Abstract
Infant mortality (IM) represents the overall health of a country or region as it relates to access to medicine, health care, and clean water in a population. IM remains understudied in many areas of Uganda, as many studies are from urban the capital (Kampala). The long-term goal of this research is the mitigation of IM and poor pregnancy outcomes in Uganda. Insights gained from geographic distribution of IM will allow adaptation of diagnosis, treatments, and interventions within the studied areas. Through using OLS and geographically weighted regression, this study has explored the significant factors and their heterogeneous and scaling effects in 2016 across Uganda. The empirical findings from this study include a significant association between IM and both being unmarried and preferring to speak Luganda when interviewed. Those unmarried may lack a social network to assist with income, childcare, and household chores representing decreased resources. Additionally, being interviewed in Luganda was associated over a large geographic area, which may represent not being comfortable in English, which is the language of education, commerce, and presumably health care, thus suggesting a disconnect with health care settings. These data suggest that strides can be made in Uganda by providing targeted resources to areas with high rates of unmarried mothers and those areas with high rates of Luganda as their language of choice.
Introduction
Infant mortality (IM), or death of an infant under one year of age, represents the overall health of a country at one point in time and allows for comparisons of population health over time [1]. IM is an important factor in population health as factors that impact infant death, such as access to medicine, health care, and clean water, also affect the health of others in the population [1]. Most (98%) of the 2017 infant deaths in the world occurred in low and middle-income countries [2,3]. In 2017, estimates showed that Sub-Saharan Africa had the highest neonatal mortality rates (death under 28 days/1000 live births) in the world at 27.2 for every 1000 births overall [4]. In Uganda 2018 estimates ranged from 27,889 to 46,361 total infant deaths [5]. These disparities and their sequelae have been previously described in parts of Uganda and other resource-limited settings [6–12].
Uganda has made significant improvements in mitigating IM. The mortality rates in Uganda were, by far, much higher in 1990 as compared to the global average rate. According to a UNICEF report, 18.4% of the children born in Uganda in 1990 died. The proportion was reduced to 4.6% in 2018 [13]. To further improve Uganda’s IM mitigation requires introducing policies that would enhance the awareness and ability of parents to provide necessary care. Some factors, such as poverty and illiteracy, are social issues that influence more than IM rates. Understanding the risk factors for IM can enhance the formulation of strategies for future mitigation. For example, one of the Ugandan government’s actions to reduce the rate of child mortality included introducing the country-wide free prenatal and postnatal services [14].
Geographically weighted regression (GWR) allows for the development of local level estimates and compares them to their neighbors to produce maps and statistics that look at the processes behind spatial heterogeneity, while still comparing to a larger unit or the global process. A GWR model can be considered a type of regression model with geographically varying parameters. One of GWR’s strengths is that it can be used in estimating parameters anywhere within the study area provided a dependent variable along with a set of one or more variables is known. Another strength of the GWR is its applicability in examining the geographical patterns of long-term conditions, which is useful in limiting long-term poor health outcomes. In this case, the long-term condition includes those that might have stemmed from exposure to work-related risks, poor housing conditions, lack of social support (being unmarried), and stress-related conditions (unintended pregnancy, young maternal age). Application of GWR in the investigation of the relationships between infant deaths and social phenomenon quantitatively helps in investigating whether there is an existing stable relationship over space or whether they vary to reflect the features of different localities within the study area, possibly representing unmeasured variables that fluctuate geographically [15]. GWR allows stimulus-response visualization and an understanding of how the relationship changes in space.
IM remains understudied within Uganda, as many of the previously cited studies are from the capital (Kampala) or central Uganda [9,16]. While several studies have looked at entire country [6,14,17,18] or rural areas, [7,11,18] none used GWR to understand the differing patterns associated with IM. Through using GWR, the long-term objective of this project is to explore geographic patterns of IM in Uganda particularly focusing on unique geographic factors associated with IM. The aims of this study were to characterize the geographic distribution of IM and associated factors in 2016 Uganda, adjusting for infant, maternal, and community risk factors.
Methods and materials
Setting
The Republic of Uganda is in East Central Africa. Uganda is a country of 241,551 square kilometers bordered by South Sudan, Kenya, Tanzania, Rwanda, and the Demographic Republic of the Congo. Over forty languages are spoken in Uganda; Swahili and Luganda are popular languages with English being the language of government and commerce [19]. Kampala is the capital city, the largest city, and the commercial capital of Uganda and is in the southwestern region (Fig 1). Also, located within southern Uganda is the historic Kingdom of Buganda, the oldest and largest centralized monarchy in Africa. While they do not manage government services, the kingdom effects important cultural factors. Finally, Uganda is well known for their acceptance of refugees and had over 1.4 million refugees located mostly in resettlement camps throughout the country in 2020 [20].
Fig 1. Study districts (n = 112) and regions (n = 4) from the Uganda 2016 Demographic and Health Survey.
The most recent estimates show a population of 41.6 million persons in the country. Three out of four Ugandan households live in rural areas [21]. Administrative districts in Uganda in 2019 consisted of four (4) regions, 15 sub-regions, 135 districts, 309 counties, one city (Kampala) council and thirteen municipalities [22]. For this study, nine Ugandan areas were included in the 2014 Census data but not in the Uganda Demographic and Health Survey (UDHS) district data in 2016 as there were112 total districts. Because of the important growth and development of Uganda, there are variations through time of the geographic location of districts in Uganda. Table 1 lists how these inconsistent 2016 districts and were allocated to be consistent from the 2014 Uganda shapefile.
Table 1. Edits made by districts Uganda infant mortality study 2016.
| District | Date Began | Before (2014) | Completed (2016) |
|---|---|---|---|
| Bunyangabu | July 1, 2017 | Bunyangabu county in Kabarole District | Merged with Kabarole District |
| Butebo | July 1, 2017 | Butebo County in Pallisa District | Merged with Pallisa District |
| Kagadi | July 1, 2016 | Part of Kibaale District | Merged with Kibaale District |
| Kakumiro | July 1, 2016 | Part of Kibaale District | Merged with Kibaale District |
| Kyotera | July 1, 2017 | Part of the Rakai District | Merged with Rakai District |
| Namisindwa | July 1, 2017 | East Bubulo County in Manafwa District | Merged with Manafwa District |
| Omoro | July 1, 2016 | Omoro County in Gulu District | Merged with Gulu District |
| Pakwach | July 1, 2017 | Part of the Nebbi District | Merged with Nebbi District |
| Rubanda | July 1, 2016 | Rubanda County in Kabale District. | Merged with Kabale District. |
Data collection
For this project, two primary datasets will be used—the 2014 Uganda Bureau of Statistics (UBOS) census shapefile and the 2016 United States Agency for International Development (USAID) Demographic and Health Survey (DHS) for Uganda. Uganda’s Bureau of Statistics administers both the Ugandan Census (completed in 2014) and the Ugandan Demographic and Health Survey (UDHS) (completed in 2016). The DHS has created a new modelled estimates for small area estimation of health and demographics [23,24]. For this study, the unit of analysis was district, thus the DHS GIS files from DHS were not used.
The UDHS was sixth in a series of DHS conducted in Uganda in 1988–89, 1995, 2000–01, 2006, and 2011 (https://dhsprogram.com/data/). The main objective of the 2016 UDHS is to provide up-to-date information on fertility and childhood mortality levels; fertility preferences; awareness, approval, and use of family planning methods; maternal and child health; domestic violence; knowledge and attitudes toward HIV/AIDS; and maternal mortality. The information collected through the 2016 UDHS proposes to assist policymakers and program managers to design and evaluating programs and strategies intended to improve the health of Uganda. Design weights were adjusted for household nonresponse and individual nonresponse to obtain the sampling weights for households and for women and men, respectively.
Dependent variable
Infant mortality is the death of an infant in the first year of life. They are categorized as yes (infant who died before their first birthday) and no (infant who lived past their first birthday). The dependent cases in this file include the children ever born of eligible women. There were 57,906 eligible women in the dataset.
Independent variables
Socio-demographic factors included mothers age, mothers educational, language spoken in interview (English or Luganda), literacy, marital status, occupation of the mother, smoking, previous death of a baby, age at first birth, contraception use, distance to health care and intended pregnancy. Pregnancy factors included number of antenatal visits, during pregnancy, given or bought iron tablets/syrup, during pregnancy took: SP/fansidar for malaria. Infant factors included sex, birth order, and birth weight. Delivery factors included pre-term birth and birth interval. Geographic factors included urban and rural areas. Household factors included overcrowding, electricity, water sources, type of cooking fuel, wealth, household has radio, household has bicycle and household has television. Father’s factors included father’s education and occupation of father.
Geographic and statistical analysis
Given the complex multifactorial nature of most infant deaths, to understand the health of a community, modeling multiple factors is necessary. When these relationships are strictly linear, we can use traditional statistics such as Ordinary Least Square (OLS) or logistic regression, however when the relationships have spatial heterogeneity, a more complex process is necessary to deal with place. In the first stage, exploratory OLS modelling was completed. The OLS provided coefficients averaged over the entire area. A test for spatial autocorrelation (Moran’s I) of the residuals was then completed to determine if the model data were or were not clustered. To control for residual autocorrelation and heteroscedasticity, a second stage, identified as necessary in the OLS estimation, used a local approach, semi-parametric and multi scale GWR [25,26]. GWR is widely used for fitting data displaying spatial non-stationarity [25,27–30]. For this study, we used the GWR in ESRI® ArcGIS Pro 3.0. We used a distance band neighborhood type with a Golden search selection method.
This study used UBOS collected DHS data for our dependent variable of infant deaths rates (number of infant death/number of births*100) at the district level. Tests for normality were performed using Proc Univariate in SAS 9.4. Additionally, exploratory analysis in ArcGIS was used to wean down to a significant exploratory analysis. Variables were initially chosen from a review of the literature then only significant variables from the OLS remained in the model. If a variable was determined to not be normally distributed a natural log was used. Each of the variables with 0 in the weighted frequency were changed to 0.00001 for analysis purposes. Significance at the 0.05 level was used.
Ethics statement
The analysis was a secondary analysis conducted on publicly available data from Uganda. The study project did not involve any significant risk to study participants. The necessary data use agreements to obtain the Uganda specific data were completed and approved. This project was reviewed by the University of Oklahoma Health Sciences Center Institutional Review Board and determined not to be human subjects’ research. The project was also reviewed and approved by the University of Salford ethics review board.
Results
Overall results
An estimated 6.1% of births died the first year of life in 2016. The range of IM was from 2.8% of births dying in Shema District to 12.4% in Kalangala District (Fig 2). The total number of polygons in the area was 112. There appears to be a strong cluster of high IM in the south central region of Uganda. In the existing literature, 31 variables were readily available for analysis from the DHS survey. Table 2 summarizes the significant Chi-square measure for each of these exploratory variables. Through exploratory analysis, only 14 variables were kept for the Ordinary Least Square analysis and the subsequent GWR analysis.
Fig 2. Infant mortality rate by district (n = 112) from Uganda 2016 Demographic and Health Survey.
Table 2. Chi-square analysis of variables related to poor infant health and risk factors from Uganda 2016 Demographic and Health Survey: Final exploratory independent variables are highlighted and italicized.
| Variable | IM |
|---|---|
| Maternal Characteristics | |
| Maternal Age | < .0001 |
| Education | < .0001 |
| Language of Interview/Maternal Native | 0.0076 |
| Literacy | < .0001 |
| Marital Status | 0.0346 |
| Occupation Mother | 0.0104 |
| Smoking | < .0001 |
| Previous death of a baby | < .0001 |
| Age at first birth | 0.0042 |
| Contraception Use/Method | 0.0256 |
| Distance to health care | 0.008 |
| Intended pregnancy | 0.0559 |
| Pregnancy | |
| Antenatal visits | < .0001 |
| During pregnancy, given or bought iron tablets/syrup | 0.0002 |
| During pregnancy took: SP/fansidar for malaria | 0.0002 |
| Infant Characteristics | |
| Sex | 0.0003 |
| Birth order | < .0001 |
| Birth weight | < .0001 |
| Delivery Characteristics | |
| Pre-term Birth | < .0001 |
| Birth Interval | < .0001 |
| Geographic Characteristics | |
| Urban/Rural | 0.0077 |
| Household Characteristics | |
| Overcrowding | 0.0002 |
| Electricity | 0.0004 |
| Water Sources | < .0001 |
| Type of cooking fuel | < .0001 |
| Wealth | 0.0002 |
| Household has: radio | 0.0044 |
| Household has: bicycle | 0.0499 |
| Household has: television | 0.0001 |
| Father’s Characteristics | |
| Father’s education | < .0001 |
| Occupation Father | 0.0408 |
Spatial Autocorrelation
Spatial Autocorrelation (Global Moran’s I) is a way to determine the randomness of geographic events. Through a Global Moran’s I, we see that IM was clustered (p-value < .00001; z = 11.823). The local Moran’s I shows a clear cluster of high-high IM in the southcentral part of Uganda and a low-low cluster in southwest Uganda (Fig 3).
Fig 3. Infant mortality clusters in study districts (n = 112) using Anselin Local Moran’s I: from Uganda 2016 Demographic and Health Survey.
Ordinary Least Square (OLS)
Using the OLS backward regression, the final models explained 33.8% of IM variance (AICc 426.105). Among infant deaths, having completed the interview in the language Luganda had the highest t-statistic (6.0938) and thus was the most important explanatory variables in the model (p-value < .0001). Being unmarried was significant (T-statistics 2.725; p-value 0.004) as well. Using the Anselin Moran’s I, IM was significant for spatial dependence or clusters (z-score 8.979015; p-value < .0001). The local Moran’s I shows several significant clusters (Fig 3); the model shows some residual low clustering in two areas of the eastern region, Nakapiripirit District in the northern region and Lwengo District in the central region. Because of spatial heterogeneity processes, as shown by local Moran’s I, it was crucial to move to the second stage of the analysis plan using GWR.
Geographically weighted regression
Using GWR, the adjusted r-squared was 74.4% and the AICc was 340.1857, suggesting that this is a better method compared to the OLS global method. The adjusted r-squared for the IM full model ranged from only 0.002% to 83.2% for districts. The optimal bandwidth size from adaptive weighting scheme was 32 (out of 112 districts), which indicated very strong local effect or scaling effect. VIF values were far less than 7.5 meaning that there are not statistically significant multicollinearity issues. The residuals were not significant in spatial autocorrelation as show by a Global Moran’s I (p-value 0.4085; z-score 0.8265). Spatial dependence for districts by proportion of women giving births in the last five years and not being married showed more impact in central Uganda (Fig 4).
Fig 4. Geographically weighted regression of infant mortality by district (n = 135) for women giving birth in the last five years residuals controlling for marital status and being interviewed in Luganda Uganda 2016 Demographic and Health Survey.
Many high r2 districts (districts that were explained well by the model) were in the south-central part of Uganda (Fig 4). There appeared to be a geographic cluster of districts with women who were unmarried when they gave birth in the last five years significantly explaining IM in central Uganda (Figs 5 and 6). The local T-statistics ranged from -0.002 to 4.742; nine districts were significant at the 95% confidence interval (4.35 or higher). Among districts where the women interviewed preferred Luganda and had given birth in the last five years, there is a geographic cluster of high IM in south and southwestern Uganda (Figs 7 and 8). The local T-statistics ranged from -1.289 to 9.800; 42 districts were significant at the 95% confidence interval (4.35 or higher).
Fig 5. T-statistic estimates for unmarried women by district (n = 135) for women giving birth in the last five years and having an infant die in the first year of life Uganda 2016 Demographic and Health Survey.
Fig 6. Parameter estimates for unmarried women by district (n = 135) for women giving birth in the last five years and having an infant die in the first year of life Uganda 2016 Demographic and Health Survey.
Fig 7. T-statistic for being interviewed in Luganda by district (n = 135) for women giving birth in the last five years and having an infant die in the first year of life Uganda 2016 Demographic and Health Survey.
Fig 8. Parameter estimates for being interviewed in Luganda by district (n = 135 for women giving birth in the last five years and having an infant die in the first year of life Uganda 2016 Demographic and Health Survey.
Discussion
GWR allows for the development of local level estimates and compares their neighbors to produce maps and statistics that look at the processes behind spatial heterogeneity, while still comparing to a larger unit or the global process. One of GWR’s strengths is that it can be used in estimating parameters anywhere within the study area provided a dependent variable along with a set of one or more variables that have already been measured. This study showed the strengths of GWR by showing that districts with high rates of being unmarried and preferring to speak Luganda for their UDHS interview were significantly associated with infant deaths, but in different locations. These are both what we consider proxy measures or measures that represent something more than just those characteristics. As we know, being unmarried at the birth of a child often represents a lack of a social network to assist with income, childcare, and household chores, thus may represent an area with decreased resources consequently in higher need. Additionally, being interviewed in Luganda was associated in a large area, which may represent not being comfortable speaking English, which is the language of education, commerce, and presumably health care, thus suggesting a disconnect with health care settings. Luganda is spoken in the highly populated south- and south-central parts of Uganda and therefore is a common spoken language.
These data suggest that strides can be made in Uganda by providing targeted resources to areas with high rates of unmarried mothers and with high rates of Luganda as their language of choice. Moreover, these districts are focused specifically on areas of west-central and south-southwestern regions of Uganda. These data suggest as well that these are areas of high IM where strong local associations exist (with high local r2 values). In short, these variables were the only two that reached significance among all variables suggesting that they are important explanatory variables, though they are not causal but associated with IM.
This study is at risk for ecological fallacy and no assumption of an individual’s or even household differences should be made. In addition, the modifiable areal unit problem (MAUP) must be considered. While these are the smallest geographic units available, they are administrative units and not natural boundaries [31–33]. By using the adjusted bandwidth MAUP, the different size of units and edge issues are less problematic, but still need to be considered [34]. The uncertain geographic context problem, that individuals interact differently not only than each other but at different times and in different contexts, must also be considered [35,36]. We used the district of residence rather than the UDHS GIS files for geographic location thus we did not need to consider the masking of GPS locations [23]. The survey utilized a complex survey design, thus understanding the weighting and sample selection is important to understand outcomes and limitation.
Despite the varied strengths of GWR, there are some potential limitations. To begin with, database errors such as selective omission from birth histories among deceased infants which may be due to interviewer bias or even respondent fatigue or bias [37]. Secondly, the GWR works effectively only in the case where independent variables have a positive correlation. The method does not work correctly when independent variables negatively correlate [15]. Another drawback of GWR is that it assumes that every relationship being modeled diverges at one single scale; this consequently limits the ability to characterize spatial context. Similarly, with GWR, the same bandwidth is presumed to be applicable for every relationship within the model, which implies that data are weighted at a standard spatial scale. GWR is unable to alleviate the occurrence of an outlier and spatial autocorrelation that tends to pose a unique challenge resulting in instability in its spatial estimates. It is not clear how cluster sampling impacts this study. Lastly we used the weighted percentages for each district, thus, there should be limited bias due to this factor.
Despite these limitations, the findings of different associations in specific geographic areas are still important due to the high rates of IM in Uganda. Moreover, this article provides some high-risk areas of IM and their associated independent variables. The results invite further studies of social and demographic characteristics associated with IM throughout Uganda [23].
Acknowledgments
We would like to acknowledge Reece Anderson who assisted with development of this paper. We would also like to thank our partners in Uganda including St. Mary’s Lacor Hospital, Gulu Regional Referral Hospital, and Monica’s Health Clinic. A special thanks to Dr. Francesca Akello and Dr. Harriet Ajilong.
Data Availability
The data that support the findings of this study are available in the USAID 2016 Uganda Demography and Health Survey https://dhsprogram.com/.
Funding Statement
The authors received no specific funding for this work.
References
- 1.Reidpath DD, Allotey P. Infant mortality rate as an indicator of population health. J Epidemiol Community Health. 2003;57(5):344–6. doi: 10.1136/jech.57.5.344 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hug L, Alexander M, You DZ, Alkema L, Alkema L, Black R, et al. National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis. Lancet Glob Health. 2019;7(6):E710–E20. doi: 10.1016/S2214-109X(19)30163-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bhutta ZA, Black RE. Global maternal, newborn, and child health—so near and yet so far. New England Journal of Medicine. 2013;369(23):2226–35. doi: 10.1056/NEJMra1111853 [DOI] [PubMed] [Google Scholar]
- 4.Hug L, Alexander M, You D, Alkema L, for Child UI-aG. National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis. The Lancet Global Health. 2019;7(6):e710–e20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Child Mortality Estimates; Country-specific neonatal deaths [Internet]. United Nations Inter-agency Group for Child Mortality Estimation. 2018. Available from: http://data.unicef.org. [Google Scholar]
- 6.Arunda MO, Agardh A, Asamoah BO. Survival of low birthweight neonates in Uganda: analysis of progress between 1995 and 2011. BMC Pregnancy Childbirth. 2018;18(1):189. doi: 10.1186/s12884-018-1831-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Byaruhanga RN, Nsungwa-Sabiiti J, Kiguli J, Balyeku A, Nsabagasani X, Peterson S. Hurdles and opportunities for newborn care in rural Uganda. Midwifery. 2011;27(6):775–80. doi: 10.1016/j.midw.2010.02.005 [DOI] [PubMed] [Google Scholar]
- 8.Kakooza-Mwesige A, Forssberg H, Eliasson AC, Tumwine JK. Cerebral palsy in children in Kampala, Uganda: clinical subtypes, motor function and co-morbidities. BMC Res Notes. 2015;8:166. doi: 10.1186/s13104-015-1125-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kaye D. Antenatal and intrapartum risk factors for birth asphyxia among emergency obstetric referrals in Mulago Hospital, Kampala, Uganda. East Afr Med J. 2003;80(3):140–3. doi: 10.4314/eamj.v80i3.8683 [DOI] [PubMed] [Google Scholar]
- 10.Milner KM, Neal EF, Roberts G, Steer AC, Duke T. Long-term neurodevelopmental outcome in high-risk newborns in resource-limited settings: a systematic review of the literature. Paediatr Int Child Health. 2015;35(3):227–42. doi: 10.1179/2046905515Y.0000000043 [DOI] [PubMed] [Google Scholar]
- 11.Hedstrom A, Ryman T, Otai C, Nyonyintono J, McAdams RM, Lester D, et al. Demographics, clinical characteristics and neonatal outcomes in a rural Ugandan NICU. BMC Pregnancy Childbirth. 2014;14:327. doi: 10.1186/1471-2393-14-327 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Ersdal HL, Mduma E, Svensen E, Perlman J. Birth asphyxia: a major cause of early neonatal mortality in a Tanzanian rural hospital. Pediatrics. 2012;129(5):e1238–43. doi: 10.1542/peds.2011-3134 [DOI] [PubMed] [Google Scholar]
- 13.UNICEF. Uganda (UGA)—Demographics, Health & Infant Mortality 2018. [Available from: https://data.unicef.org/country/uga/. [Google Scholar]
- 14.Kaberuka W, Mugarura A, Tindyebwa J, Bishop DS. Factors determining child mortality in Uganda. International Journal of Social Economics. 2017;44(5):633–42. [Google Scholar]
- 15.Brunsdon C, Fotheringham S, Charlton M. Geographically Weighted Regression. Journal of the Royal Statistical Society: Series D (The Statistician). 1998;47(3):431–43. [Google Scholar]
- 16.Abdallah Y, Namiiro F, Mugalu J, Nankunda J, Vaucher Y, McMillan D. Is facility based neonatal care in low resource setting keeping pace? A glance at Uganda’s National Referral Hospital. Afr Health Sci. 2016;16(2):347–55. doi: 10.4314/ahs.v16i2.2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Asiimwe JB, Nyegenye W, Muyingo E. Trends and determinants of neonatal mortality in Uganda: further analysis of the demographic and health surveys. Rockville, Maryland, USA: ICF; 2019. [Google Scholar]
- 18.Waiswa P, Kallander K, Peterson S, Tomson G, Pariyo GW. Using the three delays model to understand why newborn babies die in eastern Uganda. Trop Med Int Health. 2010;15(8):964–72. doi: 10.1111/j.1365-3156.2010.02557.x [DOI] [PubMed] [Google Scholar]
- 19.Tembe J, Norton B. English education, local languages and community perspectives in Uganda. Dreams and realities: Developing countries and the English language. 2011:114–36. [Google Scholar]
- 20.UNHCR. Uganda January 2020. The United Nations High Commission for Refugees; 2020. [Google Scholar]
- 21.Uganda Bureau of Statistics. World Population Day Celebrations Saturday, July 11, 2020: Leveraging Uganda’s Population Dynamics for a Resilient Future amidst COVID-19. Kampala, UG: Uganda Bureau of Statistics,; 2020. [Google Scholar]
- 22.Uganda Bureau of Statistics. Uganda in Figures. Kampala, UG: Uganda Bureau of Statistics,; 2019. [Google Scholar]
- 23.Burgert-Brucker CR, Dontamsetti T, Mashall A, Gething P. Guidance for use of The DHS Program modeled map surfaces. Rockville, Maryland, USA: ICF International; 2016. [Google Scholar]
- 24.Gething P, Tatem A, Bird t, Burgert-Brucker CR. Creating spatial interpolation surfaces with DHS data. Rockville, Maryland, USA: ICF International; 2015. [Google Scholar]
- 25.Fotheringham AS, Brunsdon C, M C. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships: Wiley; 2002. [Google Scholar]
- 26.Fotheringham AS, Yang WB, Kang W. Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers. 2017;107(6):1247–65. [Google Scholar]
- 27.Chandra H, Salvati N, Chambers R. Small area prediction of counts under a non-stationary spatial model. Spat Stat-Neth. 2017;20:30–56. [Google Scholar]
- 28.Brunsdon C, Fotheringham AS, Charlton ME. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis. 2010;28(4):281–98. [Google Scholar]
- 29.Su S, Gong Y, Tan B, Pi J, Weng M, Cai Z. Area Social Deprivation and Public Health: Analyzing the Spatial Non-stationary Associations Using Geographically Weighed Regression. Soc Indic Res. 2016;133(3):819–32. [Google Scholar]
- 30.Weng M, Pi J, Tan B, Su S, Cai Z. Area Deprivation and Liver Cancer Prevalence in Shenzhen, China: A Spatial Approach Based on Social Indicators. Soc Indic Res. 2016;133(1):317–32. [Google Scholar]
- 31.Diez Roux AV, Mair C. Neighborhoods and health. Ann N Y Acad Sci. 2010;1186:125–45. doi: 10.1111/j.1749-6632.2009.05333.x [DOI] [PubMed] [Google Scholar]
- 32.Schuurman N, Bell N, Dunn JR, Oliver L. Deprivation indices, population health and geography: an evaluation of the spatial effectiveness of indices at multiple scales. J Urban Health. 2007;84(4):591–603. doi: 10.1007/s11524-007-9193-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Wong D. The modifiable areal unit problem (MAUP). The SAGE handbook of spatial analysis. 2009:105–23. [Google Scholar]
- 34.Propastin P, Kappas M, Erasmi S. Application of Geographically Weighted Regression to Investigate the Impact of Scale on Prediction Uncertainty by Modelling Relationship between Vegetation and Climate. IJSDIR. 2008;3:73–94. [Google Scholar]
- 35.Kwan MP. The Uncertain Geographic Context Problem. Annals of the Association of American Geographers. 2012;102(5):958–68. [Google Scholar]
- 36.Flowerdew R, Manley D, Sabel C. Neighbourhood effects on health: Does it matter where you draw the boundaries? Soc Sci Med. 2008;66(6):1241–55. doi: 10.1016/j.socscimed.2007.11.042 [DOI] [PubMed] [Google Scholar]
- 37.Uganda Bureau of Statistics—UBOS, ICF. Uganda Demographic and Health Survey 2016. Kampala, Uganda: UBOS and ICF; 2018. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available in the USAID 2016 Uganda Demography and Health Survey https://dhsprogram.com/.