGROUND ...................................................................................................... 17
Livestock Ownership and Child Nutrition ................................................................ 21
Dietary Diversity and Child Nutrition ....................................................................... 23 Haiti......................................................................................................................... 27
Geography ........................................................................................................ 27
Environment and Climate ................................................................................. 28 Poverty ............................................................................................................. 29
Livelihood and Food Production ....................................................................... 29 Water, Hygiene, and Sanitation ........................................................................ 31 Undernutrition ................................................................................................... 32
Theoretical Framework ........................................................................................... 33
Proximate, Underlying, and Distal Factors ....................................................... 33 Basic Factors .................................................................................................... 36
Data Overview ........................................................................................................ 37
Figures .................................................................................................................... 41
2 I -LIVESTOCK OWNERSHIP AND DIETARY DIVERSITY OF CU5 IN RURAL HAITIAN HOUSEHOLDS ........................................................................................ 48
Introduction ............................................................................................................. 48 Research Objective ................................................................................................ 50 Methods .................................................................................................................. 50 Results .................................................................................................................... 52
Discussion .............................................................................................................. 57
3 II - LIVESTOCK OWNERSHIP, WASH, AND CU5 NUTRITION STATUS IN RURAL HAITIAN HOUSEHOLDS .......................................................................... 71
Introduction ............................................................................................................. 71 Research Objective ................................................................................................ 73 Methods .................................................................................................................. 73
7
Results .................................................................................................................... 76
Discussion .............................................................................................................. 79
Figures .................................................................................................................... 83
4 III - SPATIAL DETERMINANTS OF CU5 LINEAR GROWTH IN RURAL HAITIAN HOUSEHOLDS ........................................................................................ 89
Introduction ............................................................................................................. 89 Research Objective ................................................................................................ 91
Methods .................................................................................................................. 92 Results .................................................................................................................... 97 Discussion .............................................................................................................. 98 Figures .................................................................................................................. 102
5 CONCLUSION ...................................................................................................... 110
Summary .............................................................................................................. 110 Strengths and Limitations ..................................................................................... 111
Future directions ................................................................................................... 112
APPENDIX
A MATERNAL KNOWLEDGE QUESTIONS ............................................................ 113
B CHAPTER 2 VALIDATION ................................................................................... 115
C CHAPTER 3 VALIDATION ................................................................................... 119
LIST OF REFERENCES ............................................................................................. 123
BIOGRAPHICAL SKETCH .......................................................................................... 137
8
LIST OF TABLES
Table page 4-1 Environmental and Spatial variables descriptions (including variable name,
definition, spatial resolution, and reference source) ......................................... 104
9
LIST OF FIGURES
Figure page 1-1 A) Map of Haiti with the Aquin and Côtes-de-Fer study communes. B) Map of
the Aquin (Flamands, Fonds des Blancs, Guirand, and Frangipane) and Côtes-de-Fer (Jamais Vu) study area sub-section. ............................................ 41
1-2 Percentage of CU5 that are stunted in Haiti compared to the DR according to the DHS66. .......................................................................................................... 42
1-3 One-health theoretical framework to understand linkages between livestock ownership and child under five nutrition in southern Haiti. Adapted from UNICEF 86. ......................................................................................................... 43
1-4 Malnutrition terminology. Definitions of the various forms of malnutrition and undernutrition from the Joint Child Malnutrition Estimates 2017 edition2. ........... 44
1-5 Five 5’s diagram (adapted from Penakapapti et al.52. ......................................... 44
1-6 Undernutrition pathway from pathogen exposure via livestock feces adapted from Penakapapti et al.52. ................................................................................... 45
1-7 Improved nutrition via livestock ownership and safe WASH practices (blocked exposure to livestock feces and improved access to safe and nutritious foods) adapted from Penakapapti et al. 52. .......................................... 46
1-8 Variable list and description for all chapters. For maternal knowledge scoring, see Appendix, Figure A-1. ..................................................................... 47
2-1 Table showing the breakdown and frequencies of each response per food item in HDDS and ASF consumption calculation. ............................................... 64
2-2 Variable descriptions. ......................................................................................... 65
2-3 Descriptive statistics of survey respondents, overall. ......................................... 66
2-4 Descriptive statistics of livestock ownership, HDDS, and ASF consumption by sub-communal section. .................................................................................. 67
2-5 Bivariate regression results for study variables and HDDS. ............................... 67
2-6 Bivariate regression results for study variables ASF consumption. .................... 68
2-7 Multivariate binary backward-stepwise logistic regression results assessing the association of model 1: livestock ownership and HDDS status. ................... 69
2-8 Multivariate binary backward-stepwise logistic regression results assessing the association of model 2: livestock ownership and ASF consumption status. . 70
10
3-1 Definitions of undernutrition adapted from WHO, UNICEF, and the World Bank. .................................................................................................................. 83
3-2 Variable Descriptions used in chapter 3 analyses .............................................. 84
3-3 Descriptive statistics of surveyed households. ................................................... 85
3-4 Descriptive Statistics of WASH characteristics (Improved “I” and Unimproved “U”) broken down by sub communal section ...................................................... 85
3-5 Bivariate regression results for study variables and CU5 Stunting ..................... 86
3-6 Multivariate binary backward-stepwise logistic regression results for model 1 assessing the association of livestock ownership and CU5 Stunting status. ...... 87
3-7 Multivariate binary backward-stepwise logistic regression results for model 2 association the of livestock ownership, WASH Factors, and CU5 Stunting status. ................................................................................................................. 88
4-1 Conceptual diagram linking CU5 growth to Haiti-specific spatial and environmental drivers. Adapted from Grace et al.163. ...................................... 102
4-2 Map of Haiti and communes Aquin and Côtes-de-Fer surveyed (in red). ......... 103
4-3 Description/ distribution of spatial and environmental covariates considered in this analysis, across the country, as well as the Aquin and Cote de Fer study site communes. ....................................................................................... 105
4-4 Village coordinates geo-referenced using Google Earth Pro. ........................... 106
4-5 Village level CU5 HAZ score distribution across Aquin and Cote de Fer study site communes. ................................................................................................ 106
4-6 Livestock species distribution across Aquin and Cote de Fer study site communes. ....................................................................................................... 107
4-7 Results from the bivariate analysis of environmental and spatial covariates and village level CU5 HAZ. ............................................................................... 108
4-8 Final multivariate linear regression model results and overall model characteristics. .................................................................................................. 108
4-9 Map of the cluster and outlier analysis (Local Moran’s) in the surveyed villages. ............................................................................................................ 109
4-10 Model Residual vs. Predicted Plot indicating a properly specified model. ........ 109
A-1 Vaffriables included in Maternal Knowledge Score calculation calculations. Note, Iron and Vitamin A are included together in the combined score. ........... 114
11
B-1 Model Fit statistics for Chapter 2 Model 1: HDDS. ........................................... 115
B-2 Summary of backwards elimination procedure in multivariate backward stepwise logistic regression for Model 1: HDDS. .............................................. 115
B-3 Predictive power statistics of Model 1: HDDS................................................... 115
B-4 Receiver Operating Characteristic Curve (ROC) showing predictive power of final model, for Model 1: HDDS. ....................................................................... 116
B-5 Receiver Operating Characteristic Curves (ROC) showing predictive power of each model step until final model for Model 1: HDDS. ................................. 116
B-6 Model Fit statistics for Chapter 2 Model 2: ASF................................................ 117
B-7 Summary of backwards elimination procedure in multivariate backward stepwise logistic regression for Model 2: ASF. ................................................. 117
B-8 Predictive power statistics of Model 2: ASF. ..................................................... 117
B-9 Receiver Operating Characteristic Curve (ROC) showing predictive power of final model, for Model 2: ASF. .......................................................................... 118
B-10 Receiver Operating Characteristic Curves (ROC) showing predictive power of each model step until final model for Model 2: ASF. ..................................... 118
C-1 Model Fit statistics for Chapter 3 Model 1: Livestock and Stunting. .................. 119
C-2 Summary of backwards elimination procedure in multivariate backward stepwise logistic regression for Chapter 3 Model 1: Livestock and Stunting. ... 119
C-3 Predictive power statistics of Chapter 3, Model 1: Livestock and Stunting. ...... 119
C-4 Receiver Operating Characteristic Curve (ROC) showing predictive power of final model, for Chapter 3, Model 1: Livestock and Stunting. ............................ 120
C-5 Receiver Operating Characteristic Curves (ROC) showing predictive power of each model step until final model for chapter 3, Model 1: Livestock and Stunting. ........................................................................................................... 120
C-6 Model Fit statistics for Chapter 3 Model 2: Livestock, WASH, and Stunting. .... 121
C-7 Summary of backwards elimination procedure in multivariate backward stepwise logistic regression for Chapter 3 Model 2: Livestock, WASH, and Stunting. ........................................................................................................... 121
C-8 Predictive power statistics of Chapter 3, Model 2: Livestock, WASH, and Stunting. ........................................................................................................... 121
12
C-9 Receiver Operating Characteristic Curve (ROC) showing predictive power of final model, for Chapter 3, Model 2: Livestock, WASH, and Stunting. .............. 122
C-10 Receiver Operating Characteristic Curves (ROC) showing predictive power of each model step until final model for chapter 3, Model 2: Livestock, WASH, and Stunting. ........................................................................................ 122
13
LIST OF ABBREVIATIONS
ASF Animal Source Food
CI Confidence Interval
CU5 Child under five years old
DDS Dietary Diversity Score
DHS Demographic and Health Surveys
EBK Empirical Bayesian Kriging
EED Environmental Enteric Dysfunction
GDP Gross Domestic Product
GIS Geographic Information System
HAZ Height for age z score
HDDS Household Dietary Diversity Score
HDI Human Development Index
IRB Institutional Review Board
JMP WHO Joint Monitoring Program
KAP Knowledge, attitudes and practices
LMIC Low- and middle-income countries
MAL-ED The Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study
MAR Missing at Random
MCMC Multi Chain Monte Carlo
MI Multiple Imputation
MODIS Moderate Resolution Imaging Spectroradiometer
14
MUAC Middle and upper arm circumference
NDVI Normalized Difference Vegetation Index
NGO Non-Governmental Organization
OLS Ordinary Least Squares
OR Odds Ratio
PCA Principle components analysis
SD Standard Deviation
SES Socio-economic status
SRTM-DEM Shuttle Radar Topography Mission Digital Elevation Model
UNICEF United Nations Children’s Fund
VIF Variance Inflation Factor
WASH Water, Hygiene and Sanitation
WAZ Weight for age z score
WHO World Health Organization
WHZ Weight for height z score
15
Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
UNDERSTANDING LIVESTOCK OWNERSHIP, DIETARY DIVERSITY AND ASF CONSUMPTION, AND THE ENVIRONMENTAL AND SPATIAL DETERMINANTS OF
NUTRITION STATUS IN CU5 IN RURAL SOUTHERN HAITI
By
Lindsey A. Laytner December 2018
Chair: Sarah L. McKune Major: Public Health
Livestock are ubiquitous in many parts of the developing world, with both humans
and domestic animals sharing close environments. Livestock have the potential to
provide nutrient-dense animal source foods (ASF) such as meat, dairy, and eggs,
providing vital micro and macronutrients to children to support their development and
growth. This is especially critical within their first 1000 days of life. However, this
potential benefit may be offset by the possibility that livestock may have the potential to
hinder growth benefits in children via child exposure to disease-causing pathogens in
their excreta. Thus, understanding the context of water, hygiene and sanitation, as well
as livestock ownership is crucial to designing positively impactful nutrition and hygiene
interventions.
Moreover, spatial and environmental factors on the landscape can influence child
growth indirectly. Understanding which environmental and spatial drivers are the most
influential on child growth is crucial to designing targeted interventions. Ultimately,
these potential associations between livestock ownership, dietary diversity and ASF
consumption, WASH, and the spatial and environmental covariates remain important
aspects to consider, yet are understudied in relation to undernutrition in Haiti. This
16
research will add to the growing body of literature to assess these associations in two
rural communes in southern Haiti.
17
CHAPTER 1 BACKGROUND
Undernutrition is a worldwide concern—one or more forms of malnutrition affect
populations within nearly every country. According to the World Health Organization
(WHO), malnutrition/undernutrition refers to “deficiencies, excesses, or imbalances in a
person’s intake of energy and/or nutrients” 1. Undernutrition is more common in low
and middle-income countries (LMICs) and disproportionately impacts children under five
(CU5). In LMIC, close to half of child mortality globally is linked to undernutrition. In
2016, the WHO estimated that 155 million CU5 in developing countries were stunted (a
sign of chronic undernutrition). Of these, 66% lived in LMIC1,2.
Combating undernutrition in all its forms is one of the greatest global health
challenges3. However, optimizing nutrition early—including the 1,000 days from
conception to a child’s second birthday—ensures the best possible start in life and
many associated long-term benefits4,5. For children living in LMICs, undernutrition is
associated with the chronic exposure to infectious disease-causing enteric and
respiratory pathogens. These pathogens, present in the environment through multiple
exposure pathways, may alter gut integrity and function, impairing absorption of
nutrients and resulting in Environmental Enteric Dysfunction (EED) 6–8. EED can
further undernutrition and likewise an increased susceptibility to and incidence of both
asymptomatic infection and symptomatic disease6–8. There is growing evidence from
the Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the
Consequences for Child Health and Development (MAL-ED) study that reducing
enteropathogen burden can improve child growth outcomes, especially if energy intake
is improved9. Other evidence suggest that these pathogens may also inhibit immune
18
responses to childhood vaccines, diminishing their effectiveness and impacting broader
child health outcomes10,11. Moreover, repeated infections by these pathogens can also
lead to cognitive and additional related developmental deficits. Therefore, the
cumulative effects of continual infection and asymptomatic colonization, undernutrition,
and impaired child growth and development have great social and economic
consequences for a child’s entire life. Unfortunately, these pathogens and diseases
place a disproportional burden on poor families and the communities where they
reside12–14.
A child is considered to be stunted if their height-for-age Z score (HAZ) is -2
(stunted) to -3 or more (severely stunted) standard deviations below the HAZ of the
WHO reference median of children worldwide 15,16. Dietary diversity has been
associated with better nutritional status of children in developing countries15,17–21, and
has an especially strong relationship to childhood stunting15. In the field of nutrition,
“dietary diversity” is a measure associated with (1) overall quality and (2) nutrient
adequacy in an individual’s dietary practices and is usually assessed through dietary
diversity scores (DDS). These measures compare the number of food groups an
individual or household consumes over a previously determined reference period15,18.
Several studies have shown that DDS is positively associated with overall dietary
quality, particularly improved micronutrient consumption in children15,18,22.
Consumption of livestock and livestock products, such as dairy, meat and fish, as
well as egg proteins provide bioavailable vitamins, such as vitamins B12, riboflavin, iron,
calcium, zinc that are essential to child nutrition23. Dietary diversity involves adequate
intake of macronutrients and micronutrients. The inclusion of ASF in the diet helps
19
prevent multiple nutrient deficiencies and any resultant, linear growth retardation24.
Children living in dietary-diverse households with quality diets are more likely to
consume animal sourced foods (ASFs)25. Previous studies looking at large datasets
have shown that livestock ownership can increase consumption of ASF in the
household through increased access, availability, and income generation14,26–31.
Health and dietary practices, including supplementation (e.g. vitamins A, iron,
etc.), are influenced by a wide array of complex interactions, including individual
knowledge, attitudes, and practices (KAP), social-cultural beliefs and psychological
factors (i.e. motivations), environmental contexts, resources, and other factors32,33.
There is growing recognition among scholars regarding the important role of structural,
environmental, cultural, social, and psychological factors that can influence a person’s
diet and dietary behaviors33,34. Decisions regarding diet and food choices are often
shaped by socio-cultural factors and cultural context beyond the individual’s personal
experience. However, careful integration of dietary KAP into education programs can
support and improve dietary practices in LMIC. Evidence from in-depth qualitative
ethnographic research in Tanzania shows that careful integration of dietary diversity into
local knowledge, attitudes beliefs and practices helped local people believe that dietary
diversity was important and felt that it could be achieved in their villages because the
nutrition messaging could easily be integrated into existing nutrition programs, local
concepts, and knowledge frameworks33.
There is little research on the complexities surrounding livestock ownership,
livestock husbandry, WASH (especially with regards to livestock husbandry), ASF
consumption, and child nutrition. While owning livestock can provide food and income-
20
livelihood security for nearly one billion poor people in developing countries35, there may
be an increased zoonotic infection risk for children in livestock-owning households
because of children’s proximity and continuous exposure to livestock and their excreta.
Studies have shown children can be exposed to (and can directly or indirectly) ingest
livestock fecal matter in Peru, Zimbabwe, and Bangladesh36–38. In a recent secondary
analysis of child stunting in Ethiopia, Bangladesh, and Vietnam, researchers found that
livestock, in particular poultry in the home overnight is associated with feces exposure.
Moreover, the presence of livestock feces is significantly and negatively associated with
child HAZ, in Ethiopia (β = −0.22), and Bangladesh (β = −0.13). This study also
suggests that livestock feces may be positively associated with diarrheal disease
symptoms in Bangladesh as well39. This potential for an increased risk of infection in
children in livestock-owning households warrants careful attention to WASH in and
around the household, especially with regards to livestock ownership and husbandry.
The research presented in this dissertation is a contribution to the small but
growing body of literature devoted to understanding the benefits and risks of livestock
ownership on CU5 health. This work serves as a baseline for understanding the
relationship between livestock ownership, dietary diversity (specifically ASF
consumption), and child stunting in the southern region of Haiti. Haiti, and the regions
presented in this dissertation are understudied, especially in regard to livestock
ownership, diet, WASH, and undernutrition.
The main research areas and hypotheses explored are as follows:
Chapter 2 focuses on whether there is a relationship between livestock ownership and dietary diversity or ASF consumption in rural Haitian households, as these are factors that may influence CU5 nutrition status and ultimately, childhood stunting. The two hypotheses are: (1) Livestock ownership is
21
associated with increased dietary diversity, and (2) Livestock ownership is associated with increased ASF consumption.
Chapter 3 focuses on whether livestock ownership has a relationship to CU5 nutrition status in rural Haitian households, and whether WASH may influence it. Hypotheses: (1) Livestock ownership is associated with decreased CU5 stunting. (2) When unimproved WASH factors are included, livestock ownership is associated with increased stunting.
Chapter 4 explores the environmental and spatial variables that may be contributing to CU5 nutrition status in rural Haitian villages. The hypotheses for this chapter are exploratory. The hypotheses for this chapter are that environmental factors are associated with CU5 growth patterns.
Livestock Ownership and Child Nutrition
Few studies have examined the direct effect of livestock ownership on child
nutrition14,28,29,31. Only one of these studies has assessed the association between
livestock ownership, DDS, ASF consumption, height-for-age z-score, and childhood
stunting. This cross-sectional study of children from Luangwa Valley, Zambia used
multilevel mixed-effects linear and logistic regression models to examine the association
between livestock types and four nutrition-related outcomes of interest40. They did not
find any statistically significant relationships between any of their livestock ownership
measures and a child’s odds of ASF consumption, height-for-age z-score, or stunting.
However, their linear models showed that while having fewer poultry was associated
with decreased child dietary diversity (β = -0.477; p<0.01) relative to owning no
livestock, as the number of chickens owned increased, a positive, significant association
with DDS (β = 0.022; p<0.01) was observed. However, livestock production can also
increase ASF intake indirectly, as seen in Kenya and Ethiopia—households that
produce livestock can have increased purchasing power for higher quality food
items28,41.
22
Livestock ownership can affect dietary intake and thus affect human growth
outcomes14. Some studies have suggested that livestock, especially chickens, can
contribute to child stunting patterns both positively and negatively39,42 . When children
have increased access to safely prepared eggs and poultry meat, they were shown to
have better nutritional outcomes43, which in turn can lead to better linear growth
outcomes. However, if increased access and availability of chickens is coupled with
poor husbandry and WASH practices, there may be an increased exposure to
pathogens, such as Campylobacter—a known cause of diarrheal diseases, and impact
growth faltering through the EED pathway 44–46. EED is a condition of chronic gut
inflammation from microbial (e.g. fecal bacteria) colonization in the gut, that have shown
to impact child nutrient absorption, growth patterns, among other adverse
developmental outcomes7,47,48.
Improved water, hygiene and sanitation (WASH) have been linked to
improvements in child health outcomes49–52, especially with regards to handwashing
and safe feces disposal47,53. However, there is limited empirical evidence about the
benefits of improved livestock WASH interventions to child nutrition status. This may be
a result of sanitation efforts focusing on human, rather than livestock excrement
containment52,54. Studies, such as the WASH Benefits Study in Bangladesh and Kenya
aimed to provide rigorous evidence on both health and developmental benefits of
WASH and nutritional interventions during a child’s first 1000 days of life55,56. However,
these studies did not find a relationship between WASH improvements and linear
growth outcomes. Despite the sanitation improvements made with these studies, the
results highlight the potential for targeting environmental exposure to feces57.
23
Given these recent studies, there are few empirical research studies that have
investigatived livestock WASH interventions that could potentially improve child nutrition
status. There may be a relationship between livestock husbandry and WASH practices,
since optimal WASH practices (e.g. hand washing, corralling livestock away from the
home) serve as a potential barrier between animals and young children39,54. Hazardous
livestock practices in low-income countries, such as corralling poultry close to children
at night38, and not separating poultry and other livestock from areas where children may
sit, crawl, play, and eat36,37 may be associated with pathogen exposure, colonization,
repeated infections, and eventually an increased risk for EED42,58.
Dietary Diversity and Child Nutrition
Several studies have found associations between DDS and child consumption
patterns or nutrition status within and across several countries in Africa and
Asia15,17,19,24,25,59–62. Each one is reviewed below.
Arimond et al. assessed dietary diversity in 11 countries across Africa and Asia.
Using Demographic and Health Surveys (DHS), these authors examined the
association between dietary diversity and HAZ for children 6 to 23 months old, while
controlling for confounding factors 17. Their bivariate and multivariate results found
significant positive associations between dietary diversity and CU5 HAZ. In the
multivariate models, 7 of the 11 countries had signficiant associations between DDS,
independent of socioeconomic factors17.
There were two studies in Kenya exploring ASF consumption and child growth.
Neuman et al., assessed the effects of ASF consumption and dietary diversity on child
growth63. This randomized, controlled feeding intervention study had three interventions
of meat, milk, or vegetable stew, and a control group who received no snack. The
24
outcome data were food intake (within 24 hours) recall surveys and anthropometric
measurements (e.g., height, weight, arm circumference, fat to muscle). The meat-
consuming group showed the greatest gain in arm muscle growth, followed by the milk-
consuming group as compared with the vegetable stew consumers (p< 0·05). The meat
group showed the least increase in fat area of all groups63. The longitudinal study by
Iannotti and Lesorogol explored the relationship between milk consumption and child
growth patterns in pastoralist communities in Samburu, Kenya. They found that milk
availability at the household level affected CU5 milk intake and anthropometry.
Specifically, that milk consumption was significantly associated with higher body mass
index z scores among youth21.
In Ethiopia, predictors of household dietary diversity and ASF consumption
patterns were assessed in the 2011 Ethiopian Welfare Monitoring Survey (WMS)59.
Dietary data were collected from 27,995 households using a questionnaire measuring
dietary diversity over the past 1 week. Household DDS (HDDS) was constructed
according to the Food and Agricultural Organization guidelines. The medianHDDS of
the surveyed households was 5 food groups, with cereals being the most commonly
(96%) consumed food group. Fish, egg, and fruits, on the other hand, were the least
consumed food groups. The ASFs were consumed in greater proportions in households
with higher HDDS. Additional factors that were identified as predictors and were
positively associated with higher HDDS included: being in the higher and middle socio-
economic strata (p<0.001), household literacy (p<0.01), urban residence (p<0.01),
male-headed households (p<0.01), larger family sizes (p<0.01) and livestock ownership
(p<0.01) 59. Another study in Ethiopia explored the effect of household food insecurity
25
on the nutritional status of under-five children 61. Food insecurity was significantly
associated with wasting (β = - 0.108, p< 0.05), and food diversity and number of meals
the child ate per day were significantly associated with increased stunting (β = 0.039, p<
0.01) and increased underweight (β= 0.035, p< 0.05), respectively61. These studies
identify factors related to increased dietary diversity and show the importance of dietary
diversity in childhood stunting and undernutrition.
In Bangladesh, Rah et al. assessed dietary diversity and child stunting in 165,111
CU5 who participated in a National Surveillance Study during 2003 - 200515. They
calculated DDS from 9 food groups consumed in the previous week and found that
compared with low DDS, when controlling for confounders, high DDS was significantly
associated with a 15, 26, and 31% reduced odds of being stunted among children aged
6–11, 12–23 and 24–59 months, respectively (odds ratio (OR) = 0.85, 95% confidence
interval (CI): 0.76–0.94; OR =0.74, 95% CI: 0.69–0.79; OR =0.69, 95% CI: 0.66–0.73)15.
In a study in Nepal, researchers investigated whether CU5 in rural farming
communities had improved dietary quality if they participated in a multi-phased
community-level, nutiriton-sensitive development intervention compared matched non-
participating CU5. The DDS was calculated using 24-hour recall for 17 foods and food
groups. The study results indicated that impacts of the intervention were heterogenous,
depending on the agro-ecological region and by season. Children in the intervention
group from the Hills region (a poor area that had livestock production) were 2.2 times as
likely to have consumed food from an additional food group, 1.27 times as likely to have
achieved minimum DDS, and 1.38 times as likely to have consumed ASF. This study
26
highlights the potential of livestock ownership to improve dietary diversity and ASF
consumption 25.
A household survey study explored the relationship between dietary diversity and
different measures of childhood undernutrition (stunting, wasting, and being
underweight) in Cambodian children aged 12-59 months19. The researchers used a
“food variety score” that ranged from 0 to 9. Greater DDS, and inclusion of ASF in their
diet, was a protective factor against CU5 stunting and underweight. After adjusting for
socioeconomic and geographical factors, researchers found that CU5 stunting was
negatively associated with DDS (OR 0.95, 95% CI 0.91-0.99, p= 0.01) and ASF
consumption was associated with reduced odds of CU5 stunting (OR 0.69, 95% CI
0.54-0.89, p< 0.01) and being underweight (OR 0.74, 95% CI 0.57-0.96, p= 0.03). On
the other hand, they found that consumption of raw milk products increased the CU5
odds of diarrheal disease (OR 1.46, 95% CI 1.10-1.92, p= 0.02), especially in poorer
households (OR1.85, 95% CI 1.17-2.93, p< 0.01)19. The authors stipulated that one
reason this finding occurred may be due to poor WASH practices, particularly around
pasteurization, storage, and parental hygiene19.
A DDS study in Indonesia found that that greater dietary diversity result in a
lower odds of childhood stunting, even after adjustment of their analysis for
demographics (OR=0.89; 95% CI=0.80–0.99)62. However, the study found lowest
consumption of ASF out of all the food groups, indicating that interventions should focus
on increasing ASF consumption to increase DDS. In another study in Indonesia, DDS
and ASF consumption was assessed in a year longitudinal observational study. The
researchers found ASF consumption to be high but did not find significant associations
27
with CU5 HAZ. However, ASF consumption was significantly associated with adequate
intake of protein and micronutrients, particularly vitamin A, calcium, and zinc24.
These studies collectively show that the linkage between DDS, ASF consumption, and
child stunting is complex, and requires long-term monitoring and inclusion of other
factors that may be impacting CU5 diet and growth.
Most of these undernutrition studies focus on countries in Asia and Africa,
although many countries in the Western Hemisphere have substantial portions of
children who are moderate to severely undernourished. A particularly overlooked
country (with some of the highest rates of poverty and undernutrition), is Haiti64,65. Haiti,
in comparison to the Dominican Republic (DR)—its neighbor on the island of
Hispaniola—has not seen but marginal improvements in nutrition status in the last
decade (Figure 1-2). Despite these statistics66, only two studies16,67 have explored
dietary diversity and children in Haiti, but these studies did not investigate livestock
ownership patterns, dietary diversity, and ASF consumption patterns in Haiti. In
addition, only one study looks at the impact of risk factors on child undernutrition
spatially68, yet there are no studies that explore the potential risks (e.g. via inadequate
WASH) associated with livestock ownership and CU5 nutrition status in Haiti. There are
no studies investigating the environmental and spatial factors associated with CU5
nutrition status or growth in Haiti, a region prone to extreme weather and devastating
hurricanes.
Haiti
Geography
Haiti is a small country (~28,000 square kilometers) located in the Western
hemisphere (Figure 1). It occupies the western third of the Caribbean Island of
28
Hispaniola while the Dominican Republic takes up the remaining two thirds of the island.
Haiti has 2 main peninsulas—North and South, with the Ile de la Gonâve between them.
Haiti’s mainland is divided into three main regions: (1) North (includes the northern
peninsula), (2) Central, and (3) South (includes the southern peninsula). Haiti also
includes severable nearby islands (i.e. Ile de la Gonâve, Ile de la Tortue (Tortuga
Island), Grande Cayemite, and Ile à Vache)69.
Environment and Climate
Due to its geographic placement, tropical climate, and topography (including
numerous rivers and streams), Haiti is extremely vulnerable to natural disasters—in
fact, nearly 90% of Haitians are at risk of natural hazards, including severe storm
flooding and periodic drought70. Over the years, the damage and resulting upheaval
from devastating natural disasters, including but not limited to widespread drought,
earthquakes, and hurricanes, have exacerbated public health, economic, and political
problems in this country.
The southern peninsula of Haiti is disproportionately impacted by natural
disasters; it is often subject to heavy rainfall, incurred most of the damage from
Hurricane Matthew (2016), and was the epicenter of the 2010 earthquake. The World
Bank assessed the costs of the damage from natural disasters as being equivalent to
32% of the country’s Gross Domestic Product (GDP). As a result, the southern region
has lost over 30% of its hospitals70,71. The agricultural, aquaculture and fishing, and
livestock industries have also been adversely affected by disaster and grossly reduced
in size. These disasters have long-term impact on the livelihoods—income,
health/wellbeing, and productivity—of the communities affected70,71.
29
Poverty
As UN International Strategy for Disaster Reduction Director Salvano Briceno
said, “It’s poverty that is at the core of these disasters”72. Despite the many
humanitarian, development, and research projects aimed to provide relief and
assistance to improve the lives of Haitians, Haiti remains the poorest country in the
Western hemisphere, with a GDP of US $1,034 73. In Haiti, poverty is profound and
complex, with many different dimensions. The country suffers from low literacy, gender
inequalities, short life expectancies, and high rates of infant, child, and maternal
mortality. Most of the country lacks direct access to electricity, an improved water
source, sanitation facilities, or healthcare73. Despite the observed economic
improvement in urban areas such as Port au Prince, over 40% of the Haitian population
live in rural areas where extreme poverty persists 70,71. If current trends of
impoverishment continue, half of the population of Haiti will be living in extreme poverty
by 2030 73. According to the 2016 United Nations Human Development Index (HDI),
Haiti ranks 163rd out of 187 countries (and its Gini coefficient is 0.619). These
measures assess the degree of variation in either the levels of human development
(e.g. health, education, and income)74. Both the HDI and Gini, together, indicate that
Haiti is one of the most poverty and inequality stricken countries in the world 73. The
World Bank estimates that 59% and 24% of Haitians are living below the national and
extreme poverty lines, respectively, and that 78% of the population is surviving on less
than $2 USD a day 70,73.
Livelihood and Food Production
Agriculture plays a central role in the Haitian economy and job market. It
employs half the national workforce and is the primary income-generating activity for
30
rural Haitians, who represent nearly half the country’s population70,75. The most
commonly grown crops in Haiti are coffee, sugar cane, cassava, yam, banana, sweet
potato, plantain, maize, mango, guava, and rice. Although over 40% of the land is being
cultivated, experts recognize that less than 20% of Haitian land is actually suitable for
agriculture, a result of numerous interrelated factors including soil infertility, soil erosion,
and land degradation, thus limiting agricultural potential 76. Haitian smallholders are
farmers who cultivate two hectares of land or less. Because they have very little lands
with poor soil quality, they have experienced a prolonged history of food insecurity 76.
Additionally, Haitian cultivators do not generally grow crops for their own
consumption, but instead for sale, which counterintuitively exacerbates food insecurity,
poor household nutrition, and poor incomes in Haiti. These poorest smallholders focus
on cultivating higher-value cash crops, like sugar and coffee, to use the earnings to
purchase cheaper but often less nutritious imported foods from markets75.
Though the majority of households in rural areas depend on agriculture as their
primary livelihood activity, the low productivity of their agricultural sector makes it
difficult for Haitians to survive on agriculture 75,77. This is attributable to both intrinsic
biophysical factors as well as historical and current anthropogenic influences, which
combine to significantly hamper domestic food production and income growth. Some of
these factors include lack of infrastructure (roads, electricity, and irrigation), limited
access to food production inputs (fertile soil, water, fertilizer, farming equipment, and
adequate extension services), ecological degradation, land gradient, rainfall patterns,
and unfavorable trade policies76,77. Despite agriculture’s important role in the lives of
31
Haitians, the government and donors (e.g. non-profits, etc.) have struggled to prioritize
or improve the sector75.
Approximately 80% of rural Haitians have access to land for agriculture and for
keeping livestock. Of these Haitians, 80% engage in animal husbandry practices 75.
Generally, goats and cattle are the most common livestock type owned by private
households within Haiti 76,78. Though horses, pigs, and poultry are also produced, these
species are not as widely kept. In Haiti, livestock are kept as a form of savings, with
sales of livestock a means to cope with economic downturns and shocks75. Small-scale
fisheries are also used and resourced, usually in small ponds or canals, but are not
commonly used for subsistence. Traditionally, Haitians have not exploited their potential
for large-scale fishing, mostly for safety and political reasons, including the post-
independence practice of living in the country’s interior, away from French invasion76.
Water, Hygiene, and Sanitation
Haiti has long struggled to meet international standards for hygiene and
sanitation. Underinvestment in the WASH sector are well documented, even in the
decades preceding the 2010 earthquake and devastation79. According to researchers,
Haiti has the lowest rate of access to improved water sources and improved WASH
infrastructure in the Western hemisphere79,80. Less than 70% of all Haitians have
access to improved water sources while 17% had sanitation access to improved
sanitation facilities in 201081—one year prior to this study’s survey. These statistics are
far below the regional average for access to improved water and sanitation in Latin
America and the Caribbean (80% coverage)81.
In 2013, the Haitian government acted toward improving WASH conditions, with
an instituted a National Plan, directed at improving WASH, healthcare services and
32
management, epidemiology and surveillance, as well as hygiene promotion. “These
include but are not limited to, increased coverage of potable water to 85% increased
access to improved excreta disposal to 90%, increase access to primary health care
from 46% to 80%” and mostly importantly “Achieve a change in the behavior of the
population to the extent that by 2022, 75% of the population will understand the
importance of washing their hands after defecating and before eating” 82. Despite these
specific goals, progress to achieve them has been slow79.
Undernutrition
In Haiti, undernutrition is still a major public health problem despite multiple
ongoing relief and aid efforts. Of the estimated 10 million people living in Haiti, only 58%
have access to an adequate amount of food83. Among the CU5, 12% are
undernourished, surviving on less than one meal a day78. According to a 2012 national
survey, nearly 65% of Haitian households experience food deprivation while food
resources are available from domestic or external (relief) sources. However,
accessibility to these food items is selective not everyone is receiving resources84.
This deprivation is exacerbated by the fact that Haiti is a food-deficient country, relying
heavily on imported food. Nearly 50% of the national food requirements are imported73.
Due to chronic insufficient access to food, among other factors, nutrient
deficiencies and stunting are widespread in Haiti84,85. However, according to the 2012
Demographic and Health Survey (DHS), the prevalence rates of all forms of
undernutrition in CU5 decreased between 2005 and 2012: the percentage of moderate
to severely stunted CU5 decreased from 29% to 22%, the percentage of wasted
children decreased from 10% to 5%, and the percentage of underweight children
decreased from 18 to 11% (Figure 1-2). Though they often represent the best
33
available national-level data, as with any DHS data published, these indicators are
limited to the sample population, and thought the DHS does try to account for everyone
proportionately, they may fail to represent the most rural, hard to reach, or
underrepresented municipalities. When studying indicators such as undernutrition, this
may constitute a significant bias, given these same characteristics— such as living in a
rural, isolated area—are often associated with being undernourished 2.
Theoretical Framework
Figure 1-3 is a conceptual diagram of the factors that contribute to CU5 nutrition
status adapted from UNICEF’s framework for child nutritional outcomes86. It reflects
relationships among various factors that contribute to child nutrition, with specific
attention to factors that matter in the Haitian context, and serves as the theoretical
framework for the research.
Nutritional status (Figure 1-4) can be quantified in numerous ways; each
approach focuses on a different aspect of inadequate nutrition. Though these nutrition
indicators all contribute to our overall understanding of nutrition, given the long-term
consequences of chronic undernutrition and its potential for prevention through ASF
consumption, this research focuses on child stunting, height for age (HAZ).
Proximate, Underlying, and Distal Factors
There are many potential factors contributing to child undernutrition in Haiti. As
mentioned previously, an insufficient supply of micro- and macro-nutrients to the human
body can restrict and retard physical and cognitive growth and development and can
lead to financial and social burdens at the societal level, as well as intergenerational
consequences13. Per the theoretical framework, the proximate factors that affect CU5
nutrition status are: (1) diet, defined as the adequate consumption behaviors of safe and
34
nutrition-dense, micro and macronutrient foods; (2) disease status, defined as the
symptomatic (observed diarrheal disease) or asymptomatic colonization (no visible sign
of disease) of a susceptible host; and (3) individual factors such as CU5 age, sex,
immunity, and genetic factors that may influence dietary patterns or susceptibility to
diseases. In this dissertation, we focused on the first two categories of proximate
factors.
Diet. Diversity Scores (DDS) are associated with overall quality and nutrient
adequacy of the diet, and are often used to assess diet in LMICs17. Nutritionists
recommend that for beneficial growth and development, an individual should consume a
diet containing a variety of foods from all the food groups, which includes: starches,
cereals, vegetables, fruit, dairy products, meat, fish, meat-protein alternatives, eggs, as
well as moderate amounts of healthy fats87. For proper diet for infants and young
children under 2 years old, the WHO and UNICEF recommends introduction of
complimentary feeding (minimum of 4 food groups) for children 6 to 23 months of age,
in addition to continued breastfeeding. An additional recommendations for this age
group is to include are iron-fortified or iron-rich foods designed for infants and young
children in their diet88.
Disease. Diarrheal diseases are a leading cause of undernutrition in children
under 2 years old and are caused by exposure to waterborne and foodborne
pathogens54. Despite established UNICEF framework of malnutrition, there is growing
evidence that reflect enteric pathogen infection in the absence of diarrheal disease is
even more common9. Prolonged exposure to these diseases and any subsequent
asymptomatic colonization can impact nutrition outcomes in CU5 by impacting the gut
35
flora, intestinal permeability, and ability to absorb nutrients from food89; ultimately,
limiting the benefits of a nutritious diet. The MAL-ED study has identified across all
studied sites that the highest episodes of infection occurring in humans come from
human bacteria such as Shigella spp, norovirus GII, rotavirus, and Campylobacter in the
children under two years of age. Much of the prominent human diarrhea-causing
pathogens identified are of zoonotic origin such as Campylobacter and Salmonella,
where the primary reservoir is poultry.
Humans are usually infected by diarrheal pathogens through a fecal-oral
pathway. Some of the critical pathways that diarrheal pathogens are transmitted can be
summarized by the 5-Fs (i.e. food, flies, fomites, fingers, fluids, and fields)90. Figures 1-
5 illustrates the 5-Fs and incorporates primary exposure (i.e. direct feces contamination)
and secondary exposure (i.e. indirect contamination of food) to diarrheal pathogens by a
child. However, with livestock generating at least 85% of the world’s animal fecal
waste52, this environmental fecal contamination can increase the potential transmission
of zoonotic and foodborne diseases14,30,52,54,90. According to the 2015 Global Burden of
Disease study by Wang et al., at least one third of CU5 mortality was attributable to
microbes that can be found in animal feces96. Moreover, livestock and domestic animal
waste can contaminate soil, public and private water, and as a consequence, can lead
to human diarrhea91,92. Increased production of livestock, which is essential for
increased access to ASF, can also create new opportunities for infectious agents to
contaminate the environments via improper livestock waste management93–95.
As mentioned previously, young children can be exposed to pathogens from
poorly managed animal feces, particularly in these communities (see Figure 1-6 and
36
Figure 1-7) which can impact CU5 growth. A recent systematic review found growing
evidence to support the importance of separating animal feces from human
environments, and limiting direct and indirect child contact with fecal-borne pathogens
54. When developing integrated WASH and/or child nutrition programs, the safe
containment and disposal of livestock excreta is often overlooked, but is likely a major
pathway of child enteric disease and growth faltering39,52.
Maternal care and knowledge practices are important in the prevention,
treatment, and management of child health. Increasing maternal knowledge of which
foods are vitamin-rich can be crucial for appropriate child care and feeding practices,
especially for young children71,97, as a child’s diet is contingent on the common feeding
practices of their mother and household members. Moreover, maternal knowledge of
disease risk factors, particularly the causal factors and methods of prevention or
treatment of diarrheal illness greatly influence child exposure to pathogens98. Maternal
hygiene behaviors, especially whether they safely prepare food and what type of
complimentary feeding practices they use can impact whether or not their child is
exposed to pathogens99,100. Additionally, maternal knowledge and practice of
preventative medical approaches to disease, such as ensuring that their child receives
vitamin A or zinc supplementation and is vaccinated, are other important factors
influencing disease risk in CU5101.
Basic Factors
As per the theoretical framework, the basic factors are the top-level drivers. Basic
factors affect immediate, underlying, and distal factors along a continuum. These
include the sociocultural, political, and large-scale economic drivers that permeate
society, as well as environmental drivers (e.g. rainfall, temperature, vegetation,
37
elevation, etc.). Societal practices, values, attitudes, and belief systems that influence
social norms and behaviors are all considered basic factors.
Data Overview
Survey Sites. This survey was conducted in the South and Southeast
departments of Haiti. The survey took place from October to November 2011 in regions
that have predominantly rural, socioeconomically disadvantaged populations located in
the commune municipalities of Aquin and Côtes-de-Fer. Both communes are remote
and isolated in mountainous areas of the country. The Saint Boniface foundation is a
longstanding non-profit in the community, offering a 60-bed primary care hospital and is
the only healthcare facility in the region. Saint Boniface sponsors several community
health interventions in the area102–104.The population of this region primarily practices
subsistence farming and also raise goats and pigs as their main source of income102. A
total of 800 households were selected for the survey using a two-stage sampling
method described elsewhere in two previously published studies using this
dataset102,104. In brief, children aged 6 to 59 months of age were randomly selected
using a census derived from the St. Boniface Hospital and their employed community
healthcare workers that serve the surrounding catchment. Only households that had a
CU5 were asked to participate in the survey. Overall, 828 women of child-bearing age
(15 to 49 years old) and their youngest child under the age of 5 years were recruited for
the study. For households with multiple children under 5, the youngest child-mother
dyad was selected for data collection.
This baseline, cross-sectional survey was conducted prior to the implementation
of pilot interventions to improve maternal and child health in the region. Prior to this
baseline survey, the overall study used a two-stage random sampling scheme. This
38
study was previously approved by the University of Florida Institutional Review Board
(IRB-02 clearance), and the St. Boniface Foundation hospital authorities in Haiti. The
survey consisted of household visits to conduct interviews and take serum (to measure
anemia) and anthropometric measurements from all eligible respondents from each
selected household. Written consent was obtained from each respondent prior to
assessment, and for CU5, consent was obtained from their parents or caregivers.
Interviews and assessments were conducted only after consent was obtained, and for
children with anemia, treatment was given free of charge102–104.
Data Cleaning and Manipulation. Survey data were cleaned and analyzed
using SAS version 9.4105. After translating the document to English, reorganizing and
reclassifying data into binary categories for analyses (see Figure 1-9), there were many
missing values. To adjust for this, skip patterns in the questionnaire were addressed.
Skip patterns are questions that were asked and depending on the respondent’s answer
will determine if the respondent will move onto the next question in that section
(sections are themes of the questionnaire such as food security or maternal health) or if
the respondent will end that section and move onto an entirely different set of questions.
Additionally, for some of the key explanatory and covariate variables in the analyses,
more than 5% were missing, even after controlling the skip patterns. Within SAS
software, any statistical procedure (e.g. regression analyses) will often exclude
observations with any missing variable values from analysis. Although analyzing only
respondents with complete data records has the advantage of simplicity (i.e. no
additional data cleaning/manipulation steps), the information contained in the
incomplete cases is lost. To adjust for this and to keep as many observations as
39
possible, while also minimizing bias, and obtain the appropriate estimates of
uncertainty, statistical imputation procedures were used in line with previous
research103.
The imputation procedure used is the Multiple Imputation (MI) procedure in SAS
9.4, which performs multiple imputation of missing data106. Similarly, Seraphin et. al.
also performed this specific MI procedure, but for an entirely different study objective
that looked at the determinants of institutional delivery among women aged 15-49
years103. We chose MI over single imputation, because single imputation does not
account for the uncertainty around the predictions of the unknown missing values, and
the resulting estimated variances of the parameter estimates will be biased toward
zero—whereas with MI, the model is unbiased by missing data because it replaces
each missing value with a set of plausible values that represent the uncertainty about
the best value to impute. After imputing the missing data, we analyzed the dataset in
SAS using customary procedures for complete data and combining the results from
these analyses into one (singular) estimate107. All missing patterns for each chapter
hypothesis was explored by sub-setting data relevant to each chapter’s specific
hypothesis, and then checking the missing patterns using means and frequency tables
of all variables in the analysis. Each missing dummy variable to run Little’s “Missing
Completely at Random” (MCAR) test110. Little’s test assesses if the missing data is
MCAR or missing at random (MAR) or not missing at random, on each variable in
question. To assess if the variable’s missingness is MCAR, the p value must be greater
than 0.05 (or not significant) and neither the variables in the dataset nor the unobserved
value of the variable itself predict whether a value will be missing111. Variables in this
40
research were considered to be MAR because of the survey design and skip patterns,
some variables in the dataset were predictive of missingness in another variable but this
wasn’t true for the variable in question (e.g. for questions to be answered more
frequently by women but not by men would indicate that the variable for “gender” would
predict missingness)112. To confirm this further, we visually inspected the data’s missing
pattern to determine whether the variables that had missing information exhibited a
monotonic trend or appeared to be MAR. Given the structure of this dataset, our
hypotheses, and previous research that used this raw data and had to impute103,104, a
Markov Chain Monte Carlo (MCMC) imputation method using Jeffreys Prior by using the
“PROC MI” procedure explored in SAS 9.4105.
Chapter Methods. We imputed the dataset separately for each chapter in line
with the hypotheses within each one. In Chapter 2, we focus on the association of
livestock ownership, dietary diversity and ASF consumption in CU5 and uses binary
multivariate logistic regression models to assess dietary diversity and ASF consumption
outcomes. In Chapter 3, we focus on the association of livestock ownership (with and
without inadequate WASH behaviors) and CU5 stunting, using multivariate logistic
regression models. In Chapter 4, we conducted an exploratory analysis to investigate
the relationship of environmental variables related to food production and spatial
associations with CU5 HAZ. To assess the relationship between CU5 HAZ and
environmental and spatial covariates, we employed a multivariate linear regression
using Ordinary Least Squares (OLS) and global and local spatial clustering and
autocorrelation detection.
41
Figures
B) Figure 1-1. A) Map of Haiti with the Aquin and Côtes-de-Fer study communes. B) Map of the Aquin (Flamands, Fonds
des Blancs, Guirand, and Frangipane) and Côtes-de-Fer (Jamais Vu) study area sub-section.
42
Figure 1-2. Percentage of CU5 that are stunted in Haiti compared to the DR according to the DHS66.
43
Figure 1-3. One-health theoretical framework to understand linkages between livestock ownership and child under five
nutrition in southern Haiti. Adapted from UNICEF 86.
44
Figure 1-4. Malnutrition terminology. Definitions of the various forms of malnutrition
and undernutrition from the Joint Child Malnutrition Estimates 2017 edition2.
Figure 1-5. Five 5’s diagram (adapted from Penakapapti et al.52.
Term Definition
Undernutrition Includes wasting or severe weight loss (low weight-for-height (WHZ)),
stunting or chronic growth retardation (low height-for-age (HAZ)), and
underweight (low weight-for-age (WAZ)), where an underweight child
may also be stunted, wasted or both
Micronutrient-related
undernutrition
Includes micronutrient deficiencies (a lack of important vitamins and
minerals-namely, Iodine, vitamin A, and iron) or micronutrient excess
Overweight Obesity and diet-related non-communicable diseases (such as heart
disease, stroke, diabetes and some cancers
45
Figure 1-6. Undernutrition pathway from pathogen exposure via livestock feces adapted from Penakapapti et al.52.
46
Figure 1-7. Improved nutrition via livestock ownership and safe WASH practices (blocked exposure to livestock feces and
improved access to safe and nutritious foods) adapted from Penakapapti et al. 52.
47
Figure 1-8. Variable list and description for all chapters. For maternal knowledge
scoring, see Appendix, Figure A-1.
Variables Description Chapter
Nutrition status and anthropometrics
CU5 Stunting Stunting was defined as: HAZ < −2.0 standard deviations (SD) below the 2006 WHO reference mean. Stunting status is a
binary score with children below -2 SD considered "stunted" while all other children considered "not stunted". Outlier
children greater than 5 SD or less than -5 SD were removed.
3
Dietary Diversity
Household Total Dietary Diversity Score
(HDDS)
Household dietary diversity was assessed using a qualitative 24-hour 18 food group consumption questionnaire, adapted to
Haiti. The binary HDDS score was calculated using the median DDS of the sample (i.e. 7). Scores that fell above the
median were considered to have a more diverse diet than the average and those that fell below the median were considered
to have a less diverse diet than the average for this sample.
2,3
ASF consumpion score Animal source food consumption as a sub-score of HDDS. Includes any consumption of livestock flesh or byproducts,
including: milk, meat, fish, and eggs. This binary scoring is based off the median ASF consumption score of the sample (i.e.
1) , those that fell above the median score were considered to have a more ASF consumption than the average. Those that
fell below the median score were considered to have a less ASF consumption than the average for this sample.
2,3
Livestock ownership
Any Species Includes any ownership of small or large ruminants, poultry or swine. This variable is binary. 2,3,4
Small Ruminants Households that owned goats and/or sheep. This variable is binary. 2,3,4
Large Ruminants Households that owned cattle, milk cows, donkeys, mules, and/or horses. This variable is binary. 2,3,4
Poultry Households that own chickens or other types of poultry. This variable is binary. 2,3,4
Swine Houesholds that own Pigs. This variable is binary. 2,3,4
Impoverishment
Impoverishment is a proxy for wealth. Wealth was assessed using the standard protocol and procedures developed
previously by Seraphin et al., The principle components were used to create a relative poverty index that captures the wealth
of the region, where everyone is considered "poor". This Impoverishment is a binary indicator ranges from 0 to 1,
representing least poor and poorest.
2,3
Transportation This variable is binary. Another proxy of impoverishment and also access. Indicates the household owned bike, car or
motorcycle.2,3
Number of CU5 living in the home This variable is binary. Indicates if the household has 1 child or more than 1 child under five years old living in the
household.2,3
Land ownership This variable is binary. Another proxy of impoverishment. Indicates if the household owns land, cultivates land or has tenure
over land.2,3
Child 2,3
Age This variable is binary. Age categories were dichotomized into 2 categories: Children 6 months to 2 years old and children
25 months to 5 years old.2,3
Vitamin A Supplementation Categories based off field data, with three categories (e.g. Vitamin A received, vitamin A not received, and do not know). 2,3
Maternal
Age Mothers ages ranged from 15 to 49 in the sample. Breakdowns of the maternal age are based off child-bearing, and
consistent with Seraphin et al., groups are: age 15-24, 25-34, and 35-49. 2,3
Employment Categories based off mothers responses, and include "farming", "steady work", "No job", and "Other". 2,3
Relationship Represents if mothers are married or not married (as a binary category). All categories outside of marriage or formal union
were considered not in a relationship.2,3
Education Represents if mothers received any formal education (e.g. primary, secondary or above) versus no formal education. 2,3
Mother and caregiver knowledge
Vitamin A and Iron Rich Food Knoweldge
Nutrition and malnutrition
Diarrhea risk*
Diarrhea prevention*
To assess maternal/caregiver knowledge surrounding nutrition, WASH and potential disease risk, four scores (each
measuring a particular knowledge construct: Nutrition (vitamin A and Iron), Nutrition and Malnutrition signs, diarrheal disease
risks, and diarrheal disease prevention). These constructs were created from questions listed in table 1-6. Each construct
was a summation of the questions in table 1-6 that were answered correctly. Mean scores were then taken for each
construct across all survey participants. To establish a knowledge score, the scores were dichotomized around these mean
scores for all study participants, per knowledge constuct, following Seraphin et al. method. The participants that fell below
the mean score were considered to be less knowledgeable while those participatns that fell above the mean score were
considered to be more knowledgeable.
2,3*
WASH
Latrine This variable is binary. This is the type of toilet facility; an improved latrine is considered to be a toilet that isn’t shared and
follows the WHO JMP standards.3
Child Stool Dispoal This variable is binary. This is based off how child stool is disposed of in the household. The variable reflect Improved child
stool practices (e.g. "Child used toilet", "Put/rinsed into toilet", "Put/rinsed into drain", versus unimproved (e.g. "threw in the
trash", "left it in the open", and "other").
3
Household Waste Disposal This variable is binary. it reflects the Household waste disposal practices are considered improved versus unimproved.
Unimproved waste disposal include answers such as: "Dump in Street/Open Space", 'burn it", and "other". Improved waste
disposal incude: "Bury it", and "Dispose of on farm/compost".
3
Water Source This variable is binary. An improved water source includes water that is piped water supply, and stored water from protected
wells, springs, public standpipes or stored rainwater.3
Distance to Water This variable is binary. Distance to water as considered improved if the water was less than 30 minutes away, round trip.
Distance to water was considered to be umimptoved if the time it took to retrieve water was over 30 minutes, round trip. 3
Water treatment This variable is binary. Water treatment is considered to be improved if the water treatment falls into categories such as
boiling the water, treating the water with chlorine or disinfectant tablets, Treatment with chlorine or equivalent, boiling of
water, solar disinfecting, etc.
3
Handwashing Practices This variable is binary. This variable is based off response from mothers and caregivers who practice safe handwashing
practices before cooking, eating or using the latrine.3
Disease Status and Prevention
Diarrheal Disease This variable is binary. It reflects the Cu5 passing of 2-3 loose or watery stools per day, within the 2 weeks of the survey. 3
Febrile Illness This variable is binary. It reflects the Cu5 having a fever within 2 weeks of the survey. 3
Breast Feeding Status This variable is binary. It reflects children that are currently breastfeeding. 3
Deworming This variable is binary. It reflects that the cu5 has recived deworming medication. 3
Environmental and Spatial
Elevation Measure of the height above seal level in meters. 4
Vegetation (NDVI) Index of vegetation conditions from NASA MODIS. Ranges from -1 (no vegetation) to 1 (complete vegetated) per 250
meters.4
Land Surface Temperature (Day and
Night)
Temperature from NASA MODIS, calculated Kelvin and converted to celsius degrees.4
Precipitation Long-term cumulative (i.e. over 3 months) rainfall data based on average monthly rainfall in milimeters from 1970 - 2000 4
Population Density measurement of the number of people per 100 meters squared. 4
Accessibility Travel-time measure of the distance to the nearest urban center. 4
Distancee to Health Facility Euclidean distance from St. Bonifcace Hospital. 4
Distancee to Roads Euclidean distance from established road network. 4
Slope Percentage rise in elevation, calculated in ArcGIS software. 4
*assessed only in chapter 3
48
CHAPTER 2 I -LIVESTOCK OWNERSHIP AND DIETARY DIVERSITY OF CU5 IN RURAL HAITIAN
HOUSEHOLDS
Introduction
According to the World Health Organization (WHO), malnutrition/undernutrition
refers to “deficiencies, excesses, or imbalances in a person’s intake of energy and/or
nutrients”1. Undernutrition is a serious problem plaguing many low- and middle-income
countries (LMICs). Haiti is one of the poorest countries in the western hemisphere, and
suffers the highest rates of undernutrition in Latin America and the Caribbean65.
Children under 5 years old (CU5) are particularly vulnerable to undernutrition. These
nutritional deficits, if chronic, can affect the development status of CU5, including their
linear growth patterns and cognitive functioning. Thus, CU5 micro- and macronutrient
deficiencies, as well as immune function and disease statuses (both symptomatic and
asymptomatic), can lead to recurring undernutrition, which can have immediate and
lasting effects on their health and well-being13. Therefore, it is essential that CU5 have
an adequate dietary intake pattern consisting of safe, nutritious, and diverse food
groups to promote and foster proper growth and development patterns.
Dietary diversity is the universal term and measure associated with (1) the overall
quality, and (2) the nutrient adequacy of a person’s dietary practices. Dietary diversity
has been shown to be a strong predictor of CU5 nutrition and has been found to show
an association with CU5 stunting (-2 to -3 standard deviations below normal Height for
Age Z scores [HAZ])15,16. Dietary diversity considers an individual or household’s
consumption of a higher number of food groups compared with a set standard amount
of food groups considered to be adequate 15,18. Usually, medium or moderate is termed
adequate [compared to low and high-dietary diversity 15,18. To assess an individual’s
49
dietary diversity, a simple tool has been developed and tested called the dietary
diversity score (DDS). The DDS counts the number of food groups consumed by an
individual or household over a given reference period.18 Several studies show a high
DDS is positively associated with overall dietary quality, particularly with improved
micronutrient consumption in CU515,22. Studies have shown that dietary diversity and
infant feeding practices vary by department (i.e. region) in Haiti, in general
demonstrating low dietary diversity and poor infant feeding practices associated with
underweight, wasting, and stunting across Haitian CU516.
Moreover, livestock has the potential to provide food and nutritional security, as
well as income and livelihood, to nearly one billion poor people in LMICs35. With the
worldwide increase in demand for livestock, owning and rearing livestock have the
potential to provide many benefits--increasing ASF access, availability, income (to
purchase ASF or other diverse foods in markets) 14,16,26–31,67,85 . In Haiti, livestock too
can potentially increase animal source food (ASF) access and consumption (especially
for CU5), reduce vulnerability and improve livelihoods with food and income14,16,26–
31,67,85. These livestock benefits may provide better nutritional statuses and overall
health outcomes for CU5 in Haiti.
Safe ASF, if available and accessible to families in need, have the potential to
improve CU5 nutrition by impacting dietary quality113. ASF such as milk, meat, fish, and
eggs are rich in bioavailable vitamin B12, riboflavin, iron, calcium, zinc, and a variety of
essential amino acids23. These are necessary for positive CU5 growth and nutrition
outcomes. Ultimately, for many vulnerable groups (i.e. CU5), ASF consumption may be
50
the only means to absorb these critical vitamins and micronutrients (especially vitamin
B12) in their diet28.
Research Objective
There is limited empirical evidence to support a relationship between small-scale
livestock production, ASF consumption, and nutritional status in children under 5 years
old in southern Haiti. Thus, the goal of this research chapter is to assess if a
household’s (with a CU5) ownership of livestock is associated with the overall
household’s dietary diversity and consumption of ASF. The hypotheses for this chapter
are: (1) household livestock ownership is associated with greater dietary diversity
scores (HDDS) in households of CU5 in rural southern Haiti; and (2) household
livestock ownership is associated with greater ASF consumption in households of CU5
in rural southern Haiti. To assess these hypotheses, this analysis investigated whether
certain livestock species or groups (i.e., small ruminant, large ruminant, poultry or
swine) were associated with either HDDS or ASF consumption in the CU5 surveyed,
when accounting for covariates.
Methods
Data for this study came from a cross-sectional, household-based survey
conducted from October to November 2011 in a predominantly rural region of about
65,000 inhabitants in the Aquin and Côtes-de-Fer communes of southern Haiti102–104.
The survey selected 828 households from the Institut Haïtien de Statistique et
d’Informatique (Haiti’s census) to participate in the survey using a random, two-staged
sampling design. The first stage included a selection of 30 out of 69 villages. In the
second stage, households within each of the village cluster were selected randomly.
51
Within each household selected to participate, a mother (or caretaker) and their CU5
was then selected to participate in the survey (n=828)102–104.
For this secondary data analysis, only observations containing a mother and CU5
(between the ages of 6 months and 59 months) pair were selected for inclusion and
further analyses. All other observations were excluded. Moreover, detailed descriptions
of variable construction (e.g. recoding, statistics and data manipulation) for variables are
referenced elsewhere in Chapter 1, subsection: Data Overview as well as Figure 1-8.
The outcome variables of interest were the (1) HDDS and (2) ASF consumption
score. In brief, the HDDS is a composite score of all food groups (Figure 2-1) consumed
by the entire household (including any CU5) within a previously defined dietary recall
period. For this study, the recall period was 24 hours, and the total raw scoring was out
of 18 food groups. In comparison to other studies that usually construct dietary diversity
score out of 9 to 12 groups (to measure low, medium and high DDS), this study assess
whether a factor is contributing to either higher HDDS or low HDDS. HDDS is recoded
into a binary outcome variable following a similar approach used by Mukherjee et al. 114,
using the median HDDS in the sample (i.e. median =7). A score of “1” was assigned to
a household that fell above this median, indicating the household members consumed a
more diverse diet than the average for this sample. A score of “0” reflected the opposite.
ASF consumption score was also assessed. This was a subset of a household’s total
DDS and was the sum of any meat product, fish, dairy or egg consumption. This raw
score was out of 6 groups. Like the binary HDDS score, the ASF consumption score
was also binarized, based off the median ASF consumption score for the sample (i.e.
52
median = 1). A score of “1” indicated more ASF consumption than the median for this
sample; a score of “0” indicated less ASF consumption than the median for this sample.
The complete list of variables (including covariates) and their description are in
Figure 2-2. The main independent variable(s) for these logistic regression models was
livestock ownership status (i.e., “household owns livestock”), particularly whether a
household owned large ruminant animals (cattle, dairy cows, horses, donkeys, and/or
mules); small ruminant animals (goats and/or sheep); or poultry and swine.
Statistical Analyses. Survey data were cleaned and analyzed using SAS
version 9.4 and R105,115. Missing values were accounted for using multiple imputation in
SAS version 9.4. Tests for collinearity including checking the variance inflation factor
for each variable as well as checking variable correlation matrices were accounted for
prior to running any analyses to remove any variables with high collinearity or
correlation. Descriptive statistics for livestock ownership and diet were then stratified by
sub-communal section within Aquin and Côtes-de-Fer (e.g. Guirand, Flamands, Fond
des Blanc, Frangipane, and Jamais Vu) in Figure 2-3. Summary statistics and bivariate
regressions results for HDDS score and ASF score, separately, against each
independent variable are presented. Variables that were statistically significant at the
p<0.2 level in bivariate analyses (Figure 2-4) were input into a backward step-wise
binomial logistic regression model. The best model was chosen by the lowest Akaike
Information Criterion (AIC) score. Odds ratios (OR) with their 95% confidence interval
(95% CI), and the p-value for significance.
Results
Descriptive. Figures 2-2 through Figure 2-3 illustrate the descriptive statistics of
the survey participants from different angles. In Figure 2-2, these survey demographics
53
highlight that 24% were landholders, with 32% owning any livestock species.
Approximately 59% of the surveyed households reported they did not have enough food
to eat in the last 4 weeks (n=257), 53% report being unable to eat the food they wanted
or preferred (n=228), and 49% report having to forego one meal each day due to food
insecurity (n=213).
Figure 2-4 shows the descriptive statistics of livestock ownership, HDDS and
ASF consumption by sub communal section. Frangipane had the largest percentage of
livestock-owning households (73%, n=128) whereas Fonds des Blancs had the lowest
number of livestock-owning households (52%, n=90). Frangipane has the highest
percentage of households that own chickens as well as pigs and small ruminant animals
compared to all other communal sections. In addition, more households had low HDDS
(56%), and even more households had low ASF consumption (64%). Figure 2-3
highlights Flamands and Jamais vu sub communal sections as having more surveyed
respondents reporting higher HDDS (42%), whereas Frangipane has the lowest amount
of surveyed respondents reporting higher HDDS(25%).
Figure 2-3 shows that of the CU5 surveyed, 41% (n=180) were under 2 years old
with 53% female (n=232). Additionally, 63% of CU5 (n=388) received a vitamin A
supplement, and 37% of CU5 were not currently breastfeeding. Among mothers
surveyed, the age ranged from 15 to 49 years old, with the largest proportion of mothers
falling into the 25 to 34 (46%, n=193) age range. More mothers had completed formal
education (68%), and 70% of mothers reported not being in a relationship. In addition,
10% of mothers were employed as farmers (n=32), 24% had some form of steady work
or part-time employment (n=75), 23% had no job or income (n=74), and 43% classified
54
their employment as “other” (n=138). While 76% of mothers were more knowledgeable
regarding the signs of undernutrition in their CU5..
Multivariate. Significant results from the bivariate logistic regressions models
(for HDDS and ASF consumption) at the p<0.2 level are shown in Figure 2-5 and Figure
2-6. These variables put into the multivariate binomial logistic regression analyses
against the key outcome variables: (1) HDDS status and (2) ASF consumption status.
Results for the final logistic regression models (p<0.05) are shown in Figure 2-7 and 2-
8.
Model 1. First model (Figure 2-7) results indicated that households owning any
livestock (i.e. ownership of any species—includes small ruminants, large ruminants,
swine and chickens/poultry) are positively associated with greater odds of having higher
HDDS compared with household’s that did not (OR 2.68, 95% CI 0.58-0.82, p<0.0001).
In particular, households that own chickens were associated with increased odds of
having higher HDDS (OR 1.45, 95% CI 1.24-1.70, p<0.0001).
Additionally, household characteristics such as land ownership were negatively
associated with odds of having higher HDDS (OR 0.69, 95% CI 0.58-0.82, p<0.0001)
while household food security indicators (measured over a 4 week recall period) all had
positive associations with odds of having higher HDDS (OR 2.89, 95% CI 2.36-3.54,
p<0.0001; OR 3.02, 95% CI 2.55-3.58, p<0.0001; OR 1.79, 95% CI 1.46-2.20,
p<0.0001, respectively). The poorest households in the impoverishment index were
negatively associated with odds of having higher HDDS (OR 0.74, 95% CI 0.67-0.82,
p<0.0001).
55
In addition, maternal characteristics such as employment, education,
relationship, knowledge, and age were statistically significant. For instance, maternal
employment such as farming, and temporary or stable work, were positively associated
with odds of having higher HDDS compared to the reference. Additionally, mothers who
are not educated demonstrate a negative association with odds of having higher HDDS
compared to mothers that are educated (OR 0.84, 95% CI 0.76-0.93) while mothers
who were not married or not in a formal relationship were positively associated with
odds of having higher HDDS compared with mothers who were married or in a
relationship (OR 1.47, 95% CI 1.32-1.63, p<0.0001). Mothers that are less
knowledgeable of the signs of malnutrition in their CU5 they had reduced odds of having
higher HDDS (OR 0.59, 95% CI 0.52-0.66, p<0.0001). Also, mothers in the 25 to 34
age categories were positively associated with odds of having higher HDDS compared
with mothers over the age of 35 to 49 (OR 1.20, 95% CI 1.06–1.36, p=0.00). Child
characteristics found to be significantly associated with odds of having higher HDDS
was male gender (OR 1.51, 95% CI 1.37-1.66, p<0.0001) and receiving vitamin A
supplementation (OR 1.48, 95% CI 1.27-1.73, p<0.0001).
Model 2. The multivariate logistic regression model assessing the association
of livestock ownership and ASF consumption (Figure 2-8) indicated that households
owning large ruminant animals or chickens were negatively associated with odds of
having higher ASF consumption (OR 0.86, 95% CI 0.78–0.96, p=0.01, and OR 0.84,
95% CI 0.75–0.94, p=0.00, respectively) In particular, households that owned swine had
a positive association with odds of having higher ASF consumption (OR 1.17, 95% CI
1.05-1.31, p=0.00).
56
Like model 1, food security indicators were also significantly and positively
associated with greater ASF consumption. For instance, households that reported
having enough food to eat (OR 2.47, 95% CI 2.02-3.02, p<0.0001) or being able to eat
the foods they wanted (OR 1.82, 95% CI 1.55–2.14, p<0.0001) or not having to eat less
food (OR 2.18, 95% CI 1.78-2.67, p<0.0001) had increased odds of having higher ASF
consumption.
Maternal characteristics such as employment, education, relationship, and
knowledge were also significantly associated with odds of ASF consumption. For
instance, mothers who reported their employment as farming (OR 2.48, 95% CI 2.07–
2.96, p<0.0001), “temporary or stable work” (OR 1.25, 95% CI 1.10–1.41, p=0.00) were
positively associated with odds of having higher ASF consumption compared to the
reference group (i.e. “other”). However, mothers who reported to not have a stable
income were also positively associated with odds of having higher ASF consumption
compared to the reference category “other” (OR 1.14, 95% CI 1.01–1.27, p=0.03).
Mothers who are not formally educated are positively associated with odds of having
higher ASF consumption (OR 1.16, 95% CI 1.05–1.28, p=0.00) compared with mothers
who were educated formally. Moreover, mothers who were not in a relationship reported
more ASF consumption for their CU5 (OR 1.34, 95% CI 1.21–1.48, p<0.0001). Mothers
with less knowledge about vitamin A or iron-rich foods had negative association with
odds of having higher ASF consumption (OR 0.65, 95% CI 0.58-0.73–1.23, p<0.0001),
and mothers who are less knowledgeable of the signs of undernutrition in their CU5 had
negative associations with odds of having higher ASF consumption (OR 0.50, 95% CI
0.44–0.57, p<0.0001).
57
Among children, male CU5 had greater odds of having higher ASF consumption
compared with female CU5 (OR 1.43, 95% CI 1.30–1.57, p<0.0001). Children under 2
years old had a negative association with odds of having higher ASF consumption (OR
0.75, 95% CI 0.69–0.83, p<0.0001).
Discussion
This study aimed to understand if livestock ownership is associated with
increased odds of having higher HDDS or higher ASF consumption, respectively. Our
analyses found that any livestock ownership, particularly chickens, was positively
associated with having odds of having higher HDDS while pigs are significantly
associated with higher odds of having higher ASF consumption. This is supported by
published literature on this topic from sub-Saharan Africa, including in Ethiopia, Uganda,
and Kenya14,29,31,59,116.
The positive association exhibited with chickens and odds of having higher
HDDS may be attributable to a few reasons. In comparison to other livestock, poultry is
a valuable commodity in Haiti serving in part as much of the country’s diet and a
common ingredient in Caribbean cuisine. It is supplied mainly by the United States and
the Dominican Republic via import. In fact, Haiti imported nearly $80 million USD worth
of poultry meat from the US alone in 2016.117 Therefore, compared to other ASF, poultry
and poultry meat are more widely available on the island for purchase. This may be a
reason why we see the advantage of owning poultry and 1.45 odds of greater HDDS.
However, other explanations or questions exist that aren’t supported or answered by
our data, such as whether poultry are more expendable in this study region of Haiti or if
owning poultry or poultry keeping is a female dominated enterprise. Ultimately, more
58
qualitative and mixed-methods research is needed in this arena to further understand
and elucidate the relationship between HDDS and owning poultry.
However, unlike HDDS, poultry ownership was exhibited a negative relationship
with ASF consumption as did owning large ruminants. One explanation for this may be
due to market-value, whereby families may be keeping these livestock to be sold in
markets or as a kept as durable assets (i.e. “living savings accounts”)—this is common
with larger ruminants, such as cattle77. Poultry disease is another possibility, that has
been found in formative research as one constraint on consumption118. However, we
cannot confirm any of these associations with the results from this study. However, it is
interesting that poultry ownership is not more indicative of ASF consumption given
poultry’s association with greater HDDS. Swine or pigs were positively associated with
increased ASF consumption. Historically, Haitians have eaten pork during certain
public engagements, such as weddings or festivals, was traditionally a part of
Caribbean culture in both Haiti and the Dominican Republic119. In this particular region,
Ayoya et al. found that the people of this region owned mostly goats and pigs for
purposes of income generation—therefore, it may be also possible that families are
consuming pork as well102. This is also consistent across the country as a whole, since
owning swine was traditionally considered a valuable asset, kept as an economic
survival strategy when times were tough or children needed school fees covered119.
More qualitative and quantitative research is needed to understand the husbandry and
consumption patterns of poultry, large ruminants, and swine in this region.
All considered, livestock ownership and HDDS seems to be complex, given our
results. Although a moderate proportion (32%) of the sampled households reared
59
livestock, it appeared this activity didn’t directly lead to greater ASF consumption, aside
from pork. This was also found by other researchers, where livestock rearing in Haiti
was found to be seen as a durable asset—kept by households for selling in emergency
situations.77 Therefore, Haitians may view livestock as an economic asset, rather than
as a food security asset, or as a way to improve diet quality or consumption patterns.
However, our analysis cannot confirm this assumption.
Our models found food security indicators were associated with greater HDDS
and ASF consumption. This finding is corroborated in Cambodia and other countries,
where food security to be correlated with overall HDDS and ASF consumption in other
countries, such as Cambodia120–123.
Our results also indicated that household land ownership had a negative
association with greater HDDS but not greater ASF consumption. This finding was not
corroborated with other studies in Haiti that have found land tenure and ownership to be
positively associtiated67. More qualitative and quantitative research is needed to
understand land tenure and ownership patterns in Haiti and correlations with livestock
ownership as well as HDDS and ASF consumption.
In addition, maternal characteristics such as employment, education,
relationship, and knowledge were significant in both models, however mothers who
weren’t formally educated in model 1 had a negative association with greater HDDS. All
others were positively associated with greater odds of higher HDDS. Generally, this
sample had more formally educated mothers (68%), more mothers who were not in
relationships (70%), and about a third of the mothers were livestock owners (32%).
Literature has shown that CU5 living in households where mothers have decision-
60
making control over assets tend to have better dietary and nutritional outcomes124.
However, this cannot be confirmed in our study. More qualitative research and mixed
methods approaches are necessary to investigate and understand the role of women in
these households, their employment, their education, and marital status in these study
regions.
Mothers who were not formally educated were positively associated with1.16
times greater odds of having higher ASF consumption in their households, compared
with mothers who were formally educated. This finding was not seen for higher HDDS,
however, Pauze et al. found respondents in Haiti with primary or secondary educations
had better HDDS scores than those without any formal education67. The positive
association with greater odds of higher ASF consumption is confusing. A study
conducted in Ghana, found that a mother’s practical knowledge about nutrition may be
more important than formal maternal education about nutrition130. Unfortunately, our
survey did not go into the specific details regarding the types of formal and informal
education, and this area needs further research to understand these associations in
these studied regions. In addition, mothers who reported to have less knowledge about
the signs of malnutrition in their CU5 illustrated a negative association with greater odds
of higher HDDS and also higher ASF consumption. This is an important finding
because it emphasizes the potential that maternal education has to improve dietary
diversity and ASF consumption which is also seen in the literature131–133. However, our
results cannot confirm this assumption. In addition, mothers who were not in stable
relationships reported 1.47 more odds of higher HDDS and 1.35 more odds of higher
ASF consumption compared with mothers who were married or in a relationship. There
61
are many possible interpretations for this finding. In particular, in Haiti there is a specific
pattern union that includes 5 different types. Only 2 of the 5 are considered “married”,
and are much more rare (18%) than the remaining 3 (over 59%)134. Ultimately, we need
more qualitative research on this topic is needed in this region of Haiti.
CU5 characteristics such as gender were significant in both models: males had
1.51 times more odds to have higher HDDS and 1.43 times more odds to have higher
ASF consumption compared with female CU5. This was also found in places such as
India and rural Ethiopia, where intra-household allocation of food, particularly ASFs,
was favored toward male CU5 and adolescents135,136. However, these assumptions
cannot be confirmed with this data. Child age under 2 years was negatively associated
with ASF consumption. This may be due to complimentary feeding practices and timing
to introduce ASFs. More research is needed to understand these dynamics in this
study population.
The measure for HDDS in this study is a relatively novel approach, designed
explicitly for this study’s objective—to assess if livestock is a predictor of higher HDDS
or higher ASF consumption in the household. Given the outstanding issues related to
the appropriate number of food groups, particularities surrounding portion sizes,
consumption frequencies, and food item delegation in the literature, there is no
universal measure for HDDS18. While the food and agriculture organization reference
guidelines are out of 12 categories137, our approach was to look at HDDS and ASF as a
binary outcome—considering only scores above and below the median to assess higher
or lower HDDS or ASF consumption compared to the median score in this study
sample, similar to other recent research on dietary diversity outcomes114.
62
Furthermore, literature has shown that in Haiti, certain meat products (e.g. goat—
especially, goat fat) and fish are used primarily to flavor dishes, rather than as main
ingredients16. For this study, the dietary diversity was assessed using a 24 hour recall
survey, and did not take into account differences in portion sizes. The dietary recall
portion of the survey, as well as the resulting HDDS and ASF consumption scores, did
not consider intra-household dynamics. Therefore, these scores did not consider the
various ways food distribution among household members can be achieved. Dietary
intake was assumed to be the same among all members of a household, including CU5,
but this may be a potential bias in our study, and more studies are warranted on this
topic. In addition, more detailed quantitative surveys are necessary: ones that
specifically document the consumption quantified and consumption frequency.
Moreover, qualitative research methods are also necessary to confirm and contextualize
the quantitative surveys. This is imperative to incorporate into future studies on these
issues to achieve a gold standard HDDS and ASF consumption scoring system.
Although results are not generalizable beyond this study population, they show
that livestock ownership is associated with greater HDDS and ASF consumption in
southern Haiti, especially with regards to poultry and swine. As articulated throughout
this chapter, few studies have investigated these relationships; therefore, this work
provides a valuable baseline for future endeavors on this topic. Thus, it is imperative
that future studies look in depth at the local circumstances of livestock ownership, the
heterogeneity within livestock ownership and livestock husbandry practices, and
location-specific, cultural drivers of CU5 dietary practices. In addition, program planners,
promoters, and implementers need to understand the local context of dietary patterns,
63
food availability, livestock development, and/or ASF consumption in households to
design better programs that improve HDDS and alleviate CU5 undernutrition.
64
Figure 2-1. Table showing the breakdown and frequencies of each response per food
item in HDDS and ASF consumption calculation.
HDDS Food Item (Disagregated) Consumed Not Consumed N/A
1 Porridge 42% 43% 15%
2 Baby Foods 45% 40% 15%
3 Grains 70% 16% 14%
4 Tubers 31% 54% 15%
5 Orange Vegetables or Pumpkin 44% 42% 14%
6 Green Leafy Vegetables 36% 49% 15%
7 Ripe Mango or Papaya 26% 60% 15%
8 Other Fruits or Vegetables 38% 47% 15%
9 Organ Meat* 26% 60% 14%
10 Red Meat* (e.g. beef, lamb, pig, goat) 28% 58% 14%
11 Poultry Meat* 26% 59% 15%
12 Eggs* 33% 52% 14%
13 Fish or Shellfish* 35% 51% 14%
14 Peas, Beans or Lentils 55% 30% 15%
15 Nuts or Seeds 22% 64% 15%
16 Milk or Cheese* 41% 46% 14%
17 Fats and Oils 70% 15% 14%
18 Other Solid Foods 72% 14% 14%
*Used to Calculate ASF consumption
65
Figure 2-2. Variable descriptions.
Variables Description
Dietary Diversity
Household Total Dietary Diversity Score (HDDS) Household dietary diversity was assessed using a qualitative 24-hour 18 food group consumption questionnaire,
adapted to Haiti. The binary HDDS score was calculated using the median DDS of the sample (i.e. 7). Scores
that fell above the median were considered to have a more diverse diet than the average and those that fell below
the median were considered to have a less diverse diet than the average for this sample.ASF consumpion score Animal source food consumption as a sub-score of HDDS. Includes any consumption of livestock flesh or
byproducts, including: milk, meat, fish, and eggs. This binary scoring is based off the median ASF consumption
score of the sample (i.e. 1) , those that fell above the median score were considered to have a more ASF
consumption than the average. Those that fell below the median score were considered to have a less ASF
consumption than the average for this sample.Livestock ownership
Any Species Includes any ownership of small or large ruminants, poultry or swine. This variable is binary.
Small Ruminants Households that owned goats and/or sheep. This variable is binary.
Large Ruminants Households that owned cattle, milk cows, donkeys, mules, and/or horses. This variable is binary.
Poultry Households that own chickens or other types of poultry. This variable is binary.
Swine Houesholds that own Pigs. This variable is binary.
Impoverishment
Impoverishment score Impoverishment is a proxy for wealth. Wealth was assessed using the standard protocol and procedures
developed previously by Seraphin et al., The principle components were used to create a relative poverty index
that captures the wealth of the region, where everyone is considered "poor". This Impoverishment is a binary
indicator ranges from 0 to 1, representing least poor and poorest. Transportation This variable is binary. Another proxy of impoverishment and also access. Indicates the household owned bike,
car or motorcycle.Number of CU5 living in the home This variable is binary. Indicates if the household has 1 child or more than 1 child under five years old living in the
household.Land ownership This variable is binary. Another proxy of impoverishment. Indicates if the household owns land, cultivates land or
has tenure over land.Child
Age This variable is binary. Age categories were dichotomized into 2 categories: Children 6 months to 2 years old and
children 25 months to 5 years old.Vitamin A Supplementation Categories based off field data, with three categories (e.g. Vitamin A received, vitamin A not received, and do not
know).Maternal
Age Mothers ages ranged from 15 to 49 in the sample. Breakdowns of the maternal age are based off child-bearing,
and consistent with Seraphin et al., groups are: age 15-24, 25-34, and 35-49.
Employment Categories based off mothers responses, and include "farming", "steady work", "No job", and "Other".
Relationship Represents if mothers are married or not married (as a binary category). All categories outside of marriage or
formal union were considered not in a relationship.
Education Represents if mothers received any formal education (e.g. primary, secondary or above) versus no formal
education. Mother and caregiver knowledge
Vitamin A and Iron Rich Food Knoweldge
Nutrition and malnutrition
To assess maternal/caregiver knowledge surrounding nutrition were created from questions listed in Chapter 1.
The scores are each dichotomized around the mean score following Seraphin et al. methods previously
developed. Scores were given to all study participants, per knowledge construct. The participants that fell below
the mean score were considered to be less knowledgeable regarding the construct compared to the average for
the sample on that construct. In contrast, participatns that fell above the mean score were considered to be
more knowledgeable than the average for the construct, for the sample.
66
Figure 2-3. Descriptive statistics of survey respondents, overall.
%
Name of Variable N (Y/N)
Land ownership 435 (24% / 76%)
Any Livestock Ownership 421 (32% / 68%)
Food Security
In the last four weeks the house had enough to eat 435 (41% / 59%)
In the last 4 weeks the household could eat the food they wanted because there was enough food 430 (47% / 53%)
In the last 4 weeks, the household did not have to eat less food because the household had enough to eat 432 (51% / 49%)
Impoverishment 439 (49% / 51%)
Maternal Characteristics
Maternal Education Status 286 (68% / 32%)
Maternal Relationship Status 399 (31% / 69%)
Maternal Knowledge Score
Vitamin A and Iron Rich Food Sources 438 (50% / 50%)
Overall Nutrition and Signs of Malnutrition 433 (76% / 24%)
Maternal Age Categories
15-24 131 31%
25-34 193 46%
35-49 92 22%
Maternal Employment Status
Farming 32 10%
Steady Work 75 24%
No Income 74 23%
Other 138 43%
Child Characteristics
Age categories 6 to 24 months (vs. 2 to 5 years old) 439 (41% / 59%)
CU5 breastfeeding 410 (37% / 63%)
CU5 Gender (Males to Females)
Female 232 53%
Male 207 47%
Vitamin A Supplementation Status
Yes 101 26%
No 243 63%
Don't Know 44 11%
67
Figure 2-4. Descriptive statistics of livestock ownership, HDDS, and ASF consumption
by sub-communal section.
Figure 2-5. Bivariate regression results for study variables and HDDS.
Section Guirand Frangipane Flamands Fond des Blancs Jamais Vu All Sections
Variable Total (N) 73 128 71 90 77 439Any Livestock Ownership 67% 73% 70% 52% 62% 65%Livestock species specific count
Large Ruminant Animals
Own 49% 41% 55% 29% 40% 42%
Do Not Own 48% 48% 37% 70% 56% 52%
Did Not Answer 3% 10% 8% 1% 4% 6%
Small Ruminant Animals*
Own 51% 58% 51% 32% 47% 46%
Do Not Own 47% 30% 42% 67% 51% 48%
Did Not Answer 3% 12% 7% 1% 3% 6%
Chickens*
Own 53% 62% 61% 46% 43% 54%
Do Not Own 45% 28% 32% 51% 52% 41%
Did Not Answer 1% 10% 7% 3% 5% 6%
Pigs*
Own 33% 43% 30% 19% 32% 32%
Do Not Own 64% 45% 62% 78% 65% 61%
Did Not Answer 3% 12% 8% 3% 3% 6%
HDDS
Household DDS above average 40% 25% 42% 36% 42% 38%
Household DDS below average 55% 30% 48% 56% 54% 49%
Household DDS no response 5% 45% 10% 8% 4% 13%
ASF
Household ASF consumption above average 37% 25% 39% 29% 27% 31%
Household ASF consumption below average 58% 30% 49% 62% 69% 55%
Household ASF consumption no response 5% 45% 12% 9% 5% 14%
*Overall mean count (per household per section)
Name of Variable
Beta
Estimate SE* p**
Land ownership 0.22 0.05 0.00
Livestock Species Specific Information
Any Livestock Ownership 0.37 0.05 0.00
Owns Large Ruminant Animals -0.25 0.05 0.00
Owns Small Ruminant Animals -0.12 0.04 0.01
Owns Chickens -0.17 0.05 0.00
Food Security
In the last four weeks the house had enough to eat -0.26 0.04 0.00
In the last 4 weeks the household could eat the food they wanted 0.58 0.04 0.00
In the last 4 weeks, the household did not have to eat less food because
the household had enough to eat
0.45 0.04 0.00
Impoverishment -0.10 0.04 0.02
Maternal Characteristics
Maternal Employment Status 0.07 0.02 0.00
Maternal Education Status -0.11 0.05 0.01
Maternal Formal Relationship Status 0.37 0.05 0.00
Maternal Age Categories -0.04 0.03 0.14
Maternal Knowledge Score
Vitamin A and Iron Rich Food Sources -0.31 0.04 0.00
Overall Nutrition and Signs of Malnutrition -0.57 0.05 0.00
Child Characteristics
Vitamin A Supplementation Status -0.15 0.04 0.00
Age categories 6 to 24 months (vs. 2 to 5 years old) -0.09 0.04 0.03
CU5 Gender (Males to Females) 0.27 0.04 0.00
*SE=Standard Error
**P-value (p<0.2)
HDDS
68
Figure 2-6. Bivariate regression results for study variables ASF consumption.
Name of Variable
Beta
Estimate SE* p**
Land ownership 0.09 0.05 0.09
Livestock Species Specific Information
Large Ruminant Animals -- -- --
Chickens -- -- --
Pigs -- -- --
Food Security
In the last four weeks the house had enough to eat -0.17 0.04 0.00
In the last 4 weeks the household could eat the food they wanted 0.43 0.04 0.00
In the last 4 weeks, the household did not have to eat less food because
the household had enough to eat
0.40 0.04 0.00
Maternal Characteristics
Maternal Employment Status 0.16 0.02 0.00
Maternal Education Status 0.14 0.05 0.00
Maternal Formal Relationship Status 0.22 0.05 0.00
Maternal Knowledge Score
Vitamin A and Iron Rich Food Sources -0.24 0.04 0.00
Overall Nutrition and Signs of Malnutrition -0.46 0.05 0.00
Child Characteristics
Age categories 6 to 24 months (vs. 2 to 5 years old) -0.22 0.04 0.00
CU5 Gender (Males to Females) 0.31 0.04 0.00
*SE=Standard Error
**P-value (p<0.2)
ASF
69
Figure 2-7. Multivariate binary backward-stepwise logistic regression results assessing
the association of model 1: livestock ownership and HDDS status.
Name of Variable OR* CI** p***
Land ownership 0.69 (0.58, 0.82) <.0001
Reference: No Land Ownership
Livestock Species Specific Information
Any Livestock Ownership 2.68 (2.19, 3.28) <.0001
Reference: No Livestock Ownership
Owns Chickens 1.45 (1.24, 1.70) <.0001
Reference: No Chicken Ownership
Food Security
In the last four weeks the house had enough to eat 2.89 (2.36, 3.54) <.0001
Reference: In the last four weeks the house did not have enough to eat
In the last 4 weeks, the household could eat the food they wanted because
there was enough food
3.02 (2.55, 3.58) <.0001
Reference: In the last 4 weeks, the household could not eat the food they
wanted because there was not enough food
In the last 4 weeks, the household did not have to eat less food because
the household had enough to eat
1.79 (1.46, 2.20) <.0001
Reference: In the last 4 weeks, the household had to eat less food
because the household did not have enough to eat
Impoverishment: Poorest 0.74 (0.67, 0.82) <.0001
Reference: Poor
Maternal Characteristics
Maternal Employment
Farming 1.60 (1.34, 1.92) <.0001
Temporary or Stable Work 1.15 (1.02, 1.31) 0.02
Reference: Other
Mother is not educated 0.84 (0.76, 0.93) 0.00
Reference: Mother is educated
Mother is not in a formal relationship 1.47 (1.32, 1.63) <.0001
Reference: Mother is in a formal relationship or married
Maternal Knowledge Score
Mother is less knowledgeable of overall nutrition and the signs of
malnutrition for their CU5
0.59 (0.52, 0.66) <.0001
Reference: Mother is more knowledgeable of overall nutrition and the
signs of malnutrition for their CU5
Maternal Age Categories
25 to 34 years 1.20 (1.06, 1.36) 0.00
Reference: 35 to 49 years old
Child Characteristics
Males 1.51 (1.37, 1.66) <.0001
Reference: Females
Vitamin A Supplementation
Child did not receive Vit. A 0.70 (0.59, 0.83) <.0001
Child did receive Vit. A 1.48 (1.27, 1.73) <.0001
Reference: Does not know if the child received Vit. A.
*OR = Odds Ratio
**CI = 95% Confidence Limits
*** = P-value (p<0.05)
Model 1: HDDS
70
Figure 2-8. Multivariate binary backward-stepwise logistic regression results assessing
the association of model 2: livestock ownership and ASF consumption status.
Name of Variable OR* CI** p***
Livestock Species Specific Information
Owns Large Ruminant Animals 0.86 (0.78, 0.96) 0.01
Reference: No Large Ruminant Ownership
Owns Chickens 0.84 (0.75, 0.94) 0.00
Reference: No Chicken Ownership
Owns Pigs 1.17 (1.05, 1.31) 0.00
Reference: No Pig Ownership
Food Security
In the last four weeks the house had enough to eat 2.47 (2.02, 3.02) <.0001
Reference: In the last four weeks the house did not have enough to eat
In the last 4 weeks, the household could eat the food they wanted because
there was enough food
1.82 (1.55, 2.14) <.0001
Reference: In the last 4 weeks, the household could not eat the food they
wanted because there was not enough food
In the last 4 weeks, the household did not have to eat less food because
the household had enough to eat
2.18 (1.78, 2.67) <.0001
Reference: In the last 4 weeks, the household had to eat less food
because the household did not have enough to eat
Maternal Characteristics
Maternal Employment
Farming 2.48 (2.07, 2.96) <.0001
Temporary or Stable Work 1.25 (1.10, 1.41) 0.00
No stable income source 1.14 (1.01, 1.27) 0.03
Reference: Other
Mother is not educated 1.16 (1.05, 1.28) 0.00
Reference: Mother is educated
Mother is not in a formal relationship 1.34 (1.21, 1.48) <.0001
Reference: Mother is in a formal relationship of married
Maternal Knowledge Score
Mother is less knowledgeable of overall nutrition and the signs of
malnutrition for their CU5
0.65 (0.58, 0.73) <.0001
Reference: Mother is more knowledgeable of overall nutrition and the
signs of malnutrition for their CU5
Child Characteristics
Males 1.43 (1.30, 1.57) <.0001
Reference: Females
Under 2 years old 0.75 (0.69, 0.83) <.0001
Reference: 2 to 5 years old
*OR = Odds Ratio
**CI = 95% Confidence Limits
*** = P-value (p<0.05)
Model 2: ASF
71
CHAPTER 3 II - LIVESTOCK OWNERSHIP, WASH, AND CU5 NUTRITION STATUS IN RURAL
HAITIAN HOUSEHOLDS
Introduction
Haiti is one of the poorest countries in the western hemisphere, suffering from
food insecurity, natural disasters, infrastructural deficits, and political instability64. The
Haitian population suffers from high rates of undernutrition in adults and CU565. The
WHO defines undernutrition as: “deficiencies, excesses, or imbalances in a person’s
intake of energy and/or nutrients”1. The term undernutrition encompasses: stunting,
wasting, underweight, and obesity (see Figure 3-1).
Stunting is defined as a deficit in height (i.e. linear growth) relative to a child’s
age (i.e. height for age)138,139. Stunting is a major health problem in children under five
years old (CU5) in many low- and middle-income countries (LMIC)140. Haiti has the
highest rates of stunting (21.9% in 2012 DHS) compared to all other undernutrition
categories, with even higher prevalence in rural (24.8% in 2012) compared to urban
areas (15.5% in 2012)85,141. Stunting, which reflects changes in a child’s growth over
months and years, is an important indicator of overall health and nutritional status,
and is considered one of the best overall indicators of CU5 well-being and social
inequalities138,139.
CU5 who are stunted have an increased risk of impaired cognitive development,
poor educational performance, and reduced economic growth and productivity in
adulthood, as well as intergenerational effects (e.g. impaired maternal reproductive
outcomes)142. Moreover, there is growing international recognition that there is a critical
window within which 70% of CU5 stunting occurs4. This window is from when the child
is in utero through their 2nd birthday (i.e., 0–23 months of age), and can continue until
72
the age of five4. There are likely multiple factors contributing this high prevalence of
stunting in Haiti, including deficiencies in micro-and macro-nutrients, improper feeding,
and recurring infections (especially diarrheal diseases) from unsanitary conditions
4,47,143.
Livestock have the potential to provide both food and nutritional security, as well
as income, livelihood, and monetary savings for nearly one billion poor people in
developing countries35. With the demand for livestock products increasing worldwide,
owning livestock has many benefits to human health and longevity, including reducing
income vulnerability, broadening livelihood alternatives, and improving human nutrition,
health, and wellbeing14,26–31. However, young children can be exposed to disease-
causing pathogens (especially diarrheal disease) from poorly managed animal feces,
particularly in communities where animals live in close proximity to humans54,90.
Observational studies in Peru, Zimbabwe, and Bangladesh have observed that CU5
living in areas where livestock free-roam, and where poor Water, sanitation and hygiene
(WASH) infrastructure exists36,144,145, frequently ingest fecal particles (either directly or
via contaminated soil). Moreover, WASH -related research has shown that children
suffering from recurring bouts of symptomatic or asymptomatic infections due to fecal
ingestion can become increasingly stunted overtime58,146. Therefore, livestock
ownership and WASH surrounding livestock may an important factor to consider when
designing WASH control methods and interventions to optimize child health and growth
outcomes. Observational studies conducted by Alive and Thrive in Ethiopia,
Bangladesh, and Vietnam have shown that poor WASH and owning livestock (e.g.
poultry) is associated with stunting in children in Ethiopia42,147. However, the value of
73
livestock on nutritional outcomes of children has also been documented21,28,43,148. The
relationship between livestock ownership and whether it ultimately alleviates or
contributes to undernutrition and CU5 stunting has not been fully explored.
Research Objective
In this chapter, we assess whether livestock ownership has a relationship to CU5
stunting in Haiti. To the best of our knowledge, livestock ownership as a factor
associated with CU5 stunting has been largely ignored in the published literature and
has not been examined in Haiti, particularly. We seek to address the following questions
in this chapter: (1) Is the presence of livestock associated with CU5 stunting in rural
Southern Haiti? Which livestock groups (e.g. small ruminants, large ruminants, poultry,
or pigs)? (2) Are WASH variables associated with CU5 stunting in rural Southern Haiti?
Which WASH variables? Our hypotheses regarding these questions are: 1) Livestock
ownership itself is associated with decreased stunting in CU5 in rural Southern Haiti; 2)
Livestock ownership along with unimproved WASH indicators are associated with
increased stunting in CU5 in rural Southern Haiti.
Methods
The dataset for this analysis comes from a cross-sectional household-based
survey conducted from October to November 2011 in a predominantly rural region of
about 65,000 inhabitants situated in the Aquin and Côtes-de-Fer communes of the
south and southeastern departments of the Haitian peninsula102–104. The survey
selected 828 households from the Institut Haïtien de Statistique et d’Informatique
(Haiti’s census) to participate in the survey using a random, two-staged sampling
design. The first stage included a selection of 30 out of 69 villages. In the second
stage, households within each of the village cluster were selected randomly. Within
74
each household selected to participate, a mother (or caretaker) and their CU5 was then
selected to participate in the survey (n=828)102–104.
For this secondary data analysis, only observations containing a mother and CU5
(between the ages of 6 months and 59 months) pair were selected for inclusion and
further analyses. All other observations were excluded. Moreover, detailed descriptions
of variable in this analysis is in Figure 3-2. For additional information regarding
recoding, statistics and data manipulation for variables in the raw dataset are
referenced elsewhere in Chapter 1, subsection: Data Overview as well as Figure 1-8.
Statistical Analyses. Survey data were cleaned and analyzed using SAS
version 9.4 and R105,115. Missing values were accounted for using multiple imputation in
SAS version 9.4. Tests for collinearity including checking the variance inflation factor
for each variable as well as checking variable correlation matrices were accounted for
prior to running any analyses to remove any variables with high collinearity or
correlation. We present summary statistics (Figure 3-3 and Figure 3-4) and bivariate
regressions results for stunting against each independent variable that were significant
at the p<0.2 level (Figure 3-5). Those variables that were statistically significant (p <0.2)
in bivariate analyses were input into a backward step-wise multivariate binomial logistic
regression model. The best model was chosen by the lowest Akaike Information
Criterion (AIC) score. We report the odds ratios (OR) with their 95% confidence interval
(95% CI), and the p-value for significance (p).
The main outcome variable of interest CU5 stunting, a binary version of CU5
Height for Age Z score (HAZ) cutoffs. According to the WHO, HAZ is a proxy for
75
assessing child growth faltering (i.e. stunting) 138. A child is considered stunted if he/she
falls below -2 standard deviations from the reference mean HAZ.
Multivariate Models. The multivariate logistic regression model assesses if (1)
livestock ownership is associated with CU5 stunting and (2) if livestock ownership with
WASH factors are associated with CU5 stunting. All models included significant
covariates as independent variables regressed in the models (see Figure 3-2 for
descriptions). The independent variables for model 1 are livestock ownership, and
model 2 includes livestock ownership and WASH factors. In brief, for the livestock
ownership variables, an “overall” livestock ownership (i.e. any species), as well as
continuous variable counts of each species (i.e. large ruminant animals [e.g. cattle, milk
cows, etc.], small ruminant animals [e.g. goats and sheep], poultry [i.e. chickens], and
swine) were included to tease out if any livestock ownership is associated, and if so,
which specific livestock types show an association. WASH variables include binarized
versions (improved vs. unimproved categories) based off the WHO Joint Monitoring
Program (JMP) standard recommendations for safe and improved drinking water,
hygiene and sanitation149,150. These include: the type of latrine used by the household,
child stool disposal practices, household waste disposal practices, water source of the
household, distance to water, water treatment practices, and handwashing practices.
The covariate variables included in our models are those that were 1) associated with
CU5 nutrition outcomes in the literature151–153, and 2) asked in the survey. We were able
to include: land ownership (i.e. land-holding of any kind), indicators of household food
security, household impoverishment, maternal knowledge, maternal marital status,
maternal employment, maternal age, as well as various child characteristics (e.g. age,
76
gender, vitamin A and deworming supplementation status, and disease status [fever,
diarrhea]).
Results
Descriptive. Only 24% of survey participants are landholders, and 32% are
owners of any livestock species (Figure 3-3). Household food security indicators show
approximately 59% of surveyed households did not have enough food to eat in the last
4 weeks (n=257), 53% were not able to eat the food they wanted or preferred (n=228),
and 49% of homes had to forego one meal each day due to food insecurity (n=213)
(Figure 3-2). Most households had a less diverse diet (56%) and report less ASF
consumption (64%) compared to the sample average.
Of the surveyed households the WASH characteristics included time to fetch
water, water source, improved toilet status, child stool disposal, household waste
disposal, and household handwashing practices. Sixty eight percent of households
reporting their round trip time to fetch fresh water to be over 30 min. of the households
only 46% had an improved toilet, 62% had improved waste disposal where as 50% had
improved child stool disposal practices. Ninety four percent of household report using
an improved water source, and 99-100% of households report handwashing practices
(after defecation and before feeding a child).
Of the children surveyed in the study, 59% of children were over the age of 2 (but
less than 59 months). Majority of the children surveyed were female 53% compared to
47% male. Sixty three percent of all children had received their vitamin A supplement
within the last 6 months. Overall stunting prevalence in this study region was 15%.
The highest prevalence of stunting was in Jamais Vu (20%) and the lowest was in
Frangipane (10%).
77
Most mothers were 25 to 34 years old (46%, n=193). The majority of mothers
had completed some type of formal education (68%, n=195), and also reported not
being in a formal relationship or married (70%). Of employed mothers, 10% were
farmers (n=32), 24% had some form of steady work or part-time employment (n=75),
23% had no income or job source (n=74), and 43% classified their employment as other
(n=138). Also, more mothers reported to be more knowledgeable of the signs of
undernutrition in their child (76%) and diarrheal disease prevention methods (82%).
Mutlivariate. Overall both models show livestock ownership is protective against
stunting (model 1: Figures 3-6 and Model 2: Figure 3- 7).
Model 1: Livestock Ownership and CU5 stunting. Figure 3-6 shows model 1
assessing the association between livestock ownership and CU5 stunting. Children
from households that owned any livestock species, small ruminant animals, and large
ruminant animals had statistically significant negative association with child under five
stunting. In particular, the households that had any livestock species had 74% lower
odds of having a CU5 stunted (OR 0.26, 95% CI 0.18-0.39, p<0.0001) while households
that owned small ruminants had 77% lower odds (OR: 0.23, 95% CI: 0.16-0.32,
p<0.0001), and households that owned large ruminants had 21% lower odds of having a
CU5 stunted than children from households that did not own these livestock groups.
Food security indicators were associated with CU5 stunting. Households that
reported not having enough to eat (OR 2.04, 95% CI 1.50-2.79, P<0.0001) or having to
eat less food were positively associated with increased odds of CU5 stunting (OR 5.71,
95% CI 4.27-7.63, P<0.0001). Households that reported they could eat the foods they
wanted were negatively associated with increased odds of CU5 stunting. Households
78
that reported to have a more diverse diet were negatively associated with increased
odds of CU5 stunting (OR 0.62, 95%CI .0.53-0.74, p<0.0001). The significant
household impoverishment indicators (e.g. not having transportation access and having
more than one child in the household) were positively associated with increased odds of
CU5 stunting (OR 2.27, 95%CI .1.81-2.86, p<0.0001; OR 1.66, 95% CI 1.37-2.01,
p<0.0001, respectively).
Households with mothers without formal education (OR 1.23, 95% CI 1.04-1.46,
p=0.02) and mothers with less knowledge of vitamin A and iron rich foods had
significantly greater odds of CU5 stunting (OR 2.18, 95% CI 1.84-2.59, p<0.0001).
Children between 6 months and 2 years of age had a negative association with
increased odds of CU5 stunting (OR 0.59, 95% CI 0.50-0.70, p<0.0001).
Model 2. Figure 3-7 shows the results for Model 2, assessing the association
between livestock ownership, WASH factors, and CU5 stunting. Results indicate that
WASH factors were negatively associated with increased odds of CU5 stunting, except
for unimproved child stool disposal (OR 2.06, 95% CI 1.51-2.82, p<0.0001). The WASH-
associated characteristic of a CU5 not having diarrhea in the last 2 weeks (OR 0.71,
95% CI 0.58-0.88, p=0.00) was negatively associated with CU5 stunting. CU5 who did
not access deworming supplementation was positively associated with CU5 stunting
(OR 2.07, 95% CI 1.68-2.56, p<0.0001). Finally, children receiving vitamin A
supplementation were associated decreased odds of stunting (OR 1.71, 95% CI 1.21-
2.42, p=0.00).
Like the first model, food insecurity indicators (e.g. the household not having
enough to eat and the household having to eat less food) were associated with
79
increased odds of CU5 stunting. Dietary diversity was associated with decreased odds
of CU5 stunting. Livestock ownership of any species and of both small and large
ruminants was also significantly negatively associated with increased odds of CU5
stunting. However, unlike model 1, pig ownership was also negatively associated with
increased odds of CU5 stunting, when WASH factors are considered (OR 0.77, 95% CI
0.62-0.97, p=0.02).
Like model 1, impoverishment indicators (e.g. no transportation access and
having more than one CU5 living in the household) were associated with CU5 stunting.
Similarly to model 1, in model 2 mothers who had no formal education were still
associated with greater odds of CU5 stunting (OR 1.22, 95% CI 1.02-1.46, p=0.03).
Poor maternal knowledge scores were all positively associated CU5 stunting (e.g. if the
mother was 1) less knowledgeable of vitamin A and iron rich foods: OR 0.1.54, 95% CI
1.17-2.02, p=0.00; 2) less knowledgeable of diarrheal disease risk: OR 1.56, 95% CI
1.29-1.88, p<0.0001; 3) less knowledgeable of diarrheal disease prevention OR 2.99,
95% CI 2.39-3.73, p<0.0001).
Discussion
Overall, our results indicated that owning livestock was associated with
decreased odds of CU5 stunting, and that, when WASH factors were considered, this
relationship extended to include additional species.
In our initial model, households that own any livestock species, specifically small
ruminant and large ruminant animals show a negative association (decreased risk) for
increased odds of CU5 stunting. When WASH factors are included (model 2), these
same livestock, as well as pigs, are also negatively associated with increased odds of
CU5 stunting. Other research has found similar negative associations with CU5
80
stunting, in particular through ASF consumption due to livestock ownership providing
increased access and availability of ASF31.
Though we did not observe any positive association between livestock ownership
and CU5 stunting, with or without WASH factors, other studies have found livestock
ownership, in particular chickens, and WASH factors to be associated with CU5
stunting42. Additionally, animal husbandry practice may also be impacting these
results, though not assessed directly by our survey. Colleagues on the ground in
southern Haiti have recognized that different livestock types are housed or kept in
different areas of the household or compound, and that overall majority of the surveyed
participants owned small ruminant animals (goats) and swine102. In particular, chickens
and pigs tend to be kept closer to human dwellings, in coops. Whereas goats and
larger livestock are usually tethered to trees, with cattle being kept further from the
home. Therefore, perhaps one explanation of the protective association we see with
livestock ownership in our findings stems from a lack of CU5 exposure to livestock and
their feces. Literature to support CU5 exposure to livestock and livestock feces, in
particular close proximity to chicken feces, demonstrate negative impact on child health,
disease, and even asymptomatic biomarkers for Environmental Enteric Dysfunction
(EED)36,39,144,145. Ultimately, livestock ownership, husbandry, and WASH practices,
especially with regards to household exposure to livestock need further exploration
through observational studies, as well as both quantitatively and qualitatively, in this
study population.
The negative association seen with WASH variables such as the household
taking less time to fetch water and the household having an improved toilet are
81
consistent with the WHO standards149,150. In contrast, households reporting unimproved
child stool disposal practices was positively associated with increased odds of CU5
stunting, which may be a result of increased diarrheal disease episodes and/or EED
from the potential human feces (i.e. child stool) exposure in and around the household
or the community from lack of JMP improved sanitation infrastructure47,145,154. Also
children who have not received a deworming supplement within the last 6 months were
positively associated CU5 stunting. This is consistent with a recently published global
empirical analysis of 45 DHS countries preschool aged children (aged 1 to 4 years old)
who received deworming treatments and were also less likely to be stunted (p=0.01).
Interventions highlighting the positive effects of deworming supplementation and
programs encouraging uptake may be one method to improve CU5 stunting in Haiti.
However, these assumptions cannot be confirmed by our analyses here, and further
research is needed to understand the contexts of these results and confirm any
associations seen in this studied region.
Moreover, mothers that had less knowledge of diarrheal disease risk were 2.99
times more likely to have increased odds of CU5 stunting compared to those that had
more knowledge of the risks. Similarly, mothers with less knowledge of diarrheal
disease prevention methods were 1.56 times more likely to have increased odds of CU5
stunting. These highlight the potential for educating women in this region about the
risks and prevention mechanisms for diarrheal disease, including proper waste disposal
(including human and livestock excreta) disposal as a mechanism to combat CU5
stunting. Unfortunately, although handwashing was significantly associated with CU5
stunting in our bivariate analyses, it was not found to be significant in our multivariate
82
model. Benefits of handwashing is well documented in the literature, and consistent
regarding the positive benefits of personal hygiene practices (i.e. handwashing) in
promoting proper child nutrition outcomes47,58,155. Safe sanitation (e.g. clean and
functional toilets) and handwashing with soap and clean water are barriers to fecal-oral
transmission because they prevent fecal exposure in the household. Many other
randomized trials of handwashing have also shown reductions in diarrheal disease as
well53.
In all, these results paint a complex picture, accentuating the need to consider
the local circumstances in any program promoting livestock development to improve
nutrition outcomes, including how livestock and WASH factors may contribute to child
undernutrition and stunting. Our mixed results in our models illustrate that protection
and risk associated with livestock, WASH, and child stunting need to be assessed
further with qualitative and quantitative methods in this rural Haitian population.
Though our results highlight WASH characteristics, especially child stool disposal, are
associated with CU5 stunting, as many other researchers have146, further investigation
is needed within the realm of livestock ownership and livestock husbandry practices as
a critical fecal exposure point for young children. Furthermore, future WASH and
nutrition program planning efforts should incorporate aspects of livestock hygiene
promotion into their development where livestock (and animal source foods) have a
potential to boost CU5 growth and economic productivity. Understanding the Haitian
context, the nuance of livestock ownership and WASH is critical for future developments
of impactful interventions to increase reduce stunting, and improve health in households
with CU5.
83
Figures
Figure 3-1. Definitions of undernutrition adapted from WHO, UNICEF, and the World Bank.
Stunting Wasting Overweight Stunted & Overweight Stunted & Wasted
HAZ WHZ WAZ HAZ & WAZ WHZ & WAZ
A child that is too short for his or her age
It is referred to as “a failure to
grow both physically and
cognitively” as a result of recurrent poor nutrition.
Stunting can have devastating
lifelong impacts on a child
affected
A child that is too thin for his or her height
It is referred to as acute
malnutrition, rapid weight loss
or the failure to gain weight. Wasting, without treatment,
puts a child at increased risk of
death
A child that is too heavy for his or her weight
It is referred to as obesity that
results from an imbalance of
calorie expenditure and intake from food and drinks. Children
suffering from obesity have
long-term risks of
noncommunicable diseases
A child suffering from both stunting and overweight
undernutrition
Research is ongoing to
determine the joint estimates and long-term effects from
these combined conditions
A child suffering from both stunting and wasting
undernutrition
Research is ongoing to
determine the joint estimates and long-term effects from
these combined conditions
84
Figure 3-2. Variable Descriptions used in chapter 3 analyses
Variables Description
Nutrition status and anthropometrics
CU5 Stunting Stunting was defined as: HAZ < −2.0 standard deviations (SD) below the 2006 WHO reference mean. Stunting
status is a binary score with children below -2 SD considered "stunted" while all other children considered "not
stunted". Outlier children greater than 5 SD or less than -5 SD were removed.
Livestock ownership
Any Species Includes any ownership of small or large ruminants, poultry or swine. This variable is binary.
Small Ruminants Households that owned goats and/or sheep. This variable is binary.
Large Ruminants Households that owned cattle, milk cows, donkeys, mules, and/or horses. This variable is binary.
Poultry Households that own chickens or other types of poultry. This variable is binary.
Swine Houesholds that own Pigs. This variable is binary.
Impoverishment
Impoverishment score Impoverishment is a proxy for wealth. Wealth was assessed using the standard protocol and procedures
developed previously by Seraphin et al., The principle components were used to create a relative poverty index
that captures the wealth of the region, where everyone is considered "poor". This Impoverishment is a binary
indicator ranges from 0 to 1, representing least poor and poorest.
Transportation This variable is binary. Another proxy of impoverishment and also access. Indicates the household owned bike,
car or motorcycle.
Number of CU5 living in the home This variable is binary. Indicates if the household has 1 child or more than 1 child under five years old living in the
household.
Land ownership This variable is binary. Another proxy of impoverishment. Indicates if the household owns land, cultivates land or
has tenure over land.
Child
Age This variable is binary. Age categories were dichotomized into 2 categories: Children 6 months to 2 years old and
children 25 months to 5 years old.
Vitamin A Supplementation Categories based off field data, with three categories (e.g. Vitamin A received, vitamin A not received, and do not
know).
Maternal
Age Mothers ages ranged from 15 to 49 in the sample. Breakdowns of the maternal age are based off child-bearing,
and consistent with Seraphin et al., groups are: age 15-24, 25-34, and 35-49.
Employment Categories based off mothers responses, and include "farming", "steady work", "No job", and "Other".
Relationship Represents if mothers are married or not married (as a binary category). All categories outside of marriage or
formal union were considered not in a relationship.
Education Represents if mothers received any formal education (e.g. primary, secondary or above) versus no formal
education.
Mother and caregiver knowledge
Vitamin A and Iron Rich Food
Knoweldge
Nutrition and malnutrition
Diarrhea risk
Diarrhea prevention
To assess maternal/caregiver knowledge surrounding nutrition, WASH and potential disease risk, four scores
(each measuring a particular knowledge construct: Nutrition (vitamin A and Iron), Nutrition and Malnutrition signs,
diarrheal disease risks, and diarrheal disease prevention). These constructs were created from questions listed
in table 1-6. Each construct was a summation of the questions in table 1-6 that were answered correctly. Mean
scores were then taken for each construct across all survey participants. To establish a knowledge score, the
scores were dichotomized around these mean scores for all study participants, per knowledge constuct, following
Seraphin et al. method. The participants that fell below the mean score were considered to be less
knowledgeable while those participatns that fell above the mean score were considered to be more
knowledgeable.
WASH
Latrine This variable is binary. This is the type of toilet facility; an improved latrine is considered to be a toilet that isn’t
shared and follows the WHO JMP standards.
Child Stool Dispoal This variable is binary. This is based off how child stool is disposed of in the household. The variable reflect
Improved child stool practices (e.g. "Child used toilet", "Put/rinsed into toilet", "Put/rinsed into drain", versus
unimproved (e.g. "threw in the trash", "left it in the open", and "other").
Household Waste Disposal This variable is binary. it reflects the Household waste disposal practices are considered improved versus
unimproved. Unimproved waste disposal include answers such as: "Dump in Street/Open Space", 'burn it", and
"other". Improved waste disposal incude: "Bury it", and "Dispose of on farm/compost".
Water Source This variable is binary. An improved water source includes water that is piped water supply, and stored water
from protected wells, springs, public standpipes or stored rainwater.
Distance to Water This variable is binary. Distance to water as considered improved if the water was less than 30 minutes away,
round trip. Distance to water was considered to be umimptoved if the time it took to retrieve water was over 30
minutes, round trip.
Water treatment This variable is binary. Water treatment is considered to be improved if the water treatment falls into categories
such as boiling the water, treating the water with chlorine or disinfectant tablets, Treatment with chlorine or
equivalent, boiling of water, solar disinfecting, etc.
Handwashing Practices This variable is binary. This variable is based off response from mothers and caregivers who practice safe
handwashing practices before cooking, eating or using the latrine.
Disease Status and Prevention
Diarrheal Disease This variable is binary. It reflects the Cu5 passing of 2-3 loose or watery stools per day, within the 2 weeks of the
survey.
Febrile Illness This variable is binary. It reflects the Cu5 having a fever within 2 weeks of the survey.
Breast Feeding Status This variable is binary. It reflects children that are currently breastfeeding.
Deworming This variable is binary. It reflects that the cu5 has recived deworming medication.
85
Figure 3-3. Descriptive statistics of surveyed households.
Figure 3-4. Descriptive Statistics of WASH characteristics (Improved “I” and
Unimproved “U”) broken down by sub communal section
%
Name of Variable N (Y/N)
Land ownership 435 (24% / 76%)
Any Livestock Ownership 421 (32% / 68%)
Food Security
In the last four weeks the house had enough to eat 435 (41% / 59%)
In the last 4 weeks the household could eat the food they wanted 430 (47% / 53%)
In the last 4 weeks, the household did not have to eat less food because the household had
enough to eat 432 (51% / 49%)
WASH Characteristics
Time to fetch water (under vs. over 30 min) 437 (32% / 68%)
Water Source Status 438 (94% / 6%)
Improved toilet Status 439 (46% / 54%)
Child Stool Disposal Status: Unimproved to Improved 435 (50% / 50%)
Household Waste Disposal Status: Unimproved to Improved 431 (62% / 38%)
Handwashing After Using the Toilet 439 (100% / 0%)
Handwashing Before Feeding CU5 439 (99% / 1%)
Impoverishment 439 (49% / 51%)
Access to Transportation 438 (24% / 76%)
Maternal Characteristics
Maternal Education Status 286 (68% / 32%)
Maternal Formal Relationship Status 399 (31% / 69%)
Maternal Knowledge Score
Vitamin A and Iron Rich Food Sources 438 (50% / 50%)
Diarrheal Disease Prevention 435 (82% / 18%)
Diarrheal Disease Risk Factors 436 (49% / 51%)
Overall Nutrition and Signs of Malnutrition 433 (76% / 24%)
Maternal Employment Status
Farming 32 10%
Steady Work 75 24%
No Income 74 23%
Other 138 43%
Maternal Age Categories
15-24 131 31%
25-34 193 46%
35-49 92 22%
Child Characteristics
Diarrheal Disease Episode in Last 2 Weeks 435 (18% / 82%)
Fever Episode in Last 2 Weeks 437 (27% / 73%)
CU5 Took Deworming Supplement 439 (54% / 46%)
Number of CU5 Stunted 439 (15% / 85%)
Age categories 6 to 24 months (vs. 2 to 5 years old) 439 (41% / 59%)
More than one CU5 living in household 437 (31% / 69%)
CU5 Gender (Males to Females)
Female 232 53%
Male 207 47%
Vitamin A Supplementation Status
Yes 243 63%
No 101 26%
Don't Know 44 11%
Guirand Jamais Vu Frangipane Flamands Fond des Blancs
Total
Name of Variable N I U N/A I U N/A I U N/A I U N/A I U N/A
Handwashing After Using the Toilet 439 100% 0% 0% 100% 0% 0% 100% 0% 0% 100% 0% 0% 100% 0% 0%
Handwashing Before Feeding CU5 439 97% 3% 0% 100% 0% 0% 100% 0% 0% 99% 1% 0% 99% 1% 0%
Water Source Status 438 97% 3% 0% 92% 8% 0% 83% 17% 0% 99% 0% 1% 95% 5% 0%
Child Stool Disposal Status: Unimproved
to Improved435 44% 53% 3% 66% 33% 1% 42% 58% 0% 39% 60% 1% 48% 52% 0%
Household Waste Disposal Status:
Unimproved to Improved431 56% 41% 3% 80% 18% 2% 62% 38% 0% 41% 58% 1% 56% 42% 3%
*I=improved status, U=unimproved status, N/A=no response
73 128 71 90 77
86
Figure 3-5. Bivariate regression results for study variables and CU5 Stunting
Name of Variable
Beta
Estimate SE* p**
Land ownership 0.13 0.04 0.00 Livestock Species Specific Information
Any Livestock Ownership 0.04 0.03 0.18 Owns Large Ruminant Animals 0.12 0.03 0.00 Owns Small Ruminant Animals -0.11 0.03 0.00 Owns Pigs -0.12 0.03 0.00
Food Security
In the last four weeks the house did not have enough to eat 0.24 0.03 0.00 In the last 4 weeks the household could eat the food they wanted -0.18 0.03 0.00 In the last 4 weeks, the household did not have to eat less food because
the household had enough to eat
-0.22 0.03 0.00
WASH Characteristics
Time to fetch water (under vs. over 30 min) -0.40 0.03 0.00 Water Source Status -0.82 0.06 0.00 Improved toilet 0.38 0.03 0.00 Child Stool Disposal Status: Unimproved 0.31 0.03 0.00 Household Waste Disposal Status: Unimproved to Improved -0.10 0.03 0.00 Handwashing Before Feeding CU5 -0.82 0.22 0.00
Dietary Diveristy
Household has diverse diet -0.22 0.03 0.00 Impoverishment
Impoverishment -0.33 0.03 0.00 Access to Transportation (no access vs. access) 0.45 0.03 0.00 More than one CU5 living in household 0.36 0.03 0.00
Maternal Characteristics
Mother is not educated -0.06 0.03 0.06 Maternal Formal Relationship Status -0.20 0.03 0.00 Maternal Knowledge Score
Vitamin A and Iron Rich Food Sources 0.21 0.03 0.00 Diarrheal Disease Prevention 0.24 0.04 0.00 Diarrheal Disease Risk -0.10 0.03 0.00
Child Characteristics
Fever Episode in Last 2 Weeks -0.37 0.03 0.00 Diarrheal Disease Episode in Last 2 Weeks -0.21 0.04 0.00 Deworming Supplementation -0.12 0.03 0.00 Vitamin A Supplementation Status 0.08 0.02 0.00
*SE=Standard Error
**P-value (p<0.20)
Stunting
87
Figure 3-6. Multivariate binary backward-stepwise logistic regression results for
model 1 assessing the association of livestock ownership and CU5 Stunting status.
Name of Variable OR* CI** p***
Livestock Species Specific Information
Any Livestock Ownership 0.26 (0.18, 0.39) <.0001
Reference: No Livestock Ownership
Owns Large Ruminant Animals 0.79 (0.64, 0.99) 0.04
Reference: No Large Ruminant Ownership
Owns Small Ruminant Animals 0.23 (0.16, 0.32) <.0001
Reference: No Small Ruminant Ownership
Food Security
In the last four weeks the house did not have enough to eat 2.04 (1.50, 2.79) <.0001
Reference: In the last four weeks the house had enough to eat
In the last 4 weeks the household could eat the food they wanted 0.28 (0.20, 0.39) <.0001
Reference: In the last 4 weeks the household could not eat the food they wanted
In the last 4 weeks, the household had to eat less food because the household did not
have enough to eat 5.71 (4.27, 7.63) <.0001
Reference: In the last 4 weeks, the household did not have to eat less food because the
household had enough to eat
Dietary Diversity
Household has diverse diet 0.62 (0.53, 0.74) <.0001
Reference: Household does not have a diverse diet
Impoverishment
No access to Transportation 2.27 (1.81, 2.86) <.0001
Reference: Access to Transportation
More than one CU5 living in household 1.66 (1.37, 2.01) <.0001
Reference: Only one CU5 living in household
Maternal Characteristics
Maternal Education
Mother is not educated 1.23 (1.04, 1.46) 0.02
Reference: Mother is educated
Maternal Knowledge Score
Mother is less knowledgeable of vitamin A and Iron Rich foods 2.18 (1.84, 2.59) <.0001
Reference: Mother is more knowledgeable of vitamin A and Iron rich foods
Child Characteristics
Under 2 years old 0.59 (0.50, 0.70) <.0001
Reference: 2 to 5 years old
*OR = Odds Ratio
**CI = 95% Confidence Limits
*** = P-value (p<0.05)
Model 1: Livestock
88
Figure 3-7. Multivariate binary backward-stepwise logistic regression results for
model 2 association the of livestock ownership, WASH Factors, and CU5 Stunting status.
Name of Variable OR* CI** p***
Livestock Species Specific Information
Any Livestock Ownership 0.36 (0.24, 0.53) <.0001
Reference: No Livestock Ownership
Owns Large Ruminant Animals 0.76 (0.60, 0.97) 0.03
Reference: No Large Ruminant Ownership
Owns Small Ruminant Animals 0.41 (0.30, 0.56) <.0001
Reference: No Small Ruminant Ownership
Owns Pigs 0.77 (0.62, 0.97) 0.02
Reference: No Pig Ownership
Food Security
In the last four weeks the house did not have enough to eat 2.30 (1.67, 3.15) <.0001
Reference: In the last four weeks the house had enough to eat
In the last 4 weeks the household could eat the food they wanted 0.38 (0.28, 0.52) <.0001
Reference: In the last 4 weeks the household could not eat the food they wanted
In the last 4 weeks, the household had to eat less food because the household did not
have enough to eat 4.11 (3.04, 5.55) <.0001
Reference: In the last 4 weeks, the household did not have to eat less food because the
household had enough to eat
Dietary Diversity
Household has diverse diet 0.54 (0.44, 0.65) <.0001
Reference: Household does not have diverse diet
WASH Characteristics
Time to fetch water (under vs. over 30 min) 0.30 (0.25, 0.35) <.0001
Reference: Time to fetch water (over 30 min vs. under)
Child Stool Disposal Status: Unimproved 2.06 (1.51, 2.82) <.0001
Child Stool Disposal Status: Improved
Improved toilet 0.49 (0.37, 0.65) <.0001
Reference: unimproved toilet
Impoverishment
No access to Transportation 2.31 (1.82, 2.94) <.0001
Reference: Access to Transportation
Only one CU5 living in household 0.78 (0.65, 0.93) 0.01
Reference: More than one CU5 living in household
Maternal Characteristics
Maternal Education
Mother is not educated 1.22 (1.02, 1.46) 0.03
Reference: Mother is educated
Maternal Knowledge Score
Mother is less knowledgeable of vitamin A and Iron Rich foods 1.54 (1.17, 2.02) 0.00
Reference: Mother is more knowledgeable of vitamin A and Iron rich foods
Mother is less knowledgeable of Diarrheal Disease Prevention 1.56 (1.29, 1.88) <.0001
Reference: Mother is more knowledgeable of Diarrheal Disease Prevention
Mother is less knowledgeable of Diarrheal Disease Risk 2.99 (2.39, 3.73) <.0001
Reference: Mother is more knowledgeable of Diarrheal Disease Risk
Child Characteristics
Child did not receive Vit. A 1.71 (1.21, 2.42) 0.00
Reference: The child did receive Vit. A.
No Diarrheal Disease in Last 2 Weeks 0.71 (0.58, 0.88) 0.00
Reference: Diarrheal Disease Episode in Last 2 Weeks
CU5 did not take a deworming supplement 2.07 (1.68, 2.56) <.0001
Reference: CU5 did take deworming supplement
*OR = Odds Ratio
**CI = 95% Confidence Limits
*** = P-value (p<0.05)
Model 2: Livestock and WASH
89
CHAPTER 4 III - SPATIAL DETERMINANTS OF CU5 LINEAR GROWTH IN RURAL HAITIAN
HOUSEHOLDS
Introduction
Undernutrition or “deficiencies, excesses, or imbalances in a person’s intake of
energy and/or nutrients1” is a leading cause of child under five (CU5) morbidity and
mortality in Haiti156. It is not the result of any single factor, but is a complex social and
ecological problem, where a child and household’s biophysical, interpersonal, and
socioeconomic factors are all interacting156. As of 2012, over 22% of Haitian CU5 are
suffering from one type of linear growth faltering (i.e. stunting) resulting from
undernutrition157. Stunting is a term and condition that is defined by the World Health
Organization (WHO) as a deficit in height (i.e. linear growth) relative to a child’s
age138,139. Thus, the term height-for-age is often used to describe child linear growth,
and is often measured using height-for-age z scores (i.e. HAZ). HAZ is a well-
documented proxy measure for assessing linear growth, especially stunted growth
outcomes (e.g. HAZ below -2 standard deviations (SD) from WHO reference
mean)138,139. CU5 stunting is considered one of the best overall indicators of CU5 well-
being and social inequalities139. Disadvantageous outcomes associated with CU5
stunting include: cognitive delay, growth impairment, psychological effects on the child
have functional consequences that can be seen into adulthood ranging from low
educational performance, lower wages or earning potential, and poor reproductive
outcome5,13,158. These short and long-term corollaries, coupled with the cycle of
poverty, natural and man-made disasters, and continued undernutrition can
unfortunately be carried forward into a child’s adulthood, and ultimately their
90
reproductive capacity-- affecting future generations by perpetuating this unfortunate
cycle.
Stunting is a public health issue in southern Haiti, in the southeastern
departments, more children are stunted than any other part of the Haitian peninsula. To
our knowledge only one study conducted by Spray et al68. has focused attention to the
burden of undernutrition, wasting, and stunting in southern Haiti (e.g. Leogane, Haiti).
The authors used ordinary least square models and geographically weighted regression
to characterize nutrition and health situation of 150 children (6-35 months old) in 33
communities using cross-sectional survey data from the Children’s Nutrition Program of
Haiti. The authors found that undernutrition occurs in pockets rather than being evenly
distributed across the population. However, despite documenting the undernutrition
using spatial ordinary least square models and geographically weighted regression,
there was limited environmental, spatial or ecological factors, outside of the household
demography, included in their analyses. Though the study was promising, in terms of
using geospatial data to improve the understanding of nutrition and underlying causes in
Leogane, it failed to determine if environmental and spatial determinants were
associated with CU5 growth.
With this mention, there is growing evidence in the literature from multiple
countries including Ethiopia159–161, Somalia162, Kenya163, Argentina164, and Nepal165 that
point to a variety of environmental and spatial factors that can lead to down-stream
stunting such as rainfall, temperature, elevation and vegetation, etc. that can impact
child nutrition through food system productivity (Figure 1 shows a conceptual diagram).
In Ethiopia, agro-ecology, rainfall and temperature were associated with child
91
stunting159–161. Similarly, the studies in Kenya found precipitation and rainfall, but not
temperature to be significantly associated with child malnutrition outcomes while. In
Somalia, Bayesian hierarchical space-time modeling approaches found rainfall
(OR = 0.99, CI 0.99- 0.995) and vegetation (OR = 0.719, CI 0.6 - 0.86) to be significantly
associated with reduced odds of CU5 stunting162. In Argentina, researchers found risk
for stunting by gestational age in newborns was associated with higher altitude (i.e.
elevation)164. Nepal, like Somalia also found vegetation to be factor explaining child
nutrition outcomes; particularly, increases in NDVI values resulting in an increase in
stunting during the child’s first year of age165.
Despite these research efforts, there are limited-to-none peer-reviewed articles to
our knowledge that attempt to look at the impacts of environmental and spatial
determinants (e.g. elevation, rainfall, temperature, vegetation, distance to roads,
distance to health facilities or access) that may be contributing to CU5 HAZ scores or
stunting in Haiti. Moreover, there are no studies to our knowledge that describe the
spatial or environmental variation and livestock ownership on the landscape.
Research Objective
Therefore, the objective of this present study is to fill this knowledge gap by
elucidating the relationship between child growth, particularly risk for stunting (i.e. HAZ
< - 2SD) with environmental and spatial factors in the south and southeastern
departments of Haiti, region with some of the highest concentrations of child stunting.
Descriptions of CU5 HAZ and livestock ownership were explored at the village level as
well as model approaches to understand the spatial variables that may be associated
with CU5 HAZ at the village level.
92
Methods
This study assesses the spatial patterns of child growth in the Aquin and Côtes-de-
Fer communes, located in Sud and Sud Est departments, in southern Haiti. These two
communal sections comprise five sub-communal sections: Guirand, Fond des Blancs,
Flamands, Frangipane, and Jamais Vu (see Figure 4-2).
Survey. The village-level data for this study is sourced from a baseline survey
conducted in partnership by the St. Boniface Foundation and UNICEF-Haiti with the
purpose to better understand the maternal/caregiver and child health, nutrition, water,
hygiene, and sanitation (WASH) in the region before implementation of a pilot program
to improve these outcomes in this study region. The survey targeted mothers and
caretakers of children under the age of 5 years (59 months)102–104. The survey used a
two-stage sampling scheme that randomly selected 800 households using the Institut
Haïtien de Statistique et d’Informatique to participate in the survey. The first stage
included a selection of 30 out of 69 villages. In the second stage, the households within
each of the village clusters were selected. Within each household a mother and child
dyad were then selected to participate in the survey; these data have been aggregated
to the village level using median values for each variable102–104.
Variable Descriptions. To capture the environmental and spatial variation of
child growth, this chapter uses spatial data from multiple sources described in Table 4-
1. In brief, this study uses survey data points from the St. Boniface and UNICEF-Haiti
MCH household surveys described elsewhere (see chapter 1, dataset overview), as
well as remote sensed data including: (1) climate, (2) vegetation, (3) elevation, (4)
accessibility, and (5) population density. Geographic information system (GIS) and
GIS-derived datasets including the country boundaries and shapefiles, as well as (6)
93
slope, (7) distance to roads, and (8) distance to the nearest healthcare center (i.e. the
St. Boniface Foundation Hospital).
Moreover, selection of these spatial covariates was largely based on availability
of raster data that closely matched the survey times, from October to November 2011 in
Haiti. Each variable listed was chosen for their environmental and spatial properties
that may affect nutrition status in children, and are plausible covariates for this area of
Haiti. Each environmental and spatial variable considered is plotted in Figure 4-3 to see
the distribution across the Haitian landscape. (1) Climate included: Long-term
precipitation (min, max, and mean) and temperature (min, max, mean for daytime, and
nighttime temperatures, respectively) from WorldClim166, and real-time rainfall (e.g. min,
max, mean, and cumulative) from CHIRPS was included because of its higher spatial
resolution and real-time properties, respectively167. (2) The NDVI, a proxy for vegetation
cover, was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)
products using the 16-day composite 168. Based on this product, we calculated the
minimum, maximum, and mean NDVI within a 2-month (64 day) period between start of
October and end of November 2011. (3) Elevation is used because it is associated with
precipitation, temperature, and rainfall. Elevation was extracted from the 90m resolution
Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) 169. (4)
Accessibility was created using travel times by the Malaria Atlas Project (MAP), and is
used as a proxy for market access and access to urban centers170. Travel times are
used in geography to assess the time it takes to get from one area to another, in this
study, we take our village coordinates and the travel times it takes to get to the nearest
urban center using open street map and population characteristics170. (5) Population
94
density was extracted at 100m resolution from WorldPop 171. Finally, land use/land
cover products by global land cover (GLC) 2000 172were expected to be an important
spatial covariate to assess stunting, however, we did not include this dataset in our
models because there was little variation across the study sub communal sections.
Household-level geospatial data (i.e. GIS coordinates) was not available for this
survey dataset. However, the sub communal coordinates were accessible and
available from the Admin UTM 04 shapefile, which can be downloaded through the
‘GADM” the global administrative boundaries database173. Specific village-level
information necessary for GIS coordinate selection was based off field survey
enumerator reporting, as well as expert inquiry with the UNICEF-Haiti and St. Boniface
staff. Following similar research by Noor et al.174, unknown village coordinates (see
Figure 4-4) were georeferenced using Google Earth Pro, and visually cross checked
using Google Earth175. Google Earth is a geospatial software application that displays a
virtual globe, which offers the ability to analyze and capture geographical data 176. To
select the appropriate coordinates for each village, these steps were taken: 1) each
individual village (n=30) was searched by name using Google Earth Pro. Each sub-
commune coordinate (n=5) was crosschecked using the Admin UTM 04 shapefile. 2)
Next, a 1 km buffer radius from each sub communal section coordinate was constructed
to identify unknown village coordinates (n=25). 3) Using the population density raster,
and Google Earth as a crosscheck, villages that were identified within the 1km radius
were used as the village-level coordinates. 4) all household-level (point) information
was then aggregated to the village level for analyses.
95
The outcome variable for this study is the aggregate village-level child under five
height for age Z score (HAZ). HAZ is a continuous proxy used by WHO for assessing
child growth patterns, in particular CU5 stunting was the main outcome variable for
these analyses (Figure 4-5)138,139. Child stunting is determined if a child under five falls
below -2 (stunted) to -3 standard deviations (severely stunted) from the mean
HAZ138,139. However, due to the aggregation process from household-level to village
level, no unit in this analysis fell below the -2 HAZ to be considered stunted. Thus,
aggregate z-scores were assessed without using the standard cut off of -2 for stunting.
Instead, those with HAZ scores above -1 were considered at less risk of CU5 stunting,
while CU5 with HAZ scores of less than -1 were considered at greater risk of CU5
stunting.
The covariates included in the model(s) are the environmental and spatial
covariates outlined above (e.g. remote sensed data including: (1) climate, (2)
vegetation, (3) elevation, (4) accessibility, and (5) population density. GIS and GIS-
derived datasets including the country boundaries and shapefiles, as well as (6) slope,
(7) distance to roads, and (8) distance to the nearest healthcare center). In the second
model, we included livestock (e.g. small and large ruminants, poultry, and swine) as
additional covariates.
Statistics. All datasets, including point and raster files where projected into R
studio software using installed packages (e.g. raster, rastervis, maps, maptools, rgdal,
sp, ggmap, RCurl, tools, gtools, usdm, foreign, and tidyverse)115. The geographic
coordinate system is GCS_WGS_1984 and the projected coordinate system (projection
for Haiti) is WGS_1984_UTM_Zone_18N. Raster datasets were projected and the
96
specific values at each village point was extracted to get the unique value per spatial
variable per village. Distance to roads, and distance to hospital was calculated using
the Euclidean distance. One final dataset was compiled with each environmental and
spatial variables value for each village point.
Figure 4-5 shows the map of village-level CU5 HAZ score distribution, whereas
Figure 4-6 shows the livestock ownership (by species) across Aquin and Côtes-de-Fer
study region. These were created to see if there are any visual relationships in the
frequency/prevalence of livestock, by type(s), and village-level CU5 HAZ in the dataset,
descriptively.
Bivariate regressions were run prior to multivariate models on each
environmental and spatial variable, as well as each livestock species, against the
dependent variable, and the village-level aggregate CU5 HAZ. Variance inflation factor
(VIF) analysis was run to see if there is any collinearity among the variables that are
significant in the bivariate analysis. Variables that were significant at p value [p]<0.2
level in the bivariate and did not have collinearity were input into a multiple linear
stepwise regression model predicting the association of CU5 HAZ (dependent variable)
as well as environmental and spatial covariates (independent variables) were run using
RStudio115. The model with the lowest Akaike information criterion (AIC) were chosen.
This model was then tested in ArcMap 10.2.2177 using Ordinary Least Squares (OLS)
regression method. Model residuals were used to calculate the root mean square error
(RMSE) for model absolute fit and validation purposes. The final model(s) residuals
were also plotted and checked for local spatial clustering/ and autocorrelation using
97
global Moran’s I was calculated using ArcGIS, ArcMap 10.2.2177, software to confirm if
any clustering or spatial autocorrelation exists.
Results
Bivariate regressions (Figure 4-7) at the p<0.2 level revealed that all livestock
species ownership was not associated with village-level CU5 HAZ scores. Therefore,
we did not include them in the modeling process (i.e. model 2 was dropped).
Figure 4-5 reveals the lowest HAZ scores (HAZ approaching -2 SD) are focally in
the Côtes-de-Fer commune, particularly, the northeast and central parts Jamais Vu sub-
communal section) where as we see higher HAZ scores across nearly the entire sub-
commune of Frangipane, as well as the eastern area of Flamands, and southeastern
part of Fond des Blancs.
Moreover, rainfall (mean, max, and cumulative), precipitation (min, mean, max,
and cumulative), temperature (min temperature during the day and min temperature
during the night), as well as elevation, distance to the nearest health facility, and
population density were associated with village-level CU5 HAZ.
The final multivariate linear regression model results (Figure 4-8) indicate that
there appears to be an environmental and spatial relationship with village-level CU5
HAZ scores in this surveyed population (overall model significance p<0.008, Adjusted
R2=0.34). Elevation, rainfall, temperature and precipitation are significantly associated
with village-level CU5 HAZ. Elevation (coefficient [β]=0.63, p<0.05) and minimum
temperature at night (β=0.55, p<0.001) were positively associated with village-level CU5
HAZ. Mean rainfall (i.e. real-time [β=0.34, p<0.05]), and maximum precipitation (i.e.
long-term rain fall) had the strongest positive association with village-level CU5 HAZ
(β=2.11, p<0.05). In contrast, cumulative long-term precipitation was negatively
98
associated with village-level CU5 HAZ scores (β= -2.14, p<0.05). Moreover, tests for
autocorrelation using the cluster and outlier analysis (Local Moran’s I, Figure 4-9) and
Global Moran’s I in ArcGIS indicate limited spatial clustering and are not significantly
spatially autocorrelated (Global Moran’s I[I] = -0.51, z= -0.91, p=0.36) among model
residuals. The RMSE was approximately 0.34 indicating that the absolute model fit was
modest (e.g. a RMSE value that indicates good fit is closest to 0). Additionally, our
model residuals versus predicted plot (Figure 4-10) indicated that our model is properly
specified, and exhibits a random pattern of our model over and under predictions.
Discussion
Overall our analysis indicate that environmental and spatial variables are
significantly associated with village-level CU5 HAZ. In particular, the strongest positive
association with village-level CU5 HAZ (β=2.11, p<0.05) was seen in maximum
precipitation/long-term rainfall pattern. However, cumulative precipitation shows the
inverse relationship seen with maximum precipitation, with a strong negative association
with village-level CU5 HAZ scores (β= -2.14, p<0.05). Evidence from Kenya also found
precipitation to predict nutrition outcomes and CU5 stunting163. However, more studies
are warranted to understand the strong negative association with cumulative
precipitation. One explanation may be related to post-2010 cholera epidemic, where
models have shown that precipitation and rainfall are associated with increased cholera
outbreaks178,179. Perhaps the negative association is related to increased diarrheal
disease in the region, associated with decreasing village-level VEL CU5 HAZ. More
research is needed to understand this relationship, and confirm any associations.
Elevation, mean rainfall, and minimum temperature at night were also positively
associated with village-level CU5 HAZ in our results. These results are consistent with
99
other published research in East Africa and Argentina162–164 linking one or more of these
environmental factors to undernutrition in CU5. One possible explanation for these
results in our study links back to Figure 1, and how these factors may be influential in
food production. Thus, via improved ability to grow, produce, access, and utilize (i.e.
consume) higher quality food, children are experiencing better nutrition/growth
outcomes (higher HAZ).
Looking at the spatial clustering in our model (local Moran’s I) we can see in
Figure 4-7 that there are some pockets of low-low clustering and high-high clustering.
However, most of the villages were not significantly clustered; with a negative global
Moran’s I (I=-0.51), indicating that the pattern of spatial clustering is more likely to be
random, but also has a tendency toward dispersion. Moran’s I is a powerful tool to
evaluate whether the pattern in your model residuals is clustered (close to +1), random
(close to 0), or dispersed (close to -1). Since our Moran’s I calculation falls in-between
0 and -1, we can assume our data is randomly distributed.
Despite livestock not being associated with HAZ in our bivariate analyses, and
not included in our multivariate models, our descriptive statistics looking at village-level
CU5 HAZ (Figure 4-3) across the study landscape show that higher village-level CU5
HAZ scores (Côtes-de-Fer, Jamais Vu section) tend to overlap with more poultry, small
ruminant, and pig ownership (Figure 4-5). These descriptive maps of livestock and HAZ
prevalence reveal some patterns in the data that may be worth investigating further,
especially since goats, and pigs are raised in this region102, and poultry is one of the
largest imports in the country117. These illustrations highlight a need for further
investigation in this region, especially with regard to the potential risk for lower HAZ
100
scores and more livestock ownership, particularly goats and poultry. Poultry have been
linked to village-level HAZ and stunting through fecal-oral pathway, where children may
be exposed to pathogens in chicken feces39. Moreover, these maps also potentially
reveal that higher HAZ values are in central-eastern Aquin (e.g. Frangipane, Flamands,
and Fond des Blancs). This finding may potentially unveil the areas of lower risk for low
HAZ scores in CU5. Future research should investigate the differences within and
between these sub-communal sections.
This study is not without its limitations. Although we find significance in some of
the environmental and spatial variables included in this model, no villages in these
communes were considered “stunted” with HAZ below -2 SD. This is likely a result of
the aggregation process of gathering all child HAZ scores for that village and assigning
the median value before linking the environmental and spatial variables to the dataset.
In addition, the process by which we georeferenced households to villages using
Google Earth Pro may be a limiting factor. Due to the survey design, the households
and villages were not georeferenced during the survey. This was a major limitation;
however, we used the most up-to-date approach taken by other geographic researchers
to geocode survey points based off limited field information as accurately as possible174.
Ultimately, we were able to locate all sub-communal sections and villages surveyed;
however, each individual household was not able to be georeferenced nor the villages
themselves, and this was the main reason why we aggregated the household data to
the village level in our analysis. With this mention, this study may be limited in sample
size since only 30 villages could be analyzed.
101
Despite these limitations, this study illustrates a promising approach for using
geospatial data to extend the scope of understanding (and potentially improving)
nutrition status (e.g. village-level CU5 HAZ) in the region. The methodology presented
in this research is important for public health programmers and research institutions
because it highlights the potential importance of capturing spatial and environmental
data, especially when designing, monitoring, and/or evaluating CU5 nutrition outcomes.
The higher the quality of environmental and spatial data, coupled with more locally
tailored data, may allow practitioners a means to better prioritize and target specific
factors that may be contributing to child nutrition outcomes, and avoid wasting
resources. Ultimately, further research is needed, especially with GIS methods, to
investigate the findings presented here, as well as other factors not presented here that
have implications on child health and nutrition status.
102
Figures
Figure 4-1. Conceptual diagram linking CU5 growth to Haiti-specific spatial and
environmental drivers. Adapted from Grace et al.163.
103
Figure 4-2. Map of Haiti and communes Aquin and Côtes-de-Fer surveyed (in red).
104
Table 4-1. Environmental and Spatial variables descriptions (including variable name, definition, spatial resolution, and reference source)
Variable Definition (Units) Spatial resolution Source
Remote-Sensed
Elevation Height above sea level (m) 90 m CGIAR SRTM 169
Normalized Difference Vegetation Index (NDVI)
Index of vegetation conditions. Ranges from -1 (no vegetation) to 1 (complete vegetated)
250 m NASA (Terra) MOD13A3 and (Aqua) MYD13A3 datasets 168
Land Surface Temperature (LST) – Day and Night time
Kelvin (converted to degree Celsius)
1 km NASA (Terra) MOD11A2 and (Aqua) MYD11A2 datasets 180
Rainfall (Seasonal 3 mos. cum.)
Actual cumulative 3-months rainfall (mm)
5 km CHIRP 167
Long-term precipitation 1970 – 2000 (seasonal 3 mos. cum.)
Long-term cumulative 3 months rainfall based on average monthly rainfall (mm) data from1970 - 2000
1 km WorldClim 166
Population Density Number of people per 100m2 100 m WorldPop 171
GIS-Derived
Accessibility Distance to nearest urban center (travel times)
1 km Malaria Atlas Project (MAP) 170
Distance to Health Facility
Distance from active health facilities during study (km)
1 km Based on field data
Distance to Roads Distance from established road-network (km)
1 km CIESIN 181
Slope Percentage rise in elevation 90 m Derived from elevation product
105
Figure 4-3. Description/ distribution of spatial and environmental covariates considered in this analysis, across the
country, as well as the Aquin and Cote de Fer study site communes.
106
Figure 4-4. Village coordinates geo-referenced using Google Earth Pro.
Figure 4-5. Village level CU5 HAZ score distribution across Aquin and Cote de Fer
study site communes.
Village locality
La Baleine
Duchard
Laborieux
Flamands
Lozandye
St Jules
Morne Franck
Gaspard
Bernadel
Buissereth
Mexi
Mouillage
Pisale
Ferdile
Gousse
Dano
Guirand
Briand
Zoranje
Marada
Coraille
Corail
Ricot
Garou
Gingembre
Antoinise_Daliquette
Landrin
Villa
Corail
Corail Lherison
Georeferenced using Google Earth Pro
Latitude Longitude
Communal
Section
18.237 -73.238 Flamands
18.244 -73.960 Flamands
18.257 -73.224 Flamands
18.250 -73.235 Flamands
18.246 -73.223 Flamands
18.280 -73.130 Fond des Blancs
18.280 -73.132 Fond des Blancs
18.282 -73.134 Fond des Blancs
18.283 -73.130 Fond des Blancs
18.284 -73.131 Fond des Blancs
18.230 -73.054 Frangipane
18.244 -73.056 Frangipane
18.221 -73.070 Frangipane
18.227 -73.071 Frangipane
18.231 -73.033 Frangipane
18.230 -73.050 Frangipane
18.3500 -73.1800 Guirand
18.3470 -73.1796 Guirand
18.3490 -73.1729 Guirand
18.3572 -73.1782 Guirand
18.3531 -73.1705 Guirand
18.2537 -72.9464 Jamais Vu
18.254 -72.9433 Jamais Vu
18.2467 -72.9463 Jamais Vu
18.2505 -72.9529 Jamais Vu
18.258 -72.959 Jamais Vu
18.258 -72.9631 Jamais Vu
18.25 -72.95 Jamais Vu
18.2455 -72.9625 Jamais Vu
18.2434 -72.956 Jamais Vu
Georeferenced using Google Earth Pro Georeferenced using Admin 4 UTM shapefile
Latitude Longitude
18.250 -73.235
18.250 -73.235
18.250 -73.235
18.250 -73.235
18.250 -73.235
18.280 -73.130
18.280 -73.130
18.280 -73.130
18.280 -73.130
18.280 -73.130
18.230 -73.050
18.230 -73.050
18.230 -73.050
18.230 -73.050
18.230 -73.050
18.230 -73.050
18.350 -73.180
18.350 -73.180
18.350 -73.180
18.350 -73.180
18.350 -73.180
18.250 -72.950
18.250 -72.950
18.250 -72.950
18.250 -72.950
18.250 -72.950
18.250 -72.950
18.250 -72.950
18.250 -72.950
18.250 -72.950
Georeferenced using Admin 4 UTM shapefile
107
Figure 4-6. Livestock species distribution across Aquin and Cote de Fer study site
communes.
108
Figure 4-7. Results from the bivariate analysis of environmental and spatial covariates
and village level CU5 HAZ.
Figure 4-8. Final multivariate linear regression model results and overall model
characteristics.
Variable β* SE** p***
cumulative rainfall 0.14 0.08 0.10
distance to hospital -0.21 0.08 0.01
elevation -0.12 0.08 0.17
max rainfall 0.16 0.08 0.06
mean rainfall 0.14 0.08 0.10
min temperature during the
day 0.12 0.08 0.17
min temperature during the
night 0.21 0.08 0.01
population density -0.13 0.08 0.14
cumulative precipitation -0.12 0.08 0.15
max precipitation -0.12 0.08 0.16
mean precipitation -0.12 0.08 0.15
min precipitation -0.11 0.08 0.19
*β = Beta (coefficient)
**SE = Standard Error
***p = P-value
Significant Bivariates
Variable β* SE** p***
(Intercept) -0.80 0.07 0.00
elevation 0.63 0.23 0.01
mean rainfall 0.34 0.13 0.01
min temperature during the
night 0.55 0.14 0.00
cumulative precipitation -2.14 0.81 0.01
max precipitation 2.11 0.81 0.02
Estimate z**** p***
Adjusted R-squared (fit) 0.34
Significance (p) 0.01
RMSE**** 0.34
Moran's I -0.51 -0.91 0.36
*β = Beta (coefficient)
**SE = Standard Error
***p = P-value
****z = z-score
*****RMSE = Root Mean Square Error
Overall Model characteristics
Final Model
109
Figure 4-9. Map of the cluster and outlier analysis (Local Moran’s) in the surveyed
villages.
Figure 4-10. Model Residual vs. Predicted Plot indicating a properly specified model.
110
CHAPTER 5 CONCLUSION
Haiti is one of the poorest countries in the western hemisphere64, and suffers
from high rates of undernutrition in adults, children, and children under five (CU5)65.
Stunted CU5 have an increased risk of many negative, life-long consequences such as
impaired mental and physical development, which can in turn affect educational
performance, and economic growth, and many more142.
CU5 micro- and macronutrient deficiencies from poor dietary diversity, and
exposure to disease (both symptomatic and asymptomatic), can lead to recurring
undernutrition, impacting short and long-term health13. Livestock have the potential to
provide both animal source foods (ASF) and nutritional security in Haiti, but with
caution, as CU5 can be exposed to disease-causing pathogens from poorly managed
animal feces, and poor WASH infrastructure in the household, and community 42,47,54,90.
Additionally, child nutrition and growth outcomes may be associated with macro-level
environmental and spatial drivers in Haiti. Therefore, considering these enviro-spatial
contexts are important when considering risk factors or determinants of child stunting in
Haiti.
Summary
The goal for chapter 2 was to understand livestock ownership and how it may
affect dietary diversity and ASF consumption. We determined that livestock ownership
does have the potential to improve dietary outcomes, including HDDS and ASF
consumption. However, the potential that livestock has to improve nutrition in CU5
could potentially be offset by poor WASH (and livestock WASH practices) in this
community of Haiti. Thus, in chapter 3 our goal was to build off chapter 2, but to assess
111
if livestock ownership is associated with CU5 stunting, both with and without WASH
factors are considered in our models. Our results indicated that owning specific
livestock groups was associated with CU5 stunting. However, when WASH factors were
considered, livestock ownership was still associated with reduced odds of CU5 stunting.
In chapter 4, our goal was to assess if spatial and environmental drivers are associated
with Child Height for Age. We were able to determine that there is a spatial and
environmental link to child growth; however, more studies are warranted to assess all
potential covariates at play.
Strengths and Limitations
This entire dataset has many strengths. First it is a large survey, targeting the
poorest and most remote villages in the St. Boniface catchment—it captured what it
intended to capture per survey initiative to understand the maternal and child health in
these regions to inform future intervention. Additionally, this work is and has been very
important for policy makers, program planners, and implementers that seek to improve
maternal and child health in the region, especially illustrating the site-specific
opportunities to intervene and improve health outcomes.
However, despite these strengths, it is a cross-sectional study, informing just a
baseline understanding of the patterns we see in this research. Therefore, no causal
inference for any of our findings can be made, nor is it generalizable to the broader
Haitian context, given the study population. Also given the nature of a household
survey allows for limitations; misreporting cannot be ruled out, and there could be bias
presented in some questions due to self-reporting.
Furthermore, due to the nature of the survey and field limitations, much of the
questions critical for our analyses were missing, including livestock ownership, dietary
112
diversity, as well as confounding variables. Though, determining that these data were
missing at random, and employing very specific imputation methods per chapter
hypothesis, this imputation may be a limitation, even though we are confident that this
method was rigorous and justified. However, imputation aside, this survey was
structured for a different purpose than the assessments seen in this dissertation, and
therefore this data may not be the most appropriate for these analyses. Further,
tailored research is needed to confirm these observations.
Future directions
All in all, these findings suggest that CU5 undernutrition in Haiti is complex, and
household diet, WASH, and spatial/environmental factors are multi-dimensionally
associated with CU5 nutrition status. Moreover, future studies should aim to assess
these dimensions together, longitudinally, through both qualitative and quantitative
methods to get a more robust picture of the undernutrition problem in Haiti.
Improvement of CU5 nutritional status in Haiti will require a multi-factorial intervention,
that encompasses many dimensions including addressing dietary practices and food
security, agricultural and livestock dimensions, maternal characteristics (e.g. maternal
knowledge and education, employment, as well as empowerment), and WASH factors
(both at the individual, household, community-levels, including proper livestock WASH
and husbandry practices).
113
APPENDIX A MATERNAL KNOWLEDGE QUESTIONS
In order to calculate maternal knowledge scores, we used the list of questions
from the St. Boniface and UNICEF-Haiti survey (Figure A-1). Respondents could
answer freely and the enumerator would record their responses to each question, and
for each correct response, the individual would get a cumulative score. We took the
median score of the sample, and dichotomized it into “knowledgeable” or “less
knowledgeable”.
114
Figure A-1. Variables included in Maternal Knowledge Score calculation calculations. Note, Iron and Vitamin A are
included together in the combined score.
Iron Vitamin A Undernutrition Diarrhea Risk Diarrhea Prevention
1 Iron-rich wheat Wheat rich in vitamin A Child is: Skinny Dirty water can cause diarrhea Consuming fresh foods can prevent
diarrhea
2 Iron-rich teff Teff rich in vitamin A Child is: Short Damaged food can cause diarrhea Drinking water can prevent diarrhea
3 Iron-rich legume Vegetables and legumes
rich in vitamin A
Child has: Old face Not washing hands before eating can
cause diarrhea
Washing hands before eating may
prevent diarrhea
4 Yellow fruits and
vegetables are rich in iron
Yellow fruits and
vegetables rich in vitamin
A
Child is: Irritable Not washing your hands with soap
after using the toilet can cause
diarrhea
Washing hands with soap after using
the toilet can prevent diarrhea
5 Other vegetables are rich
in iron
Other vegetables rich in
vitamin A
Child has: Hair changes
color
Not using toilets can cause diarrhea Washing hands with ash after using
the toilet can prevent diarrhea
6 Fish are rich in iron Fish rich in vitamin A Child has: Hollow eyes Not breastfeeding your child for at
least two years can cause diarrhea
Using clean toilets can prevent
diarrhea
7 Iron-rich meat Meat rich in vitamin A Child has: Edema of the
legs
Lacking vaccinations can cause
diarrhea
Breastfeeding your child for two years
may prevent diarrhea
8 Eggs are rich in iron Eggs rich in vitamin A Other things can cause diarrhea Good vaccination-status can prevent
diarrhea
9 Milk is rich in iron Milk rich in vitamin A Do not know the causes of diarrhea Other things can prevent diarrhea
10 The other fruits are rich in
iron
Other fruits rich in vitamin
A
Do not know how to prevent diarrhea
11 Iron-rich oil / butter Oil / butter rich in vitamin
A
12 Iron-rich salt Salt rich in vitamin A
13 Other iron-rich foods Other foods rich in
vitamin A
14 Do not know any iron-rich
foods
Do not know foods that
are rich in vitamin A
115
APPENDIX B CHAPTER 2 VALIDATION
Figure B-1. Model Fit statistics for Chapter 2 Model 1: HDDS.
Figure B-2. Summary of backwards elimination procedure in multivariate backward
stepwise logistic regression for Model 1: HDDS.
Figure B-3. Predictive power statistics of Model 1: HDDS.
Criterion Intercept Only
Intercept and
Covariates
AIC 10,757.79 9,991.03
SC 10,764.75 10,123.21
-2 Log L 10755.788 9,953.03
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 802.76 18 <.0001
Score 762.32 18 <.0001
Wald 692.09 18 <.0001
Chi-Square DF Pr > ChiSq
8.50 4 0.07
Model Fit Statistics
Testing Global Null Hypothesis: BETA=0
Residual Chi-Square Test
Step Effect Removed DF
Number
In
Wald Chi-
Square Pr > ChiSq
1 Owns Small Ruminant Animals 1 17 0.69 0.41
2 Vitamin A and Iron Rich Food Sources 1 16 1.91 0.17
3 Owns Large Ruminant Animals 1 15 2.94 0.09
4 Age categories 6 to 24 months (vs. 2 to 5
years old)
1 14 2.96 0.09
Summary of Backward Elimination
Percent Concordant 68.00 Somers' D 0.36
Percent Discordant 31.90 Gamma 0.36
Percent Tied 0.10 Tau-a 0.18
Pairs 15,050,800 c 0.68
Association of Predicted Probabilities and Observed
Responses
116
Figure B-4. Receiver Operating Characteristic Curve (ROC) showing predictive power
of final model, for Model 1: HDDS.
Figure B-5. Receiver Operating Characteristic Curves (ROC) showing predictive power
of each model step until final model for Model 1: HDDS.
117
Figure B-6. Model Fit statistics for Chapter 2 Model 2: ASF.
Figure B-7. Summary of backwards elimination procedure in multivariate backward
stepwise logistic regression for Model 2: ASF.
Figure B-8. Predictive power statistics of Model 2: ASF.
Criterion Intercept Only
Intercept and
Covariates
AIC 10,676.47 10,202.45
SC 10,683.41 10,306.69
-2 Log L 10674.465 10,172.45
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 502.01 14 <.0001
Score 483.51 14 <.0001
Wald 450.14 14 <.0001
Chi-Square DF Pr > ChiSq
1.48 2 0.48
Model Fit Statistics
Testing Global Null Hypothesis: BETA=0
Residual Chi-Square Test
Step Effect Removed DF
Number
In
Wald Chi-
Square Pr > ChiSq
1 Land ownership 1 13 0.00 0.97
2 Vitamin A and Iron Rich Food Sources 1 12 1.48 0.22
Summary of Backward Elimination
Percent Concordant 64.70 Somers' D 0.30
Percent Discordant 35.20 Gamma 0.30
Percent Tied 0.10 Tau-a 0.15
Pairs 14,822,496 c 0.65
Association of Predicted Probabilities and Observed
Responses
118
Figure B-9. Receiver Operating Characteristic Curve (ROC) showing predictive power
of final model, for Model 2: ASF.
Figure B-10. Receiver Operating Characteristic Curves (ROC) showing predictive
power of each model step until final model for Model 2: ASF.
119
APPENDIX C CHAPTER 3 VALIDATION
Figure C-1. Model Fit statistics for Chapter 3 Model 1: Livestock and Stunting.
Figure C-2. Summary of backwards elimination procedure in multivariate backward
stepwise logistic regression for Chapter 3 Model 1: Livestock and Stunting.
Figure C-3. Predictive power statistics of Chapter 3, Model 1: Livestock and Stunting.
Criterion Intercept Only
Intercept and
Covariates
AIC 4,510.37 4,046.00
SC 4,517.19 4,134.72
-2 Log L 4,508.37 4,020.00
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 488.37 12 <.0001
Score 457.28 12 <.0001
Wald 394.75 12 <.0001
Chi-Square DF Pr > ChiSq
5.49 7 0.60
Model Fit Statistics
Testing Global Null Hypothesis: BETA=0
Residual Chi-Square Test
Step Effect Removed DF
Number
In
Wald Chi-
Square Pr > ChiSq
1 Land ownership 1 18 0.01 0.94
2 Owns Pigs 1 17 0.23 0.63
3 Overall Nutrition and Signs of
Malnutrition
1 16 0.30 0.58
4 Owns Chickens 1 15 0.46 0.50
5 Impoverishment 1 14 0.52 0.47
6
Maternal Formal Relationship Status
1 13 0.79 0.37
7 CU5 Gender (Males to Females) 1 12 3.18 0.07
Summary of Backward Elimination
Percent Concordant 73.40 Somers' D 0.47
Percent Discordant 26.40 Gamma 0.47
Percent Tied 0.20 Tau-a 0.09
Pairs 4,270,000 c 0.74
Association of Predicted Probabilities and Observed
Responses
120
Figure C-4. Receiver Operating Characteristic Curve (ROC) showing predictive power
of final model, for Chapter 3, Model 1: Livestock and Stunting.
Figure C-5. Receiver Operating Characteristic Curves (ROC) showing predictive power
of each model step until final model for chapter 3, Model 1: Livestock and Stunting.
121
Figure C-6. Model Fit statistics for Chapter 3 Model 2: Livestock, WASH, and Stunting.
Figure C-7. Summary of backwards elimination procedure in multivariate backward
stepwise logistic regression for Chapter 3 Model 2: Livestock, WASH, and Stunting.
Figure C-8. Predictive power statistics of Chapter 3, Model 2: Livestock, WASH, and
Stunting.
Criterion Intercept Only
Intercept and
Covariates
AIC 4,523.35 3,755.36
SC 4,530.18 3,905.70
-2 Log L 4521.345 3,711.36
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 809.98 21 <.0001
Score 830.19 21 <.0001
Wald 645.22 21 <.0001
Chi-Square DF Pr > ChiSq
52.52 7 <.0001
Model Fit Statistics
Testing Global Null Hypothesis: BETA=0
Residual Chi-Square Test
Step Effect Removed DF
Number
In
Wald Chi-
Square Pr > ChiSq
1 Handwashing Before Feeding CU5 1 26 0.00 0.99
2 Water Source Status 1 25 0.00 0.97
3
Maternal Formal Relationship Status
1 24 0.01 0.94
4 Land ownership 1 23 0.03 0.87
5 Household Waste Disposal Status:
Unimproved to Improved
1 22 0.07 0.79
6 Impoverishment 1 21 0.55 0.46
7 Fever Episode in Last 2 Weeks 1 20 3.72 0.05
Summary of Backward Elimination
Percent Concordant 79.20 Somers' D 0.59
Percent Discordant 20.80 Gamma 0.59
Percent Tied 0.00 Tau-a 0.11
Pairs 4,312,000 c 0.79
Responses
Association of Predicted Probabilities and Observed
122
Figure C-9. Receiver Operating Characteristic Curve (ROC) showing predictive power
of final model, for Chapter 3, Model 2: Livestock, WASH, and Stunting.
Figure C-10. Receiver Operating Characteristic Curves (ROC) showing predictive
power of each model step until final model for chapter 3, Model 2: Livestock, WASH, and Stunting.
123
LIST OF REFERENCES
1. WHO. WHO | Malnutrition. WHO (2017).
2. UNICEF, WHO & The World Bank. Levels and Trends in Child Malnutrition. Joint Child Malnutrition Estimates edition (2017). doi:10.1016/S0266-6138(96)90067-4
3. WHO | WHO child growth standards and the identification of severe acute malnutrition in infants and children. WHO (2014).
4. Leroy, J. L., Ruel, M., Habicht, J.-P. & Frongillo, E. A. Linear Growth Deficit Continues to Accumulate beyond the First 1000 Days in Low- and Middle-Income Countries: Global Evidence from 51 National Surveys. J. Nutr. (2014). doi:10.3945/jn.114.191981
5. Martorell, R. Improved nutrition in the first 1000 days and adult human capital and health. Am. J. Hum. Biol. 29, e22952 (2017).
6. Syed, S., Ali, A. & Duggan, C. Environmental Enteric Dysfunction in Children: A Review. J. Pediatr. Gastroenterol. Nutr. 1 (2016). doi:10.1097/MPG.0b013e318228574e
7. Crane, R. J., Jones, K. D. J. & Berkley, J. A. Environmental enteric dysfunction: An overview. Food Nutr. Bull. (2015). doi:10.1177/15648265150361S113
8. Owino, V. et al. Environmental Enteric Dysfunction and Growth Failure/Stunting in Global Child Health. Pediatrics (2016). doi:10.1542/peds.2016-0641
9. Caulfield, L. E. Relationship between growth and illness, enteropathogens and dietary intakes in the first 2 years of life: findings from the MAL-ED birth cohort study. BMJ Glob. Heal. (2017). doi:10.1136/bmjgh-2017-000370
10. Valdez, Y., Brown, E. M. & Finlay, B. B. Influence of the microbiota on vaccine effectiveness. Trends in Immunology (2014). doi:10.1016/j.it.2014.07.003
11. Praharaj, I., John, S. M., Bandyopadhyay, R. & Kang, G. Probiotics, antibiotics and the immune responses to vaccines. Philosophical Transactions of the Royal Society B: Biological Sciences (2015). doi:10.1098/rstb.2014.0144
12. Dewey, K. G. & Begum, K. Long-term consequences of stunting in early life. Matern. Child Nutr. 7, 5–18 (2011).
13. Victora, C. G. et al. Maternal and child undernutrition: consequences for adult health and human capital. The Lancet 371, 340–357 (2008).
14. Mosites, E. M. et al. The relationship between livestock ownership and child stunting in three countries in eastern Africa using national survey data. PLoS One 10, (2015).
124
15. Rah, J. H. et al. Low dietary diversity is a predictor of child stunting in rural Bangladesh. Eur. J. Clin. Nutr. (2010). doi:10.1038/ejcn.2010.171
16. Heidkamp, R. A., Ayoya, M. A., Teta, I. N., Stoltzfus, R. J. & Marhone, J. P. Complementary feeding practices and child growth outcomes in Haiti: an analysis of data from Demographic and Health Surveys. Matern. Child Nutr. (2015). doi:10.1111/mcn.12090
17. Arimond, M. & Ruel, M. T. Dietary Diversity Is Associated with Child Nutritional Status: Evidence from 11 Demographic and Health Surveys. J. Nutr. (2004). doi:10.1093/jn/134.10.2579
18. Ruel, M. T. Is dietary diversity an indicator of food security or dietary quality? A review of measurement issues and research needs. Food Nutr. Bull. (2003). doi:10.1177/156482650302400210
19. Darapheak, C., Takano, T., Kizuki, M., Nakamura, K. & Seino, K. Consumption of animal source foods and dietary diversity reduce stunting in children in Cambodia. Int. Arch. Med. 6, (2013).
20. Neumann, C. G. et al. Animal source foods improve dietary quality, micronutrient status, growth and cognitive function in Kenyan school children: background, study design and baseline findings. J. Nutr. 133, 3941S–3949S (2003).
21. Iannotti, L. & Lesorogol, C. Animal milk sustains micronutrient nutrition and child anthropometry among pastoralists in Samburu, Kenya. Am. J. Phys. Anthropol. (2014). doi:10.1002/ajpa.22547
22. Steyn, N., Nel, J., Nantel, G., Kennedy, G. & Labadarios, D. Food variety and dietary diversity scores in children: are they good indicators of dietary adequacy? Public Health Nutr. (2006). doi:10.1079/PHN2005912
23. Murphy, S. P. & Allen, L. H. Animal Source Foods to Improve Micronutrient Nutrition and Human Function in Developing Countries. J. Nutr 133, 3965–3971 (2003).
24. Muslimatun, S. & Wiradnyani, L. A. A. Dietary diversity, animal source food consumption and linear growth among children aged 1–5 years in Bandung, Indonesia: a longitudinal observational study. British Journal of Nutrition (2016). doi:10.1017/S0007114515005395
25. Darrouzet-Nardi, A. F. et al. Child dietary quality in rural Nepal: Effectiveness of a community-level development intervention. Food Policy (2016). doi:10.1016/j.foodpol.2016.03.007
26. Randolph, T. F. et al. Role of livestock in human nutrition and health for poverty reduction in developing countries. J. Anim. Sci. 85, 2788–800 (2007).
125
27. Thornton, P. K. Livestock production: recent trends, future prospects. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 365, 2853–2867 (2010).
28. Jin, M. & Iannotti, L. L. Livestock production, animal source food intake, and young child growth: The role of gender for ensuring nutrition impacts. Soc. Sci. Med. 105, 16–21 (2014).
29. Azzarri, C., Zezza, A., Haile, B. & Cross, E. Does Livestock Ownership Affect Animal Source Foods Consumption and Child Nutritional Status? Evidence from Rural Uganda. J. Dev. Stud. 51, 1034–1059 (2015).
30. Thumbi, S. M. et al. Linking human health and livestock health: A ‘one-health’ platform for integrated analysis of human health, livestock health, and economic welfare in livestock dependent communities. PLoS One 10, (2015).
31. Hetherington, J. B., Wiethoelter, A. K., Negin, J. & Mor, S. M. Livestock ownership, animal source foods and child nutritional outcomes in seven rural village clusters in Sub-Saharan Africa. Agric. Food Secur. 6, 9 (2017).
32. Köster, E. P. Diversity in the determinants of food choice: A psychological perspective. Food Qual. Prefer. (2009). doi:10.1016/j.foodqual.2007.11.002
33. Powell, B., Bezner Kerr, R., Young, S. L. & Johns, T. The determinants of dietary diversity and nutrition: Ethnonutrition knowledge of local people in the East Usambara Mountains, Tanzania. J. Ethnobiol. Ethnomed. (2017). doi:10.1186/s13002-017-0150-2
34. Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: The importance of targeting automatic processes. Science (2012). doi:10.1126/science.1226918
35. Swanepoel, F., Stroebel, a. & Moyo, S. The role of livestock in developong communities: Enhancing multifunctionality. Proceedings of the satellite symposium (2008).
36. Marquis, G. S. et al. Fecal contamination of shanty town toddlers in households with non-corralled poultry, Lima, Peru. Am. J. Public Health (1990). doi:10.2105/AJPH.80.2.146
37. Mbuya, M. N. N. et al. Design of an intervention to minimize ingestion of fecal microbes by young children in Rural Zimbabwe. Clin. Infect. Dis. 61, S703–S709 (2015).
38. George, C. M. et al. Fecal markers of environmental enteropathy are associated with animal exposure and caregiver hygiene in Bangladesh. Am. J. Trop. Med. Hyg. (2015). doi:10.4269/ajtmh.14-0694
126
39. Headey, D. et al. Is exposure to animal feces harmful to child nutrition and health outcomes? A multicountry observational analysis. American Journal of Tropical Medicine and Hygiene 96, 961–969 (2017).
40. Dumas, S. E., Kassa, L., Young, S. L. & Travis, A. J. Examining the association between livestock ownership typologies and child nutrition in the Luangwa Valley, Zambia. PLoS One (2018). doi:10.1371/journal.pone.0191339
41. Ayele, Z. & Peacock, C. Improving access to and consumption of animal source foods in rural households: the experiences of a women-focused goat development program in the highlands of Ethiopia. J. Nutr. 133, 3981S–3986S (2003).
42. Headey, D. & Hirvonen, K. Is exposure to poultry harmful to child nutrition? An observational analysis for rural Ethiopia. PLoS One 11, (2016).
43. Iannotti, L. L. et al. Eggs in Early Complementary Feeding and Child Growth: A Randomized Controlled Trial. Pediatrics 140, e20163459 (2017).
44. Amour, C. et al. Epidemiology and Impact of Campylobacter Infection in Children in 8 Low-Resource Settings: Results from the MAL-ED Study. Clin. Infect. Dis. (2016). doi:10.1093/cid/ciw542
45. J.A., P.-M. et al. Prevalence of non-jejuni/coli campylobacter species detected by enzyme immunoassay and culture in the mal-ed cohort study. American Journal of Tropical Medicine and Hygiene (2013).
46. Platts-Mills, J. A. et al. Pathogen-specific burdens of community diarrhoea in developing countries: A multisite birth cohort study (MAL-ED). Lancet Glob. Heal. (2015). doi:10.1016/S2214-109X(15)00151-5
47. Humphrey, J. H. Child undernutrition, tropical enteropathy, toilets, and handwashing. Lancet (2009). doi:10.1016/S0140-6736(09)60950-8
48. Harper, K. M., Mutasa, M., Prendergast, A. J., Humphrey, J. & Manges, A. R. Environmental enteric dysfunction pathways and child stunting: A systematic review. PLoS Negl. Trop. Dis. 12, (2018).
49. Clasen, T. F. et al. Interventions to improve disposal of human excreta for preventing diarrhoea. in Cochrane Database of Systematic Reviews (2010). doi:10.1002/14651858.CD007180.pub2
50. Stocks, M. E. et al. Effect of Water, Sanitation, and Hygiene on the Prevention of Trachoma: A Systematic Review and Meta-Analysis. PLoS Med. 11, (2014).
51. Strunz, E. C. et al. Water, Sanitation, Hygiene, and Soil-Transmitted Helminth Infection: A Systematic Review and Meta-Analysis. PLoS Med. 11, (2014).
127
52. Penakalapati, G. et al. Exposure to Animal Feces and Human Health: A Systematic Review and Proposed Research Priorities. Environ. Sci. Technol. 51, 11537–11552 (2017).
53. Curtis, V. & Cairncross, S. Effect of washing hands with soap on diarrhoea risk in the community: A systematic review. Lancet Infectious Diseases (2003). doi:10.1016/S1473-3099(03)00606-6
54. Zambrano, L. D., Levy, K., Menezes, N. P. & Freeman, M. C. Human diarrhea infections associated with domestic animal husbandry: A systematic review and meta-analysis. Transactions of the Royal Society of Tropical Medicine and Hygiene 108, 313–325 (2014).
55. Luby, S. P. et al. Effects of water quality, sanitation, handwashing, and nutritional interventions on diarrhoea and child growth in rural Bangladesh: A cluster randomised controlled trial. The Lancet Global Health (2018). doi:10.1016/S2214-109X(17)30490-4
56. Null, C. et al. Effects of water quality, sanitation, handwashing, and nutritional interventions on diarrhoea and child growth in rural Kenya: A cluster-randomised controlled trial. The Lancet Global Health (2018). doi:10.1016/S2214-109X(18)30005-6
57. Arnold, B. F., Null, C., Luby, S. P. & Colford, J. M. Implications of WASH Benefits trials for water and sanitation – Authors’ reply. The Lancet Global Health (2018). doi:10.1016/S2214-109X(18)30229-8
58. Mbuya, M. N. N. & Humphrey, J. H. Preventing environmental enteric dysfunction through improved water, sanitation and hygiene: An opportunity for stunting reduction in developing countries. Maternal and Child Nutrition (2016). doi:10.1111/mcn.12220
59. Workicho, A. et al. Household dietary diversity and Animal Source Food consumption in Ethiopia: evidence from the 2011 Welfare Monitoring Survey. BMC Public Health 16, 1192 (2016).
60. Parappurathu, S., Kumar, A., Bantilan, M. C. S. & Joshi, P. K. Food consumption patterns and dietary diversity in eastern India: evidence from village level studies (VLS). Food Secur. (2015). doi:10.1007/s12571-015-0493-2
61. Motbainor, A., Worku, A. & Kumie, A. Stunting is associated with food diversity while wasting with food insecurity among underfive children in East and West Gojjam Zones of Amhara Region, Ethiopia. PLoS One (2015). doi:10.1371/journal.pone.0133542
62. Mahmudiono, T., Sumarmi, S. & Rosenkranz, R. R. Household dietary diversity and child stunting in East Java, Indonesia. Asia Pac. J. Clin. Nutr. (2017). doi:10.6133/apjcn.012016.01
128
63. Neumann, C. G. et al. Meat supplementation increases arm muscle area in Kenyan schoolchildren. Br. J. Nutr. (2013). doi:10.1017/S0007114512003121
64. The World Bank. Investing in people to fight poverty in Haiti : Reflections for evidence-based policy making (English). (2014).
65. Black, R. E. et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451 (2013).
66. ICF International. The DHS Program: Demographic and Health Surveys - Stata Compiler. DHS Model Questionnaires (2016).
67. Pauzé, E., Batal, M., Philizaire, Y., Blanchet, R. & Sanou, D. Determinants of diet quality among rural households in an intervention zone of Grande Anse , Haiti. Food Secur. 1123–1134 (2016). doi:10.1007/s12571-016-0615-5
68. Spray, A. L., Eddy, B., Hipp, J. A. & Iannotti, L. Spatial analysis of undernutrition of children in léogâne commune, haiti. Food Nutr. Bull. 34, 444–461 (2013).
69. Haggerty, R. A. Haiti: A Country Study. Washington: GPO for the Library of Congress (1989).
70. The World Bank. Haiti: Overview. (2018). Available at: http://www.worldbank.org/en/country/haiti/overview.
71. The World Bank. Promoting Nutrition Security in Haiti: An Assessment of Pre- and Post-Earthquake Conditions for the Way Forward. Nutrition 74 (2010).
72. UN radio & ReliefWeb. Poverty is behind Haiti’s vulnerability to natural disasters.
73. WFP. Haiti transitional interim country strategic plan (year 2018). (2018).
74. UNDP. Human Development Index (HDI). Human Development Reports (2018). Available at: http://hdr.undp.org/en/content/human-development-index-hdi.
75. Oxfam International. Planting Now: Agricultural challenges and opportunities for Haiti’s reconstruction. (2010).
76. Lawless, R. & Ferguson, J. A. Haiti. Encyclopædia Britannica (2018).
77. Baro, M. Food Insecurity and Livelihood Systems in Northwest Haiti. 9, 1–34 (2002).
78. Heifer International. Heifer Haiti. Available at: https://www.heifer.org/ending-hunger/our-work/countries/north-america/haiti.html. (Accessed: 31st July 2018)
79. Gelting, R., Bliss, K., Patrick, M., Lockhart, G. & Handzel, T. Water , Sanitation and Hygiene in Haiti : Past , Present , and Future. 89, 665–670 (2013).
129
80. USAID. Water, Sanitation, and Hygiene Sector Status and Trends Assessment in Haiti. USAID, Dec. 2014 (2014).
81. WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation. Progress on Drinking Water and Sanitation. Update (2012). doi:978-924-1503279
82. Ministry of public health and population: national directorate for water supply and sanitation. National Plan for the Elimination of Cholera in Haiti: 2013-2022.
83. Bargout, R. N. & Raizada, M. N. Soil nutrient management in Haiti, pre-Columbus to the present day: Lessons for future agricultural interventions. Agriculture and Food Security (2013). doi:10.1186/2048-7010-2-11
84. Cayemittes, M. et al. Survey on mortality, morbidity and use of services EMMUS-V: Haiti 2012. Enquete mortalite, morbidite et utilisation des services EMMUS-V: Haiti 2012. (2013).
85. Ayoya, M. A., Heidkamp, R., Ngnie-Teta, I., Pierre, J. M. & Stoltzfus, R. J. Child malnutrition in Haiti: progress despite disasters. Glob. Heal. Sci. Pract. (2013). doi:10.9745/GHSP-D-13-00069
86. UNICEF. Improving child nutrition: The achievable imperative for global progress. Div. Commun. UNICEF (2013). doi:978-92-806-4686-3
87. Schönfeldt, H. C., Pretorius, B. & Hall, N. The impact of animal source food products on human nutrition and health.: Find articles, e-books in one search. S. Afr. J. Anim. Sci. (2013). doi:10.4314/sajas.v43i3.11
88. UNICEF. Infant and young child feeding. UNICEF data (2018). Available at: https://data.unicef.org/topic/nutrition/infant-and-young-child-feeding/.
89. Crane, R. J., Jones, K. D. J. & Berkley, J. A. Environmental enteric dysfunction: An overview. Food Nutr. Bull. 36, S76–S87 (2015).
90. Ercumen, A. et al. Animal Feces Contribute to Domestic Fecal Contamination: Evidence from E. coli Measured in Water, Hands, Food, Flies, and Soil in Bangladesh. Environ. Sci. Technol. 51, 8725–8734 (2017).
91. Barnes, A. N., Davaasuren, A., Baasandagva, U. & Gray, G. C. A systematic review of zoonotic enteric parasitic diseases among nomadic and pastoral people. PLoS ONE (2017). doi:10.1371/journal.pone.0188809
92. Barnes, A. N. Domestic animals and drinking water contamination in peri-urban households of kisumu, kenya. (University of Florida, 2016).
93. Greger, M. The human/animal interface: Emergence and resurgence of zoonotic infectious diseases. Critical Reviews in Microbiology 33, 243–299 (2007).
130
94. Engering, A., Hogerwerf, L. & Slingenbergh, J. Pathogen–host–environment interplay and disease emergence. Emerg. Microbes Infect. 2, e5 (2013).
95. Lindahl, J. & Grace, D. The consequences of human actions on risks for infectious diseases: a review. Infect. Ecol. Epidemiol. 5, 11 (2015).
96. Wang, H. et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1459–1544 (2016).
97. Block, S. A. Maternal nutrition knowledge versus schooling as determinants of child micronutrient status. Oxf. Econ. Pap. (2007). doi:10.1093/oep/gpm001
98. George, C. M. et al. Risk factors for diarrhea in children under five years of age residing in Peri-urban Communities in Cochabamba, Bolivia. Am. J. Trop. Med. Hyg. (2014). doi:10.4269/ajtmh.14-0057
99. Dikassa, L. et al. Maternal behavioural risk factors for severe childhood diarrhoeal disease in kinshasa, zaire. Int. J. Epidemiol. (1993). doi:10.1093/ije/22.2.327
100. Bilenko, N., Fraser, D. & Naggan, L. Maternal knowledge and environmental factors associated with risk of diarrhea in Israeli Bedouin children. Eur. J. Epidemiol. (1999). doi:10.1023/A:1007649820423
101. Vikram, K., Vanneman, R. & Desai, S. Linkages between maternal education and childhood immunization in India. Soc. Sci. Med. (2012). doi:10.1016/j.socscimed.2012.02.043
102. Ayoya, Mohamed Ag; Ngnie- Teta, Ismael; Seraphin Marie Nancy; Mamadoultaibou, Aissa; Boldon, Ellen; Saint- Fleur, Jean Ernest; Koo, Leslie; Bernard, S. Prevalence and Risk Factors of Anemia Among Children 6- 59 Mpnths Old in Haiti. Hindawi Publ. (2013). doi:10.1155/2013/502968
103. Seraphin, M. N. et al. Determinants of Institutional Delivery Among Women of Childbearing Age in Rural Haiti. Matern. Child Health J. (2015). doi:10.1007/s10995-014-1646-1
104. Seraphin, M. N. et al. Childhood anemia in Rural Haiti: the potential role of community health workers. Glob. Heal. Res. policy (2017). doi:10.1186/s41256-016-0022-7
105. SAS Institute. SAS version 9.4. SAS Institute Inc. (2012).
106. SAS Institute Inc. Base SAS® 9.4 Procedures Guide. Statistical Procedures (2013). doi:10.1016/B978-0-444-59425-9.00014-7
131
107. SAS. The MI procedure. in SAS/STAT 13.1 User’s Guide (2013).
108. Schunk, D. A Markov chain Monte Carlo algorithm for multiple imputation in large surveys. AStA Adv. Stat. Anal. (2008). doi:10.1007/s10182-008-0053-6
109. Zhang, P. Multiple imputation: Theory and method. Int. Stat. Rev. (2003). doi:10.1111/j.1751-5823.2003.tb00213.x
110. Little, R. J. A. A test of missing completely at random for multivariate data with missing values. J. Am. Stat. Assoc. (1988). doi:10.1080/01621459.1988.10478722
111. UCLA: Statistical Consulting Group. Introduction to SAS. Available at: https://stats.idre.ucla.edu/sas/modules/sas-learning-moduleintroduction-to-the-features-of-sas/. (Accessed: 29th October 2018)
112. UCLA: Statistical Consulting Group. Introduction to SAS.
113. Ruel, M. T. Animal Source Foods to Improve Micronutrient Nutrition and Human Function in Developing Countries Operationalizing Dietary Diversity : A Review of Measurement Issues. J. Nutr. (2003).
114. Mukherjee, A., Paul, S., Saha, I., Som, T. & Ghose, G. Dietary diversity and its determinants: A community-based study among adult population of Durgapur, West Bengal. Med. J. Dr. D.Y. Patil Vidyapeeth 11, 296–301 (2018).
115. RStudio Team, -. RStudio: Integrated Development for R. [Online] RStudio, Inc., Boston, MA URL http//www. rstudio. com (2015). doi:10.1126/science.aad6351
116. Mosites, E. M. The influence of livestock ownership and health on the nutritional status of children in Eastern Africa. ProQuest Diss. Theses 69 (2015). doi:10.1017/CBO9781107415324.004
117. US Department of Agriculture. The Caribbean Import Market for Poultry.
118. Pachón, H. et al. Constraints on the delivery of animal-source foods to infants and young children: Case studies from five countries. Food Nutr. Bull. (2007). doi:10.1177/156482650702800211
119. Murray, G. The Haitian Pig Slaughter.
120. Bukania, Z. N. et al. Food insecurity and not dietary diversity is a predictor of nutrition status in children within semiarid agro-ecological zones in Eastern Kenya. J. Nutr. Metab. (2014). doi:10.1155/2014/907153
121. Swindale, A. & Bilinsky, P. Household Dietary Diversity Score (HDDS) for measurement of household food access: Indicator guide. Food Nutr. Tech. Assist. … (2006). doi:10.1017/CBO9781107415324.004
132
122. Ruel, M. T. & Alderman, H. Nutrition-sensitive interventions and programmes: How can they help to accelerate progress in improving maternal and child nutrition? Lancet (2013). doi:10.1016/S0140-6736(13)60843-0
123. McDonald, C. M. et al. Household food insecurity and dietary diversity as correlates of maternal and child undernutrition in rural Cambodia. Eur. J. Clin. Nutr. (2014). doi:10.1038/ejcn.2014.161
124. Njuki, J. & Sanginga, P. C. Women, livestock ownership and markets: Bridging the gender gap in eastern and Southern Africa. Women, Livestock Ownership and Markets: Bridging the gender gap in Eastern and Southern Africa (2013). doi:10.4324/9780203083604
125. Carlson, G. J., Kordas, K. & Murray-Kolb, L. E. Associations between women’s autonomy and child nutritional status: A review of the literature. Maternal and Child Nutrition (2015). doi:10.1111/mcn.12113
126. Cunningham, K. et al. Women’s empowerment in agriculture and child nutritional status in rural Nepal. Public Health Nutr. (2015). doi:10.1017/S1368980015000683
127. Cunningham, K., Ruel, M., Ferguson, E. & Uauy, R. Women’s empowerment and child nutritional status in South Asia: a synthesis of the literature. Matern. Child Nutr. (2014). doi:10.1111/mcn.12125
128. Gardella, A. Gender Assessment for USAID/Haiti Country Strategy Statement. (2006).
129. Savy, M., Martin-Prével, Y., Sawadogo, P., Kameli, Y. & Delpeuch, F. Use of variety/diversity scores for diet quality measurement: Relation with nutritional status of women in a rural area in Burkina Faso. Eur. J. Clin. Nutr. (2005). doi:10.1038/sj.ejcn.1602135
130. Appoh, L. Y. & Krekling, S. Maternal nutritional knowledge and child nutritional status in the Volta Region of Ghana. Matern. Child Nutr. (2005). doi:10.1111/j.1740-8709.2005.00016.x
131. Smith, L. C. & Haddad, L. Reducing Child Undernutrition: Past Drivers and Priorities for the Post-MDG Era. World Dev. (2015). doi:10.1016/j.worlddev.2014.11.014
132. Oduor, F. O., Boedecker, J., Kennedy, G., Mituki-Mungiria, D. & Termote, C. Caregivers’ nutritional knowledge and attitudes mediate seasonal shifts in children’s diets. Maternal and Child Nutrition (2018). doi:10.1111/mcn.12633
133. Frongillo, E. A., de Onis, M. & Hanson, K. M. Socioeconomic and demographic factors are associated with worldwide patterns of stunting and wasting of children. J. Nutr. (1997). doi:10.1093/jn/127.12.2302
133
134. Daumerie, B. & Madsen, E. The effects of a very young age structure in Uganda. Country case study. The Shape of things to Come Series (2010).
135. Aurino, E. Do boys eat better than girls in India? Longitudinal evidence on dietary diversity and food consumption disparities among children and adolescents. Econ. Hum. Biol. (2017). doi:10.1016/j.ehb.2016.10.007
136. Herrador, Z. et al. Cross-Sectional Study of Malnutrition and Associated Factors among School Aged Children in Rural and Urban Settings of Fogera and Libo Kemkem Districts, Ethiopia. PLoS One 9, e105880 (2014).
137. FAO. Guidelines for measuring household and individual dietary diversity. Fao (2010). doi:613.2KEN
138. WHO and UNICEF. WHO child growth standards and the identification of severe acute malnutrition in infants and children: A Joint Statement by the World Health Organization and the United Nations Children’s Fund. 1–12 (2009).
139. de Onis, M. & Branca, F. Childhood stunting: A global perspective. Maternal and Child Nutrition (2016). doi:10.1111/mcn.12231
140. Unicef/ & WHO/Wolrd Bank. Levels and Trends in Child malnutrition: Key findings of the 2016 edition. Midwifery (2016). doi:10.1016/S0266-6138(96)90067-4
141. Ayoya, M. A. et al. Précis of nutrition of children and women in Haiti: Analyses of data from 1995 to 2012. Ann. N. Y. Acad. Sci. (2014). doi:10.1111/nyas.12373
142. Stewart, C. P., Iannotti, L., Dewey, K. G., Michaelsen, K. F. & Onyango, A. W. Contextualising complementary feeding in a broader framework for stunting prevention. Matern. Child Nutr. (2013). doi:10.1111/mcn.12088
143. Caulfield, L. E., Richard, S. a, Rivera, J. a, Musgrove, P. & Black, R. E. Stunting , Wasting , and Micronutrient Deficiency Disorders. in Disease Control Priorities in Developing Countries 551–567 (2006). doi:10.1093/ije/dyq085
144. Ngure, F. M. et al. Formative research on hygiene behaviors and geophagy among infants and young children and implications of exposure to fecal bacteria. Am. J. Trop. Med. Hyg. 89, 709–716 (2013).
145. George, C. M. et al. Unsafe Child Feces Disposal is Associated with Environmental Enteropathy and Impaired Growth. J. Pediatr. (2016). doi:10.1016/j.jpeds.2016.05.035
146. Cumming, O. & Cairncross, S. Can water, sanitation and hygiene help eliminate stunting? Current evidence and policy implications. Maternal and Child Nutrition (2016). doi:10.1111/mcn.12258
134
147. Baker, J., Sanghvi, T., Hajeebhoy, N., Martin, L. & Lapping, K. Using an evidence-based approach to design large- scale programs to improve infant and young child feeding. Food Nutr. Bull. (2013). doi:10.1177/15648265130343S202
148. Iannotti, L. L., Lutter, C. K., Bunn, D. A. & Stewart, C. P. Eggs: The uncracked potential for improving maternal and young child nutrition among the world’s poor. Nutr. Rev. 72, 355–368 (2014).
149. WHO and UNICEF. Progress on sanitation and drinking-water - 2014 update. … Monitoring Programme for water supply and sanitation. (2014). doi:http://apps.who.int/iris/bitstream/10665/81245/1/9789241505390_eng.pdf?ua=1
150. WHO. Progress on Drinking Water, Sanitation and Hygiene. World Health Organization (2017). doi:10.1111/1463-1652.00063
151. Black, R. E., Morris, S. S. & Bryce, J. Where and why are 10 million children dying every year? Lancet 361, 2226–2234 (2003).
152. Vella, V. et al. Determinants of child nutrition and mortality in north-west Uganda. Bull. World Health Organ. (1992).
153. Arthur, S. S. et al. Tackling malnutrition: A systematic review of 15-year research evidence from INDEPTH health and demographic surveillance systems. Glob. Health Action (2015). doi:10.3402/gha.v8.28298
154. Bawankule, R., Singh, A., Kumar, K. & Pedgaonkar, S. Disposal of children’s stools and its association with childhood diarrhea in India. BMC Public Health (2017). doi:10.1186/s12889-016-3948-2
155. Nizame, F. A. et al. Handwashing before food preparation and child feeding: A missed opportunity for hygiene promotion. Am. J. Trop. Med. Hyg. (2013). doi:10.4269/ajtmh.13-0434
156. Black, R. E. et al. Maternal and child undernutrition: global and regional exposures and health consequences. The Lancet 371, 243–260 (2008).
157. L’Enfance, I. H. de, Petion-Ville, DHS Haiti & MEASURE & ICF International. Haiti Enquete Mortalite, Morbidite, et Utilization de Services (EMMUS-V). (2012).
158. Piper, J. D. et al. Water , sanitation and hygiene ( WASH ) interventions : effects on child development in low- and middle-income countries ( Protocol ). Cochrane Database Syst Rev (2017). doi:10.1002/14651858.CD012613.www.cochranelibrary.com
159. Hagos, S., Lunde, T., Mariam, D. H., Woldehanna, T. & Lindtjørn, B. Climate change, crop production and child under nutrition in Ethiopia; a longitudinal panel study. BMC Public Health 14, 884 (2014).
135
160. Gebreyesus, S. H., Mariam, D. H., Woldehanna, T. & Lindtjørn, B. Local spatial clustering of stunting and wasting among children under the age of 5 years: Implications for intervention strategies. Public Health Nutr. (2016). doi:10.1017/S1368980015003377
161. Haile, D., Azage, M., Mola, T. & Rainey, R. Exploring spatial variations and factors associated with childhood stunting in Ethiopia: Spatial and multilevel analysis. BMC Pediatr. (2016). doi:10.1186/s12887-016-0587-9
162. Kinyoki, D. K. et al. Environmental predictors of stunting among children under-five in Somalia: Cross-sectional studies from 2007 to 2010. BMC Public Health (2016). doi:10.1186/s12889-016-3320-6
163. Grace, K., Davenport, F., Funk, C. & Lerner, A. M. Child malnutrition and climate in Sub-Saharan Africa: An analysis of recent trends in Kenya. Appl. Geogr. (2012). doi:10.1016/j.apgeog.2012.06.017
164. Martínez, J. I., Román, E. M., Alfaro, E. L., Grandi, C. & Dipierri, J. E. Geographic altitude and prevalence of underweight, stunting and wasting in newborns with the INTERGROWTH-21st standard. Jornal de Pediatria (2018). doi:10.1016/j.jped.2018.03.007
165. Shively, G., Sununtnasuk, C. & Brown, M. Environmental variability and child growth in Nepal. Heal. Place (2015). doi:10.1016/j.healthplace.2015.06.008
166. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. (2017). doi:10.1002/joc.5086
167. Funk, C. et al. A global satellite-assisted precipitation climatology. Earth Syst. Sci. Data (2015). doi:10.5194/essd-7-275-2015
168. Didan, K. MOD13A1 MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS LP DAAC (2015).
169. Jarvis, A., H.I., Reuter, A., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database. CGIAR CSI Consort. Spat. Inf. (2016). doi:10.1016/S0550-3213(96)00589-5
170. Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature (2018). doi:10.1038/nature25181
171. Stevens, F. R. et al. WorldPop-RF, Version 2b.1.1. figshare (2015). doi:doi:10.6084/m9.figshare.1491490
172. Bartholomé, E., Belward, A. S. & F. Achard. Global Land Cover 2000 database. European Commission,Joint Research Centre (2003).
173. GADM. Database of Global Administrative Areas, version 3.6. Univ. Calif. (2018).
136
174. Noor, A. M. et al. The changing risk of Plasmodium falciparum malaria infection in Africa: 2000-10: A spatial and temporal analysis of transmission intensity. Lancet (2014). doi:10.1016/S0140-6736(13)62566-0
175. Sullivan, D. Google Earth Pro. Geospatial Solutions (2009). doi:10.1056/NEJMoa1509342
176. Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. (2017). doi:10.1016/j.rse.2017.06.031
177. ESRI Environmental Systems Research Institute. ArcGIS Desktop: Release 10.2.2. Redlands CA (2014).
178. Rinaldo, A. et al. Reassessment of the 2010–2011 Haiti cholera outbreak and rainfall-driven multiseason projections. Proc. Natl. Acad. Sci. 109, 6602 LP-6607 (2012).
179. Eisenberg, M. C., Kujbida, G., Tuite, A. R., Fisman, D. N. & Tien, J. H. Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches. Epidemics (2013). doi:10.1016/j.epidem.2013.09.004
180. Wan, Z., Hook, S. & Hulley, G. MOD11A2 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006 [Data set]. NASA EOSDIS LP DAAC (2015).
181. Center for International Earth Science Information Network - CIESIN. Global Roads Open Access Data Set, Version 1 (gROADSv1). NASA Socioecon. Data Appl. Cent. (2013). doi:http://dx.doi.org/10.7927/H4VD6WCT
137
BIOGRAPHICAL SKETCH
Lindsey Amanda Laytner was born and raised in Fort Lauderdale, FL. She went
on to pursue her Bachelor of Art (BA) in Anthropology, a Master in Public Health (MPH)
in Social and Behavioral Sciences, and a Doctor of Philosophy in Environmental and
Global Public Health from the University of Florida. Throughout her academic career,
Lindsey has been fortunate to work in East Africa and the Caribbean. She spent 3
months in the highlands of Ethiopia as an archeologist, and on a multi-sectoral WASH
campaign in Kisumu, Kenya where she trained enumerators, field assistants, and
oversaw the administration of an 800-household survey that involved the University of
Florida, Great Lakes University in Kisumu, CDC-Kemri, and the London School of
Hygiene and Tropical medicine based in Kisumu, Kenya. During her doctoral studies,
she has worked on a variety of projects concerning WASH, livestock husbandry and
animal source food consumption, child health outcomes (i.e. particularly, stunting). Her
work has included consulting for PATH, the Bill and Melinda Gates Foundation, USAID,
UNICEF, and the World Bank. Her ongoing desire is to continue her work in the WASH-
One Health research and programming arena, using community outreach and
engagement principles to design impactful WASH interventions and communication
tools that focus on human, animal, and environmental health through the WASH
interface.
