Diarrheal Diseases in Rural and Urban Uganda: Examining the Association Between Temperature and Rainfall Anomalies and Diarrheal Incidence

By Quinn Adams University of Colorado at Boulder

A thesis submitted to the University of Colorado at Boulder In partial fulfillment of the requirements to receive Honors designation in Environmental Studies May 2019

Thesis Advisors: Colleen Reid, Geography, Committee Chair Dale Miller, Environmental Studies Balaji Rajagopalan, Civil, Environmental and Architectural Engineering

ABSTRACT

Diarrheal diseases are the second leading cause of childhood mortality world-wide and are among the top three leading causes of childhood mortality in Uganda. Prior research suggests a relationship between weather and diarrheal incidence. This thesis attempts to distinguish how short-term temperature and precipitation anomalies in 2016 impacted diarrheal incidence in Uganda. I combined household-level data from the 2016 Uganda Demographic Health Survey with gridded meteorological data obtained from the International Research Institute Data Library. I then performed logistic regressions over various temporal ranges in order to assess the association between diarrheal disease incidence and temperature and precipitation anomalies in Uganda in 2016. Confounding variables including wealth quintile, toilet type, drinking water type, rural versus urban status, presence of electricity, and floor type were used in my regression analysis to adjust the relationship between meteorological anomalies and diarrheal incidence for household and geographic characteristics that could also influence diarrhea. My results indicate a borderline statistically significant positive relationship between higher temperatures during Uganda’s rainy season and diarrheal incidence in the following months (OR = 1.66). The relationship between precipitation and diarrheal incidence was not statistically significant in this study. I also found that household variables played a significant role in diarrheal contraction and may explain some of the disparities in diarrheal incidence between rural and urban communities. These results could be used to inform policymakers about when to implement advanced measures for dealing with predicted weather events and subsequent peaks in diarrhea. The results could also facilitate improvements in sanitation facilities and can raise further questions about the relationship between climate and diarrhea.

PREFACE

Inspiration for this research stemmed from a long-time interest of mine in health sciences combined with my undying love for the environment. This project is the perfect intersection of health and environmental sciences and is in line with my goals to study environmental health in graduate school.

The inspiration for this specific topic arose from the Introduction to Global Public Health course taught by Professor Colleen Reid. In this class I was introduced to the environmental health field and was particularly struck by the increasing need for environmental health studies to mitigate the uncertainties surrounding climate change and health. With the extensive help of

Professor Reid over the past eight months, I was able to narrow my interest from a broad, general goal to study environmental health to a very focused glance into the impact of precipitation and temperature on diarrheal disease in Uganda in 2016. This is a topic that I am excited to expand on in the future through a master’s degree or PhD thesis.

I chose to study diarrheal diseases in particular because, though they are of the most easily treated diseases, they are still the cause of millions of deaths yearly on a global scale.

There are links between diarrheal contraction and weather events and long-term climate, so by studying these impacts, I can develop a general understanding for how weather and climate impact diarrhea and how to use this knowledge to improve public health.

Uganda was my country of choice for a few reasons. I was particularly inspired by my sisters work in maternal and child health with the NGO Jhpiego in Uganda for the past year. She was also of great help in the search for health data and in the background research process because of her unique knowledge and experience living in the country.

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ACKNOWLEDGMENTS

I would like to thank my advisors first and foremost for supporting me throughout this project and for devoting their time toward helping me achieve this goal. I would like to thank

Dale Miller for keeping me on track and meeting with me weekly to provide support and advice throughout the process. I would like to thank Balaji Rajagopalan for taking time to meet with me to help me understand the potential of this project and for providing the source of the climate data used in this study. Additionally, I would like to acknowledge Phillip White for taking many hours to teach me ArcGIS. Finally, I would like to give a big thanks to Colleen Reid for spending countless hours inspiring the development of my project, for being patient with me in my learning process, and for providing extensive feedback in my writing and analysis. I am grateful to have worked with such an inspiring group without whom this project would not have been a success.

Next, I would like to thank my friends and family for their undying support, encouragement and patience over the last eight months. I am especially grateful to my family for supporting my academics and for providing me unique opportunities to learn. Thanks to my mom for keeping me sane throughout the process, for providing valuable feedback on my drafts and for always being my number one fan. Thanks to my dad for supporting my project and for reading and critiquing my drafts. Finally, thanks to my sister for inspiring my interest in public health, for providing constant advice, and for being an incredible role model for me to look up to.

Overall, this process enhanced my academic and overall college experiences exponentially and I am so grateful to have been provided the opportunity to complete an honors thesis with the support from an incredible group of people.

Table of Contents Abstract...... i Preface...... iii Acknowledgements...... iv List of Acronyms...... vi 1| Introduction...... Page 1 2| Background...... Page 3 Uganda...... Page 3 Geography and Demographic Background in Uganda...... Page 3 Rural Versus Urban Characteristics in Uganda...... Page 4 Uganda Climate...... Page 5 Temperature and Precipitation Changes and Forecasts in Uganda...... Page 6 Diarrheal Disease in Uganda...... Page 7 Diarrheal Disease Incidence...... Page 8 Global Burden of Diarrheal Disease...... Page 8 Climate Change and Diarrheal Disease Incidence...... Page 10 Impact of Precipitation on Diarrheal Disease Incidence...... Page 11 Impact of Temperature on Diarrheal Disease Incidence...... Page 12 Review of Comparable Literature...... Page 13 3| Methodology...... Page 15 Health Data...... Page 15 Household Risk Factors...... Page 16 Meteorological Data...... Page 18 Spatial Analysis...... Page 19 Regression Analysis...... Page 20 4| Results...... Page 22 Precipitation...... Page 22 Temperature...... Page 23 Diarrheal Disease...... Page 23 Preliminary Spatial Analyses...... Page24 Statistical Association Between Meteorological Variables and Diarrheal Incidence...... Page 31 Statistical Association Between Household Variables and Diarrheal Incidence...... Page 32 5| Discussion...... Page 35 6| Limitations...... Page 39 7| Recommendations...... Page 41 8| Conclusions...... Page 43 9| Bibliography...... Page 45 10| Appendices...... Page 49

Appendix A: Regional prevalence of Diarrhea from 2016 Ugandan DHS Report Appendix B: Confounding Variable Code Sheet Appendix C: Administrative Regions from the Ugandan DHS 2016. Appendix D: Complete List of Logistic Regression Results for Diarrhea and Meteorological variables

LIST OF ACRONYMS

DHS: Demographic Health Survey

NOAA: National Oceanic and Atmospheric Association

IRI: International Research Institute

DALY: Disability Adjusted Life Year

IPUMS: Integrated Public Use Microdata Series

ITCZ: Intertropical Convergence Zone

WHO: World Health Organization

USAID: United States Agency for International Development

UNICEF: United Nations Children’s Fund

UNFPA: United Nations Population Fund

1| INTRODUCTION

Countries in sub-Saharan Africa are particularly likely to be impacted by climate variability associated with climate change. Climate change may cause an increase in severe weather events like drought or flooding as well as increasingly variable temperatures, which have the potential to influence public health outcomes (Horn, et. al., 2018). Because sub-Saharan

Africa is known to have one of the highest global burdens of disease due to the high rates of communicable disease, paired with increasing prevalence of non-communicable diseases, these climate fluctuations are likely to impact diseases in this region with greater intensity (“The

African Regional Health Report: The Health of the People”, 2017). Though diarrheal disease leads to fewer premature deaths in sub-Saharan Africa today than 20 years ago, it still remains a leading cause of death and disease incidence in this region (“The Global Burden of Disease:

Main Findings for Sub-Saharan Africa, 2013). Nearly 11 percent of deaths in children under five can be attributed to diarrheal diseases globally, with 90 percent of these deaths occurring in sub-

Saharan Africa (“Africa Key Facts and Figures for Childhood Mortality”, 2012).

This study attempts to quantify the relationship between temperature and precipitation anomalies and the incidence of diarrheal disease in children under the age of five in rural and urban regions in Uganda in 2016. Understanding the relationship between meteorological fluctuations and diarrheal disease could inform understanding of how climate changes could affect diarrheal disease prevalence in Uganda. Uganda has a tropical climate that is frequently rainy with peak rainfall during the wet months: March through May and September through

November. There are two distinct dry seasons from December to February and from June to July.

Uganda sits just north of the equator and is located in a region that is impacted by high weather

variability, making it an interesting spatial case study of how different weather patterns, specifically changes in temperature and precipitation, are associated with diarrheal disease incidence over 15 different administrative regions spanning the country. I hypothesize that higher temperature is associated with higher diarrheal incidence after adjustment for household and behavioral characteristics because of the increased reproductive abilities of bacteria and pathogens in warmer weather. I also hypothesize that the impact of precipitation on diarrhea will be variable: higher levels of precipitation will be associated with increased diarrhea due to increased pathogen exposure from flooding, or that lower levels of precipitation could lead to higher levels of diarrhea due to the increased use of unsafe drinking water as a result of water scarcity. Finally, I hypothesize that rural areas will be disproportionately impacted by increased rates of diarrhea.

In my research, I retrieved diarrheal incidence data from the Ugandan Demographic

Health Survey (DHS) data modified by the Integrated Public Use Microdata Series (IPUMS), which was collected from June 20th through December 16th in 2016; average monthly temperature anomalies from the National Oceanic and Atmospheric Administration (NOAA) through the International Research Institute for Climate Prediction (IRI) data library for the year

2016; and average monthly precipitation anomalies from the IRI data library as well. With these data sets, I used ArcGIS to assign mean rainfall and temperature anomalies to each of the 15 administrative regions laid out by the DHS and performed statistical regression analysis in R to determine how diarrhea is influenced by these meteorological factors. Because diarrheal incidence is also impacted by factors other than climate, the regression analysis was adjusted for confounding factors including primary drinking water source, rural versus urban residence, improved or unimproved toilet facility, wealth quintile, floor type, and presence of electricity.

2| BACKGROUND

Uganda

Geography and Demographic Background in Uganda

The republic of Uganda lies just North of the equator in East Africa, ranging from about

1.5˚S to 4.2˚N and about 29.6˚E to 35˚W. Uganda is made up of 241,039 square kilometers that have been administratively separated between 112 districts (Uganda Bureau of Statistics (UBOS) and ICF, 2018). Uganda is landlocked and is surrounded by volcanic hills on the Eastern border shared with Kenya and mountainous regions on the Western border shared with the Democratic

Republic of Congo. About 13 percent of the total land area is used for national parks and game reserves (Global Security, 2015) and nearly one fifth of the total land area is covered by water and swampland with access to Lake Victoria on the Southeastern border: the second largest freshwater lake in the world (World Atlas, n.d.).

Uganda has one of the youngest and fastest growing populations in the world with a fertility rate of 5.4 children per woman (Uganda Bureau of Statistics (UBOS) and ICF, 2018).

With a population of 40,853,749 people, nearly 50 percent of Uganda’s overall population is under the age of fifteen and nearly 80 percent of the population is under the age of 30. (Central

Intelligence Agency, 2018). High fertility, in part, can be explained by high infant mortality (The

World Bank, 2011). But, reducing the fertility rate is difficult for a number of reasons: the weak coordination of contraceptives and family planning in the government, inequality between men and women, the large population of women under 18 who will soon enter reproductive age, lack of support from influential religious institutions, and misconceptions about family planning

(Uganda, USAID, 2016) to name a few. While the age specific fertility rate for Ugandan women aged 15 through 19 has declined overall, it remains significantly higher in rural areas compared

to urban areas (Uganda, USAID, 2016). According to the World Bank report of 2011, economies and income per capita would flourish under lower fertility scenarios. Increasing income per capita has the potential to move people up the economic ladder, resulting in less risky living conditions for a larger percentage of the population.

Uganda is a site for refugees of conflict from neighboring countries such as South Sudan,

Burundi and the Democratic Republic of the Congo, and currently hosts over 1.5 million refugees; creating more areas with the potential for unsafe and unsanitary living conditions due to overcrowding (UNHCR, 2019).

Rural versus Urban Characteristics in Uganda Most of the Ugandan population lives in rural settings despite increasing economic growth and recent urbanization efforts. Just around 20 percent of the population lives in urban areas, but because Uganda is moving from an agricultural-based economy to a service-based economy, fairly rapid urbanization took place at a rate of 5.4 percent per year beginning in 2002

(Mukwaya et. al., 2012). But it is important to note that, while the economy is shifting and urbanization is increasing, employment in agriculture is also increasing even as its contribution to the economy decreases. This indicates that modern, urban employment opportunities are not abundant enough to support the massive increases in population entering the workforce

(Mukwaya et. al., 2012).

A prominent number of the Ugandan population resides in rural areas, so the significant disparities between rural and urban communities in Uganda are important to note. For example, according to the 2016 DHS, only 18 percent of rural households had electricity compared to 59 percent in urban households. Additionally, there is more reliable access to improved drinking water sources like piped water, covered wells, and rainwater in urban areas (91 percent) versus rural areas (74 percent) (Uganda Bureau of Statistics (UBOS) and ICF, 2018). In addition to the

lack of improved water sources, more than half of rural households do not use any methods to treat their drinking water (Uganda Bureau of Statistics (UBOS) and ICF, 2018). The 2016 DHS report also discusses the use of unimproved sanitation facilities, including non-flushing toilet facilities and unventilated pit latrines, depicting a significant disparity in urban areas (25 percent use) versus rural areas (65 percent use) (Uganda Bureau of Statistics (UBOS) and ICF, 2018).

Finally, nearly half of rural families are in the lowest two wealth quintiles compared to more than half of urban families existing in the highest wealth quintile (Uganda Bureau of Statistics

(UBOS) and ICF, 2018). These statistics imply a sharp contrast between urban and rural settings and lifestyles.

Uganda Climate Uganda, sitting just North of the equator, experiences a highly variable climate with mostly tropical conditions and two distinct wet seasons and two distinct dry seasons. The wet seasons occur from March through May and from September through November and the dry seasons occur from December through February and June through Early August (Uganda

Wildlife Authority, 2018). Uganda occupies many different climate classifications such as tropical savanna, tropical monsoon, tropical rainforest, oceanic and subtropical highland oceanic climates; but is dominated particularly by tropical savanna (“Uganda climate,” 2015). Uganda is also home to a multitude of large lakes and there is high variability in rainfall across the country.

One possible explanation for Uganda’s high weather variability nationwide is the existence of the mountainous highlands of Uganda and Kenya mixed with the large volume of lakes in the region, which could contribute to the breaking up of air masses over East Africa

(McHugh, 2004). Because of the minor convergence zone over Northern Uganda, the Northeast regions experience a drier climate than much of the rest of the country (Future Climate for

Africa, 2016). This variance can also be explained by prevailing wind patterns and the large

range in elevation levels from just over 600m above sea level to over 5,000m at the peak of

Mount Rwenzori (Future Climate of Africa, 2016). Much of the Southern border is occupied by

Lake Victoria, including Kampala: the most densely populated part of the country. Typically, evaporation is approximately equal to precipitation over the lake, but global atmospheric water vapor and pressure are expected to increase with increases in greenhouse gas concentrations bringing the potential for an increase in rainfall in this area (Akurut, Willems, & Niwagaba,

2014). The meteorology in Uganda is heavily influenced by seasonal migration of the tropical rain belt which helps to explain the twice a year rainy seasons experienced in equatorial Africa

(Nicholson, 2018).

Temperature and Precipitation Changes and Forecasts in Uganda Beginning primarily in the early 1980’s, Uganda, along with the rest of East Africa, has experienced a warming trend. By the end of this century, the subtropics are projected to see an increase in temperature by 4˚C (Kolstad & Johansson, 2011). Although Uganda has not experienced a significant change in daily maximum temperatures, according to one study there has been a notable increase in daily minimum temperatures particularly within the last three decades (Christy, 2013). Increasing temperatures allow for more water vapor in the atmosphere which could indicate the possibility of increased severe rainfall events (World Bank Group, n.d.).

In East Africa, general circulation models suggest a 10-20% increase in precipitation with unknown shifts in rainfall distribution (Kisakye & Van der Bruggen, 2018). Other studies suggest, though, that there is a regional scale drying trend in East Africa that began around 1999 that resulted from increased variability in Pacific Decadal Oscillation (Lyon & Vigaud, 2017). It is clear that there is uncertainty with how climate systems will be influenced as a result of climate change.

In Uganda in particular, this brings major uncertainty with drinking water quality as reliance on rainwater harvesting has become a major strategy for rural communities in insuring drinking water access (Kisakye & Van der Bruggen, 2018). Using surface water, like rain water, as a primary water source puts children at a greater risk for diarrheal disease than using improved water sources like wells (Ssenyonga, Muwonge, Twebaze, & Mutyabule, 2010).

Diarrheal Disease in Uganda

In Uganda, pneumonia, diarrhea and malaria are the leading causes of child morbidity and mortality. From 2007 to 2017, deaths from diarrheal diseases have increased by three percent in Uganda (Institute for Health Metrics and Evaluation, 2017). The Ugandan DHS determined that diarrheal prevalence is highest for children between 6 and 11 months of age (Government of

Uganda, 2016). In general, children under the age of five are especially susceptible to climate- influenced diseases like diarrhea (Musengimana et al., 2016). The probability of a child in

Uganda dying from any cause, including diarrhea, before the age of five is 45 percent higher for rural infants than for urban infants (World Health Organization, Inter-parliamentary Union,

Parliament of the Republic of Uganda, & The Partnership for Maternal, Newborn & Child

Health, 2007). Children in rural communities are also seven percent less likely to receive oral rehydration therapy (ORT) than children living in urban areas (UNICEF, 2018). According to the

2016 Ugandan DHS, 20 percent of children under the age of five were reported to have diarrhea in the two weeks prior to being surveyed and 14,493 cases were observed at the time of the survey (Government of Uganda, 2016). The 2016 DHS survey showed that prevalence of diarrhea was highest in the Teso region in Eastern Uganda, with 29 percent of children having diarrhea in the two weeks preceding the survey and was lowest in the Bunyoro region in Western

Uganda (See Appendix C), with only 10 percent of children having diarrhea in the two weeks preceding the survey (Government of Uganda, 2016).

Widely recognized household-based risk factors for diarrhea include low levels of access to safe drinking water, poor personal hygienic practices, and unsafe disposal of feces (Izale,

2015). The Ugandan DHS determined that disposing of stools in a safe manner increased with the child’s age (Government of Uganda, 2016). Specifically, the DHS survey found that 44 percent of feces were disposed of safely for children age 0-1 months while 95 percent of feces were disposed of safely for children age 18-23 months (Government of Uganda, 2016). Proper disposal of feces may also be determined by wealth quintile as well as urban versus rural living conditions. Only 74 percent of families in the lowest wealth quintile practiced safe disposal while 86 percent of families in the highest wealth quintile practiced safe disposal. Children living in urban areas are approximately 10 percent more likely to have their last fecal matter disposed of in a safe manner (Government of Uganda, 2016). It is especially important to note the differences between rural and urban populations because only 23.8 percent of Uganda’s population resides in urban areas, indicating that the majority of the population living in rural areas may experience more adverse effects of increases in diarrheal disease (Central Intelligence

Agency, 2018).

Diarrheal Disease Incidence

Global Burden of Diarrheal Disease

Even though diarrheal diseases are some of the most treatable communicable diseases, they are still one of the leading causes of death globally - especially in children (Horn, et al.,

2018). Diarrhea leads to death by extreme dehydration. In 2015, 2,195 children died daily as a

result of diarrhea ("Global Diarrhea Burden| CDC", 2015). Diarrhea is ranked as number four as of 2010 in DALYs (Murray et al., 2012), with diarrhea as the leading cause of childhood malnutrition and the second leading cause of death in children (Rabassa, Skoufias, & Jacoby,

2014). A disability adjusted life year (DALY) is the sum of healthy years lost due to premature death and disability and a measurement of the disparity in the population’s current health and the populations ideal health (Metrics: Disability-Adjusted Life Year (DALY), 2014). Until 2003, mortality associated with diarrhea contributed to about 2.5 million deaths per year in children under five, and still, diarrheal disease causes around 700,000 deaths per year in developing countries globally (Musengimana et al., 2016). Mortality from diarrhea in children under the age of five was highest in sub-Saharan Africa and South Asia (Troeger et al., 2017).

Though diarrheal disease incidence moved from the second leading cause of death worldwide to the fourth with a 51 percent decline over the last 20 years, rates of diarrhea are increasing in sub-Saharan Africa. Increases in diarrheal disease incidence and burden due to diarrhea seen in sub-Saharan Africa are likely a result of increasing populations. (Murray et al.,

2012). According to the United Nations Children’s Fund, over 60 percent of deaths related to diarrhea can be attributed to poor sanitation practices and lack of access to safe drinking water sources (UNICEF, 2018).

In 2015, the leading causes of diarrheal deaths included rotavirus, shigella, and salmonella in order of highest impact (Troeger et al., 2017). Rotavirus infection, the number one cause of diarrheal diseases in children, is highly infectious and infection can be attributed primarily to poor sanitation practices (Government launches new rotavirus vaccine to protect children in Uganda from diarrhea, 2018). The global burden of diarrheal disease has decreased as a result of the implementation of sanitation facilities and increased access to safe drinking water

sources in certain areas, but it is still widely recognized as one of the leading causes of death in children worldwide (Troeger et al., 2017).

Climate Change and Diarrheal Disease Incidence

According to previous studies, there is a significant connection between climate variability and the burden of water and vector-borne diseases (Teklehaimanot et al., 2004). It is likely that infectious disease incidence, particularly climate-sensitive diseases like vector or waterborne diseases, will be exacerbated in a number of ways by anthropogenic climate change.

Diarrheal disease is one of the top climate sensitive diseases; primarily because varying climate patterns have the potential to lead to lower levels of water availability, compromised drinking water sources, and poor hygiene practices which increase risk of infection (World Health

Organization, 2018). The relationship between climate change and diarrheal disease is especially complicated to measure because it is influenced by a number of confounding factors and transmission routes that aren’t necessarily climate related, but still impact infection rates (Thiam et al., 2017).

This complicated relationship indicates that the link between climate factors and diarrheal disease will fluctuate based on location; household indicators like wealth and sanitation practices for example; and also, on the different agents of transmission that cause diarrheal diseases. For example, households in lower wealth quintiles with poor access to sanitation facilities and lower levels of education may be more strongly impacted by extreme weather events like drought and flooding, leading to higher infection rates for those vulnerable communities (Hashizume et al., 2008). Additionally, diarrhea caused by rotavirus could be impacted by climate change differently than diarrhea caused by pathogenic E. Coli and

Salmonella, for example (Musengimana, Mukinda, Machekano, & Mahomed, 2016). Because each diarrheal causing pathogen has unique characteristics and transmission can vary from food- borne infection, water-borne infection, or person to person contact, it is likely that climate will have a varying impact on each pathogen (Harley, et. al., 2011). It is possible, though, that increasing temperatures could increase the reproductive and survival abilities of a multitude of viral, bacterial and parasitic pathogens that lead to diarrheal disease; suggesting that increasing temperatures may have a positive association with pathogen concentration of many forms and subsequent higher rates of human infection (Singh, et. al., 2001). The impact of other forms of climate variability remain inconsistent and unclear: the influence of climate factors such as rainfall, air pressure, or relative humidity on diarrhea are reliant on sanitation infrastructure as well as pathogen characteristics, and may not have a direct one-to-one correlation with disease incidence, making diarrheal incidence as a result of these factors more difficult to measure

(Kolstad & Johansson, 2011).

Already, increases in temperature and rainfall intensity are being experienced in East

Africa as a result of climate change (Shongwe et. al., 2011). These changes in temperature and rainfall have the ability to alter geographic range and seasonality of disease-causing pathogens and also have the potential to increase human exposure (Carlton, Woster, DeWitt, Goldstein, &

Levy, 2016).

Impact of Precipitation on Diarrheal Disease Incidence

Precipitation specifically can impact diarrheal disease prevalence in a number of ways.

Heavy precipitation, leading to increased flooding and rainfall runoff, leads to increased pathogen exposure in freshwater sources, contamination of water sources (Godfrey, et. al., 2013) and increases in deterioration and access to sanitation facilities (Bandyopadhyay, Kanji & Wang,

2012). Another result of increased rainfall is the increased opportunity for transmission by flies and other vectors, as breeding season is correlated with the rainy season (Azage M, Kumie A,

Worku A & Bagtzoglou A, 2017). Because of this, increased rainfall could increase the breeding capabilities of various pathogens and vectors. In 2017, Azage M., et. al. found that the overarching reason for the association between increased rainfall and diarrheal incidence is the contamination of drinking water sources as a result of increased rainfall runoff. On the other hand, droughts are expected to increase in certain areas of Sub-Saharan Africa which has the potential to decrease sanitation practices, like hand washing, and increase the need to use unsafe drinking water (Bandyopadhyay, Kanji & Wang, 2012).

Impact of Temperature on Diarrheal Disease Incidence

Temperature has been found to have a significant impact on diarrheal disease incidence

(Carlton et al., 2016). For example, increased temperatures may have an effect on pathogen multiplication in food and water sources (Singh et al., 2001). The influence of temperature on diarrheal incidence is generally more prominent in rural areas. The findings that show higher rates of diarrhea in urban areas may be explained by population growth and overcrowding and the subsequent lack of timely access to health care and may therefore have a lower association with temperature than rural areas (Thiam et al., 2017). Previous studies find that a 5˚C increase in weekly average temperatures in Cape Town, South Africa could lead to an increase in diarrheal diseases up to 40 percent (Musengimana et al., 2016). Increased temperatures could also lead to changes in behavior such as higher water consumption and decreased sanitation practices, thus allowing for easier transmission of diarrhea (Onozuka & Hagihara, 2015).

Review of Comparable Literature Past studies on this topic show that rates of diarrheal disease are positively correlated with temperature increases, but that there is a more complicated relationship between diarrheal disease rates and precipitation (Mellor et al., 2016). A study conducted in Benin determined that higher diarrheal prevalence was found under more scarce water conditions primarily because it increased water consumption overall, leading to higher probability of consuming water from contaminated sources (Pande, Keyzer, Arouna, & Sonneveld, 2008). Similarly, research done in

Taiwan looking at the relationship between diarrhea and climatic variables shows that increasing maximum temperatures have a positive association with diarrheal morbidity, especially in children and older adults due to increased exposure from increased temperature coupled with their weaker immune systems (Chou, et. al., 2010). In their 2011 study, Kolstad and Johansson found similar results in six regions including North America, North Africa, the Middle East,

Equatorial Africa, Southern Africa and Southern Asia. They determined through a climate model that, with a projected 4˚C increase in temperature with climate change in these regions, there will likely be a 22-29 percent increase in diarrheal incidence nearing the end of the century

(Kolstad & Johansson, 2011). Through past studies, it is clear that there is a consensus about the positive correlation between diarrheal disease incidence and temperature increases, but findings regarding the association between precipitation and diarrheal disease are less consistent (Horn, et. al., 2018).

Some studies found that low levels of precipitation are associated with higher levels of diarrheal incidence (Singh et al., 2001; Bandyopadhyay, Kanji & Wang, 2012), while others found that higher precipitation levels were associated with higher incidence rates due to the potential for increased pathogen exposure (Godfrey, et. al., 2013). A study that took place in

Botswana found that diarrheal incidence was highest in the dry season and that incidence in the

wet season will likely decrease (Alexander, et. al., 2013). Their possible explanations for higher diarrheal incidence during the dry season include increased water intake by people, increased fly density, or decreased sanitation as a result of water economizing among other possible factors

(Alexander, et. al., 2013). In their 2001 study in Fiji, Singh et. al. found that lower rainfall levels were associated with a higher incidence of diarrhea due to disruption of drinking water availability. Interestingly, they also found that higher levels of rainfall had a positive association with diarrhea immediately after the rainfall event but led to decreased incidence the month after

(Singh, et. al., 2001). A likely explanation for this is that high levels of rainfall lead to increased pathogen exposure from fecal matter being flushed into the water supply, but eventually increases in drinking water quality once fecal matter was flushed out (Singh, et. al., 2001).

Similarly, Moors et. al. in their 2013 study in the Ganges Basin of Northern India, found that both decreased precipitation and extreme rainfall events might increase diarrheal incidence by 3 percent when combined with influences of increased temperatures. A study done in Rwanda using the Rwandan DHS also determined a negative association between precipitation and diarrhea. The authors found that higher runoff decreased diarrheal incidence for households with unimproved toilet facilities by cleaning the household environment; likely as a result of Rwanda- specific infrastructure (Mukabutera, et. al., 2016).

Godfrey et. al. looked at cholera outbreaks in Uganda from 2007 to 2011 and, though their findings show the highest number of outbreaks occurring with high levels of precipitation and flooding, there were no seasonal trends in outbreaks and number of cases varied each year

(Godfrey, et. al., 2013). A Mozambique study looking at the association between precipitation and diarrheal incidence found a positive association with rainfall and diarrheal incidence (Horn, et. al., 2018). Their paper explained that differing results regarding the relationship between

precipitation and rainfall may be a result of varying pathogen burdens by country. In

Mozambique specifically, they found that inadequate access to sanitation facilities coupled with rainfall and increasing human exposure to pathogens, together explain the positive association that they found (Horn, et. al., 2018). The lack of consensus found regarding the relationship between precipitation and diarrhea in different countries and regions suggests a need for more analyses to better understand this relationship.

3| METHODOLOGY

Health Data

Health data for this study was collected from the 2016 Ugandan DHS which was implemented by the Uganda Bureau of Statistics in collaboration with the Ministry of Health.

Funding was provided for this survey by the government of Uganda, the United States Agency for International Development (USAID), the United Nations Children’s Fund (UNICEF), and the

United Nations Population Fund (UNFPA) (Government of Uganda, 2016). The DHS focuses on maternal and child health and provides nationwide data in over 90 countries on HIV/AIDS, diarrheal diseases, malaria, household characteristics, gender, nutrition, family planning and more. I selected data from the 2016 survey in Uganda on diarrheal incidence at the household level and household characteristics. The DHS breaks Uganda into 15 different administrative regions. Surveyors interviewed 18,506 mothers aged 15 through 49 throughout these 15 different administrative regions. Data collection took place from June 20, 2016 to December 16, 2016

(Government of Uganda, 2016).

For my analysis, I used IPUMS-DHS which codes the DHS data previously described consistently to allow for analysis over space and time (Uganda Bureau of Statistics, 2016).

IPUMS integrates data by coding variables consistently across countries and over time allowing

for the study of change and cross-country and regional comparison to be conducted more simply.

IPUMS’s consistent coding also makes dataset merging and customization more straightforward.

For this analysis, I used data at the household level which provides more detail on household and individual characteristics compared to administrative-level data.

The dataset being used in this study indicates prevalence of recent diarrhea among children under five, using the question in the survey as follows: “did the child have diarrhea in the two weeks preceding the survey?” (Uganda Bureau of Statistics (UBOS) and ICF, 2018).

This data is available at the household level with code 0 indicating that the child did not have diarrhea in the two weeks preceding the survey and code 1 indicating that the child did have diarrhea in the two weeks preceding the survey.

Household Risk Factors

Because diarrheal prevalence can be attributed to household risk factors, which could be statistically related to meteorological factors through geographic location, the following confounding variables, additionally collected through the Ugandan Demographic Health Survey, were included in the regression models to adjust the relationship between meteorology and diarrheal disease for these factors. A confounding variable is a variable that influences both the dependent and independent variable and therefore can lead to an unanticipated association between the exposure and the outcome. In order to avoid any false correlations from the influence of confounding variables, controlling for the confounders described below adjusts the relationship between temperature and precipitation anomalies and diarrheal disease.

The urban versus rural (URBAN) variable assigns households to urban areas (0) and rural areas (1). The geographic location (DISTRICT) variable assigns each household to one of the 15 administrative regions laid out by the DHS. The floor type (FLOOR) variable is a measurement

of wealth and classifies household floors based on its material: earth, sand and dung (1); wood planks, palm and bamboo (2); polished wood, cement, ceramic tiles, carpet, stone, and brick (3); and any other not previously mentioned floor types (4). The electricity (ELECTRC) variable determines if a household has electricity (1) or does not have electricity (0). The variable describing sanitation facilities (TOILETTYPE) determines the use of the following types of sanitation facilities by each household: no facility (0); flush toilets (1); non-flushing toilets/compostable toilets (2); pit toilet latrine (3); unimproved/bucket toilet (4); other, not previously mentioned toilet facility (5). The variable describing each households primary drinking water source (DRINKWTR), uses the following categories for analysis: piped water (0); well water (1); surface water (2) ; rainwater (3); water purchased from supplier i.e. bottled water or tank truck (4); or other, not previously mentioned primary drinking water source (5). Finally, the wealth quintile (WEALTHQ) variable classifies each household as a part of the poorest (1), poorer (2), middle (3), richer (4), or richest (5) wealth quintiles.

Temperature Diarrhea

Geographic Precipitation Location Wealth (with floor type and Rural/Urban electricity as Livelihood Sanitation indicators (toilet and

Figure 1: Possible pathways depicting the contribution of household factors, precipitation, and temperature on diarrheal disease contraction. This figure shows the complicated relationships among common variables that lead to diarrheal incidence.

Meteorological Data

For my meteorological variables I used temperature and precipitation anomalies averaged monthly over the year 2016. Meteorological anomalies quantify the difference in a meteorological variable compared to the long-term average of that variable. This average is typically 30 years or more of daily data. A positive anomaly indicates that a given day’s observed temperatures or precipitation is higher than the 30-year average that it is being compared to. A negative anomaly indicates that a given day’s temperature or precipitation is lower than the 30-year average it is being compared.

Data for temperature anomalies in Uganda were obtained from the IRI data library. I used a gridded GeoTIFF image format with each grid at a spatial resolution of 0.1 degrees latitude by

0.1 degrees longitude. I retrieved monthly temperature anomaly data over the course of 12 months beginning in January of 2016 and ending in December of 2016 in order to compare monthly temperature anomalies from the DHS data year, 2016. Diarrheal disease incidence is commonly associated with a temporal lag which indicates that temperature patterns months and even years prior to the date of the survey could have impacted diarrheal infection rates. I used average annual precipitation and temperature anomaly data for each administrative region, and also used averages for seasonal ranges to account for a lagged association. First, I used the average temperature anomaly over the months of January through June to account for meteorological influence before the survey began in mid-June. Additionally, I used the average temperature anomaly for the months of March through May. This is Uganda’s pronounced wet season occurring before the start of the survey.

I received monthly average precipitation anomaly values for Uganda from the National

Oceanic and Atmospheric Administration’s Climate Prediction Center, obtained through the IRI

data library. Gridded GeoTiff image format was also used for these data with a spatial resolution of 2.0 degrees latitude by 2.0 degrees longitude. Monthly precipitation averages were obtained over a period of 12 months beginning in January of 2016 and ending in December of 2016. I used the same seasonal ranges to calculate annual average precipitation anomaly, wet season precipitation anomaly, and average precipitation anomaly for only the months prior to the start of the survey. These seasonal ranges allow for the analysis of precipitation anomaly data as described previously to account for temporal lags between precipitation exposure and diarrheal incidence.

Spatial Analysis

I created choropleth maps using the DHS statcompiler software to provide a visual representation of diarrheal incidence and DHS confounding variables in each of the 15 administrative regions.

ArcGIS software (ESRI, 2018) was used to spatially merge gridded GeoTiff meteorological data with the 15 administrative regions of the DHS data. In order to match the spatial resolution of temperature and precipitation data, I used the resample tool which changed the size of the cells for temperature from 2.0 x 2.0 to 0.1 x 0.1 to match the resolution of the precipitation data while maintaining the integrity of the data. I used the zonal statistics tool in the model builder feature to assign the mean temperature and mean precipitation anomalies to each of the 15 administrative regions laid out by the DHS. I then exported, as a comma separated values file (csv), the meteorological variables assigned to each administrative region with the table tool. The exported data tables were then merged with my health datasets obtained from

IPUMS-DHS.

Regression Analysis

In order to determine the relationship between temperature and rainfall anomalies and diarrheal disease incidence that occurred in 2016, I performed a mixed effect logistic regression analysis of the association between diarrheal incidence in 2016 with temperature and precipitation anomalies for the same year (2016), and separately for specific lagged seasonal meteorological measures. All models were adjusted for the following confounding variables: urban versus rural status, floor type as a measure of wealth, electricity versus no electricity present in the home, improved versus unimproved toilet facilities, primary source of drinking water, and wealth quintile. I found that the confounding variable, GEO_UG2016, describing regional location was collinear with precipitation and temperature variables, indicating that there is a strong, linear relationship between region and temperature and region and precipitation. In order to avoid collinearity, this variable was removed from regression models.

As informed by prior research, there is typically a lag associated with the incubation and infection periods of diarrheal diseases; but because the survey that ascertained diarrheal incidence was conducted over a period of around six months without documentation of when each household was surveyed, I was unable to associate a given child’s infection with an accurate temporal lag. Instead I ran regressions across the surveyed population with yearly and seasonal temperature and precipitation anomaly averages for 2016 in an attempt to assess longer- term lagged relationships. More specifically, I separately assessed models with the following meteorological variables at different time points during 2016: (1) yearly averages of temperature and precipitation anomalies, (2) averages of temperature and precipitation anomalies from

January 2016 through June 2016 (accounting for all of the months before the start date of the

survey), and (3) averages of temperature and precipitation anomalies from March to May

(Uganda’s wet season that takes place just before the survey season began).

The DHS data is not a random sample of the population of Uganda, so survey weights supplied by the DHS were used with the survey package to adjust for sampling design so that my results can be considered true associations of the population at the national level. For example, regions with smaller populations were oversampled in order to account for accurate contribution of women surveyed to the regions size (Uganda Bureau of Statistics (UBOS) and ICF, 2018).

The use of IPUMS-DHS made survey weights more simple because it included and explained the uses of the strata (IDHSSTRATA), person (PERWEIGHT) and DHS identification (DHISID) weights in my dataset, which were integral in my survey design. The strata variable

(IDHSSTRATA) is an identifier unique to DHS survey data that combines geographically similar data from specific countries for each specified year. The person variable (PERWEIGHT) is a weight that provides an accurate numeric representation of the surveyed population. Finally, the DHS identification (DHSID) variable was used to identify clusters across the sampled population and is the connector between DHS shapefiles and IPUMS-DHS data. These weights were used in my survey design and were ultimately used to make the statistical findings from surveyed respondents more representative of the population as a whole.

To do my regressions, I used the survey package in R (Lumley, 2019), which can be used to analyze complex survey-based datasets. I used this package to compute summary statistics and to perform logistic regression using the survey-weighted generalized linear model (Lumley,

2019). Statistical analyses for this study were done using R version 3.4.2 (R Core Team, 2017).

4| RESULTS

This section will begin by outlining the precipitation and temperature anomalies for the year of

2016. My next table shows the percentage and count of diarrheal incidence per administrative district. Then, I present choropleth maps for diarrheal incidence and each confounding variable to give a visual representation of characteristics that exist in each administrative region. Next, I present the results of my summary statistics and finally, I present the results of my logistic regressions analyzing the relationship between diarrheal disease and temperature and precipitation anomalies and confounding household variables.

Precipitation

The average precipitation over the year of 2016 was higher than the long-term average for every month. The long-term average started in 1950 and ended in 2015. Positive anomalies indicate that higher levels of precipitation were observed in 2016 than the average of precipitation values for each location from 1950-2015.

Temperature

The average annual temperature measurement for the year of 2016 was lower than normal indicating that, with the exceptions of May, July, and September, each month exhibited cooler temperature levels than the same long-term average previously described. These negative anomalies indicate that temperature was lower in most months of 2016 than the average of temperature values for each location from 1950-2015.

Diarrheal Disease

In the year 2016, there was an observed total of 14,493 reported cases of diarrhea in children under the age of five over the time span of late June through December from the 15 administrative regions in Uganda based on the DHS survey. Of the mothers surveyed, 20 percent reported their child having diarrhea within the two weeks preceding the survey.

Table 1: Diarrheal Incidence Levels by Administrative Region (n=14,493)

Region Percentage with Diarrhea Number of Children South Central 19.9 1,808 North Central 16.7 1,537 Kampala 15.5 554 Busoga 27.3 1,430 Bukedi 17.9 1,016 Bugisu 14.3 733 Teso 29.2 911 Karamoja 24.0 394 Lango 20.5 765 Acholi 24.4 713 West Nile 15.8 1,005 Bunyoro 10.1 845 Tooro 22.0 1,140 Kigezi 15.8 484 Ankole 16.6 1,157 Total 19.5 14,493

Preliminary Spatial Analysis

In visual comparison of the diarrheal incidence map and maps representing various confounding variables, I was able to make some inferences about how diarrhea is related to social factors. There is an apparent positive association between diarrhea and population in the lowest wealth quintile (Figure 3), and population with unimproved toilet facilities (Figure 4).

There is an apparent negative association between diarrheal disease and high percentages of households with electricity (Figure 5) and between diarrheal disease and unimproved drinking water sources (Figure 6).

Figure 2: Percentage of Children aged three years to five years who had diarrhea within the two weeks preceding the survey. Map information and compilation obtained from statcompiler software by Esri using DHS data by USAID. Copyright © Esri. All rights reserved.

Figure 2 shows the spatial distribution of diarrheal incidence in 2016 in Uganda, depicting the highest prevalence in the Northeast. There is also a relatively high percentage of children with diarrhea in administrative regions bordering Lake Victoria. The lowest percentages of children who had diarrhea two weeks prior to the survey in 2016 exist in Bunyoro and Bugiso.

Figure 3: Percentage of Ugandan population in the lowest wealth quintile. Map information and compilation obtained from statcompiler software by Esri using DHS data by USAID. Copyright © Esri. All rights reserved.

As shown in figures 2 and 3, the Southwest region of Uganda has the lowest percent of the population living in the lowest wealth quintile, which generally correlates with a lower incidence level of diarrhea. In the Southwest regions, 18.3 percent or fewer children had diarrhea in the two weeks preceding the 2016 DHS survey. This apparent positive association between high diarrheal disease incidence and the population in the lowest wealth quintile is expected because income is a major driver of health disparities: the population in the lowest wealth quintile is more likely to use unimproved sanitation sources, a large indication of infection rates, and are less likely to visit the doctor or have access to resources that help maintain health

(Woolf, et. al., 2015).

Figure 4: Percentage of Ugandan population using an unimproved toilet source. Map information and compilation obtained from statcompiler software by Esri using DHS data by USAID. Copyright © Esri. All rights reserved.

Figures 2 and 4 also suggests a positive association between diarrheal disease incidence and the use of an unimproved toilet facility. According to the DHS, improved toilet facilities include non-shared toilet facilities such as “flush/pour flush toilets to piped sewer systems, septic tanks, and pit latrines; ventilated improved pit (VIP) latrines; pit latrines with slabs; and composting toilets” (Uganda Bureau of Statistics (UBOS) and ICF, 2018). Figure 4 shows that

Karamoja, Bugisu, Lango, and the West Nile region present the highest prevalence of unimproved toilet facilities which correlates with the high diarrheal incidence seen in the

Northwest region of Uganda (as seen in figure 2). This association was also anticipated because unimproved toilet facilities can facilitate infection by bacteria from fecal matter thus increasing the incidence of diarrhea.

Figures 2 and 5 suggest a negative association between the presence of electricity in each household and diarrheal incidence. The highest percentage of children with diarrhea according to the DHS is in the Northeastern region which is also where the lowest percentage of households have electricity (fewer than 13.4 percent). The presence of electricity can be an indication of wealth, so it is expected that the areas where larger percentages of the population are in the lowest wealth quintiles, as seen in figure 3, would correspond with areas with the lowest percentage of households with electricity (Figure 5).

Figure 6: Percentage of population whose primary source of drinking water is unimproved. Map information and compilation obtained from statcompiler software by Esri using DHS data by USAID. Copyright © Esri. All rights reserved.

The comparison of figures 2 and 6 shows that there is an apparent negative association between diarrheal incidence and use of an unimproved primary drinking water source. The DHS defines improved drinking water sources as “piped water, public taps, standpipes, tube wells, boreholes, protected dug wells and springs, and rainwater. Households that use bottled water for drinking are classified as using an improved source only if the water they use for cooking and hand washing comes from an improved source” (Uganda Bureau of Statistics (UBOS) and ICF,

2018). In general, Western Uganda has a higher percentage using unimproved drinking water source (18.7 percent or higher) where generally lower rates of diarrhea are seen (figure 2). This negative correlation is unexpected as pathogen exposure is likely to increase with the use of more likely contaminated drinking water (Cabral, 2010).

Table 2: Summary Statistics of DHS variables among administrative region (N=15)

Confounding Factor Mean (%) Count of Surveyed Respondents

Had Diarrhea Recently 19.959 14,493

Rural Urban Status Urban 21.272 3,094 Rural 78.728 11,398

Administrative Region Kampala 3.7193 604 Central 1 12.2851 1113 Central 2 10.3978 1100 Busoga 9.8564 1219 Bukedi 6.8859 1001 Bugishu 5.0154 722 Teso 6.1516 1038 Karamoja 2.8427 787 Lango 5.3885 895 Acholi 4.9927 839 West Nile 6.9275 973 Bunyoro 6.0258 990 Tooro 7.9835 1058 Ankole 8.1488 970 Kigezi 3.3790 632 Floor Type Floor (natural) 66.613300 9992 Floor (rudimentary) 0.488366 70 Floor (finished) 32.799015 3868 Floor (other) 0.099319 11

Electricity Does not have electricity (0) 73.603 10670 Has electricity (1) 26.397 3271

Toilet Type No facility 7.18963 1406 Flush toilet 2.55222 313 Non-flushing toilet 0.14106 24 Pit toilet latrine 89.26454 12079 Unimproved toilet (Bucket/hanging 0.51177 63 latrine) Other 0.34078 56

Drinking Water Source Piped water 18.76637 2274 Well water 57.65364 8240 Surface water 21.66502 3170 Rainwater 1.08625 127 Purchased from a supplier 0.60716 88 Other source 0.22155 42

Wealth Quintile Poorest 22.747 3762 Poorer 21.070 3046 Middle 19.234 2664 Richer 17.735 2326 Richest 19.214 2143

Statistical Association Between Meteorological Variables and Diarrheal Incidence

As seen in tables 3 and 4 below, there is no statistically significant relationship in 2016 in

Uganda between diarrheal disease incidence and meteorological indicators except for temperature anomalies averaged over the rainy season – March through May (Table 3). The regression shows that there is a positive association between diarrheal disease and temperature anomaly averaged over March, April and May. By exponentiating the coefficient from the logistic regression, I found the odds ratio (OR = 1.66) which shows that, by holding other predictor variables constant, there is a 66% increased odds of a child getting diarrheal disease during mid-June through mid-December for a one unit increase in the average temperature anomaly during the previous March to May. This odds ratio was borderline significant

(p=0.05838) which indicates that the chance that the true association between average temperature anomaly during the previous March to May and childhood diarrheal disease in

Uganda is null is only slightly larger than 5%.

Table 3: Odds Ratios for Diarrheal Disease in Uganda in 2016 Regression including Climate

Variables (Temperature and Precipitations at Various Temporal Lags).

Variable Odds Ratio p-value 95% CI (2.5%, 97.5%)

Annual temperature anomaly 0.985 0.96115 (0.525, 1.845)

Annual precipitation anomaly 1.187 0.11756 (0.958, 1.471)

Average temperature anomaly 1.659 0.05838 . (0.983, 2.801) (March-May)

Average precipitation anomaly 1.012 0.86410 (0.884, 1.158) (March-May)

Average temperature anomaly 0.711 0.41237 (0.315, 1.605) (January- June)

Average precipitation anomaly 1.059 0.65360 (0.825, 1.358) (January-June) Signif. codes: 0 = ‘***’ 0.001 = ‘**’ 0.01 = ‘*’ 0.05 = ‘.’ 0.1 = ‘ ’

Statistical Association Between Household Variables and Diarrheal Incidence

Across all of the models, statistical significance was discovered in all regressions for positive associations between diarrheal disease and wealth in the 3rd (middle wealth), 4th (richer), and 5th (richest) quintiles; between diarrheal disease and the use of flush toilets; and between diarrheal disease and rudimentary floor materials like wood planks, palm, or bamboo. Statistical significance was found for negative associations between diarrheal disease and drinking water sourced from surface water. Table 4 shows the results for the odds ratios between diarrheal disease and the confounding variables used in this study with no meteorological variables included. Because the results were consistent for these variables across all regressions including those with meteorological variables, the results shown in table 4 are representative of the overall impact of these confounders on diarrheal disease incidence regardless of the temporal lags of the

climate variables (See appendix D for results for the odds ratios found between diarrheal disease and confounding variables on all of the seasonal ranges used).

The results of the regression regarding the relationship between confounding variables and diarrheal incidence showed that higher wealth quintiles (middle, rich, richest) were negatively associated with diarrheal incidence. I found the richest wealth quintile (OR = 0.64) to have a stronger negative association than the richer wealth quintile (OR = 0.75) and an even stronger negative association than the middle wealth quintile (OR = 0.83). As wealth quintile increases, the odds of contracting diarrhea decrease; indicating that increased wealth reduces the risk of diarrhea. My results from this regression also confirm the unanticipated negative association from figure 5 between drinking water obtained from surface water and diarrheal disease incidence (OR = 0.76) suggesting that the use of surface water as a drinking water source, as compared to using piped water, decreased the risk of diarrhea in this dataset. As expected, my results show a significant negative association between flush toilets and diarrhea

(OR = 0.61), which suggests that the use of flush toilets, as compared to no toilets, decreased the risk of contracting diarrhea. The results show that there is a positive relationship between diarrhea and unfinished floor types. The significance of this relationship is borderline (p > 0.05) but shows that children in households with unfinished flooring, as compared to floors made of cement, are more likely to contract diarrhea (OR = 1.176).

Table 4: Odds Ratios for Diarrheal Disease in Uganda in 2016 Regression Including

Confounding Variables

Variable Odds Ratio p-value 95% CI (2.5%, 97.5%)

Wealth Quintile 2 (Poorer) 0.959 0.57386 (0.826, 1.106)

Wealth Quintile 3 (Middle) 0.827 0.01920 * (0.706, 0.969)

Wealth Quintile 4 (Richer) 0.746 0.00602 ** (0.605, 0.919)

Wealth Quintile 5 (Richest) 0.635 0.00328 ** (0.469, 0.859)

Urban/Rural Status 1.082 0.38574 (0.894 1.279)

Drinking water from well water 0.936 0.46322 ( 0.763 1.099)

Drinking water from surface water 0.756 0.00794 ** (0.616, 0.928)

Drinking water from rainwater 0.770 0.44878 (0.398 1.523)

Drinking water purchased from 0.659 0.28744 (0.311 1.432) supplier

Drinking water from other sources 2.448 0.03863 * (1.041, 5.532)

Use of Flush Toilets 0.599 0.02488 * (0.374, 0.915)

Non-flushing/Composting toilet 0.804 0.51530 (0.408 1.542)

Pit latrine toilet 0.895 0.18248 (0.742 1.032)

Unimproved (bucket) toilet 1.463 0.29845 (0.693 2.808)

Other toilet source 0.734 0.45542 (0.321 1.629)

Has electricity 1.041 0.62864 (0.893 1.234)

Natural Floor (Earth, sand, dung) 1.429 0.19224 (0.849 2.488)

Rudimentary Floor (palm, bamboo) 1.167 0.08093 . (0.972 1.372)

Finished Floor (Ceramics, Stone, 3.476 0.16227 (0.615 20.059) Carpet, Bricks, Cement)

Signif. codes: 0 = ‘***’ 0.001 = ‘**’ 0.01 = ‘*’ 0.05 = ‘.’ 0.1 = ‘ ’

5| DISCUSSION

My results indicate that temperature and precipitation anomalies were typically not associated with diarrheal disease incidence in Uganda in 2016 at a statistically significant level.

The results do show a statistically significant relationship between temperature and diarrheal disease incidence when using temperature averages over Uganda’s rainy season: March through

May. More specifically, there is a 66 percent increase in risk of a child getting diarrheal disease from mid-June to mid-December with every unit increase in temperature over Uganda’s rainy season. My results show that higher temperatures during the rainy season lead to higher rates of diarrhea in the following months from June through December; so, increased diarrheal disease as a result of temperatures associated with the rainy season describes a possible burden on the health-care infrastructure in the subsequent months. It is important to note that these results may not be indicative of the influence of temperature on diarrheal incidence in Uganda generally because 2016 was an anomalously rainy and cold year, so these results may not translate to the impacts of temperature and precipitation in other years. For a more accurate and holistic understanding of this relationship, more research needs to be done in order to determine how meteorological factors impact diarrhea during warmer and drier years in Uganda as well.

I hypothesized that temperature would increase diarrheal disease incidence and that precipitation would have a variable impact on diarrheal incidence. My results do indicate that there is a positive association between temperature and diarrheal incidence, which is also the common consensus found in the literature. Azage, et. al. found in their 2017 study that diarrhea- causing bacteria can multiply more rapidly in warmer months, leading to higher incidence rates

(Azage, et. al., 2017).

The results of other studies indicate that it is difficult to measure the impact of precipitation on diarrheal incidence. On one hand, more rainfall could lead to increased risk of diarrhea because heavy precipitation can flush fecal matter into areas where humans may become exposed to diarrhea-causing pathogens (Horn, et. al, 2018). On the other hand, droughts have been known to increase cases of diarrhea because droughts can inhibit access to adequate drinking water (Jong-Wook, 2004) and decrease hygienic activities such as hand washing (Azage et. al., 2017). My findings regarding the relationship between precipitation and diarrhea were insignificant, possibly as a result of these divergent impacts, and therefore need further research is needed to reach any conclusions. While these findings cannot confirm or reject my hypothesis that precipitation will have a varying impact on diarrheal disease incidence, they do highlight the complexity of the association between precipitation and diarrhea.

My results show statistical significance in the relationships between diarrhea and socioeconomic status by wealth quintile, toilet type, floor type and drinking water sources.

Largely, my findings regarding the relationship between household characteristics and diarrheal incidence are what one would expect, except for the impact of drinking water source.

The results describing the relationship between diarrhea and drinking water source were unanticipated. I found that use of surface water as a drinking water source had a negative correlation with diarrheal incidence. The DHS defines surface water as an unimproved source for drinking water which is known to increase the risk of waterborne disease (Uganda Bureau of

Statistics (UBOS) and ICF, 2018). A possible explanation for the negative association found could be that behavior may change based on the water source. For example, because surface water may be presumed to be unsafe, higher boiling or other treatment practices might be observed. In Uganda, however, there is a higher percent use of unimproved drinking water

sources in rural areas (26%) than in urban areas (9%) and the DHS found that just over half of rural households (54%) do not treat their water (Uganda Bureau of Statistics (UBOS) and ICF,

2018). In order to make any significant predictions about the impact of drinking water source on the incidence of diarrhea, further assessment regarding the impacts of social behaviors in addition to household characteristics are needed.

I found that socioeconomic status had an expected influence on the incidence of diarrhea.

The DHS determines wealth quintiles based on the amount and type of consumer goods a household possesses (Uganda Bureau of Statistics (UBOS) and ICF, 2018) and it is expected that the more resources one has, the better their general health is likely to be. Because there is lower access to health care and vaccine coverage among people in the lowest two wealth quintiles, there is more opportunity for the disproportionate incidence of diarrhea in these groups (Chang, et. al., 2018). In Uganda, a higher percentage of people living in the lowest wealth quintile reside in rural areas compared to urban centers (Uganda Bureau of Statistics (UBOS) and ICF, 2018).

Only nine percent of households in rural areas are in the highest wealth quintile compared to 59 percent in urban areas (Uganda Bureau of Statistics (UBOS) and ICF, 2018). This information combined with my findings that lower wealth leads to higher rates of diarrhea suggests that more families are at risk in rural areas.

Floor type is a common predictor of wealth, so it was expected that rudimentary floor types (wood planks, palm or bamboo), as compared to floors made of cement, would also have a positive association with diarrheal disease. Having unfinished flooring is an indication of lower wealth quintiles and though the significance is borderline (p = 0.08093), this relationship shows that unfinished flooring increases the risk of diarrheal disease (OR = 1.176). Rural households are more likely to have unfinished floors made of Earth materials whereas 59 percent of urban

household floors are made of cement or other improved flooring types (Uganda Bureau of

Statistics (UBOS) and ICF, 2018). This predicts the possibility of unequal distribution of wealth from rural to urban settings and thus, the unequal distribution of diarrheal infection. In addition to floor materials being a predictor of wealth, unfinished floors enhance the survival and spread of pathogens in the home. A study done in Eastern Ethiopia notes that floors can act as an indirect pathway for pathogen transmission through increased microbial load in unfinished flooring (Hashi, et. al., 2016). Titiunik and colleagues found that replacing dirt floors with cement in Mexico reduced diarrhea by 49 percent (Titiunik, et. al., 2007).

My results for the association between flush toilets and diarrhea were also expected.

There was a negative association for this relationship indicating that children in households with flush toilets are less likely to contract diarrhea. The DHS categorizes flush toilets as an improved sanitation facility which is an important way to safely dispose of children’s stools, thus limiting the spread of disease (Uganda Bureau of Statistics (UBOS) and ICF, 2018). Just under 20 percent of households in Uganda in 2016 used improved toilet facilities and that number is even lower (16 percent) in rural communities. Because sanitation is a significant indication of diarrheal contraction, the high number of households with unimproved sanitation facilities is a good indication of high rates of diarrheal disease. Flush toilets alone have been found to reduce childhood diarrhea by 3.4 to 10 percent in the rural Philippines, suggesting the importance of improved sanitation as a way to reduce diarrheal incidence (Capuno, 2011).

Because rural households were at higher risk in my study for all of the significant household confounders; wealth quintile, toilet type, floor type and drinking water source; it’s possible that rural communities are at higher risk for diarrheal disease. Though more research needs to be done focusing more specifically on the disparity of household characteristics

between rural and urban regions and their impact on diarrheal disease, my results appear to corroborate my hypothesis that rural areas are at higher risk for diarrheal diseases than urban areas.

6| LIMITATIONS

My study has several limitations. One of the overarching limitations to this study was the lack of information regarding when women were surveyed. The DHS reveals that surveys took place from mid-June to mid-December, with no indication of when each different survey question was asked or when each mother was surveyed. There is often a temporal lag associated with diarrheal infection, but because of the lack of information regarding specific infection dates for each child observed in the survey, I was not able to determine with confidence through this study how infection is influenced by various specific lags for each child. In general, it is also difficult to perform a study on certain diseases that aren’t always reported, like diarrhea, because it could produce an underestimation of diarrhea-related mortality (Sarkar et. al., 2014).

Additionally, the gridded climate data that I used from the IRI data library had a resolution of 0.1 degrees latitude x 0.1 degrees longitude for precipitation and 2.0 degrees latitude x 2.0 degrees longitude for temperature. Because Uganda is approximately 5.5 degrees wide by 6.7 degrees long, this resolution was not sufficient for my analysis. Had the grid size for the meteorological data been smaller, it may have picked up more local information and would have given a more accurate representation in the differences in weather between the 15 administrative regions defined by the DHS over the course of 2016. This information would have been helpful in making more concrete conclusions about how diarrhea was influenced by meteorological versus confounding variables.

I was also unable to look at behavioral data regarding boiling water, washing vegetables, hand washing, or stool disposal methods which are critical factors in contraction of diarrhea, because these data are not available in the DHS data. While DHS publications may mention prevalence of these behaviors in rural or urban regions generally, the data at finer administrative spatial units was not available for my statistical analysis (Uganda Bureau of Statistics (UBOS) and ICF, 2018). Ideally, all of the data would have been available at the individual level, as opposed to the household level, because that would have provided more specific details about each person’s behavior in order to make comparisons between urban and rural regions.

The relationships I found between diarrheal disease and weather in Uganda over the course of 2016 and between diarrheal disease and confounding variables are likely context specific and therefore, I cannot draw large scale conclusions about the impact of climate on diarrheal disease beyond Uganda and beyond 2016 without studying this at larger spatial and temporal scales. My study instead, looks at associations between shorter-term meteorological factors and diarrheal incidence within one country and I therefore do not have the ability to make any broad conclusions. Additionally, the relationships that I found to be statistically insignificant in Uganda in 2016 could be significant in other locations, on a larger regional scale or over a longer period of time. For better understanding of how diarrhea is impacted by meteorological variables, future research should expand upon this work by looking back at previous years to develop an understanding about how changes in climate impact fluctuations in diarrheal incidence. In addition, other meteorological variables such as drought, for example, may be important to study to understand the impacts of climate extremes and diarrhea more holistically.

7| RECOMMENDATIONS

This particular research project could be implemented at a larger scale by looking at trends in Uganda over a longer time span. It could also be enhanced by looking at the relationship of diarrheal incidence and climate for a greater number of countries over the course of many years to assess spatial differences. Additionally, the use of more meteorological factors such as relative humidity or extreme weather events could help to determine how other climate systems could impact disease. Looking at disease incidence over the course of many years will also open the possibility of modeling future impacts of climate factors on disease, which will be informative in terms of mitigation and adaptation measures to a changing climate.

It is possible that my results can provide insight into how meteorological factors will impact diarrheal disease in different communities. This area research could be of importance in determining mitigation strategies to combat the impacts of climate change on health on a global scale. More importantly, though, this type of research could stimulate more interest in the environmental health field, as climate change having adverse impacts on disease incidence is possible. In the future, it will be necessary to investigate how our changing climate will impact disease in order to implement infrastructure and policy measures to reduce negative impacts.

Again, this can be done by looking at the methods used in this paper over a larger scale over a longer period of time.

Research in environmental health is difficult due to the lack of publicly accessible health data or lack of health records especially in developing countries. Weekly and monthly incidence data is often privatized at the facility level and is therefore not available for non-healthcare workers or researchers. Many developing countries are likely to be more strongly impacted by the effects of climate change due to structural inabilities to react quickly to severe weather events

especially in disruption of power and access to water; due to lack of resources at the household and sometimes institutional levels; and due to economic reliance on climate-based activities like farming and fishing, among other variables. Despite this, research being done in this these regions is limited. In particular, there are relatively few studies that have quantified the relationship between climatic variables like rainfall, surface temperature, and relative humidity and diarrheal diseases. From previous studies that have been done, though, there is a clear lack of consensus about the effect of precipitation on diarrheal disease incidence and there is little known in general about how changing climate systems will influence pathogen-induced and water-borne illnesses in the future.

By continuing similar research, policy inferences can be made as well as advanced measures for dealing with predicted weather events and the subsequent peaks in diarrheal incidence. My results indicate that there is a borderline significant link between higher temperatures during the rainy season (March-May) in Uganda and contraction of diarrheal disease anytime from June through December. With this information, policymakers and public health professionals could be able to implement heightened sanitation practices over anomalously warm rainy seasons to limit the subsequent infection of diarrhea. The link between diarrheal disease and poor sanitation practices is clear and should provide incentive for implementation of improved sanitation and toilet facilities at the national level for developing countries experiencing these same high rates of diarrheal incidence and mortality. There is also incentive to implement rotavirus vaccinations as this pathogen is one of the leading causes of diarrhea (“Government launches new rotavirus vaccine to protect children in Uganda from diarrhea”, 2018). Because it is possible that global access to clean drinking water will likely be reduced by climate change impacts (“Climate Change | UN-Water”, n.d.), it is important to

understand and establish infrastructure that will enhance access to reliable and clean drinking water sources in developing countries, providing the potential to reduce the overall global burden of diarrheal disease.

8| CONCLUSIONS

The unknowns associated with climate change have the potential to impact human health and disease incidence through the combination of infrastructure, social influences, and climate variability. This is especially true for diseases that are influenced heavily by behavior and are also linked to climatic factors, like diarrhea (Bhandari, et. al., 2012). This thesis examined the ways in which temperature and precipitation anomalies for the year 2016 adjusted for household characteristics impacted diarrheal disease incidence in children under five years old in 15 administrative regions in Uganda. Through prior research and the results of my study, I found that social factors have a strong influence on diarrheal disease incidence. My conclusions regarding the influence of meteorology on diarrheal disease in this study cannot be proven without further research because of the influence of many confounding factors on health combined with the limitations of this study outlined above. I predicted that temperature would have a positive association with diarrheal incidence and that the impact of precipitation would be variable. My results show that increased temperatures during the rainy season (March-May) have a borderline significant relationship with increased diarrheal incidence from mid-June to mid-

December. My hypothesis is partially confirmed with this result, but further research needs to be done in order to accept these results on a broader scale. With my results depicting higher incidence rates following high temperatures during the rainy season, preparation measures could be implemented during anomalously warm rainy seasons to combat the rise in diarrheal

incidence to be experienced in the following months. By furthering this research, policymakers could make even more informed decisions about how to implement mitigation measures improve rates of diarrheal incidence as climate change progresses. Though I found no statistical significance among the relationship between precipitation and diarrhea, my results highlight the complex relationship between diarrhea and precipitation and provide incentive to assess this relationship further.

My results on the influence of social factors and household characteristics were more conclusive and suggest that rural and poorer families are generally at greater risk for contracting diarrhea as a result of heightened prevalence of unfinished, rudimentary floor types; decreased sanitation; decreased use of improved drinking water sources; and fewer families in the upper wealth quintiles in rural areas.

While my results are not conclusive enough to determine the exact relationship between temperature and precipitation and diarrhea, they do bring forth further questions regarding the relationship between climate, social factors, behavior, and diarrhea. These results can be used to make predictions based on the sanitation levels and social factors that exist in rural areas compared to urban areas as seen in this study, combined with the relationship I did see between temperature influence and diarrheal incidence. But, because there is little consensus on the impact of precipitation on diarrheal disease and because my results for temperature were only borderline significant, it will be informative to expand on this research.

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10| APPENDIX

Appendix A: Regional prevalence of diarrhea obtained from the 2016 Ugandan Demographic Health Survey final report.

Appendix B: confounding variables re-coded for analysis with descriptions.

Appendix C: Ugandan administrative regions used in this study as described by DHS, retrieved from the 2016 final report for the Ugandan Demographic Health Survey.

Appendix D: Logistic Regression results for association between diarrhea and temperature and precipitation anomalies.

Odds Ratios for Diarrheal Disease in Uganda from January 2016 through December 2016:

Regression including annual temperature anomaly

Variable Odds Ratio p-value 95% CI (2.5%, 97.5%)

Middle wealth quintile 0.827 0.01961 * (0.706, 0.969)

Richer wealth quintile 0.746 0.00616 ** (0.605, 0.919)

Richest wealth quintile 0.635 0.00333 ** (0.469, 0.859) drinking water from surface water 0.756 0.00790 ** (0.615, 0.929) drinking water from other sources 2.448 0.03861 * (1.050, 5.708) flush toilets 0.599 0.02479 * (0.383, 0.936) rudimentary floor types 1.167 0.08113 . (0.981, 1.388) annual temperature anomaly 0.985 0.96115 (0.525, 1.845)

Signif. codes: 0 = ‘***’ 0.001 = ‘**’ 0.01 = ‘*’ 0.05 = ‘.’ 0.1 = ‘ ’

Odds Ratios for Diarrheal Disease in Uganda from January 2016 through December 2016:

Regression Including Annual Precipitation Anomaly

Variable Odds Ratio p-value 95% CI (2.5%, 97.5%)

Middle wealth quintile 0.832 0.02155 * (0.711, 0.973)

Richer wealth quintile 0.750 0.00678 ** (0.610, 0.923)

Richest wealth quintile 0.641 0.00386 ** (0.475, 0.866) drinking water from surface water 0.756 0.00777 ** (0.616, 0.928) drinking water from other sources 2.399 0.04038 * (1.041, 5.532) flush toilets 0.585 0.01901 * (0.374, 0.915) annual precipitation anomaly 1.187 0.11756 (0.958, 1.471)

Signif. codes: 0 = ‘***’ 0.001= ‘**’ 0.01 =‘*’ 0.05 =‘.’ 0.1 =‘ ’

Odds Ratios for Diarrheal Disease in Uganda from January 2016 through December 2016:

Regression including Temperature Anomaly March - May (Uganda’s Rainy Season)

Variable Odds Ratio p-value 95% CI (2.5%, 97.5%)

Middle wealth quintile 0.807 0.00890 ** (0.687, 0.947)

Richer wealth quintile 0.725 0.00289 ** (0.587, 0.895)

Richest wealth quintile 0.616 0.00175 ** (0.456, 0.834) drinking water from surface water 0.759 0.00918 ** (0.617, 0.933) drinking water from other sources 2.439 0.03919 * (1.047, 5.685) flush toilets 0.607 0.02906 * (0.383, 0.936) rudimentary floor types 1.176 0.06637 . (0.989, 1.398)

Average temperature anomaly 1.659 0.05838 . (0.983, 2.801)

(March-May)

Signif. codes: 0 = ‘***’ 0.001 = ‘**’ 0.01 = ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Odds Ratios for Diarrheal Disease in Uganda from January 2016 through December 2016:

Regression including Precipitation Anomaly March - May (Uganda’s Rainy Season)

Variable Odds Ratio p-value 95% CI (2.5%, 97.5%)

Middle wealth quintile 0.829 0.02078 * (0.707, 0.971)

Richer wealth quintile 0.748 0.00684 ** (0.606, 0.922)

Richest wealth quintile 0.638 0.00367 ** (0.472, 0.863) drinking water from surface water 0.756 0.00789 ** (0.615, 0.929) drinking water from other sources 2.445 0.03841 * (1.051, 5.691) flush toilets 0.599 0.02489 * (0.383, 0.936) rudimentary floor types 1.167 0.08078 . (0.982, 1.387) average precipitation anomaly 1.012 0.86410 (0.884, 1.158)

(march-may)

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Odds Ratios for Diarrheal Disease in Uganda from January 2016 through December 2016:

Regression including Temperature Anomaly January - June

Variable Coefficient p-value 95% CI (2.5%, 97.5%)

Middle wealth quintile 0.832 0.02435 * (0.709, 0.976)

Richer wealth quintile 0.750 0.00763 ** (0.608, 0.926)

Richest wealth quintile 0.639 0.00398 ** (0.472, 0.866) drinking water from surface water 0.753 0.00725 ** (0.613, 0.926) drinking water from other sources 2.452 0.03804 * (1.052, 5.712) flush toilets 0.596 0.02360 * (0.381, 0.932) rudimentary floor types 1.166 0.08324 . (0.980, 1.387) average temperature anomaly 0.711 0.41237 (0.315, 1.605)

(january through june)

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’

Odds Ratios for Diarrheal Disease in Uganda from January 2016 through December 2016:

Regression including Precipitation Anomaly January - June

Variable Standard Error p-value 95% CI (2.5%, 97.5%)

Middle wealth quintile 0.828 0.01944 * (0.707, 0.969)

Richer wealth quintile 0.747 0.00623 ** (0.607, 0.920)

Richest wealth quintile 0.638 0.00356 ** (0.472, 0.862) drinking water from surface water 0.756 0.00780 ** (0.615, 0.928) drinking water from other sources 2.439 0.03830 * (1.051, 5.662) flush toilets 0.595 0.02334 * (0.380, 0.930) rudimentary floor types 1.166 0.08064 . (0.982, 1.387) average precipitation anomaly 1.059 0.65360 (0.825, 1.358)

(january through june)

Signif. codes: 0 = ‘***’ 0.001 = ‘**’ 0.01 = ‘*’ 0.05 = ‘.’ 0.1 = ‘ ’