DHS ANALYTICAL

STUDIES 73

VARIATIONS IN HEALTH OUTCOMES 

WITH ALTERNATIVE MEASURES OF 

URBANICITY, USING DEMOGRAPHIC 

AND HEALTH SURVEYS 2013-18

August 2020

This publication was produced for review by the United States Agency for International Development. It was prepared by

Shireen Assaf and Christina Juan.

DHS Analytical Studies No. 73 

Variations in Health Outcomes 

with Alternative Measures of Urbanicity, 

Using Demographic and Health Surveys 2013-18 

Shireen Assaf1

Christina Juan1

ICF 

Rockville, Maryland, USA 

August 2020 

1

 ICF, The DHS Program 

Corresponding author: Shireen Assaf, International Health and Development, ICF, 530 Gaither Road, 

Suite 500, Rockville, MD 20850, USA; phone: +1 301-407-6500; fax: +1 301-407-6501; email: 

Shireen.assaf@icf.com 
Acknowledgments: The authors wish to thank Thomas Pullum and Wanli Fang for their comments on the report. 

We would also like to thank Tom Fish for producing the maps.

Editor: Diane Stoy 

Document Production: Chris Gramer 

This study was carried out with support provided by the United States Agency for International Development 

(USAID) through The DHS Program (#720-OAA-18C-00083). The views expressed are those of the authors and do 

not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate population, 

health, and nutrition programs. Additional information about The DHS Program can be obtained from ICF, 

530 Gaither Road, Suite 500, Rockville, MD 20850 USA; telephone: +1 301-407-6500, fax: +1 301-407-6501, 

email: info@DHSprogram.com, internet: www.DHSprogram.com. 

Recommended citation: 

Assaf, Shireen, and Christina Juan. 2020. Variations in Health Outcomes with Alternative Measures of Urbanicity, 

Using Demographic and Health Surveys 2013-18. DHS Analytical Studies No. 73. Rockville, Maryland, USA: ICF. 
iii 

CONTENTS 

TABLES ........................................................................................................................................................ v 

FIGURES .................................................................................................................................................... vii 

PREFACE .................................................................................................................................................... xi 

ABSTRACT ............................................................................................................................................... xiii 

1 BACKGROUND OF THE REPORT ................................................................................................ 1 

2 DATA AND METHODS ................................................................................................................... 7 

2.1 Data ..................................................................................................................................... 7 

2.2 Variables ............................................................................................................................. 8 

2.2.1 Urbanicity variables ................................................................................................ 8 

2.2.2 Outcome variables ................................................................................................. 9 

2.3 Methods ............................................................................................................................ 10 

3 RESULTS ...................................................................................................................................... 13 

3.1 Bangladesh ....................................................................................................................... 13 

3.2 Democratic Republic of Congo ......................................................................................... 19 

3.3 India .................................................................................................................................. 25 

3.4 Kenya ................................................................................................................................ 31 

3.5 Nigeria ............................................................................................................................... 37 

3.6 Senegal ............................................................................................................................. 43 

3.7 Summary of in-depth analysis ........................................................................................... 48 

3.8 Results from further analysis of 30 surveys ...................................................................... 49 

3.8.1 Modern contraceptive use .................................................................................... 49 

3.8.2 At least four ANC visits ........................................................................................ 51 

3.8.3 Three doses of DPT vaccine ................................................................................ 53 

3.8.4 Minimum acceptable diet ..................................................................................... 55 

DISCUSSION AND CONCLUSION ............................................................................................................ 59 

REFERENCES ............................................................................................................................................ 63 

APPENDIX .................................................................................................................................................. 67 

v 

TABLES 

Table 1 Surveys included in the analysis ................................................................................ 7 

Table 2 Crosstabulations of urbanicity variables with health outcomes in Bangladesh 

2014 DHS ................................................................................................................ 14 

Table 3 Crosstabulations of urbanicity variables with health outcomes in DRC 2013-14 

DHS .......................................................................................................................... 20 

Table 4 Crosstabulations of urbanicity variables with health outcomes in India 2015-16 

DHS .......................................................................................................................... 26 

Table 5 Crosstabulations of urbanicity variables with health outcomes in Kenya 2014 

DHS .......................................................................................................................... 32 

Table 6 Crosstabulations of urbanicity variables with health outcomes in Nigeria 2018 

DHS .......................................................................................................................... 38 

Table 7 Crosstabulations of urbanicity variables with health outcomes in Senegal 

2016 DHS ................................................................................................................ 44 

Appendix Table 1 Description of urbanicity variables among all interviewed women age 15-49 

for Bangladesh 2014 DHS, DRC 2013-14 DHS, India 2015-16 DHS, Kenya 

2014 DHS, Nigeria 2018 DHS, and Senegal 2016 DHS ......................................... 67 

Appendix Table 2 Unadjusted and adjusted odds ratios of the urbanicity variables with health 

outcomes in Bangladesh 2014 DHS ........................................................................ 68 

Appendix Table 3 Unadjusted and adjusted odds ratios of the urbanicity variables with health 

outcomes in DRC 2013-14 DHS .............................................................................. 68 

Appendix Table 4 Unadjusted and adjusted odds ratios of the urbanicity variables with health 

outcomes in India 2015-16 DHS .............................................................................. 69 

Appendix Table 5 Unadjusted and adjusted odds ratios of the urbanicity variables with health 

outcomes in Kenya 2014 DHS ................................................................................. 69 

Appendix Table 6 Unadjusted and adjusted odds ratios of the urbanicity variables with health 

outcomes in Nigeria 2018 DHS ............................................................................... 70 

Appendix Table 7 Unadjusted and adjusted odds ratios of the urbanicity variables with health 

outcomes in Senegal 2016 DHS .............................................................................. 70 

Appendix Table 8 Adjusted odds ratios for modern contraceptive use ................................................ 71 

Appendix Table 9 Adjusted odds ratios for having at least four ANC visits .......................................... 72 

Appendix Table 10 Adjusted odds ratios for having all three DPT vaccination doses............................ 73 

Appendix Table 11 Adjusted odds ratios for minimum acceptable diet .................................................. 74 

vii 

FIGURES 

Figure 1 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) 

urban poor cluster variables in Bangladesh 2014 DHS. .......................................... 13 

Figure 2 Percent distribution of the urbanicity variables in the Bangladesh 2014 DHS ........ 14 

Figure 3 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in 

Bangladesh 2014 DHS ............................................................................................ 15 

Figure 4 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in 

Bangladesh 2014 DHS ............................................................................................ 16 

Figure 5 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in 

Bangladesh 2014 DHS ............................................................................................ 17 

Figure 6 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in 

Bangladesh 2014 DHS ............................................................................................ 18 

Figure 7 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) 

urban poor cluster variables in the DRC 2013-14 DHS ........................................... 19 

Figure 8 Percent distribution of the urbanicity variables in the DRC 2013-14 DHS .............. 20 

Figure 9 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in 

the DRC 2013-14 DHS ............................................................................................ 21 

Figure 10 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in 

the DRC 2013-14 DHS ............................................................................................ 22 

Figure 11 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in 

the DRC 2013-14 DHS ............................................................................................ 23 

Figure 12 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in 

the DRC 2013-14 DHS ............................................................................................ 24 

Figure 13 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) 

urban poor cluster variables in India 2015-16 DHS ................................................. 25 

Figure 14 Percent distribution of the urbanicity variables in India 2015-16 DHS .................... 26 

Figure 15 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in 

India 2015-16 DHS .................................................................................................. 27 

Figure 16 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in 

India 2015-16 DHS .................................................................................................. 28 

Figure 17 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in 

India 2015-16 DHS .................................................................................................. 29 

Figure 18 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in 

India 2015-16 DHS .................................................................................................. 30 

Figure 19 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) 

urban poor cluster variables in Kenya 2014 DHS .................................................... 31 

Figure 20 Percent distribution of the urbanicity variables in Kenya 2014 DHS ....................... 32 

Figure 21 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in 

Kenya 2014 DHS ..................................................................................................... 33 
viii 

Figure 22 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in 

Kenya 2014 DHS ..................................................................................................... 34 

Figure 23 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in 

Kenya 2014 DHS ..................................................................................................... 35 

Figure 24 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in 

Kenya 2014 DHS ..................................................................................................... 36 

Figure 25 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) 

urban poor cluster variables in Nigeria 2018 DHS .................................................. 37 

Figure 26 Percent distribution of the urbanicity variables in Nigeria 2018 DHS ...................... 38 

Figure 27 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in 

Nigeria 2018 DHS .................................................................................................... 39 

Figure 28 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in 

Nigeria 2018 DHS .................................................................................................... 40 

Figure 29 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in 

Nigeria 2018 DHS .................................................................................................... 41 

Figure 30 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in 

Nigeria 2018 DHS .................................................................................................... 42 

Figure 31 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) 

urban poor cluster variables in Senegal 2016 DHS ................................................. 43 

Figure 32 Percent distribution of the urbanicity variables in Senegal 2016 DHS .................... 44 

Figure 33 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in 

Senegal 2016 DHS .................................................................................................. 45 

Figure 34 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in 

Senegal 2016 DHS .................................................................................................. 46 

Figure 35 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in 

Senegal 2016 DHS .................................................................................................. 47 

Figure 36 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in 

Senegal 2016 DHS .................................................................................................. 48 

Figure 37 Adjusted odds ratios for the urban poor cluster variable (reference: urban nonpoor) and mCPR in 30 DHS surveys ....................................................................... 50 

Figure 38 Adjusted odds ratios for the SMOD variable (reference: urban centers) and 

mCPR in 30 DHS surveys ........................................................................................ 51 

Figure 39 Adjusted odds ratios for the urban poor cluster variable (reference: urban nonpoor) and ANC4 in 30 DHS surveys ........................................................................ 52 

Figure 40 Adjusted odds ratios for the SMOD variable (reference: urban centers) and 

ANC4 in 30 DHS surveys ......................................................................................... 53 

Figure 41 Adjusted odds ratios for the urban poor cluster variable (reference: urban nonpoor) and DPT3 in 30 DHS surveys ........................................................................ 54 

Figure 42 Adjusted odds ratios for the SMOD variable (reference: urban centers) and 

DPT3 in 30 DHS surveys ......................................................................................... 55 
ix 

Figure 43 Adjusted odds ratios for the urban poor cluster variable (reference: urban nonpoor) and MAD in 30 DHS surveys .......................................................................... 56 

Figure 44 Adjusted odds ratios for the SMOD variable (reference: urban centers) and 

MAD in 30 DHS surveys .......................................................................................... 57 

xi 

PREFACE 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be 

useful to policymakers and program managers in low- and middle-income countries. DHS Analytical 

Studies serve this objective by providing in-depth research on a wide range of topics, typically including 

several countries and applying multivariate statistical tools and models. These reports are also intended to 

illustrate research methods and applications of DHS data that may build the capacity of other researchers. 

The topics in this series are selected by The DHS Program in consultation with the U.S. Agency for 

International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 

xiii 

ABSTRACT 

The literature has firmly established the link between urban/rural place of residence and health indicators, 

and the association of poor health outcomes and health services in rural areas. However, few studies have 

looked at gradients of urban areas or urbanicity, which have been described as the impact of living in 

those areas at a given time. In this report, we used several measures of urbanicity to study four health 

indicators: using modern contraceptives (mCPR); having four or more antenatal care (ANC4) visits for 

the most recent birth among women; completing three doses of an immunization that protects children 

against diphtheria, pertussis, and tetanus (DPT3); and providing the minimum acceptable diet (MAD) for 

children. The urbanicity measures include the urban/rural dichotomous measure, SMOD, which uses 

satellite data and population density to identify rural, peri-urban (suburbs), and urban centers; nightlights, 

which measures the level of luminosity; and a variable constructed from DHS data which splits urban 

areas into urban poor and urban non-poor clusters. Data from 30 countries with a recent DHS were used 

for the analysis. Of these, an in-depth analysis was also performed on six DHS surveys: Bangladesh 2014, 

Democratic Republic of the Congo (DRC) 2013-14, India 2015-16, Kenya 2014, Nigeria 2016, and 

Senegal 2016. The in-depth analysis examined the urbanicity variables and used unadjusted and adjusted 

regression to examine associations between the urbanicity variables and the health indicators. The 

analysis of the 30 surveys used adjusted regressions to examine the association between SMOD and the 

urban poor cluster variable with the health outcomes. The findings show few significant associations, 

particularly those with DPT3 and MAD. The urban poor cluster variables exhibited the most significant 

associations, particularly with mCPR. However, results were country specific, with some countries 

exhibiting large significant differences that favored the urban non-poor or urban centers. For example, in 

Haiti and Burundi, there were more than 80% lower odds of MAD for children who live in urban poor 

clusters compared to the urban non-poor. Some limitations of the analysis include small sample sizes for 

certain categories of the urbanicity variables and for the DPT3 and MAD outcomes. In addition, the 

analysis included health services indicators, which may exhibit few differences within an urban 

environment compared to health outcomes indicators. 

Key words: urbanicity, SMOD, nightlights, urban poor, urban poverty, family planning, maternal and 

child health 

1 

1 BACKGROUND OF THE REPORT 

Urbanization is a term that is widely used to define a change in density, size, and heterogeneity of cities 

(Cyril, Oldroyd, and Renzaho 2013; Vlahov and Galea 2002). The process of urbanization has rapidly 

increased at global and regional levels. Among the approximately 7.6 billion people in the world, 6.4 

billion live in less developed regions (DESA 2018), or low- and middle-income countries (LMICs). 

According to 2018 estimates, more than half of the world’s population (55%) live in urban areas (DESA 

2018). As the world’s population continues to grow, two-thirds (66%) of the population are forecasted to 

live in urban areas by 2050 (World Health Organization and UN-Habitat 2016). Furthermore, most of 

rapid urban growth will continue to occur in LMICs, and at a faster pace in those regions compared to 

others, with an estimated 8.5 billion out of 9.8 billion living in LMICs by 2050 (Cyril, Oldroyd, and 

Renzaho 2013; DESA 2018). 

The widely used term urbanization is a process through which cities change in terms of their size, 

heterogeneity, and density. This concept differs from the lesser-known concept of urbanicity, which refers 

to the impact of living in urban areas at a given time (Cyril, Oldroyd, and Renzaho 2013; Vlahov and 

Galea 2002). Urbanicity is static compared with the notion that describes a city’s evolving characteristics 

that occur over time. Previous research has assessed urbanicity by the number of addresses per square 

kilometer (Verheij, Maas, and Groenewegen 2008). Urbanicity contrasts existing cities and surrounding 

areas, and highlights differences between a city and surrounding towns or peri-urban areas. The concepts 

of urbanicity and urbanization are complementary. Urbanicity can also be viewed through the lens of 

three domains: the social environment, the physical environment, and access to health and social services, 

in which the prevalence of conditions such as respiratory disease is more characteristic of urban areas 

compared to the nonurban areas (Vlahov and Galea 2002). 

A number of different scales have been used to measure urbanization and urbanicity. Studies have 

typically relied on the measurement of urbanization through a dichotomous rural-urban residence 

variable. Statistical analyses that include this two-category variable are limited, however, in terms of their 

ability to capture the heterogeneity that likely takes place to varying degrees in urban environments 

(Jones-Smith and Popkin 2010). Such classifications are also inherently country-specific and dependent 

on an unstandardized mix of characteristics such as a functioning economy, population density, and 

administrative regions (Dahly and Adair 2007; Vlahov and Galea 2002). Countries like Singapore classify 

all areas as urban (Dahly and Adair 2007). 

Although countless studies assess urbanization or the impact of urbanization through the rural-urban 

residence dichotomy, there is a dearth of research that measures urbanization’s complex and more 

nuanced features beyond these two categories. An urbanicity index, for example, measures the degree to 

which an environment is urban at one point in time and examines how urban living affects health 

outcomes. Through a systematic review that examined measurement properties of scales that evaluate 

urbanicity, Cyril et al. (2013) discussed studies from Africa, Asia, and Europe that included from 7 to 12 

items in the scales. Previous research assessed urbanicity with items related to remote sensing, the 

collection of neighborhood characteristics, or single-dimension proxy measures such as population 

density. Allender et al. (2008), for example, developed a 7-item scale based on components such as the 

existence of phone services, access to public transportation, the presence of health facilities such as a 
2 

maternal health clinic, and the presence of gas stations. In contrast to single-dimension proxy measures, 

Dahly and Adair (2007) developed an urbanicity scale based on an a priori conceptual framework (Cyril, 

Oldroyd, and Renzaho 2013) and available data across survey years (Dahly and Adair 2007). Other 

measures of urbanicity include access to infrastructure and services such as education, electricity, and 

water. In contrast to defining residence areas by administrative definitions, another urbanicity measure 

(SMOD) uses data from the Global Human Settlement Layer, which uses population density, built cover, 

and settlement size to define cities, towns, suburbs (or peri-urban areas), and rural areas (European 

Commission 2020). 

Previous studies have shown the linkages between urbanization and urbanicity with health outcomes. As 

cities become more populous and booming with growth in the economy, education, and infrastructure, so 

too are the changes in health trends that have both beneficial and deleterious effects. Urbanization is 

associated with an increase in access to and utilization of health services (Cyril, Oldroyd, and Renzaho 

2013). With more people living in more densely populated areas, there is greater opportunity for 

innovation, care, and caregiving as well as the growth of businesses. Better access to health care has a 

greater impact on reducing child mortality (Harttgen and Misselhorn 2006), and lowering rates of 

untreated hypertension (Vorster 2002), while rural environments are usually more deprived of the 

facilities that address these and other health issues. Research has supported the claim that, on average, 

urban residents have better health outcomes compared to residents in rural areas (Van de Poel, O’Donnell, 

and Van Doorslaer 2007). For example, a 33-country study that examined anthropometric indicators of 

child growth (such as the prevalence of stunting, wasting, and underweight) found that urban children had 

better nutrition than children in rural areas (Von Braun 1993). Further, more urbanized countries tend to 

have a lower prevalence of underweight preschoolers compared to countries with less urbanization, 

although malnutrition in large cities is similar to rural areas in some countries (Von Braun 1993). Von 

Braun (1993) reflected on the poor living conditions, low-income households, and difficulty in meeting 

demand for services in rapidly growing cities. 

Another study that compared overall childhood mortality between urban and rural children in 12 

countries, including Senegal, Egypt, Mexico, and Thailand, found that children in rural areas have 1.6 

times greater odds of dying before the age five compared to children in the urban areas (Cleland, Bicego, 

and Fegan 1992). Urbanization is linked with poor health outcomes, which makes the effects of this 

process less clear. Women and men in urban areas have been shown to have higher mean body mass index 

(BMI) compared to other groups (Vorster 2002), and a higher probability of reporting poor health from 

social isolation, overcrowding, pollution, and other unhygienic living conditions found at higher levels of 

urbanization (Van de Poel, O'Donnell, and Van Doorslaer 2012). In urban environments, city officials are 

responsible for managing global health issues and demographic transitions, such as water contamination, 

health pandemics, pollution, communicable and noncommunicable diseases, and slum upgrading (World 

Health Organization and UN-Habitat 2016). Globally, government and city officials are directing and 

managing the effects of COVID-19 (Corburn et al. 2020), in which cities have largely been the epicenter 

of the current public health crisis (Branas et al. 2020). During demographic transitions, the urban 

environment may affect health, and can modify effects of unpredicted stressors on cities (Vlahov and 

Galea 2002). 

To measure urbanization and urbanicity and their relationship with health, studies have traditionally relied 

on the dichotomous urban and rural area variable (Damasceno et al. 2009; Peen et al. 2010). Thus, use of 
3 

a surrogate measure such as the urban/rural dichotomy is limited in terms of assessing stability and 

consistency across time and countries (Cyril, Oldroyd, and Renzaho 2013). Beyond this generic measure, 

previous studies have looked at urbanization in a more nuanced way—as living standards that vary more 

within urban settings, for example, with greater income inequality compared to rural areas, as well as 

health programs that may overlook urban poor residents while targeting their rural counterparts (Van de 

Poel, O’Donnell, and Van Doorslaer 2007). 

Menon et al. (2000) used DHS data from 11 countries to test the hypothesis that intra-urban differentials 

across socioeconomic status quintiles in child stunting are greater than intra-rural differentials, and that 

stunting prevalence among the urban and rural poor is equally high (Menon, Ruel, and Morris 2000). 

Malnutrition in urban areas was greatest among the poorest urban populations. A systematic review of 11 

studies investigated the association between urbanization and undernutrition and overweight nutrition 

outcomes in children (Eckert and Kohler 2014). Eckert and Kohler found that urbanization was associated 

with a lower risk of undernutrition and a higher risk of overweight outcomes in children. In addition, risk 

factors for chronic diseases were more common in urban areas. A 47-country study that used DHS data 

examined the magnitude of rural-urban disparities and differences in the degree of socioeconomic 

inequality in child nutritional status and under-5 mortality, and found that urban poor in a number of 

countries have higher stunting and under-5 mortality rates than the rural poor (Van de Poel, O’Donnell, 

and Van Doorslaer 2007). In Ghana, researchers analyzed life history calendar data, and found that urban 

women had lower odds of giving birth than rural women, with particular effects of urbanization on the 

first birth (White et al. 2008). White et al. (2008) interpret this decline in fertility as reflective of 

opportunities such as employment and education, delay in marriage and other norms of living in an urban 

environment, and access to health care services. It also depends on the association between urbanization 

and health outcomes in terms of harmful or beneficial effects. In contrast to the study by Cleland et al. 

(1992), which relied on the urban-rural residence variable and found that urban children had a lower risk 

of dying before their fifth birthday compared to their rural counterparts, another study found that under-5 

mortality increased with more urban population growth, which worsened as levels of urban disadvantage 

grew (Antai and Moradi 2010). 

Previous studies of urbanicity have explored the relationship between cities and health. One study 

analyzed DHS data from 30 countries to understand urbanicity based on population density and 

associations with malnutrition, obesity and anemia in women of childbearing age, and stunting in 

preschool-age children (Jones, Acharya, and Galway 2016). These indicators, which examined the impact 

of cities on health and were constructed using geolocated DHS data to create cluster-level mean 

population densities, found that malnourishment among women and children (including the prevalence of 

overweight and obesity outcomes) was higher among those in urban and peri-urban areas, compared with 

women of childbearing age and preschool-age children in rural areas. Peri-urban areas were also 

associated with an increased odds of child stunting compared to urban areas, which suggested problems in 

health infrastructure, food, and other amenities in peri-urban areas (Jones, Acharya, and Galway 2016). 

There was a similar or higher prevalence of stunting among children in the peri-urban areas as compared 

to their rural and semi-rural counterparts (Jones, Acharya, and Galway 2016). Another study investigated 

urbanicity scales and their associations with noncommunicable diseases (NCDs), nutrition, physical 

activity, and exposure to pollution through a systematic review of urbanicity scales (Cyril, Oldroyd, and 

Renzaho 2013). One study in this review conducted a systematic review and found that high urbanicity—

in which five of the nine studies used population density and the others used composite measures—was 
4 

associated with a sedentary lifestyle, high BMI, and diabetes mellitus regardless of sex (Allender et al. 

2008). 

Another study conducted in Austria found strong statistical evidence for an association between 

urbanicity and musculoskeletal diseases such as osteoporosis and arthritis. The prevalence of these 

diseases decreased with increasing levels of urbanicity (Vavken and Dorotka 2011). The authors suggest 

that although greater levels of urbanicity in LMICs is associated with poorer living conditions, more 

specialized health care in more industrialized countries such as Austria may attenuate risk factors 

associated with living in urban environments. To assess the degree of change in urban features across 

communities, urbanicity was assessed on a continuum for its association with adult BMI in China (JonesSmith and Popkin 2010). Women who lived in communities that transitioned from a low initial urban 

score to higher levels of urbanicity had higher odds of overweight and obesity outcomes (Jones-Smith and 

Popkin 2010). In addition to understanding the net effect of urbanization on health behaviors and 

outcomes such as physical activity and hypertension by using longitudinal data in China, another study 

that examined self-assessed health found that urbanization increased the probability of reporting 

symptoms of illness or disease (Van de Poel, O'Donnell, and Van Doorslaer 2012). 

With increasing urbanization comes a growth in slums (Crocker-Buque et al. 2017; Pörtner and Su 2018; 

Unger and Riley 2007). According to the United Nations Human Settlements Program (UN-HABITAT), 

863 million people—or one-third—of the urban population in developing countries live in slums (Habitat 

2013; Pörtner and Su 2018). More than three-fourths (78%) of the least developed countries’ urban 

population live in slums (Unger and Riley 2007). A slum household is defined by UN-HABITAT as a 

group of people who live under the same roof in an urban area, lacking at least one or more of the 

following: 

1. Durable housing of a permanent nature that protects against extreme climate conditions 

2. Sufficient living space, which means not more than three people sharing the same room 

3. Easy access to safe water in sufficient amounts at an affordable price 

4. Access to adequate sanitation in the form of a private or public toilet shared by a reasonable 

number of people 

5. Security of tenure that prevents forced evictions (United Nations Human Settlements Programme 

2006-2007) 

This is a global problem that continues to worsen. Within LMICs, there are certain regions such as subSaharan Africa (SSA) where most of its urban population live in slums (Mberu et al. 2016). 

Approximately 56% of Kenya’s urban population lived in slums in 2014, and Kibera, one of the largest 

slums in the world, is located in Nairobi (Mberu et al. 2016). Countries in Southeast Asia such as 

Bangladesh and India also have high proportions of their urban populations living in slums, such as 55% 

in Bangladesh in 2014 (Mberu et al. 2016). In Bangladesh and Kenya, malnutrition and childhood illness 

mortality and morbidity indicators were worse in slums compared to urban and rural communities (Mberu 

et al. 2016). Another study found that children under age 5 who live in slums in Senegal lack basic water, 

sanitation, and hygiene (WASH) services, and that this creates greater risks of waterborne and 

gastrointestinal conditions such as diarrhea (Thiam et al. 2017). 
5 

While some studies have shown a more straightforward relationship between living in slums and health 

outcomes, others have illustrated a more complex relationship between the two. Understanding contextual 

and compositional effects that are linked to living in slums and their associations with child health 

outcomes is important to consider. Slums had been found to have better maternal health services than 

rural communities (Mberu et al. 2016). After adjusting for wealth and other health variables, children in 

urban areas in India were the tallest on average, while children living in slums were taller than their rural 

counterparts (Pörtner and Su 2018). A 45-country DHS analysis between 2000-2009 examined 

associations between urban slum residency—constructed through characteristics such as water, sanitation, 

and housing conditions—and infant mortality (Kyu et al. 2013). The authors found that living in a slum 

neighborhood is associated with higher infant mortality, although this was attenuated among children born 

to women who had received antenatal care (ANC) from trained providers. This key finding by Kyu et al. 

(2013) differs from a 73-country DHS study that found slum children to have lower mortality and stunting 

risks than their rural counterparts, although their health risks were worse compared to urban children 

(Fink, Günther, and Hill 2014). Similar to other studies that examined slums and child health outcomes, 

observed differences across a large portion of these countries can be explained by differences in wealth, 

health access, and maternal education (Fink, Günther, and Hill 2014). 

We seek to understand if it is worse in terms of health behaviors and outcomes for resource-constrained 

people to live in rural areas or urban areas. We examine this central question through a number of 

variables that assess urbanicity in gradients. 

Our study seeks to answer the following research questions: 

1. Do peri-urban areas have worse health outcomes than urban areas, and is this disparity similar to 

or different from that between rural and urban areas? 

2. Do people in urban poor areas experience worse health outcomes compared to their urban nonpoor counterparts, and if so, is this disparity similar to or different from rural and urban non-poor 

contexts? 

7 

2 DATA AND METHODS 

2.1 Data 

Data from countries that had a DHS survey from 2014 to the present were included in the analysis. 

Countries with a population of fewer than 10 million and no GPS data collected during the DHS survey 

were excluded, which left 30 surveys for the analysis. Six countries were selected for in-depth analysis: 

Bangladesh, DRC, India, Kenya, Nigeria, and Senegal (Agence Nationale de la Statistique et de la 

Démographie - ANSD/Sénégal and ICF 2017; International Institute for Population Sciences - IIPS/India 

and ICF 2017; Kenya National Bureau of Statistics et al. 2015; Ministère du Plan et Suivi de la Mise en 

œuvre de la Révolution de la Modernité - MPSMRM/Congo, Ministère de la Santé Publique - 

MSP/Congo, and ICF International 2014; National Population Commission - NPC and ICF 2019; 

Research et al. 2016). The results for the six countries are discussed country by country, with further 

analysis of all 30 surveys summarized. Table 1 summarizes the sample sizes. 

Table 1 Surveys included in the analysis 

Country DHS survey 

Number of 

households 

interviewed 

Number of 

women 

age 15-49 

interviewed 

Angola 2015-16 16,109 14,379 

Bangladesh* 2014 17,300 17,863 

Benin 2017-18 14,156 15,928 

Burundi 2016-17 15,977 17,269 

Cambodia 2014 15,825 17,578 

Chad 2014-15 17,233 17,719 

Congo Democratic Republic (DRC)* 2013-14 18,171 18,827 

Egypt 2014 28,175 21,762 

Ethiopia 2016 16,650 15,683 

Ghana 2014 11,835 9,396 

Guatemala 2014-15 21,383 25,914 

Guinea 2018 7,912 10,874 

Haiti 2016-17 13,405 14,371 

India* 2015-16 601,509 699,686 

Jordan 2017-18 18,802 14,689 

Kenya* 2014 36,430 31,079 

Malawi 2015-16 26,361 24,562 

Mali 2018 9,510 10,519 

Myanmar 2015-16 12,500 12,885 

Nepal 2016 11,040 12,862 

Nigeria* 2018 40,427 41,821 

Pakistan 2017-18 11,869 12,364 

Philippines 2017 27,496 25,074 

Rwanda 2014-15 12,699 13,497 

Senegal* 2016 4,437 8,865 

South Africa 2016 11,083 8,514 

Tanzania 2015-16 12,563 13,266 

Uganda 2016 19,588 18,506 

Zambia 2018 12,831 13,683 

Zimbabwe 2015 10,534 9,955 

Note: *countries selected for in-depth analysis. 
8 

2.2 Variables 

2.2.1 Urbanicity variables 

We include the place of residence variable available in the DHS datasets that has the urban and rural 

categories. This variable is based on the country’s statistical office classification. This is usually based on 

the most recent census conducted in the country and may not always be up to date. For instance the 

Zambia 2018 DHS used the Census of Population and Housing of the Republic of Zambia conducted in 

2010 (see the sample frame description in Appendix A of the 2018 Zambia DHS final report) (Zambia 

Statistics Agency - ZSA et al. 2020). 

The analysis uses two variables from external data sources to describe the level of urbanicity at the cluster 

level: SMOD derived from the Global Human Settlement Model grid (GHS-SMOD) (Pesaresi and Freire 

2016) and a nightlights composite produced by the National Oceanic and Atmospheric Administration 

(NOAA) (National Centers for Environmental Information 2015). The most recent dataset for these two 

variables is from 2015, which we used in this analysis because it is closest to the survey years for the 

countries in the analysis. 

The SMOD variable represents the degree of urbanization and is constructed with data on the Global 

Human Settlement Layer (GHSL) built-up areas and population density. This information is used to 

produce three categories: urban centers (cities), urban clusters (peri-urban: towns or suburbs), and rural, 

for an area within a 2 km (urban) or 10 km (rural) buffer that surrounds the DHS survey cluster location. 

For a graphical description of this variable, please see https://ghsl.jrc.ec.europa.eu/data.php?sl=4/. 

The nightlights variable measures the average level of luminosity in an area within the 2 km (urban) or 10 

km (rural) buffer that surrounds the DHS survey cluster location during nighttime hours. This variable is 

often related to urbanicity, although it also measures economic activity. Nightlights is a continuous 

variable. The higher the value, the higher the level of urbanicity. After examining the distribution of this 

variable in the six countries for the in-depth analysis, we divided the distribution into three categories: 0-1 

(the lowest level of urbanicity or rural areas), 2-10 (representing peri-urban areas), and more than 10 

(representing urban areas). Standardizing the categories of nightlights across the countries was performed 

in order to easily make cross-country comparisons. 

A third variable was constructed from variables available in the DHS data that represent the urban poor. 

Due to the DHS displacement procedures, we cannot with certainty identify the slum areas within an 

urban area. However, identifying the urban poor can be a close proxy. The UN HABITAT definition of a 

slum household is one that lacks one or more of the following: durable housing of permanent nature, 

sufficient living space for not more than three persons per room, access to safe water, access to adequate 

sanitation, and security of tenure that prevents forced evictions (United Nations Human Settlements 

Programme 2006-2007). The DHS data contain information on all these areas except for security of 

tenure. Using this definition as a guide, an urban poor household is identified as lacking two or more of 

the following: a household made of durable material for the floor, wall, and roof; access to improved 

water; access to improved sanitation; and fewer than three persons per sleeping room. An urban poor 

cluster is defined as a cluster with more than 50% of urban poor households. This definition was also used 

by Van de Poel et al. (2007). This variable was analyzed at the cluster level instead of remaining at the 
9 

household level in order to define areas that may be considered slum-like areas (i.e., the urban poor). The 

urban poor cluster variable in this study has three categories: rural, urban poor, and urban non-poor. 

The India survey included two slum variables in the dataset. One was the census-based definition of a 

slum, and the second was the observation of a slum area by the survey team. A slum variable was 

constructed that combines the observation and census definitions into the following categories: rural, 

urban slum, and urban non-slum. 

One limitation of this analysis arises from the DHS displacement procedures, which displaces urban 

clusters up to 2 km. Since our main independent variables focused on urban area locations, this 

displacement could weaken the associations we observe in the analysis. 

2.2.2 Outcome variables 

Four outcomes were selected that cover family planning and maternal and child health outcomes. The 

definitions of these outcomes are presented below. 

Modern contraceptive prevalence rate (mCPR): The proportion of women age 15-49, currently in a 

union, who are using a modern contraceptive method. Modern contraceptive methods include pills, 

intrauterine devices (IUDs), injections, implants, diaphragms, female and male condoms, female and male 

sterilization, foam or jelly, and the lactational amenorrhea method (LAM). The mCPR may also include 

other modern contraceptive methods that are country-specific or less commonly used, but were reported 

by the respondent and identified in the datasets as modern methods. 

Four or more ANC visits (ANC4): The proportion of women age 15-49 who gave birth in the past 2 

years, and who had at least four ANC visits for the most recent birth. 

Completion of three doses of DPT vaccine (DPT3): The proportion of children age 12-23 months who 

received all three doses of the DPT vaccine. The DPT3 immunization is selected for the indicator because 

children who receive this vaccine generally have received all other recommended immunizations. 

Minimum Acceptable Diet (MAD): This indicator is defined among the youngest children age 6-23 

months living with the mother, and uses the definitions of minimum dietary diversity and minimum meal 

frequency as follows: 

 Breastfed children—minimum dietary diversity and minimum meal frequency as defined below. 

 Non-breastfed children—minimum dietary diversity but excluding the dairy products category, 

and minimum meal frequency and 2 or more milk feeds. 

Minimum dietary diversity: The child is fed five out of eight food groups during the day or night before 

the survey. The food groups are: 1. breast milk; 2. grains, roots, and tubers; 3. legumes and nuts; 4. dairy 

products (milk, yogurt, and cheese); 5. flesh foods (meat, fish, poultry, and liver/organ meats); 6. eggs; 

7. vitamin A rich fruits and vegetables; 8. other fruits and vegetables. 
10 

Minimum meal frequency: 

 2 or more solid or semisolid feeds for breastfeeding children age 6-8 months, or 

 3 or more solid or semisolid feeds for breastfeeding children age 9-23 months, or 

 4 or more solid or semisolid or milk feeds for nonbreastfeeding children age 6-23 months. 

2.3 Methods 

The analysis has two parts, which include the in-depth analysis of six countries and further analysis of 30 

countries. The 30 countries in the further analysis include the six countries from the in-depth analysis. 

The in-depth analysis involved a further assessment of the urbanity variables and use of unadjusted and 

adjusted regression models. The results for this analysis are discussed country by country and include a 

summary of the findings. The further analysis involved fitting only the adjusted regression models and 

using the SMOD and urban poor cluster variable. These results are discussed by outcome and are 

described below. 

Descriptive statistics of the urbanicity variables were produced for the six countries in the in-depth 

analysis. In addition, descriptive maps were constructed using ArcGIS software to show the distribution 

of the urbanicity variables across country regions. Crosstabulation of the urbanicity variables with the 

four outcomes of interest was performed to observe associations. Statistical tests determined whether 

there were significant disparities in the health outcomes by the urbanicity variables for each survey. 

To determine the magnitude of the associations, logistic regressions were fit for each outcome and each 

urbanicity variable separately. Unadjusted and adjusted regressions were fit for the six countries selected 

for the in-depth analysis, while only adjusted regression was performed for the 30 surveys. The adjusted 

regressions included the following control variables for the mCPR and ANC4 outcomes: woman’s age at 

birth for the most recent birth, number of living children, woman’s education level, and region. For the 

DPT3 and MAD outcomes, the same controls were used along with the sex of the child and the child’s 

age in months. The wealth index was not included in the models because it was found to be highly 

correlated with the urbanicity variables. Four unadjusted and adjusted models were fit for each outcome 

for the six countries in the in-depth analysis: 

 Model 1: Place of residence (rural, urban) 

 Model 2: SMOD (rural, peri-urban, urban centers) 

 Model 3: Nightlights (0-1 2-10, >10) 

 Model 4: Urban poverty cluster (rural, urban poor, urban non-poor) 

For India, an additional model was fit for the country-specific slum variable available in the dataset: 

 Model 5: India slums (rural, urban slum, urban non-slum) 

For Models 1-3, the reference was the most urban category (urban, urban centers, and greater than 10 

nightlights). For Model 4, the reference was urban non-poor and for Model 5, urban non-slum. 
11 

Models 2 and 4 were fit for the adjusted logistic regressions performed on all 30 surveys. The same 

controls were used, with the results summarized for each outcome separately. This further analysis on 30 

surveys attempted to find a pattern for these urbanicity variables. 

All analyses took into account the sampling design and sampling weights and were performed with Stata 

16 software. 

13 

3 RESULTS 

The results will first report the in-depth analysis performed on the recent DHS surveys from Bangladesh, 

DRC, India, Kenya, Nigeria, and Senegal. This is followed by the results from the further analysis of the 

30 DHS surveys. Appendix Table 1 shows the exact estimates for the percent distribution of the urbanicity 

variables summarized in the figures for the six countries in the in-depth analysis. All regression results are 

summarized in the results section using figures. Appendix Tables 2-7 provide the exact estimates for the 

regression results for the in-depth analysis and Appendix Tables 8-11 show the regression results for the 

30 countries analysis. 

3.1 Bangladesh 

Figure 1 shows the geographic distribution of the urbanicity variables in Bangladesh. The maps show a 

concentration of highly urban areas in the Dhaka region across the three variables. Figure 2 summarizes 

the percent distribution of women age 15-49 according to the urbanicity variables. We see general 

agreement in the rural category for all variables, which ranges from 65% for the SMOD rural category to 

approximately 70% for the rural category with the remaining variables. Approximately 30% of women 

resided in urban centers and 5% in peri-urban clusters. Almost a quarter of women lived in urban nonpoor clusters, while 3% lived in urban poor clusters. Having a high level of nightlights is comparable to a 

highly urban area. However, only 9% of women were found to be in clusters with greater than 10 

nightlights, which did not agree with the urban categories for the remaining variables. 

Figure 1 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) urban poor 

cluster variables in Bangladesh 2014 DHS. 

(a) SMOD (b) Nightlights (c) Urban poor cluster

 
14 

Figure 2 Percent distribution of the urbanicity variables in the Bangladesh 2014 DHS 

Just over half (54%) of women age 15-49 in Bangladesh are using a modern contraceptive method. Small 

differences were observed between the urbanicity variables and mCPR as shown in Table 2. This was also 

observed in the regression results in Figure 3. In Models 1 and 3, women who reside in rural areas had 

lower odds of mCPR compared to the reference. However, mCPR for women in rural areas according 

Models 2 and 4 were not significantly different from the reference. There were also no statistically 

significant differences between women who reside in peri-urban areas compared to those in urban centers 

(Model 2), between women with 2-10 level of nightlights compared to greater than 10 (Model 3), and 

between the urban poor and urban non-poor (Model 4). This was true for both the unadjusted and adjusted 

models. 

Table 2 Crosstabulations of urbanicity variables with health outcomes in Bangladesh 2014 DHS 

Modern contraceptive use At least four ANC visits 

Three doses of DPT 

vaccine Minimum acceptable diet 

Variable % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value 

Total 54.1 [52.8,55.3] 31.5 [28.9,34.1] 91.3 [88.7,93.3] 23.1 [21.0,25.4] 

Place of residence * *** ** 

Rural 53.2 [51.7,54.8] 26.0 [23.1,29.1] 90.4 [87.1,93.0] 21.0 [18.6,23.6] 

Urban 56.2 [54.1,58.3] 47.1 [42.1,52.0] 93.6 [90.5,95.7] 29.1 [25.0,33.7] 

SMOD *** * 

Rural 53.9 [52.4,55.3] 26.4 [23.4,29.5] 89.6 [85.8,92.5] 22.6 [20.0,25.6] 

Peri-urban 50.7 [46.1,55.4] 33.6 [22.6,46.7] 95.1 [83.5,98.7] 23.3 [15.5,33.4] 

Urban centers 55.1 [52.5,57.7] 43.0 [37.4,48.7] 94.1 [91.2,96.1] 24.1 [20.3,28.3] 

Nightlights * *** 

0-1 53.0 [51.6,54.5] 26.4 [23.7,29.3] 90.3 [86.9,92.8] 22.2 [19.7,24.9] 

2-10 57.2 [54.2,60.0] 44.4 [38.2,50.8] 94.0 [89.9,96.5] 23.5 [19.3,28.4] 

>10 56.1 [52.1,60.1] 51.2 [37.4,64.8] 94.9 [89.5,97.6] 31.2 [22.2,41.7] 

Urban poor cluster * *** *** 

Rural 53.2 [51.7,54.8] 26.0 [23.1,29.1] 90.4 [87.1,93.0] 21.0 [18.6,23.6] 

Urban poor 56.6 [51.0,62.0] 28.8 [19.1,41.1] 94.3 [82.1,98.4] 20.4 [13.4,29.9] 

Urban non-poor 56.2 [53.9,58.4] 49.2 [43.8,54.5] 93.5 [90.2,95.8] 30.1 [25.6,35.1] 

Note: *p<0.05,**p<0.01, ***p<0.001 

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Rural Peri-urban Urban

centers

0-1 2-10 >10 Rural Urban

poor

Urban

non-poor

Place of residence SMOD Nightlights Urban poor cluster
15 

Figure 3 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in Bangladesh 2014 

DHS 

Approximately one-third (32%) of women in Bangladesh age 15-49 attended at least four ANC visits for 

their most recent birth. Table 2 shows that the percentage of women with ANC4 increases with increasing 

level of urbanicity. The gap between the lowest level of urbanicity and the highest is approximately 25 

percentage points with a range between 25% and 50%. However, in the regression results shown in Figure 

4, we find no statistically significant differences in the odds of ANC4 between peri-urban and urban 

centers for the SMOD variable (Model 2) and the 2-10 nightlights and greater than 10 nightlights (Model 

3). Women living in urban poor clusters had approximately 40% lower odds of ANC4 compared to the 

urban non-poor women (adjusted Model 4). 
16 

Figure 4 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in Bangladesh 2014 

DHS 

Over 90% of children age 12-23 months in Bangladesh had all three doses of the DPT immunization. As 

shown in Table 2, there were almost no differences by the urbanicity variables. This finding is also 

confirmed by the regression results shown in Figure 5. There were no significant differences between the 

categories and the reference category for all the urbanicity variables in the four models. This was true for 

both the unadjusted and the adjusted models. One exception is the rural category for the SMOD variable, 

where children living in rural clusters had significantly lower odds of DPT3 in the unadjusted model 

compared to women living in urban centers. 
17 

Figure 5 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in Bangladesh 2014 

DHS 

Fewer than a quarter (23%) of the youngest children age 6-23 months who live with their mother had a 

minimum acceptable diet. Table 2 shows that this only differed significantly by the place of residence and 

the urban poverty cluster variables. The highest percentage of MAD was found in children living in urban 

non-poor clusters (30%) and in clusters with greater than 10 nightlights (31%). Children living in rural, 

peri-urban areas, and urban poor clusters had similar levels of MAD (21-23%). The regression results in 

Figure 6 show several significant findings. In all models except Model 2 with the SMOD variable, rural 

children have lower odds of having MAD compared to the reference. In addition, children living in 

clusters with 2-10 nightlights had lower odds of MAD compared to children living in clusters with greater 

than 10 nightlights in the adjusted model. There were no significant differences in MAD between children 

living in peri-urban areas compared to urban centers, and children living in urban poor clusters compared 

to urban non-poor in both the adjusted and unadjusted models. 
18 

Figure 6 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in Bangladesh 2014 

DHS 

 
19 

3.2 Democratic Republic of Congo 

Figure 7 displays the geographic distribution of SMOD, nightlights, and urban poor cluster variables in 

the DRC based on 2013-14 DHS data. Across these indicators, urbanicity appears to cluster in Kinshasa 

in the southwestern corner of the country, in areas along the border with Zambia in the southeastern 

region, and in northeastern DRC. The SMOD and urban poor cluster variables appear to show more 

variation of urbanicity than the nightlights variable. 

Figure 7 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) urban poor 

cluster variables in the DRC 2013-14 DHS 

(a) SMOD (b) Nightlights (c) Urban poor cluster

Note: The upper left box is an enlarged image of Kinshasa and surrounding clusters. 

Figure 8 shows the percent distribution of women age 15-49 according to four indicators to assess 

gradients of urbanicity: place of residence, SMOD, nightlights, and the urban poor cluster variable. There 

appears to be general agreement in the rural category for all variables, ranging from 62%—for both place 

of residence and the urban poor cluster variable—to 71% in the lowest (0-1) nightlights category. 

According to the SMOD indicator, nearly one-third (32%) of women in the DRC resided in urban centers, 

compared to 4% living in peri-urban areas. Based on the urban poor cluster indicator, 17% of women 

lived in urban poor clusters, and more than one-fifth (22%) of women lived in urban non-poor clusters. 

The percentage of women living in the highest urban area categories across the urbanicity indicators 

displayed a wider range compared to their rural counterparts, from 21% in clusters with greater than 10 

nightlights and urban non-poor clusters to 38% in the urban category of place of residence. 
20 

Figure 8 Percent distribution of the urbanicity variables in the DRC 2013-14 DHS 

Only 8% of women age 15-49 in the DRC are using a modern contraceptive method. This differed 

significantly by all urbanicity variables with ranges from as low as 5% to the highest of 18% in urban 

non-poor clusters (Table 3). Women living in peri-urban clusters had a slightly higher percentage of 

mCPR (17%) compared to women living in the urban centers (15%). A larger difference was found 

between women living in clusters with 2-10 nightlights (17%) compared to women living in clusters with 

greater than 10 nightlights (10%). The regression results summarized in Figure 9 show that in Models 1, 

2, and 4, women living in rural clusters have lower odds of mCPR compared to their respective reference 

categories (approximately 70% lower odds in these models). The remaining categories were not 

significant in the adjusted models. This indicates that there was no difference in mCPR between women 

living in the peri-urban and urban centers (Model 1), 2-10 nightlights and greater than 10 nightlights 

(Model 3), and urban poor and urban non-poor (Model 4). 

Table 3 Crosstabulations of urbanicity variables with health outcomes in DRC 2013-14 DHS 

Modern contraceptive use At least four ANC visits 

Three doses of DPT 

vaccine Minimum acceptable diet 

Variable % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value 

Total 7.8 [6.9,8.9] 46.6 [44.3,48.9] 60.7 [57.4,63.9] 8.4 [7.1,9.9] 

Place of residence *** *** *** ** 

Rural 4.6 [3.7,5.8] 41.3 [38.3,44.3] 54.3 [49.9,58.6] 6.8 [5.3,8.5] 

Urban 14.6 [12.7,16.7] 58.4 [55.1,61.5] 74.2 [70.0,78.0] 12.1 [9.5,15.2] 

SMOD *** *** *** ** 

Rural 5.5 [4.4,6.8] 41.0 [38.0,44.1] 56.2 [52.0,60.3] 6.8 [5.3,8.6] 

Peri-urban 17.1 [4.3,48.3] 41.4 [21.6,64.5] 70.6 [23.3,95.0] 15.2 [3.9,44.5] 

Urban centers 14.9 [13.3,16.7] 61.9 [58.3,65.5] 76.7 [71.8,80.9] 11.9 [8.9,15.8] 

Nightlights *** *** ** * 

0-1 6.4 [5.4,7.7] 42.9 [40.2,45.7] 57.4 [53.6,61.1] 7.4 [6.0,9.0] 

2-10 17.3 [12.5,23.3] 58.1 [47.1,68.3] 79.1 [69.0,86.5] 15.4 [8.6,26.0] 

>10 10.0 [8.0,12.4] 58.5 [54.6,62.2] 66.3 [56.5,74.8] 10.0 [7.0,14.1] 

Urban poor cluster *** *** *** ** 

Rural 4.6 [3.7,5.8] 41.3 [38.3,44.3] 54.3 [49.9,58.6] 6.8 [5.3,8.5] 

Urban poor 11.6 [8.8,15.1] 49.3 [45.9,52.8] 62.3 [56.5,67.7] 11.2 [8.3,14.9] 

Urban non-poor 17.5 [14.9,20.4] 68.1 [63.6,72.2] 87.0 [82.9,90.3] 13.0 [8.8,18.7] 

Note: *p<0.05,**p<0.01, ***p<0.001 

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Rural Peri-urban Urban

centers

0-1 2-10 >10 Rural Urban

poor

Urban

non-poor

Place of residence SMOD Nightlights Urban poor cluster
21 

Figure 9 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in the DRC 2013-14 

DHS 

Approximately half (47%) of women age 15-49 in the DRC have attended at least four ANC visits for 

their most recent birth. This ranged from approximately 41% to 68% by the urbanicity variables as shown 

in Table 3. Women living in peri-urban clusters had the same level of ANC4 as women living in rural 

clusters (both approximately 41%). In Figure 10, we see lower odds of ANC4 in all categories compared 

to the reference for all models except for the 2-10 nightlights category in Model 3. In addition, the odds 

ratios of the categories for SMOD and the urban poor cluster were of similar magnitude, which indicated 

a similar level association with ANC4 compared to the reference. In Model 2, women living in peri-urban 

clusters and rural clusters had 50% and 40% lower odds of ANC4, respectively, when compared to urban 

centers. In Model 4, women living in urban poor and rural clusters had approximately 50% lower odds of 

ANC4 compared to women in the urban non-poor clusters. 
22 

Figure 10 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in the DRC 2013-14 

DHS 

Almost two-thirds (61%) of children age 12-23 months had all three doses of DPT vaccine. This differed 

by all urbanicity variables and ranged from a low of 54% in rural areas to a high of 87% for children 

living in the urban non-poor clusters (Table 3). The regression results summarized in Figure 11 show that 

in Models 1, 2, and 4, children living in rural clusters have lower odds of receiving DPT3 compared to the 

reference categories. In Models 1 and 2, children living in rural areas had approximately 50% lower odds 

of DPT3 compared to urban and urban centers. However, in Model 4, rural children had 80% lower odds 

compared to children in the urban non-poor clusters. In Model 2, children in peri-urban clusters did not 

differ significantly from children in urban centers in DPT3 uptake. In Model 4, we see that children living 

in urban poor clusters had 70% lower odds and children living in rural clusters had 80% lower odds of 

DPT3 compared to children in the urban non-poor clusters. No significant results were found in Model 3. 
23 

Figure 11 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in the DRC 2013-14 

DHS 

Only 8% of children age 6-23 months in the DRC had a minimum acceptable diet. This differed 

significantly by all urbanicity variables with ranges from approximately 7% in rural areas to the highest 

percentage found for children living in urban non-poor clusters at 13% (Table 3). The regression results 

summarized in Figure 12 show that in Models 1, 2, and 4, children living in rural clusters had 

approximately 50% lower odds of MAD compared to the reference categories. The remaining categories 

across the urbanicity indicators were not significant in the unadjusted and adjusted models. This indicates 

that there was no difference in MAD between children living in peri-urban and urban centers (Model 1), 

2-10 nightlights and greater than 10 nightlights (Model 3), and urban poor and urban non-poor (Model 4). 
24 

Figure 12 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in the DRC 2013-14 

DHS 

 
25 

3.3 India 

Figure 13 displays the geographic distribution of SMOD, nightlights, and urban poor cluster variables in 

India based on 2015-16 DHS data. Across these three indicators, urbanicity appears to center and then 

break apart from cities including New Delhi in the northwestern region, Kolkata along the border of 

Bangladesh, Mumbai, and Chennai. Urban centers and clusters with greater than 10 nightlights (in 

Figures 13a and 13b) share similar patterns in India in terms of urbanicity levels. Figure 13c illustrates a 

clustering of the urban poor in cities like New Delhi as well as along the southern coastline. 

Figure 13 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) urban poor 

cluster variables in India 2015-16 DHS 

(a) SMOD (b) Nightlights (c) Urban poor cluster

Figure 14 shows the percent distribution of women in India age 15-49 according to place of residence, 

SMOD, nightlights, the urban poor cluster variable, and a country-specific slum indicator with gradients 

of urbanicity. The rural category for all variables is in general agreement, with ranges from 57% in the 

lowest (0-1) nightlights category to 69% according to SMOD. According to the SMOD indicator, 28% of 

women in India resided in urban centers, with 3% of women in the peri-urban areas. The urban poor 

cluster indicator displays similar patterns seen with SMOD, with 32% of women living in urban non-poor 

clusters, and 3% of women living in urban poor clusters. Similarly, the slum variable agrees with all 

categories found in SMOD and the urban poor cluster indicators; 33% of women live in non-slum areas 

and 2% live in slums. 
26 

Figure 14 Percent distribution of the urbanicity variables in India 2015-16 DHS 

Almost half (48%) of women age 15-49 in India are using a modern contraceptive method. As shown in 

Table 4, mCPR differed significantly across all urbanicity variables. However, the differences were 

minimal with ranges from approximately 45% to 54% for all variables. Figure 15 shows the unadjusted 

and adjusted regression results for Models 1-4. For India, an additional fifth model was fit for the slum 

variable in the India dataset. For all models, the most rural category had lower odds of mCPR compared 

to the reference. Women living in peri-urban areas did not differ significantly from women in urban 

centers in mCPR use (Model 2). However, women living in clusters with 2-10 nightlights had 10% lower 

odds of mCPR compared to women living in clusters with greater than 10 nightlights (Model 3). Women 

living in urban poor clusters had 20% lower odds of mCPR compared to women living in urban non-poor 

clusters. This odds ratio was also similar to the odds ratio for rural women (approximately 0.8 as shown in 

Appendix Table 4). In Model 5, there is no statistical difference in mCPR between women living in urban 

slums compared to women living in urban non-slums. 

Table 4 Crosstabulations of urbanicity variables with health outcomes in India 2015-16 DHS 

Modern contraceptive use At least four ANC visits 

Three doses of DPT 

vaccine Minimum acceptable diet 

Variable % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value 

Total 47.8 [47.5,48.0] 50.4 [49.9,50.9] 78.4 [77.8,78.9] 9.8 [9.4,10.2] 

Place of residence *** *** ** *** 

Rural 46.0 [45.7,46.3] 44.5 [43.9,45.0] 77.7 [77.1,78.3] 8.9 [8.6,9.3]

Urban 51.2 [50.7,51.8] 66.1 [65.0,67.3] 80.2 [78.9,81.5] 12.0 [11.1,12.9] 

SMOD *** *** *** *** 

Rural 45.8 [45.5,46.1] 44.5 [43.9,45.0] 77.4 [76.8,77.9] 8.9 [8.5,9.2]

Peri-urban 54.2 [52.2,56.1] 69.8 [66.3,73.1] 85.0 [81.2,88.1] 18.3 [15.3,21.8]

Urban centers 52.1 [51.4,52.7] 68.7 [67.2,70.1] 80.9 [79.4,82.4] 11.8 [10.7,12.9] 

Nightlights *** *** *** ** 

0-1 45.3 [44.9,45.6] 44.2 [43.7,44.8] 76.7 [76.1,77.4] 9.2 [8.8,9.6]

2-10 50.2 [49.7,50.8] 55.6 [54.5,56.8] 81.5 [80.5,82.5] 10.7 [9.9,11.4]

>10 52.6 [51.7,53.5] 69.5 [67.5,71.4] 80.4 [78.1,82.4] 10.8 [9.4,12.4] 

Urban poor cluster *** *** *** *** 

Rural 46.0 [45.7,46.3] 44.5 [43.9,45.0] 77.7 [77.1,78.3] 8.9 [8.6,9.3] 

Urban poor 45.7 [44.2,47.2] 48.2 [45.2,51.2] 72.7 [69.2,76.0] 11.6 [9.7,13.9]

Urban non-poor 51.8 [51.2,52.4] 68.7 [67.5,70.0] 81.3 [79.9,82.6] 12.1 [11.1,13.1] 

Indian slums *** *** ** *** 

Rural 46.0 [45.7,46.3] 44.5 [43.9,45.0] 77.7 [77.1,78.3] 8.9 [8.6,9.3]

Urban slum 54.2 [50.7,57.8] 75.9 [70.5,80.5] 70.2 [59.8,78.8] 14.8 [10.0,21.4]

Urban non-slum 51.1 [50.5,51.7] 65.6 [64.5,66.8] 80.7 [79.4,81.9] 11.9 [11.0,12.8] 

Note: *p<0.05,**p<0.01, ***p<0.001 

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Rural Peri-urban Urban

centers

0-1 2-10 >10 Rural Urban

poor

Urban

non-poor

Rural Slum Non-slum

Place of residence SMOD Nightlights Urban poor cluster Slum
27 

Figure 15 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in India 2015-16 DHS 

Half of women age 15-49 attended at least four ANC visits for their most recent birth. This differed 

significantly by all urbanicity variables with the lowest percentages found in rural areas (45%). The 

highest percentage was found for women residing in urban slums at 76%, followed by women living in 

clusters with greater than 10 nightlights (70%) and the urban non-poor (69%) (Table 4). In Figure 16, 

women in rural areas had lower odds of ANC4 compared to the reference in all models. In Model 2, there 

was no statistical difference in ANC4 between women in peri-urban compared to the urban centers. 

Women living in clusters with 2-10 nightlights had 27% lower odds of ANC4 compared to women living 

in clusters with greater than 10 nightlights (Model 3). In Model 4, women living in urban poor clusters 

had 32% lower odds of ANC4 compared to women in urban non-poor clusters. This odds ratio was 

similar to the odds for rural women with 37% lower odds of ANC4 compared to the urban non-poor. For 

Model 5, the unadjusted model indicated that women living in urban slums had 1.7 times higher odds of 

ANC4 compared to women in urban non-slums. However, after adjusting for controls, there was no 

significant difference found between women living in urban slums and urban non-slums (Appendix Table 

4). 
28 

Figure 16 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in India 2015-16 DHS 

Over three-quarters (78%) of children age 12-23 months had all three doses of DPT vaccine. This differed 

significantly by all the urbanicity variables, although the differences were small ranging from 77% in 

rural areas to 85% in the peri-urban areas (Table 4). Figure 17, in contrast, shows few significant findings. 

In all five unadjusted models, the rural category had significantly lower odds of DPT3 compared to the 

reference. However, the significance was lost in the adjusted models. In Model 2, there was no statistical 

difference in DPT3 between children living in peri-urban clusters compared to children in urban centers. 

In Model 3, we see no significant difference in DPT3 for children living in clusters with 2-10 nightlights 

compared to those with greater than 10 nightlights in both unadjusted and adjusted models. Children 

living in urban poor clusters (Model 4) and children living in urban slums (Model 5) had significantly 

lower odds of DPT3 in both the unadjusted and adjusted models compared to the urban non-poor and 

urban non-slums, respectively. 
29 

Figure 17 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in India 2015-16 DHS 

Only 10% of children age 6-23 months had a MAD in India. This differed significantly by all urbanicity 

variables, although the differences were small (Table 4). The percentages of MAD by urbanicity variables 

ranged from 9% in rural areas to 12% for most of the other categories, and reached 18% for peri-urban 

areas. The regression results summarized in Figure 18 show that children living in rural areas had 

significantly lower odds of MAD compared to the reference in all the models. In unadjusted Model 2, 

children living in peri-urban areas had 1.7 times significantly higher odds of MAD compared to urban 

centers. However, this significance was lost in the adjusted model. In adjusted Model 5, children living in 

urban slums had 1.9 significantly higher odds of MAD compared to children living in urban non-slums. 

However, the significance was marginal with a wide confidence interval (95% C.I. 1.1, 3.2). 
30 

Figure 18 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in India 2015-16 DHS 

 
31 

3.4 Kenya 

Figure 19 displays the geographic distribution of SMOD, nightlights, and urban poor cluster variables in 

Kenya based on 2014 DHS data. Across these three indicators, urbanicity appears to cluster in and around 

Nairobi and Kisumu, with variation of urbanicity appearing more prominently along main arterial roads 

according to SMOD and the urban poor cluster variables. In the surrounding Nairobi area, for example, 

there are more urban centers and peri-urban areas along major roads (see Figure 19 (a)). 

Figure 19 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) urban poor 

cluster variables in Kenya 2014 DHS 

(a) SMOD (b) Nightlights (c) Urban poor cluster

Figure 20 presents the percent distribution of women age 15-49 according to the four indicators. There is 

some variation in the rural category across the indicators, ranging from 55% for SMOD to 72% in the 

lowest (0-1) nightlights category. According to the SMOD indicator, 23% of women in Kenya resided in 

urban centers and peri-urban areas. The distribution of women in Kenya living in the middle (2-10) 

nightlights category as well as in the highest (greater than 10) nightlights category was 14% in both 

subgroups. Based on the urban poor cluster indicator, 18% of women lived in urban poor clusters, and 

more than one-fifth (23%) in urban non-poor clusters. The percentage of women living in the highest 

urban area categories across the urbanicity indicators had a wider range compared to their rural 

counterparts, from 14% in clusters with greater than 10 nightlights to 41% in the urban place of residence. 
32 

Figure 20 Percent distribution of the urbanicity variables in Kenya 2014 DHS 

Over half (53%) of women age 15-49 in Kenya use a modern contraceptive method. This differed 

significantly by place of residence, SMOD, and urban poor cluster variables, but not by nightlights (Table 

5). However, the differences were small with ranges from approximately 50% in rural areas to 58% in 

urban areas, and 60% in the peri-urban areas. The regressions summarized in Figure 21 show that in 

Models 1, 2, and 4, women living in rural clusters had approximately 20% lower odds of mCPR 

compared to the reference categories. The remaining categories were not significant in the unadjusted and 

adjusted models. This indicates that there was no difference in mCPR between women living in periurban and urban centers (Model 1) and the urban poor and urban non-poor (Model 4). Nightlights were 

not a significant predictor of mCPR (Model 3). 

Table 5 Crosstabulations of urbanicity variables with health outcomes in Kenya 2014 DHS 

Modern contraceptive use At least four ANC visits 

Three doses of 

DPT vaccine Minimum acceptable diet 

Variable % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value 

Total 53.2 [52.1,54.3] 54.7 [53.2,56.3] 89.9 [88.5,91.1] 10.9 [9.8,12.1] 

Place of residence *** *** *** 

Rural 50.9 [49.5,52.2] 48.7 [46.8,50.6] 89.2 [87.5,90.7] 8.1 [7.1,9.2] 

Urban 56.9 [55.0,58.7] 65.6 [63.1,68.1] 91.2 [88.3,93.3] 16.0 [13.5,18.9] 

SMOD *** *** *** 

Rural 49.4 [48.0,50.7] 50.9 [49.0,52.9] 89.0 [87.2,90.5] 8.7 [7.6,10.0] 

Peri-urban 59.9 [57.8,61.9] 52.5 [49.4,55.6] 91.9 [89.1,94.0] 11.0 [8.9,13.5] 

Urban centers 56.4 [53.3,59.5] 69.7 [65.7,73.4] 90.5 [85.7,93.9] 18.2 [14.1,23.1] 

Nightlights *** *** 

0-1 52.4 [51.2,53.6] 50.4 [48.7,52.1] 89.8 [88.4,91.1] 9.0 [8.0,10.2] 

2-10 56.1 [53.6,58.7] 68.9 [65.0,72.5] 89.0 [82.9,93.1] 15.2 [11.4,19.8] 

>10 55.0 [50.4,59.5] 69.4 [63.5,74.8] 91.1 [84.6,95.0] 20.0 [13.9,27.7] 

Urban poor cluster *** *** *** 

Rural 50.9 [49.5,52.2] 48.7 [46.8,50.6] 89.2 [87.5,90.7] 8.1 [7.1,9.2] 

Urban poor 57.8 [55.4,60.1] 59.5 [56.1,62.8] 90.8 [87.0,93.6] 12.6 [9.5,16.5] 

Urban non-poor 56.1 [53.3,58.9] 71.5 [67.8,74.8] 91.5 [86.9,94.5] 19.3 [15.5,23.7] 

Note: *p<0.05,**p<0.01, ***p<0.001 

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Rural Peri-urban Urban

centers

0-1 2-10 >10 Rural Urban

poor

Urban

non-poor

Place of residence SMOD Nightlights Urban poor cluster
33 

Figure 21 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in Kenya 2014 DHS 

Over half (55%) of women age 15-49 had at least four ANC visits for their most recent birth. This 

differed significantly by all the urbanicity variables and ranged from approximately 49% in rural areas to 

the highest percentage of 72% found in women living in urban non-poor clusters (Table 5). The regression 

results in Figure 22 show that in Models 1-4, women living in rural clusters have lower odds of ANC4 

compared to the reference categories (approximately 30% lower odds in Models 1-3 and 40% in Model 

4). In adjusted Model 2, women living in peri-urban clusters have approximately 40% lower odds of 

ANC4 and women living in rural clusters have 30% lower odds of ANC4 compared to women living in 

urban clusters. In Model 3, there was no statistical difference in ANC4 between women living in clusters 

with 2-10 nightlights and those with greater than 10 nightlights. In adjusted Model 4, women living in 

urban poor clusters had approximately 25% lower odds of ANC4 and women living in rural clusters had 

approximately 40% lower odds of ANC4 compared to women living in urban non-poor clusters. 
34 

Figure 22 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in Kenya 2014 DHS 

Approximately 90% of children age 12-23 months in Kenya had three doses of DPT. This did not differ 

significantly by the urbanicity variables shown in Table 5. The regression results in Figure 23 confirmed 

this finding. None of the urbanicity variables was found to be a significant predictor of DPT3 in either the 

unadjusted or adjusted models. 
35 

Figure 23 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in Kenya 2014 DHS 

Only 11% of children age 6-23 months in Kenya had a MAD. This differed significantly by all the 

urbanicity variables with ranges from 8% in rural areas to a high of 20% for children living in clusters 

with greater than 10 nightlights (Table 5). In Figure 24, for Model 1 and Model 4, children living in rural 

areas had 40% lower odds of MAD compared to children living in urban and urban non-poor clusters 

respectively. In Model 2, children in rural and peri-urban clusters had lower odds of MAD compared to 

urban centers in the unadjusted models, although this significance was lost in the adjusted models. The 

SMOD and nightlights variables (Models 2 and 3) were not significant predictors of MAD. In Model 4, 

there was no significant difference in MAD between children living in urban poor clusters and the urban 

non-poor clusters. 
36 

Figure 24 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in Kenya 2014 DHS 

 
37 

3.5 Nigeria 

Figure 25 displays the geographic distribution of SMOD, nightlights, and urban poor cluster variables in 

Nigeria based on 2018 DHS data. Urbanicity indicators appear to cluster in cities like Abuja, Lagos, 

Ibadan, and Kano, as well as in other places with access to a main road. According to the urban poor 

cluster indicator map (Figure 25(c)), there is a large prevalence of people—across the variable 

categories—living in the southwestern section of Nigeria. 

Figure 25 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) urban poor 

cluster variables in Nigeria 2018 DHS 

(a) SMOD (b) Nightlights (c) Urban poor cluster

Figure 26 presents the percent distribution of women age 15-49 according to the four urbanicity 

indicators. There appears to be some variation in the rural category across the indicators, ranging from 

55% for place of residence and urban poor cluster indicators to 69% in the lowest (0-1) nightlights 

category. According to the SMOD indicator, more than one-third (34%) of women in Nigeria reside in 

urban centers, compared to 5% who live in peri-urban areas. One-fifth of women in Nigeria live in the 

middle (2-10) nightlights category, and 12% live in the highest (greater than 10) nightlights category. 

Based on the urban poor cluster indicator, 8% of women lived in urban poor clusters, compared with 38% 

who live in urban non-poor clusters. The percentage of women living in the highest urban area categories 

across the urbanicity indicators displayed a wider range compared to their rural counterparts, from 8% in 

urban poor clusters to 46% in the urban place of residence. 
38 

Figure 26 Percent distribution of the urbanicity variables in Nigeria 2018 DHS 

Only 12% of women age 15-49 use a modern contraceptive method. This differed significantly by all 

urbanicity variables and ranged from 8% in rural areas to 20% in highly urban areas and the urban nonpoor (Table 6). Figure 27 shows that women living in rural clusters had lower odds of mCPR compared to 

the reference in Models 1-4 (approximately 30-35% lower odds). In Model 2, while women living in periurban clusters had lower odds of mCPR compared to urban centers in the unadjusted model, this 

significance was lost in the adjusted model. In Model 3, there was no statistical difference between 

women living in clusters with from 2-10 nightlights and women living in clusters with greater than 10 

nightlights. In Model 4, women living in urban poor clusters had approximately 30% lower odds of 

mCPR compared to women living in urban non-poor clusters. The odds were similar to that of women in 

rural clusters who had approximately 35% lower odds of mCPR compared to women in the urban nonpoor clusters. 

Table 6 Crosstabulations of urbanicity variables with health outcomes in Nigeria 2018 DHS 

Modern contraceptive use At least four ANC visits 

Three doses of DPT 

vaccine Minimum acceptable diet 

Variable % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value 

Total 12.0 [11.4,12.7] 56.2 [54.5,57.8] 50.1 [47.9,52.3] 11.0 [10.1,12.0] 

Place of residence *** *** *** *** 

Rural 7.8 [7.1,8.6] 45.8 [43.8,47.9] 38.4 [35.9,41.0] 8.6 [7.7,9.5] 

Urban 18.2 [17.1,19.4] 72.7 [70.5,74.8] 67.9 [64.5,71.2] 14.8 [12.9,16.9] 

SMOD *** *** *** *** 

Rural 8.6 [7.9,9.4] 48.5 [46.5,50.6] 40.9 [38.5,43.4] 9.0 [8.1,9.9] 

Peri-urban 13.5 [10.8,16.6] 62.7 [56.4,68.7] 50.4 [42.1,58.7] 11.1 [7.3,16.6] 

Urban centers 20.2 [19.0,21.6] 76.2 [73.9,78.4] 73.3 [69.1,77.2] 16.7 [14.3,19.6] 

Nightlights *** *** *** *** 

0-1 9.3 [8.6,10.0] 51.3 [49.4,53.3] 44.3 [42.0,46.7] 9.3 [8.5,10.2] 

2-10 19.0 [17.4,20.8] 72.9 [69.8,75.9] 64.5 [59.5,69.2] 12.6 [10.2,15.4] 

>10 20.1 [18.0,22.4] 66.8 [61.7,71.6] 69.8 [61.6,76.9] 21.7 [16.9,27.4] 

Urban poor cluster *** *** *** *** 

Rural 7.8 [7.1,8.6] 45.8 [43.8,47.9] 38.4 [35.9,41.0] 8.6 [7.7,9.5] 

Urban poor 9.8 [7.1,13.3] 61.1 [55.3,66.5] 54.1 [41.8,66.0] 12.6 [7.7,20.0] 

Urban non-poor 20.1 [19.0,21.3] 75.6 [73.5,77.6] 71.3 [67.9,74.5] 15.4 [13.4,17.5] 

Note: *p<0.05,**p<0.01, ***p<0.001 

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Rural Peri-urban Urban

centers

0-1 2-10 >10 Rural Urban

poor

Urban

non-poor

Place of residence SMOD Nightlights Urban poor cluster
39 

Figure 27 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in Nigeria 2018 DHS 

More than half of women (56%) age 15-49 had at least four ANC visits for their most recent birth. This 

differed by all urbanicity variables with ranges from 46% in rural areas to 76% in urban centers and urban 

non-poor clusters (Table 6). The regression results in Figure 28 show that in Models 1, 2, and 4, women in 

rural areas had significantly lower odds of ANC4 compared to the reference. In Model 3, women living in 

clusters with 0-1 nightlights had lower odds of ANC4 compared to women living in clusters with greater 

than 10 nightlights, but this was found only in the unadjusted model. All remaining categories for the 

urbanicity variables were not significant except for a marginal significance of the 2-10 nightlights 

category in the adjusted model (OR 1.3, 95% C.I. 1.0,1.8). Therefore, there was no statistical difference in 

ANC4 found between women living in peri-urban clusters compared to urban centers and between the 

urban poor and the urban non-poor. 
40 

Figure 28 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in Nigeria 2018 DHS 

Half of children age 12-23 in Nigeria had three doses of DPT. This differed by all urbanicity variables and 

ranged from 38% in rural areas to 73% in urban centers (Table 6). The results in Figure 29 show that 

children living in rural areas had lower odds of receiving DPT compared to the reference in Models 1-4 

(between 30-50% lower odds). In the unadjusted Model 2, children living in peri-urban areas had lower 

odds of DPT3 compared to urban centers, although this significance was lost in the adjusted model. In 

Model 3, children living in clusters with 2-10 nightlights had approximately 40% lower odds of DPT3 

compared to children living in clusters with greater than 10 nightlights in the adjusted model. In the 

unadjusted Model 4, children living in urban poor clusters had lower odds of DPT3 compared to children 

living in urban non-poor clusters. This significance was lost in the adjusted model. 
41 

Figure 29 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in Nigeria 2018 DHS 

Only 11% of children age 6-23 months in Nigeria had a minimum acceptable diet. This differed 

significantly across all urbanicity variables and ranged from 9% in rural areas to a high of 22% in clusters 

with greater than 10 nightlights (Table 6). The range of children age 6-23 months who had a MAD was 

between 15-17% in urban, urban centers, and urban non-poor clusters. In Figure 30, in all models, 

children living in rural clusters had lower odds of MAD compared to the reference (approximately 30-

35% lower odds in Models 1, 2, and 4 and 57% lower odds in Model 3). In Model 2, there was no 

significant difference in MAD for children living in the peri-urban areas compared to urban centers. In 

Model 3, children living in clusters with 2-10 nightlights had 50% lower odds of MAD compared to 

children living in clusters with greater than 10 nightlights. This odds ratio was also similar to the odds 

ratio for the rural category. In Model 4, there was no statistical difference in MAD for children living in 

urban poor clusters compared to children living in the urban non-poor clusters. 
42 

Figure 30 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in Nigeria 2018 DHS 

 
43 

3.6 Senegal 

Figure 31 displays the geographic distribution of SMOD, nightlights, and urban poor cluster variables in 

Senegal based on 2016 DHS data. Urbanicity indicators appear on the western part of the country, most 

notably in Dakar, where we see the highest prevalence of urban centers according to SMOD, the highest 

(>10) nightlights category, and the highest concentration of urban poor non-poor clusters. 

Figure 31 Maps showing the geographic distribution of (a) SMOD, (b) nightlights, and (c) urban poor 

cluster variables in Senegal 2016 DHS 

(a) SMOD (b) Nightlights (c) Urban poor cluster

Figure 32 presents the percent distribution of women age 15-49 according to place of residence, SMOD, 

nightlights, and the urban poor cluster variable. There is general agreement in the rural category across 

the indicators, ranging from 51%—according to place of residence, the lowest (0-1) nightlights category, 

and urban poor cluster—to 59% according to SMOD. According to the SMOD indicator, the percentage 

of women living in peri-urban areas in Senegal is less than half a percent, compared to 41% living in 

urban centers. Thirty percent of women live in the middle (2-10) nightlights category, and 19% live in the 

highest (greater than 10) nightlights category. Based on the urban poor cluster indicator, 3% of women 

lived in urban poor clusters, compared with 47% who live in urban non-poor clusters. The percentage of 

women living in the highest urban area categories across the urbanicity indicators displayed a wider range 

compared to their rural counterparts, from 19% in clusters with greater than 10 nightlights to 49% in the 

urban place of residence. 
44 

Figure 32 Percent distribution of the urbanicity variables in Senegal 2016 DHS 

Fewer than a quarter (23%) of women age 15-49 use a modern contraceptive method. This differed 

significantly by all urbanicity variables and ranged from approximately 17% in rural areas to 34% in 

highly urban areas (Table 7). There were fewer than 50 unweighted observations for women living in 

peri-urban areas, which made it difficult to provide a reliable estimate. The regression results in Figure 33 

show that women living in rural areas had approximately 40-50% lower odds of mCPR compared to the 

reference in Models 1-4. No other categories were significant. Therefore, there was no statistical 

difference in mCPR between women living in peri-urban clusters compared to urban centers, between 2-

10 nightlights and greater than 10 nightlights, and urban poor compared to urban non-poor. 

Table 7 Crosstabulations of urbanicity variables with health outcomes in Senegal 2016 DHS 

Modern contraceptive use At least four ANC visits 

Three doses of DPT 

vaccine Minimum acceptable diet 

Variable % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value % [C.I.] p-value 

Total 23.1 [21.2,25.0] 52.2 [48.6,55.7] 89.5 [86.2,92.0] 6.9 [5.5,8.7] 

Place of residence *** *** * ** 

Rural 17.2 [15.4,19.1] 44.2 [40.2,48.3] 87.3 [82.9,90.7] 5.4 [4.2,7.0] 

Urban 31.3 [27.7,35.1] 67.9 [62.6,72.8] 94.0 [89.4,96.7] 10.0 [6.9,14.3] 

SMOD *** *** Rural 17.7 [16.0,19.6] 45.2 [41.5,49.0] 87.5 [83.4,90.6] 6.0 [4.8,7.4] 

Peri-urban ND ND ND ND 

Urban centers 33.2 [29.0,37.7] 71.3 [65.2,76.7] 95.1 [88.6,98.0] 9.7 [6.0,15.2] 

Nightlights *** *** * 0-1 18.0 [16.2,20.0] 43.9 [40.3,47.7] 86.1 [81.7,89.6] 6.2 [4.9,7.8] 

2-10 26.9 [24.3,29.8] 62.4 [54.9,69.3] 96.7 [92.9,98.5] 7.5 [4.5,12.1] 

>10 34.2 [25.4,44.4] 75.6 [62.8,85.1] (94.1) [74.6,98.8] 9.7 [4.0,21.6] 

Urban poor cluster *** *** * * 

Rural 17.2 [15.4,19.1] 44.2 [40.2,48.3] 87.3 [82.9,90.7] 5.4 [4.2,7.0] 

Urban poor 21.2 [14.1,30.7] 62.5 [50.5,73.2] 82.6 [59.4,93.9] 10.5 [4.1,24.1] 

Urban non-poor 31.9 [28.1,35.9] 68.2 [62.6,73.4] 94.9 [89.8,97.5] 9.9 [6.7,14.5] 

Notes: ND - not displayed because it is based on fewer than 25 observations. Figures in parentheses are based on 25-50 observations. 

*p<0.05,**p<0.01, ***p<0.001 

0

10

20

30

40

50

60

70

80

90

100

Rural Urban Rural Peri-urban Urban

centers

0-1 2-10 >10 Rural Urban

poor

Urban

non-poor

Place of residence SMOD Nightlights Urban poor cluster
45 

Figure 33 Unadjusted and adjusted odds ratios of the urbanicity variables and mCPR in Senegal 2016 DHS 

Slightly over half (52%) of women age 15-49 had at least four ANC visits for their most recent birth. This 

differed significantly by all urbanicity variables and ranged from approximately 44% in rural clusters to 

up to 76% in clusters with greater than 10 nightlights (Table 7). There were fewer than 50 unweighted 

observations for women living in peri-urban areas, which made it difficult to provide a reliable estimate. 

In Figure 34, women living in rural areas in Models 1, 2, and 4 had approximately 30% lower odds of 

ANC4 compared to the reference. In Model 2, we find no significant difference in ANC4 between women 

living in peri-urban clusters compared to urban centers. In Model 4, there was no significant difference in 

ANC4 between women living in urban poor clusters compared to urban non-poor clusters. Nightlights 

was not a significant predictor of ANC4. 
46 

Figure 34 Unadjusted and adjusted odds ratios of the urbanicity variables and ANC4 in Senegal 2016 DHS 

Almost 90% of children age 12-23 months had all three doses of DPT vaccine. This differed with a 

marginal significance by place of residence, nightlights, and the urban poor variable, but did not differ 

significantly by the SMOD variable, which had an insufficient number of observations in the peri-urban 

category to provide a reliable estimate (Table 7). The differences were also small and ranged from 

approximately 87% in rural areas to 94% in highly urban areas and the urban non-poor. Figure 35 

summarizes the regression results for Models 1, 2, and 4. Model 3 was omitted from the figure since the 

confidence interval for the 2-10 nightlights category was too wide to be displayed and can be found in 

Appendix Table 7. In this table, children living in clusters with 2-10 nightlights had 7 times the odds of 

DPT3 compared to children living in clusters with greater than 10 nightlights. However, since the 

confidence interval for this estimate was very large (95% C.I. 1.5, 36.6), this marginal significance should 

be viewed with caution. Figure 35 shows that in Models 1, 2, and 4—all models presented in this figure—

children living in rural areas had lower odds of DPT3 compared to the reference in the unadjusted models. 

However, this significance was lost in the adjusted models. In Model 2, the peri-urban category was 

automatically omitted from the regression due to the small number of observations. In Model 4, there was 

no statistical difference in DPT3 between children living in urban poor clusters compared to children in 

the urban non-poor clusters. 
47 

Figure 35 Unadjusted and adjusted odds ratios of the urbanicity variables and DPT3 in Senegal 2016 DHS 

Only 7% of children age 6-23 months in Senegal had a minimum acceptable diet. This differed 

significantly by place of residence and the urban poor cluster variable but not by SMOD or nightlights 

(Table 7). Percentages ranged from approximately 5% in rural areas to 10% in urban and urban non-poor 

clusters. The results in Figure 36 show that in Model 1 and 4, children in rural clusters had 50% lower 

odds of MAD compared to children living in urban and urban non-poor clusters respectively. In Model 3, 

nightlights was not a significant predictor of MAD. In Model 2, the peri-urban category was 

automatically omitted from the model due to the few available observations. In Model, 4, there was no 

statistically significant difference in MAD between children in living in urban poor clusters and children 

in the urban non-poor clusters. 
48 

Figure 36 Unadjusted and adjusted odds ratios of the urbanicity variables and MAD in Senegal 2016 DHS 

3.7 Summary of in-depth analysis 

The results across most surveys and outcomes show that women and children who live in rural areas had 

worse outcomes compared to their urban counterparts. However, the differences among urban subcategories were not always apparent. These generalizations should be viewed with caution because the 

analysis includes only six countries. For the SMOD variable, there were no significant differences in 

mCPR, DPT3, and MAD between women or children living in peri-urban clusters and urban centers 

across all countries. For ANC4, only in the DRC and Kenya was there a significant difference between 

women living in peri-urban clusters and urban centers. For the nightlights variable, there were few 

significant differences in the outcomes between clusters with 2-10 nightlights and those greater than 10 

nightlights, for mCPR only in India, for ANC4 only in India and marginally significant in Nigeria, and for 

only MAD in Nigeria. 

Differences between the urban poor and urban non-poor were mostly apparent for the ANC4 outcome. 

Significant differences between these two categories were found in Bangladesh, the DRC, India, and 

Kenya. For mCPR, significant differences between the urban poor and urban non-poor were only found in 

India and Nigeria, and for DPT3 only in the DRC and India. No significant differences in MAD between 

the urban poor and urban non-poor were found in any country. 

For India, a slum variable available in the dataset was included in the analysis. There was agreement only 

in the results between the slum variable and the urban poor cluster variable for the DPT3 outcome. 

Children living in urban poor clusters or in urban slums had lower odds of DPT3 compared to urban nonpoor or urban non-slums. For mCPR and ANC4, a significant difference was found between women 

living in urban poor clusters and urban non-poor clusters, although no significant differences were found 
49 

between urban slums and urban non-slums in the adjusted models. Finally, for the MAD outcome, there 

was no significant difference between children living in urban poor clusters compared to urban non-poor 

clusters. However, there were higher odds of children with MAD who live in urban slums compared to 

urban non-slums, although the significance was marginal. 

3.8 Results from further analysis of 30 surveys 

A further analysis of 30 countries was performed with data from the surveys listed in Table 1. The 

analysis involved fitting adjusted logistic regressions for each outcome with the SMOD variable in one 

model and the urban poverty cluster variable in another model. The same control variables used for the indepth regression analyses were used for these models. The results are summarized in Figures 37-44 below 

and Appendix Tables 8-11. The surveys in the figures are sorted by the upper bound of urban poor and 

peri-urban categories respectively for each outcome. This was done so that the surveys that show 

significance in these categories with respect to the outcome appear first in the list; if the Odds Ratios are 

below one, they will also have an asterisk next to the survey name on the y-axis. For some surveys there 

were no urban poor clusters identified among women and children. 

3.8.1 Modern contraceptive use 

Figure 37 shows the adjusted odds ratios for the urban poverty cluster variable and mCPR. For nine 

countries (Burundi, Egypt, Tanzania, Angola, Guatemala, Benin, India, Nigeria, and Uganda), women 

living in urban poor clusters had lower odds of mCPR compared to urban non-poor women. The lowest 

odds ratio was found in Tanzania, where women in urban poor clusters had 62% lower odds of mCPR 

compared to urban non-poor, while rural women had 25% lower odds compared to urban non-poor 

women. For the remaining countries where significance was found, the odds ranged from approximately 

20% to 50% lower odds of mCPR for women residing in urban poor clusters compared to urban non-poor. 

For a few countries (Benin, India, Nigeria, and Uganda), the odd ratios of urban poor women were also 

similar to the odds ratios for women who reside in rural clusters. This implies that women who reside in 

rural and urban poor clusters have a similar risk of non-use of modern contraceptives compared to the 

urban non-poor. In South Africa we observe that women residing in urban poor clusters had more than 3 

times the odds of mCPR compared to urban non-poor women. However, this significance was marginal 

and had a wide confidence interval, and therefore should be interpreted with caution. While significant 

differences in mCPR between the urban poor and urban non-poor were detected in 10 surveys, significant 

differences between rural and urban non-poor were found in 22 surveys. 
50 

Figure 37 Adjusted odds ratios for the urban poor cluster variable (reference: urban non-poor) and mCPR 

in 30 DHS surveys 

Note: South Africa had an upper bound that did not fit in the above figure. The adjusted odds ratio for 

peri-urban was 3.5 with 95% C.I. 1.1,11.3. Please see Appendix Table 8. 

Figure 38 shows the adjusted odds ratios for the SMOD variable and mCPR. The figure shows that 

women who reside in peri-urban clusters in Guatemala, Benin, and Guinea had significantly lower odds 

of mCPR compared to women in the urban centers. In Guinea, women from peri-urban areas had 66% 

lower odds of using mCPR compared to urban centers, while women residing in rural areas had 41% 

lower odds. In Benin, the odds for peri-urban women for mCPR were 35% lower compared to women in 

urban centers, and in Guatemala the odds were 28% lower. Also, for both Benin and Guatemala, the odds 

ratios for peri-urban and rural categories were very similar, indicating a similar association with mCPR 

compared to urban centers. In Egypt, women from peri-urban areas had 16% higher odds of mCPR 

compared to women in urban centers. There was no significant difference in mCPR between women in 

peri-urban areas and urban centers for the remaining countries. While significant differences in mCPR 

between the peri-urban and urban centers were found in four surveys, significant differences between the 

rural and urban centers were found in 19 surveys. 

0 0.5 1 1.5 2 2.5 3 3.5

South Africa 2016

Chad 2014-15

Rwanda 2014-15

Haiti 2016-17

Ethiopia 2016

Philippines 2017

Bangladesh 2014

Cambodia 2014

Myanmar 2015-16

Malawi 2015-16

Zambia 2018

Ghana 2014

Guinea 2018

Kenya 2014

Senegal 2016

Nepal 2016

Pakistan 2017-18

Congo Democratic Republic 2013-14

Mali 2018

Uganda 2016

Nigeria 2018

India 2015-16

Benin 2017-18

Guatemala 2014-15

Tanzania 2015-16

Egypt 2014

Angola 2015-16

Burundi 2016-17

Jordan 2017-18

Zimbabwe 2015

Urban poor Rural
51 

Figure 38 Adjusted odds ratios for the SMOD variable (reference: urban centers) and mCPR in 30 DHS 

surveys 

3.8.2 At least four ANC visits 

Figure 39 shows the odds ratios of ANC4 for women living in urban poor and rural clusters compared to 

those in the urban non-poor category. Nine surveys from the 30 showed a significant difference between 

women living in urban poor versus urban non-poor clusters in ANC4. In the DRC, Pakistan, India, Benin, 

Angola, Kenya, and Bangladesh, women living in urban poor clusters had lower odds of ANC4 compared 

to women in urban non-poor clusters (ranging from 26% to 60% lower odds). For Pakistan, India and 

Benin, the odds ratios for the urban poor and the rural clusters were very similar, which were similarly 

decreased odds of mCPR compared to the urban non-poor. In Egypt and Guatemala, women from urban 

poor clusters had 81% and 64% higher odds respectively of ANC4 compared to the urban non-poor. For 

both these countries, there was no significant difference in ANC4 observed between women residing in 

rural clusters compared to urban non-poor clusters. While significant differences in ANC4 between the 

urban poor and urban non-poor were detected in nine surveys, significant differences between rural and 

urban non-poor were found in 20 surveys. 

0 0.5 1 1.5 2 2.5 3 3.5

Haiti 2016-17

Mali 2018

Burundi 2016-17

Chad 2014-15

Congo Democratic Republic 2013-14

Ghana 2014

Rwanda 2014-15

Ethiopia 2016

Philippines 2017

Cambodia 2014

Myanmar 2015-16

Malawi 2015-16

Egypt 2014

Zambia 2018

Jordan 2017-18

Pakistan 2017-18

Tanzania 2015-16

Uganda 2016

Nepal 2016

Nigeria 2018

Senegal 2016

Zimbabwe 2015

Angola 2015-16

Kenya 2014

Bangladesh 2014

India 2015-16

South Africa 2016

Guinea 2018

Guatemala 2014-15

Benin 2017-18

Peri-urban Rural
52 

Figure 39 Adjusted odds ratios for the urban poor cluster variable (reference: urban non-poor) and ANC4 

in 30 DHS surveys 

Figure 40 shows that women who reside in peri-urban clusters in Ghana, Kenya, the DRC, Benin, 

Burundi, and Ethiopia had lower odds of ANC4 compared to women in urban centers (between 36% to 

67% lower odds). In Benin, the odds ratio for peri-urban and rural clusters was very similar; women from 

both categories had approximately 40% lower odds of ANC4 compared to urban centers. In Chad, women 

who lived in peri-urban areas had almost 2.6 times greater odds of ANC4 compared to women in urban 

centers, while rural women had approximately 50% lower odds of ANC4. Significant differences in 

ANC4 between the peri-urban and urban centers were detected in seven surveys, while significant 

differences between rural and urban centers were found in 17 surveys. 

0 0.5 1 1.5 2 2.5 3 3.5

Egypt 2014

Senegal 2016

Guatemala 2014-15

Rwanda 2014-15

Uganda 2016

Malawi 2015-16

Tanzania 2015-16

Chad 2014-15

Cambodia 2014

Philippines 2017

Guinea 2018

South Africa 2016

Burundi 2016-17

Zambia 2018

Nepal 2016

Nigeria 2018

Haiti 2016-17

Ethiopia 2016

Ghana 2014

Myanmar 2015-16

Mali 2018

Bangladesh 2014

Kenya 2014

Angola 2015-16

Benin 2017-18

India 2015-16

Pakistan 2017-18

Congo Democratic Republic 2013-14

Jordan 2017-18

Zimbabwe 2015

Urban poor Rural
53 

Figure 40 Adjusted odds ratios for the SMOD variable (reference: urban centers) and ANC4 in 30 DHS 

surveys 

Note: Chad had an upper bound that did not fit in the above figure. The adjusted odds ratio for periurban was 2.6 with 95% C.I. 1.2, 5.3. Please see Appendix Table 9. 

3.8.3 Three doses of DPT vaccine 

Figure 41 summarizes the results of the adjusted logistic regression for the urban poor cluster variable and 

DPT3 in children age 12-23 months. Children who reside in urban poor clusters in the DRC, Angola, 

India, and Nepal had lower odds of DPT3 compared to children from urban non-poor clusters. The largest 

differences were found in the DRC, where children in urban poor clusters had 73% lower odds and rural 

children 80% lower odds of DPT3 compared to the urban non-poor. In Pakistan, children living in urban 

poor clusters had 2.8 times higher odds of DPT3 compared to urban non-poor, although there was no 

significant difference found for rural children. Significant differences in DPT3 between the urban poor 

and urban non-poor were detected in five surveys, and in eight surveys between rural and urban non-poor. 

Figure 42 shows that there were no significant differences in DPT3 between children living in peri-urban 

clusters and urban centers for all the surveys in the analysis. Seven surveys showed a significant 

0 0.5 1 1.5 2 2.5 3 3.5

Chad 2014-15

Cambodia 2014

Philippines 2017

South Africa 2016

Mali 2018

Nepal 2016

Haiti 2016-17

Senegal 2016

Guinea 2018

Zambia 2018

Zimbabwe 2015

Guatemala 2014-15

Uganda 2016

Jordan 2017-18

Nigeria 2018

Angola 2015-16

Myanmar 2015-16

Egypt 2014

Rwanda 2014-15

Tanzania 2015-16

Pakistan 2017-18

Bangladesh 2014

Malawi 2015-16

India 2015-16

Ethiopia 2016

Burundi 2016-17

Benin 2017-18

Congo Democratic Republic 2013-14

Kenya 2014

Ghana 2014

Peri-urban Rural
54 

difference in DPT3 between rural children and children living in urban centers. Of interest was the higher 

odds of DPT3 found for children living in rural areas in Egypt and Malawi compared to the urban centers 

(more than twice the odds in both surveys). 

Figure 41 Adjusted odds ratios for the urban poor cluster variable (reference: urban non-poor) and DPT3 

in 30 DHS surveys 

Note: Bangladesh and South Africa had upper bounds that did not fit in the above figure. Please see 

Appendix Table 10. 

0123456

South Africa 2016

Bangladesh 2014

Pakistan 2017-18

Ghana 2014

Mali 2018

Ethiopia 2016

Zambia 2018

Cambodia 2014

Uganda 2016

Philippines 2017

Kenya 2014

Senegal 2016

Chad 2014-15

Myanmar 2015-16

Burundi 2016-17

Tanzania 2015-16

Benin 2017-18

Nigeria 2018

Guatemala 2014-15

Haiti 2016-17

Guinea 2018

Nepal 2016

India 2015-16

Angola 2015-16

Congo Democratic Republic 2013-14

Jordan 2017-18

Zimbabwe 2015

Egypt 2014

Malawi 2015-16

Rwanda 2014-15

Urban poor Rural
55 

Figure 42 Adjusted odds ratios for the SMOD variable (reference: urban centers) and DPT3 in 30 DHS 

surveys 

Note: Egypt, Pakistan, Ethiopia, Rwanda, and Zimbabwe had upper bounds that did not fit in the 

above figure. Please see Appendix Table 10. 

3.8.4 Minimum acceptable diet 

Figure 43 summarizes the results of the adjusted logistic regression for the urban poverty cluster and 

MAD in children age 6-23 months. In the Philippines, data were not collected on feeding practices for 

young children that are needed to compute this indicator. The results show that in Burundi, Haiti, and 

Guatemala, children living in urban poor clusters had lower odds of MAD compared to children in urban 

non-poor clusters. For Burundi and Guatemala, the odds of MAD were also lower for rural compared to 

urban non-poor children. In Haiti and Burundi, the differences were relatively large, with more than 80% 

lower odds of MAD for children living in urban poor clusters compared to urban non-poor in both 

surveys. In Burundi, this difference was larger than the difference between rural and urban non-poor 

children; rural children had approximately 50% lower odds of MAD compared to urban non-poor. In 

Haiti, there was no significant difference in MAD between the rural and urban non-poor children. 

0123456

Zimbabwe 2015

Rwanda 2014-15

Ethiopia 2016

Pakistan 2017-18

Egypt 2014

Zambia 2018

Bangladesh 2014

Myanmar 2015-16

Tanzania 2015-16

Haiti 2016-17

Malawi 2015-16

Guatemala 2014-15

Ghana 2014

Cambodia 2014

Guinea 2018

Nepal 2016

Jordan 2017-18

Uganda 2016

Burundi 2016-17

Angola 2015-16

Congo Democratic Republic 2013-14

Kenya 2014

Philippines 2017

South Africa 2016

India 2015-16

Benin 2017-18

Nigeria 2018

Chad 2014-15

Mali 2018

Senegal 2016

Peri-urban Rural
56 

Significant differences in MAD between the urban poor and urban non-poor were detected in three 

surveys, and in 14 surveys between rural and urban non-poor. 

Figure 43 Adjusted odds ratios for the urban poor cluster variable (reference: urban non-poor) and MAD in 

30 DHS surveys 

Note: Tanzania, South Africa, and Ghana had upper bounds that did not fit in the above figure. 

Please see Appendix Table 11. 

Figure 44 shows that only Rwanda had lower odds of MAD for children living in peri-urban clusters 

compared to urban centers. In addition, the odds ratio was similar to the odds ratio for rural children 

(approximately 50% lower odds of MAD for both categories). In Tanzania and Mali, there were higher 

odds of MAD for children living in peri-urban areas compared to the urban centers. In Mali, since the 

confidence interval was very wide due to the small sample size, the figure should be interpreted with 

caution. In Mali, rural children also had higher odds of MAD compared to urban centers, while there was 

no significant difference in MAD between rural children and children in urban centers in Tanzania. 

Significant differences in MAD between the peri-urban and urban centers were detected in three surveys, 

and in 11 surveys between rural and urban centers. 

0 0.5 1 1.5 2 2.5 3 3.5

Ghana 2014

South Africa 2016

Tanzania 2015-16

Zambia 2018

Senegal 2016

Mali 2018

Myanmar 2015-16

Chad 2014-15

Ethiopia 2016

Cambodia 2014

Nigeria 2018

Malawi 2015-16

Nepal 2016

Pakistan 2017-18

India 2015-16

Bangladesh 2014

Congo Democratic Republic 2013-14

Uganda 2016

Benin 2017-18

Egypt 2014

Kenya 2014

Angola 2015-16

Rwanda 2014-15

Guatemala 2014-15

Haiti 2016-17

Burundi 2016-17

Jordan 2017-18

Zimbabwe 2015

Guinea 2018

Philippines 2017

Urban poor Rural
57 

Figure 44 Adjusted odds ratios for the SMOD variable (reference: urban centers) and MAD in 30 DHS 

surveys 

Note: Several countries had upper bounds that did not fit in the above figure. For Mali, the adjusted 

odds ratio for rural areas was 4.5 and for peri-urban it was 16.5. Please see Appendix Table 11. This 

indicator was not available for the Philippines. 

0 0.5 1 1.5 2 2.5 3 3.5

Mali 2018

Zambia 2018

Ethiopia 2016

Myanmar 2015-16

Burundi 2016-17

Tanzania 2015-16

South Africa 2016

Ghana 2014

Cambodia 2014

Uganda 2016

Congo Democratic Republic 2013-14

Zimbabwe 2015

Guinea 2018

Pakistan 2017-18

Bangladesh 2014

Haiti 2016-17

Egypt 2014

Jordan 2017-18

India 2015-16

Benin 2017-18

Kenya 2014

Angola 2015-16

Guatemala 2014-15

Nigeria 2018

Malawi 2015-16

Nepal 2016

Rwanda 2014-15

Chad 2014-15

Senegal 2016

Philippines 2017

Peri-urban Rural

59 

DISCUSSION AND CONCLUSION 

The world’s population continues to grow, with two-thirds estimated to be living in urban areas by 2050, 

and even more rapid growth anticipated to take place in LMICs (Cyril, Oldroyd, and Renzaho 2013; 

DESA 2018; World Health Organization and UN-Habitat 2016). To date, numerous studies have sought to 

understand how the process of urbanization or the impact of living in urban areas, or urbanicity, affect 

various populations. However, many of these studies rely on the dichotomous urban/rural residence 

variable to convey the complex changes and conditions of environments. 

In this study, we examine several measures of urbanicity through a 30-country analysis of DHS data that 

focused on indicators related to the health behavior and service provision outcomes of women and 

children in LMICs. Health indicators assessed were mCPR; mothers who presented for at least four ANC 

visits for their most recent birth; children who have completed three doses of the DPT vaccine; and 

children who have received the minimum acceptable diet. In addition to an analysis of the dichotomous 

urban/rural residence variable, we evaluate indicators with more nuance—considering country-specific 

context and including measures to gauge urban poor clusters, SMOD, and luminosity levels of nightlights. 

In our analysis of India, we also assessed a country-specific slum variable. Among the 30 countries in our 

study, we conducted a more in-depth analysis in six of these countries, and fit unadjusted and adjusted 

logistic regression models to evaluate the associations between the urbanicity indicators and health (and 

in some cases, service provision) outcomes. One aim of this in-depth analysis was to understand whether, 

and to what extent, associations were due to controls such as the woman’s educational level or the region 

of residence. 

We used urbanicity variables beyond the two-category urban/rural variable, and ascertained that rural 

areas generally have worse health outcomes, compared to their urban area counterparts. Moreover, interurban differentials are noteworthy. We detected differences between peri-urban and urban centers, as well 

as those between urban poor and urban non-poor areas. In Haiti and Burundi, the differences were 

notable—and worse in the urban poor category in terms of receiving the minimum acceptable diet. More 

specifically, there was an 80% reduction in the odds of children receiving the minimum acceptable diet 

among those in urban poor clusters compared to their urban non-poor counterparts. Across the urbanicity 

indicators, we found that the urban poor cluster classification or urbanicity exhibited the strongest 

statistical evidence of association with the health indicators, followed by SMOD, and the nightlights 

variable. Through an analysis of a number of urbanicity indicators in terms of health, one aim of this 

study was to assess whether, and to what extent, the needs of people living in varying types of 

environments can be better addressed. 

As expected, health outcomes were better in the more highly urban areas and non-poor settings. A key 

finding of our study was how country-specific they were, with some countries exhibiting large significant 

differences in health outcomes in favor of those living in urban non-poor or urban centers. This meant that 

health was more improved in these areas. Based on the urban poor cluster and slum indicator in our 

analysis of India data, children living in urban poor clusters and in urban slums had significantly lower 

odds of completing three doses of the DPT immunization in both the unadjusted and adjusted models, 

compared to the urban non-poor and urban non-slums, respectively. Overarching patterns were difficult to 

determine because the indicators depended heavily on the health system’s infrastructure of each country. 
60 

More specifically, our study examined health service provision indicators (specifically mCPR and ANC4), 

because these services may be more likely to be provided in urban areas as a whole. This is different from 

focusing on health indicators such as respiratory disease where inter-urban disparities may exist more 

prominently (Vlahov and Galea 2002). This also aligns with previous research that found access to and 

utilization of health services to be predictive of positive health outcomes such as lower rates of infant and 

child mortality and hypertension (Cyril, Oldroyd, and Renzaho 2013; Harttgen and Misselhorn 2006; Kyu 

et al. 2013; Vorster 2002). Our findings may not be related to population density or poverty. Rather, we 

posit that the availability and access to health services in urban versus rural areas are important for most 

countries in our analysis.

Some of our findings were more unexpected in terms of peri-urban and rural areas that we assumed would 

have worse health outcomes. Interestingly, and in contrast to what we might expect about better health 

service outcomes in more urban areas, we found statistical evidence in a few countries for better health 

service provision outcomes in the peri-urban areas. In Chad, for example, women who lived in peri-urban 

areas had nearly 2.6 times greater odds of having at least four ANC visits compared to women in the 

urban center areas, while rural women had approximately 50% lower odds of four ANC visits compared 

to their urban center counterparts. These findings may reflect decentralization efforts in countries like 

Chad, as well as enhanced policies and programming that were targeted to certain disadvantaged areas. 

Interestingly, we observed better outcomes in rural areas compared to urban areas in addition to the better 

outcomes observed in peri-urban areas. Children living in Indian slums also have almost twice the odds of 

receiving MAD compared to children living in urban non-slums. Previous research describes outreach 

efforts for reaching vulnerable populations of slum dwellers by delivering health services and education 

to areas where the residents live. More specifically, outreach that involves residents of slums as partners 

in redressing issues of inequity and disempowerment from living in slums has been important (Unger and 

Riley 2007). 

Based on the literature and our in-depth analyses, we learned that socioeconomic factors could be more 

predictive of better health outcomes than the urbanicity indicators that reflect a more macro-level 

approach. When comparing the unadjusted and adjusted logistic regression models, we find that 

urbanicity variables lost significance after adding controls, such as attending at least four ANC visits in 

India. It could be that slum dwellers are socioeconomically disadvantaged, but are not necessarily less 

educated (Kyu et al. 2013), and therefore women are performing their ANC visits regardless of whether or 

not they live in slums. Kyu et al. (2013) studied the role of household wealth and maternal education in 

the maternal use of ANC services as a means to alleviate the harmful effects associated with living in a 

slum. It is important to consider and understand contextual and compositional effects of living in slums 

and their associations with child health outcomes. Relatedly, levels of maternal education and household 

wealth in this study were lower among peri-urban residents than their urban resident counterparts. This 

calls for the need to investigate and account for socioeconomic factors when examining urbanicity (Jones, 

Acharya, and Galway 2016). 

Although this study extends our knowledge of gradients of urban environments and their relationships 

with health-related outcomes, there are limitations to our analyses. First, the nightlights variable 

performed poorly as an indicator that assessed the relationship between urbanicity and health outcomes. 

The manner in which the nightlights variable was categorized did not adequately capture differences, and 

it is possible that the measure does not capture differences in levels of urbanicity in these settings. 
61 

Furthermore, when comparing the highest nightlights (greater than 10) category with the other indicators 

that sought to measure the most densely populated areas, this particular category did not appear to align 

with corresponding categories in the other urbanicity indicators that denote the highest levels of 

urbanicity. The nightlights variable may have been limited by the small number of observations with high 

levels of luminosity in the countries that were analyzed. Other methods could have been used to 

categorize the nightlights variable; however, these methods would be country specific and not suitable for 

cross-country comparisons. 

A second limitation of our study was sample size, particularly with the MAD and DPT3 health indicators 

and some other categories of the urbanicity variables. As a result, several odds ratios were automatically 

omitted from regressions because of an insufficient number of observations. A third limitation of this 

study is related to the displacement procedures followed by the DHS. Specifically, urban clusters are 

displaced by up to 2 km. Given that one of our main exposure variables was focused on urban area 

locations, this displacement may have potentially weakened the associations in our analyses. Similarly, 

displacement of these geolocated areas may increase the difficulty in identifying slums, including finding 

agreement in India between the urban poor cluster variable and the country-specific slum variable. Fourth, 

another possible limitation involved using data from 2015 for SMOD and nightlights when the DHS data 

were collected at different time periods. Given that urbanicity refers to the impact of living in urban areas 

at a given time (Cyril, Oldroyd, and Renzaho 2013; Vlahov and Galea 2002), it is possible that there is a 

temporal issue with possible rapid changes in development that we are unable to fully capture and adjust 

for at this time. The final possible limitation is in the construction of the urban poverty cluster variable. 

The place of residence variable available in the DHS data was used to identify rural and urban clusters as 

a first step in coding this variable. However, place of residence is determined by the statistical agency and 

usually using the most recent census for the country. This information may be out of date, and some rural 

clusters, for example, may have become urban clusters over time. This may cause some misclassification 

of clusters as rural or urban. 

To our knowledge, this is the first time that DHS data has been used to examine the role of urbanicity on 

health and service provision outcomes through the use of the urban poor cluster variable and other 

urbanicity indicators. This study examines health outcomes in relation to more commonly used indicators 

such as the dichotomous urban/rural residence variable as well as less frequently used indicators such as 

SMOD. Based on our multi-country analyses, we suggest that more research is needed to further examine 

why some countries showed strong statistical evidence of association between gradients of urbanicity and 

their health outcomes. We hypothesize that more health services are required in the urban poor cluster or 

peri-urban areas based on our study’s findings. For example, in countries such as Angola and India, the 

urban poor category had lower odds of modern contraceptive uptake, at least 4 ANC visits, and receiving 

the required number of DPT immunizations among children, compared to their urban non-poor 

counterparts. These key findings add to the current literature that suggests the importance of focusing on 

this urban poor subgroup (Crocker-Buque et al. 2017; Menon, Ruel, and Morris 2000; Pörtner and Su 

2018), chiefly in terms of health facility-based interventions that we examined. Finally, we suggest that 

more research be conducted to examine the relationship between urbanicity variables and health 

indicators that are not related to service provision. This could help to explain the effect of the urban 

environment on the physical health of individuals. 

63 

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67 

APPENDIX Appendix Table 1 Description of urbanicity variables among all interviewed women age 15-49 for Bangladesh 2014 DHS, DRC 2013-14 DHS, 

India 2015-16 DHS, Kenya 2014 DHS, Nigeria 2018 DHS, and Senegal 2016 DHS 

Bangladesh 2014 DRC 2013-14 India 2015-16 Kenya 2014 Nigeria 2018 Senegal 2016 

Variable % [C.I.] N % [C.I.] N % [C.I.] N % [C.I.] N % [C.I.] N % [C.I.] N Place of residence 

Rural 71.7 [69.4,73.9] 11,696 61.6 [57.6,65.5] 12,000 65.4 [65.0,65.8] 494,951 59.2 [57.6,60.7] 19,465 54.2 [52.4,56.0] 24,837 50.9 [47.8,54.0] 5,578 Urban 28.3 [26.1,30.6] 6,167 38.4 [34.5,42.4] 6,827 34.6 [34.2,35.0] 204,735 40.8 [39.3,42.4] 11,614 45.8 [44.0,47.6] 16,984 49.1 [46.0,52.2] 3,287 

SMOD 

Rural 65.3 [60.9,69.4] 12,081 64.7 [60.3,68.8] 12,070 68.8 [68.3,69.4] 540,371 54.9 [52.2,57.5] 19,946 61.4 [58.9,63.9] 27,423 59.1 [54.8,63.3] 6,882 

Peri-urban 4.9 [3.2,7.5] 727 3.6 [1.9,6.9] 611 3.0 [2.7,3.3] 20,855 22.5 [20.2,25.0] 7,192 4.9 [3.7,6.5] 1,988 0.2 [00.0,1.6] 42 Urban centers 29.8 [26.0,33.9] 5,022 31.7 [27.8,35.8] 4,718 28.2 [27.6,28.7] 135,271 22.6 [20.6,24.7] 3,778 33.7 [31.0,36.4] 11,442 40.7 [36.5,45.0] 1,941 

Nightlights 

Median [min, max] 0.3 [0,23.2] 17,830 0 [0,36.8] 17,399 0.8 [0,106.2] 696,497 0.1 [0,51.2] 30,916 0.1 [0,36.4] 40,853 0.9 [0,40.7] 8,865 0-1 72.6 [68.7,76.3] 12,862 70.5 [65.5,75.0] 14,424 56.7 [56.1,57.3] 443,444 71.8 [69.7,73.8] 25,634 69.0 [66.2,71.7] 30,800 50.9 [45.7,56.0] 6,502 2-10 18.6 [15.2,22.6] 3,872 8.7 [5.6,13.3] 1,162 25.3 [24.6,25.9] 177,879 14.1 [12.1,16.5] 3,826 19.5 [17.1,22.1] 7,396 30.4 [22.4,39.9] 1,964 >10 8.7 [6.9,11.0] 1,129 20.8 [16.8,25.6] 3,241 18.1 [17.5,18.6] 78,363 14.0 [12.4,15.9] 1,619 11.5 [9.3,14.2] 3,625 18.7 [13.0,26.2] 399 

Urban poor cluster 

Rural 71.7 [69.4,73.9] 11,696 61.6 [57.6,65.5] 12,000 65.4 [65.0,65.8] 494,951 59.2 [57.6,60.7] 19,465 54.2 [52.4,56.0] 24,837 50.9 [47.8,54.0] 5,578 

Urban poor 2.5 [1.5,4.1] 641 16.5 [13.3,20.3] 3,433 3.1 [2.8,3.3] 23,605 17.9 [15.9,20.1] 6,804 7.6 [5.6,10.2] 2,989 2.6 [1.7,4.0] 586 

Urban non-poor 25.8 [23.4,28.3] 5,526 21.9 [18.1,26.2] 3,394 31.6 [31.1,32.0] 181,130 22.9 [20.5,25.6] 4,810 38.2 [36.0,40.5] 13,995 46.5 [43.1,49.9] 2,701 

Slum variable for India 

Rural - - - - 65.4 [65.0,65.8] 494,951 - - - - - - Slum - - - - 1.6 [1.4,1.9] 5,201 - - - - - - Non-slum - - - - 33.0 [32.6,33.4] 199,534 - - - - - - 
68 

Appendix Table 2 Unadjusted and adjusted odds ratios of the urbanicity variables with health outcomes in Bangladesh 2014 DHS 

Modern contraceptive use At least four ANC visits Three doses of DPT vaccine Minimum acceptable diet 

Variable UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] Place of residence 

Rural 0.89* [0.8,0.98] 0.87* [0.78,0.98] 0.39*** [0.31,0.51] 0.45*** [0.35,0.57] 0.65 [0.38,1.11] 0.89 [0.51,1.54] 0.65** [0.5,0.84] 0.71* [0.55,0.93] Urban 1 1 1 1 1 1 1 1 

SMOD 

Rural 0.95 [0.84,1.08] 0.87 [0.75,1.01] 0.48*** [0.36,0.63] 0.5*** [0.37,0.67] 0.54* [0.31,0.95] 0.65 [0.35,1.2] 0.92 [0.7,1.22] 0.96 [0.7,1.31] 

Peri-urban 0.84 [0.68,1.03] 0.88 [0.71,1.09] 0.67 [0.39,1.15] 0.75 [0.46,1.23] 1.2 [0.35,4.11] 1.34 [0.37,4.83] 0.96 [0.56,1.62] 1.03 [0.61,1.71] Urban centers 1 1 1 1 1 1 1 1 

Nightlights 

0-1 0.88 [0.74,1.05] 0.81* [0.67,0.98] 0.34*** [0.2,0.6] 0.37*** [0.21,0.64] 0.5 [0.22,1.16] 0.74 [0.31,1.78] 0.63 [0.4,1.01] 0.59* [0.36,0.96] 2-10 1.04 [0.85,1.28] 0.96 [0.78,1.19] 0.76 [0.42,1.38] 0.69 [0.39,1.22] 0.84 [0.33,2.14] 0.95 [0.36,2.49] 0.68 [0.41,1.13] 0.57* [0.35,0.94] >10 1 1 1 1 1 1 1 1 

Urban poor cluster 

Rural 0.89* [0.8,0.99] 0.89 [0.79,1] 0.36*** [0.28,0.47] 0.42*** [0.33,0.55] 0.66 [0.37,1.15] 1.02 [0.58,1.79] 0.62*** [0.47,0.81] 0.7* [0.53,0.92] 

Urban poor 1.02 [0.8,1.29] 1.19 [0.87,1.63] 0.42*** [0.26,0.67] 0.61* [0.38,0.98] 1.15 [0.32,4.19] 2.72 [0.61,12.12] 0.59 [0.34,1.04] 0.76 [0.42,1.37] 

Urban non-poor 1 1 1 1 1 1 1 1 

Note: *p<0.05, **p<0.01, ***p<0.001 

Appendix Table 3 Unadjusted and adjusted odds ratios of the urbanicity variables with health outcomes in DRC 2013-14 DHS 

Modern contraceptive use At least four ANC visits Three doses of DPT vaccine Minimum acceptable diet 

Variable UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] Place of residence 

Rural 0.28*** [0.21,0.38] 0.37*** [0.27,0.5] 0.5*** [0.42,0.6] 0.69*** [0.56,0.84] 0.41*** [0.31,0.54] 0.52*** [0.37,0.72] 0.53** [0.37,0.76] 0.54** [0.37,0.8] Urban 1 1 1 1 1 1 1 1 

SMOD 

Rural 0.33*** [0.25,0.44] 0.44*** [0.3,0.65] 0.43*** [0.35,0.52] 0.61*** [0.48,0.79] 0.39*** [0.29,0.53] 0.56* [0.36,0.87] 0.54** [0.36,0.81] 0.54** [0.37,0.79] Peri-urban 1.18 [0.58,2.37] 1.06 [0.57,1.98] 0.43*** [0.28,0.68] 0.53** [0.34,0.85] 0.73 [0.27,1.96] 0.92 [0.37,2.29] 1.33 [0.62,2.84] 0.93 [0.34,2.54] Urban centers 1 1 1 1 1 1 1 1 

Nightlights 

0-1 0.62** [0.44,0.86] 0.94 [0.62,1.43] 0.53*** [0.44,0.66] 0.74* [0.59,0.93] 0.69 [0.43,1.09] 0.91 [0.53,1.56] 0.72 [0.46,1.11] 0.7 [0.39,1.25] 2-10 1.88** [1.22,2.9] 1.57 [0.97,2.55] 0.98 [0.65,1.49] 0.95 [0.65,1.4] 1.92* [1.04,3.53] 1.46 [0.68,3.12] 1.64 [0.8,3.35] 1.4 [0.61,3.2] >10 1 1 1 1 1 1 1 1 

Urban poor cluster 

Rural 0.23*** [0.17,0.31] 0.31*** [0.21,0.45] 0.33*** [0.26,0.42] 0.45*** [0.33,0.6] 0.18*** [0.12,0.26] 0.2*** [0.12,0.32] 0.49** [0.3,0.79] 0.46** [0.28,0.76] Urban poor 0.62** [0.43,0.89] 0.76 [0.49,1.16] 0.46*** [0.36,0.59] 0.53*** [0.4,0.69] 0.25*** [0.16,0.37] 0.27*** [0.17,0.43] 0.85 [0.49,1.45] 0.79 [0.46,1.34] 

Urban non-poor 1 1 1 1 1 1 1 1 

Note: *p<0.05, **p<0.01, ***p<0.001 
69 

Appendix Table 4 Unadjusted and adjusted odds ratios of the urbanicity variables with health outcomes in India 2015-16 DHS 

Modern contraceptive use At least four ANC visits Three doses of DPT vaccine Minimum acceptable diet 

Variable UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] Place of residence 

Rural 0.81*** [0.79,0.83] 0.87*** [0.85,0.9] 0.41*** [0.39,0.43] 0.66*** [0.62,0.71] 0.86** [0.79,0.94] 1.05 [0.96,1.15] 0.72*** [0.65,0.79] 0.84** [0.76,0.93]Urban 1 1 1 1 1 1 1 1 

SMOD 

Rural 0.78*** [0.75,0.8] 0.85*** [0.82,0.88] 0.37*** [0.34,0.39] 0.63*** [0.57,0.68] 0.8*** [0.72,0.89] 1.04 [0.93,1.16] 0.73*** [0.65,0.82] 0.86** [0.76,0.96]

Peri-urban 1.09 [0.99,1.2] 0.98 [0.89,1.08] 1.06 [0.88,1.27] 0.82 [0.67,1] 1.33 [0.99,1.79] 1.01 [0.73,1.4] 1.69*** [1.32,2.15] 1.24 [0.95,1.61] Urban centers 1 1 1 1 1 1 1 1 

Nightlights 

0-1 0.75*** [0.72,0.78] 0.78*** [0.74,0.81] 0.35*** [0.32,0.38] 0.56*** [0.49,0.63] 0.81** [0.7,0.93] 0.96 [0.83,1.12] 0.84* [0.71,0.98] 0.86 [0.73,1.01]2-10 0.91*** [0.87,0.95] 0.91** [0.87,0.96] 0.55*** [0.5,0.61] 0.73*** [0.65,0.84] 1.08 [0.92,1.26] 1.15 [0.98,1.35] 0.98 [0.83,1.17] 0.89 [0.75,1.06] >10 1 1 1 1 1 1 1 1 

Urban poor cluster 

Rural 0.79*** [0.77,0.82] 0.86*** [0.83,0.89] 0.36*** [0.34,0.39] 0.63*** [0.58,0.68] 0.8*** [0.73,0.88] 1.01 [0.91,1.12] 0.71*** [0.64,0.79] 0.86** [0.77,0.96]

Urban poor 0.78*** [0.72,0.85] 0.82*** [0.75,0.89] 0.42*** [0.36,0.49] 0.68*** [0.58,0.79] 0.61*** [0.5,0.75] 0.77* [0.62,0.96] 0.96 [0.76,1.21] 1.16 [0.93,1.44] 

Urban non-poor 1 1 1 1 1 1 1 1 

Indian slums 

Rural 0.82*** [0.8,0.84] 0.87*** [0.84,0.9] 0.42*** [0.4,0.44] 0.67*** [0.63,0.72] 0.83*** [0.76,0.91] 1.03 [0.93,1.13] 0.73*** [0.66,0.8] 0.87** [0.79,0.96]Urban slum 1.13 [0.98,1.31] 0.85 [0.71,1.02] 1.65** [1.24,2.19] 1.4 [0.99,1.96] 0.56* [0.35,0.9] 0.61* [0.38,0.98] 1.29 [0.82,2.03] 1.89* [1.11,3.22] Urban non-slum 1 1 1 1 1 1 1 1 

Note: *p<0.05, **p<0.01, ***p<0.001 

Appendix Table 5 Unadjusted and adjusted odds ratios of the urbanicity variables with health outcomes in Kenya 2014 DHS 

Modern contraceptive use At least four ANC visits Three doses of DPT vaccine Minimum acceptable diet 

Variable UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] Place of residence 

Rural 0.78*** [0.71,0.86] 0.81*** [0.74,0.89] 0.5*** [0.43,0.57] 0.69*** [0.6,0.8] 0.8 [0.57,1.13] 0.95 [0.66,1.35] 0.46*** [0.36,0.59] 0.69** [0.54,0.9] Urban 1 1 1 1 1 1 1 1 

SMOD 

Rural 0.75*** [0.65,0.87] 0.83* [0.71,0.96] 0.45*** [0.37,0.55] 0.7** [0.57,0.86] 0.84 [0.52,1.38] 1 [0.54,1.86] 0.43*** [0.31,0.6] 0.72 [0.48,1.08]

Peri-urban 1.15 [0.99,1.35] 0.94 [0.8,1.11] 0.48*** [0.38,0.6] 0.63*** [0.5,0.8] 1.18 [0.67,2.1] 1.05 [0.5,2.2] 0.55** [0.38,0.81] 0.82 [0.52,1.3] Urban centers 1 1 1 1 1 1 1 1 

Nightlights 

0-1 0.9 [0.75,1.09] 0.97 [0.76,1.23] 0.45*** [0.34,0.59] 0.67* [0.45,1] 0.86 [0.46,1.62] 0.56 [0.17,1.78] 0.4*** [0.26,0.62] 0.59 [0.35,1]2-10 1.05 [0.84,1.3] 1.13 [0.89,1.43] 0.97 [0.71,1.33] 1.04 [0.69,1.56] 0.79 [0.36,1.75] 0.39 [0.12,1.26] 0.72 [0.42,1.22] 0.73 [0.43,1.25] >10 1 

Urban poor cluster 

Rural 0.81** [0.71,0.92] 0.85* [0.73,0.98] 0.38*** [0.31,0.46] 0.57*** [0.46,0.71] 0.77 [0.47,1.27] 0.98 [0.58,1.67] 0.37*** [0.27,0.5] 0.59** [0.41,0.85]

Urban poor 1.07 [0.92,1.25] 1.07 [0.9,1.28] 0.59*** [0.47,0.73] 0.74* [0.58,0.94] 0.92 [0.49,1.72] 1.06 [0.56,1.98] 0.6* [0.4,0.91] 0.75 [0.47,1.19] 

Urban non-poor 1 1 1 1 1 1 1 1 

Note: *p<0.05, **p<0.01, ***p<0.001 
70 

Appendix Table 6 Unadjusted and adjusted odds ratios of the urbanicity variables with health outcomes in Nigeria 2018 DHS 

Modern contraceptive use At least four ANC visits Three doses of DPT vaccine Minimum acceptable diet 

Variable UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] Place of residence 

Rural 0.38*** [0.33,0.43] 0.69*** [0.61,0.79] 0.32*** [0.28,0.36] 0.68*** [0.59,0.77] 0.29*** [0.24,0.35] 0.71*** [0.59,0.86] 0.54*** [0.44,0.66] 0.74** [0.6,0.92] Urban 1 1 1 1 1 1 1 1 

SMOD 

Rural 0.37*** [0.33,0.42] 0.72*** [0.62,0.83] 0.29*** [0.25,0.35] 0.71*** [0.61,0.82] 0.25*** [0.2,0.32] 0.61*** [0.48,0.77] 0.49*** [0.39,0.61] 0.67** [0.53,0.85] 

Peri-urban 0.61** [0.46,0.82] 0.89 [0.69,1.15] 0.53*** [0.37,0.75] 1.04 [0.78,1.39] 0.37*** [0.24,0.58] 0.67 [0.44,1.03] 0.62* [0.39,1] 0.69 [0.43,1.1] 

Urban centers 1 1 1 1 1 1 1 1 

Nightlights 

0-1 0.41*** [0.34,0.49] 0.68*** [0.58,0.8] 0.52*** [0.41,0.68] 0.98 [0.76,1.25] 0.34*** [0.23,0.51] 0.53*** [0.37,0.75] 0.37*** [0.26,0.53] 0.43*** [0.31,0.6] 2-10 0.94 [0.77,1.14] 0.88 [0.74,1.04] 1.34 [0.98,1.83] 1.34* [1.01,1.77] 0.79 [0.49,1.26] 0.58** [0.39,0.87] 0.52** [0.34,0.79] 0.51** [0.34,0.76] >10 1 1 1 1 1 1 1 1 

Urban poor cluster 

Rural 0.34*** [0.3,0.38] 0.66*** [0.57,0.76] 0.27*** [0.24,0.31] 0.66*** [0.57,0.76] 0.25*** [0.21,0.31] 0.69*** [0.56,0.85] 0.52*** [0.43,0.63] 0.74* [0.58,0.93] 

Urban poor 0.43*** [0.29,0.64] 0.70* [0.53,0.92] 0.51*** [0.38,0.67] 0.9 [0.67,1.2] 0.48** [0.27,0.83] 0.87 [0.53,1.44] 0.8 [0.45,1.42] 0.97 [0.53,1.77] 

Urban non-poor 1 1 1 1 1 1 1 1 

Note: *p<0.05, **p<0.01, ***p<0.001 

Appendix Table 7 Unadjusted and adjusted odds ratios of the urbanicity variables with health outcomes in Senegal 2016 DHS 

Modern contraceptive use At least four ANC visits Three doses of DPT vaccine Minimum acceptable diet 

Variable UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] UOR [C.I.] AOR [C.I.] Place of residence 

Rural 0.45*** [0.37,0.56] 0.59*** [0.47,0.75] 0.38*** [0.28,0.5] 0.69** [0.54,0.88] 0.44* [0.21,0.89] 0.95 [0.49,1.86] 0.52** [0.32,0.84] 0.5* [0.3,0.84] Urban 1 1 1 1 1 1 1 1 

SMOD 

Rural 0.43*** [0.35,0.54] 0.55*** [0.42,0.7] 0.33*** [0.24,0.46] 0.67** [0.52,0.87] 0.36* [0.14,0.92] 1.06 [0.43,2.61] 0.59 [0.34,1.04] 0.56 [0.28,1.12] Peri-urban 0.72** [0.59,0.87] 0.82 [0.58,1.15] 0.81 [0.61,1.06] 1.15 [0.75,1.78] - - - - Urban centers 1 1 1 1 1 1 1 1 

Nightlights 

0-1 0.42*** [0.28,0.63] 0.53** [0.35,0.8] 0.25*** [0.14,0.44] 0.67 [0.32,1.36] 0.39 [0.09,1.73] 3.06 [0.53,17.82] 0.62 [0.26,1.47] 0.4 [0.11,1.45] 2-10 0.71 [0.46,1.08] 0.9 [0.64,1.26] 0.53* [0.29,0.99] 0.99 [0.5,1.94] 1.83 [0.36,9.44] 7.39* [1.49,36.58] 0.75 [0.28,1.99] 0.81 [0.24,2.68] >10 1 1 1 1 1 1 1 1 

Urban poor cluster 

Rural 0.44*** [0.36,0.55] 0.58*** [0.46,0.74] 0.37*** [0.28,0.5] 0.72* [0.56,0.92] 0.37* [0.16,0.84] 0.81 [0.4,1.65] 0.52* [0.32,0.86] 0.5* [0.29,0.88] Urban poor 0.57* [0.36,0.91] 0.79 [0.5,1.24] 0.78 [0.46,1.33] 1.41 [0.79,2.51] 0.26* [0.07,0.89] 0.45 [0.11,1.86] 1.06 [0.42,2.69] 1.04 [0.4,2.67] 

Urban non-poor 1 1 1 1 1 1 1 1 

Note: *p<0.05, **p<0.01, ***p<0.001 
71 

Appendix Table 8 Adjusted odds ratios for modern contraceptive use 

Urban poor cluster 

(ref. urban non-poor) 

SMOD 

(ref. urban centers) 

Country Rural Urban poor Rural Peri-urban 

Angola 2015-16 0.17*** [0.1,0.3] 0.5*** [0.35,0.7] 0.4*** [0.29,0.57] 0.58 [0.3,1.12] 

Bangladesh 2014 0.89 [0.79,1] 1.19 [0.87,1.63] 0.87 [0.75,1.01] 0.88 [0.71,1.09] 

Benin 2017-18 0.62*** [0.5,0.77] 0.63** [0.47,0.82] 0.58*** [0.46,0.72] 0.65** [0.48,0.87] 

Burundi 2016-17 0.65*** [0.53,0.78] 0.49*** [0.36,0.68] 0.63** [0.47,0.85] 1.2 [0.68,2.12] 

Cambodia 2014 1.2* [1.04,1.39] 1.2 [0.92,1.58] 1.18 [0.95,1.47] 1.1 [0.82,1.48] 

Chad 2014-15 0.72 [0.44,1.18] 1.53 [0.97,2.42] 0.54** [0.36,0.81] 0.87 [0.36,2.1] 

Congo Democratic Republic 2013-14 0.31*** [0.21,0.45] 0.76 [0.49,1.16] 0.44*** [0.3,0.65] 1.06 [0.57,1.98] 

Egypt 2014 0.7 [0.49,1.01] 0.49*** [0.34,0.71] 1.18** [1.04,1.34] 1.16* [1,1.33] 

Ethiopia 2016 0.65 [0.39,1.08] 1.26 [0.79,1.99] 0.68 [0.39,1.19] 0.67 [0.26,1.71] 

Ghana 2014 1.28* [1.04,1.59] 0.74 [0.41,1.33] 1.41* [1.09,1.84] 1.16 [0.73,1.85] 

Guatemala 2014-15 0.69*** [0.61,0.78] 0.57*** [0.43,0.74] 0.73*** [0.62,0.86] 0.72** [0.59,0.88] 

Guinea 2018 0.65* [0.44,0.98] 0.41 [0.13,1.31] 0.59* [0.38,0.9] 0.34* [0.12,0.98] 

Haiti 2016-17 1.12 [0.85,1.47] 1.41 [0.98,2.02] 1.23 [0.95,1.59] 1.65 [0.84,3.26] 

India 2015-16 0.86*** [0.83,0.89] 0.82*** [0.75,0.89] 0.85*** [0.82,0.88] 0.98 [0.89,1.08] 

Jordan 2017-18 0.94 [0.78,1.12] - 0.95 [0.81,1.13] 1.06 [0.84,1.32] 

Kenya 2014 0.85* [0.73,0.98] 1.07 [0.9,1.28] 0.83* [0.71,0.96] 0.94 [0.8,1.11] 

Malawi 2015-16 0.88 [0.76,1.01] 0.97 [0.65,1.45] 0.83* [0.69,0.99] 1.08 [0.87,1.35] 

Mali 2018 0.66** [0.52,0.84] 0.58 [0.33,1] 0.84 [0.63,1.11] 1.1 [0.45,2.67] 

Myanmar 2015-16 0.98 [0.82,1.17] 1.16 [0.89,1.52] 0.96 [0.82,1.12] 1 [0.69,1.46] 

Nepal 2016 0.77** [0.66,0.91] 0.89 [0.65,1.22] 0.78* [0.63,0.97] 0.91 [0.71,1.17] 

Nigeria 2018 0.66*** [0.57,0.76] 0.7* [0.53,0.92] 0.72*** [0.62,0.83] 0.89 [0.69,1.15] 

Pakistan 2017-18 0.82* [0.7,0.96] 0.79 [0.52,1.21] 0.86* [0.74,0.99] 0.87 [0.59,1.28] 

Philippines 2017 1.22* [1.05,1.42] 1.07 [0.7,1.64] 1.46*** [1.23,1.73] 1.22 [0.99,1.5] 

Rwanda 2014-15 0.93 [0.76,1.14] 1.36 [0.89,2.09] 1.05 [0.79,1.39] 1.27 [0.91,1.79] 

Senegal 2016 0.58*** [0.46,0.74] 0.79 [0.5,1.24] 0.55*** [0.42,0.7] 0.82 [0.58,1.15] 

South Africa 2016 1.05 [0.85,1.29] 3.52* [1.1,11.28] 0.79 [0.61,1.02] 0.73 [0.51,1.05] 

Tanzania 2015-16 0.75** [0.63,0.9] 0.38** [0.19,0.74] 0.72** [0.57,0.91] 0.9 [0.63,1.28] 

Uganda 2016 0.71*** [0.6,0.84] 0.77* [0.6,0.99] 0.76** [0.63,0.93] 0.83 [0.55,1.24] 

Zambia 2018 0.86* [0.75,0.99] 1.12 [0.91,1.39] 0.97 [0.83,1.14] 0.79 [0.47,1.33] 

Zimbabwe 2015 0.91 [0.78,1.06] - 0.84 [0.67,1.04] 0.85 [0.62,1.15] 

Note: *p<0.05, **p<0.01, ***p<0.001 

 
72 

Appendix Table 9 Adjusted odds ratios for having at least four ANC visits 

Urban poor cluster 

(ref. urban non-poor) 

SMOD 

(ref. urban centers) 

Country Rural Urban poor Rural Peri-urban 

Angola 2015-16 0.47*** [0.38,0.58] 0.71** [0.55,0.92] 0.59*** [0.48,0.72] 0.81 [0.48,1.37] 

Bangladesh 2014 0.42*** [0.33,0.55] 0.61* [0.38,0.98] 0.5*** [0.37,0.67] 0.75 [0.46,1.23] 

Benin 2017-18 0.61*** [0.49,0.76] 0.66** [0.51,0.86] 0.59*** [0.45,0.77] 0.62** [0.46,0.85] 

Burundi 2016-17 0.97 [0.8,1.18] 0.85 [0.55,1.32] 0.91 [0.71,1.16] 0.64* [0.45,0.9] 

Cambodia 2014 0.86 [0.56,1.32] 0.97 [0.5,1.87] 0.81 [0.42,1.56] 1 [0.36,2.75] 

Chad 2014-15 0.61* [0.4,0.91] 1.32 [0.9,1.95] 0.53*** [0.38,0.74] 2.58* [1.24,5.37] 

Congo Democratic Republic 2013-14 0.45*** [0.33,0.6] 0.53*** [0.4,0.69] 0.61*** [0.48,0.79] 0.53** [0.34,0.85] 

Egypt 2014 0.59 [0.28,1.25] 1.81* [1.1,2.99] 1.27 [0.99,1.63] 0.9 [0.62,1.3] 

Ethiopia 2016 0.22*** [0.1,0.48] 0.47 [0.21,1.06] 0.21** [0.08,0.51] 0.33* [0.11,0.96] 

Ghana 2014 0.55** [0.37,0.81] 0.46 [0.2,1.05] 0.35*** [0.21,0.59] 0.41** [0.21,0.77] 

Guatemala 2014-15 1.08 [0.84,1.39] 1.64* [1.12,2.39] 1.16 [0.83,1.61] 1.12 [0.75,1.67] 

Guinea 2018 0.49*** [0.37,0.63] 0.88 [0.45,1.72] 0.5*** [0.37,0.68] 1.08 [0.66,1.75] 

Haiti 2016-17 0.46*** [0.33,0.64] 0.59 [0.31,1.11] 0.43** [0.27,0.69] 0.71 [0.28,1.81] 

India 2015-16 0.63*** [0.58,0.68] 0.68*** [0.58,0.79] 0.63*** [0.57,0.68] 0.82 [0.67,1] 

Jordan 2017-18 1.21 [0.79,1.83] - 1.06 [0.67,1.67] 0.95 [0.55,1.64] 

Kenya 2014 0.57*** [0.46,0.71] 0.74* [0.58,0.94] 0.7** [0.57,0.86] 0.63*** [0.5,0.8] 

Malawi 2015-16 0.76* [0.6,0.95] 0.9 [0.4,2.02] 0.75 [0.55,1.01] 0.72 [0.52,1.02] 

Mali 2018 0.43*** [0.33,0.55] 0.55 [0.3,1.02] 0.44*** [0.33,0.58] 1.35 [0.83,2.19] 

Myanmar 2015-16 0.28*** [0.18,0.44] 0.58 [0.33,1.04] 0.35*** [0.19,0.62] 0.63 [0.29,1.36] 

Nepal 2016 0.7* [0.5,0.98] 0.74 [0.45,1.2] 1.31 [0.64,2.69] 0.95 [0.46,1.95] 

Nigeria 2018 0.66*** [0.57,0.76] 0.9 [0.67,1.2] 0.71*** [0.61,0.82] 1.04 [0.78,1.39] 

Pakistan 2017-18 0.43*** [0.32,0.57] 0.4** [0.23,0.7] 0.43*** [0.32,0.56] 0.76 [0.47,1.24] 

Philippines 2017 1.39 [0.99,1.96] 0.62 [0.22,1.73] 1.33 [0.9,1.98] 1.43 [0.82,2.49] 

Rwanda 2014-15 0.98 [0.72,1.35] 1.36 [0.77,2.39] 0.98 [0.67,1.42] 0.83 [0.53,1.3] 

Senegal 2016 0.72* [0.56,0.92] 1.41 [0.79,2.51] 0.67** [0.52,0.87] 1.15 [0.75,1.78] 

South Africa 2016 1.05 [0.71,1.55] 0.28 [0.05,1.65] 0.96 [0.59,1.57] 1.1 [0.55,2.2] 

Tanzania 2015-16 0.61*** [0.48,0.78] 0.88 [0.39,2.01] 0.62** [0.45,0.84] 0.75 [0.44,1.28] 

Uganda 2016 0.94 [0.75,1.16] 1.37 [0.91,2.06] 1.24 [0.94,1.65] 1.02 [0.62,1.67] 

Zambia 2018 1.18 [0.96,1.45] 0.68 [0.38,1.22] 1.18 [0.92,1.52] 1.28 [0.94,1.75] 

Zimbabwe 2015 0.89 [0.67,1.16] - 1.07 [0.66,1.74] 1.01 [0.61,1.68] 

Note: *p<0.05, **p<0.01, ***p<0.001 

 
73 

Appendix Table 10 Adjusted odds ratios for having all three DPT vaccination doses 

Urban poor cluster 

(ref. urban non-poor) 

SMOD 

(ref. urban centers) 

Country Rural Urban poor Rural Peri-urban 

Angola 2015-16 0.37*** [0.25,0.55] 0.62* [0.43,0.9] 0.57** [0.4,0.81] 1.14 [0.56,2.34] 

Bangladesh 2014 1.02 [0.58,1.79] 2.72 [0.61,12.12] 0.65 [0.35,1.2] 1.34 [0.37,4.83] 

Benin 2017-18 0.79 [0.54,1.15] 0.92 [0.59,1.44] 0.8 [0.52,1.23] 0.66 [0.38,1.14] 

Burundi 2016-17 1.41 [0.61,3.23] 0.63 [0.25,1.57] 0.72 [0.23,2.24] 0.39 [0.06,2.39] 

Cambodia 2014 0.67 [0.31,1.44] 0.87 [0.28,2.71] 0.5 [0.16,1.58] 0.82 [0.2,3.3] 

Chad 2014-15 0.73 [0.4,1.35] 1.05 [0.59,1.85] 0.61* [0.39,0.94] 0.16 [0.03,1.02] 

Congo Democratic Republic 2013-14 0.2*** [0.12,0.32] 0.27*** [0.17,0.43] 0.56* [0.36,0.87] 0.92 [0.37,2.29] 

Egypt 2014 0.23* [0.06,0.89] - 2.36* [1.17,4.76] 2.09 [0.61,7.14] 

Ethiopia 2016 0.59 [0.18,1.96] 1.06 [0.25,4.46] 0.61 [0.16,2.32] 1.76 [0.29,10.6] 

Ghana 2014 1.61* [1.01,2.58] 1.97 [0.69,5.65] 1.17 [0.64,2.14] 1.48 [0.62,3.57] 

Guatemala 2014-15 0.98 [0.65,1.48] 0.57 [0.28,1.2] 1.22 [0.66,2.23] 1.58 [0.69,3.6] 

Guinea 2018 0.72 [0.48,1.09] 0.49 [0.22,1.12] 0.75 [0.5,1.12] 1.6 [0.8,3.21] 

Haiti 2016-17 0.56** [0.36,0.86] 0.62 [0.33,1.17] 0.56* [0.34,0.91] 1.19 [0.37,3.85] 

India 2015-16 1.01 [0.91,1.12] 0.77* [0.62,0.96] 1.04 [0.93,1.16] 1.01 [0.73,1.4] 

Jordan 2017-18 1.35 [0.79,2.3] - 1.31 [0.71,2.44] 1.32 [0.66,2.64] 

Kenya 2014 0.98 [0.58,1.67] 1.06 [0.56,1.98] 1 [0.54,1.86] 1.05 [0.5,2.2] 

Malawi 2015-16 2.26*** [1.47,3.47] - 2.66** [1.51,4.68] 1.6 [0.71,3.62] 

Mali 2018 0.93 [0.46,1.85] 1.79 [0.63,5.02] 1.69 [0.93,3.05] - 

Myanmar 2015-16 0.56 [0.29,1.08] 0.84 [0.4,1.77] 0.97 [0.47,2.01] 1.73 [0.64,4.73] 

Nepal 2016 0.9 [0.56,1.46] 0.47* [0.22,0.97] 2.05 [0.75,5.57] 1.12 [0.41,3.04] 

Nigeria 2018 0.69*** [0.56,0.85] 0.87 [0.53,1.44] 0.61*** [0.48,0.77] 0.67 [0.44,1.03] 

Pakistan 2017-18 0.9 [0.59,1.38] 2.81** [1.35,5.82] 0.82 [0.53,1.26] 3.05 [0.92,10.04] 

Philippines 2017 0.92 [0.6,1.41] 0.83 [0.34,2.01] 1.07 [0.67,1.72] 0.93 [0.48,1.82] 

Rwanda 2014-15 1.4 [0.45,4.3] - 2.04 [0.41,10.2] 1.48 [0.17,13.05] 

Senegal 2016 0.81 [0.4,1.65] 0.45 [0.11,1.86] 1.06 [0.43,2.61] - 

South Africa 2016 1.02 [0.62,1.66] 5.72 [0.47,69.34] 0.98 [0.58,1.66] 0.83 [0.4,1.75] 

Tanzania 2015-16 0.42** [0.24,0.72] 0.51 [0.17,1.52] 0.59 [0.33,1.07] 0.92 [0.18,4.68] 

Uganda 2016 1.41 [0.98,2.04] 1.16 [0.67,2.03] 1.11 [0.69,1.78] 1.07 [0.45,2.58] 

Zambia 2018 0.97 [0.54,1.73] 0.98 [0.34,2.79] 1.03 [0.53,2.02] 1.24 [0.29,5.35] 

Zimbabwe 2015 1.17 [0.64,2.13] - 1.49 [0.67,3.34] 3.37 [0.67,17] 

Note: *p<0.05, **p<0.01, ***p<0.001 

 
74 

Appendix Table 11 Adjusted odds ratios for minimum acceptable diet 

Urban poor cluster 

(ref. urban non-poor) 

SMOD 

(ref. urban centers) 

Country Rural Urban poor Rural Peri-urban 

Angola 2015-16 0.54** [0.36,0.8] 0.7 [0.46,1.06] 0.66* [0.45,0.97] 0.51 [0.21,1.23] 

Bangladesh 2014 0.7* [0.53,0.92] 0.76 [0.42,1.37] 0.96 [0.7,1.31] 1.03 [0.61,1.71] 

Benin 2017-18 0.77 [0.51,1.16] 0.75 [0.46,1.2] 0.92 [0.63,1.36] 0.9 [0.51,1.56] 

Burundi 2016-17 0.48*** [0.33,0.69] 0.13*** [0.04,0.38] 0.46** [0.28,0.76] 2.3 [0.89,5.95] 

Cambodia 2014 0.78 [0.5,1.22] 0.82 [0.38,1.77] 0.72 [0.45,1.15] 1.14 [0.5,2.63] 

Chad 2014-15 0.58 [0.21,1.6] 0.96 [0.41,2.22] 0.79 [0.34,1.82] - 

Congo Democratic Republic 2013-14 0.46** [0.28,0.76] 0.79 [0.46,1.34] 0.54** [0.37,0.79] 0.93 [0.34,2.54] 

Egypt 2014 0.8 [0.48,1.33] 0.47 [0.18,1.2] 1 [0.81,1.23] 1.27 [0.95,1.7] 

Ethiopia 2016 0.37* [0.15,0.9] 0.98 [0.5,1.93] 0.78 [0.31,1.97] 1.37 [0.28,6.84] 

Ghana 2014 1.05 [0.67,1.64] 0.91 [0.21,4.05] 1.22 [0.72,2.08] 1.01 [0.37,2.72] 

Guatemala 2014-15 0.68*** [0.56,0.84] 0.59** [0.43,0.82] 0.67** [0.5,0.9] 0.86 [0.61,1.22] 

Guinea 2018 0.4** [0.21,0.8] - 0.43* [0.2,0.93] 0.29 [0.04,2.13] 

Haiti 2016-17 0.92 [0.56,1.53] 0.18* [0.04,0.75] 0.68 [0.35,1.32] 0.62 [0.22,1.7] 

India 2015-16 0.86** [0.77,0.96] 1.16 [0.93,1.44] 0.86** [0.76,0.96] 1.24 [0.95,1.61] 

Jordan 2017-18 1.45* [1.05,2.01] - 1.61* [1.08,2.38] 1.08 [0.7,1.67] 

Kenya 2014 0.59** [0.41,0.85] 0.75 [0.47,1.19] 0.72 [0.48,1.08] 0.82 [0.52,1.3] 

Malawi 2015-16 0.48*** [0.33,0.7] 0.8 [0.37,1.74] 0.5** [0.31,0.81] 0.56 [0.31,1.02] 

Mali 2018 0.98 [0.39,2.43] 0.93 [0.38,2.28] 4.53* [1.03,19.86] 16.48** [3.42,79.41] 

Myanmar 2015-16 0.75 [0.44,1.28] 1.05 [0.49,2.26] 1.15 [0.6,2.19] 2.37 [0.89,6.28] 

Nepal 2016 1.21 [0.92,1.6] 0.98 [0.57,1.7] 0.81 [0.51,1.28] 0.56* [0.33,0.94] 

Nigeria 2018 0.74* [0.58,0.93] 0.97 [0.53,1.77] 0.67** [0.53,0.85] 0.69 [0.43,1.1] 

Pakistan 2017-18 0.95 [0.66,1.37] 0.87 [0.5,1.51] 0.92 [0.63,1.34] 0.88 [0.45,1.73] 

Philippines 2017 NA NA NA NA 

Rwanda 2014-15 0.52*** [0.39,0.71] 0.53 [0.27,1.03] 0.45** [0.28,0.73] 0.52* [0.28,0.94] 

Senegal 2016 0.5* [0.29,0.88] 1.04 [0.4,2.67] 0.56 [0.28,1.12] - 

South Africa 2016 0.72 [0.47,1.09] 0.24 [0.01,3.93] 1.19 [0.71,2.01] 1.92 [0.91,4.08] 

Tanzania 2015-16 0.92 [0.61,1.37] 1.47 [0.6,3.58] 1.51 [0.96,2.37] 2.16* [1.12,4.16] 

Uganda 2016 0.84 [0.59,1.2] 0.81 [0.49,1.34] 0.83 [0.54,1.29] 1.28 [0.64,2.55] 

Zambia 2018 0.76 [0.51,1.13] 1.5 [0.68,3.31] 0.87 [0.53,1.43] 2.27 [0.73,7.05] 

Zimbabwe 2015 0.77 [0.47,1.25] - 1.16 [0.61,2.22] 1.26 [0.63,2.53] 

NA = Not available 

Note: *p<0.05, **p<0.01, ***p<0.001