Health Research Program
Author(s):
Shireen Assaf, PhD, MS
Organization:
Senior Research Data Analysis Manager at the Demographic and Health Surveys (DHS) Program
Photo credit: Sara A. Holtz

Two-thirds of the global population are forecasted to live in urban areas by 2050 so it is important to understand the relationship between living in urban areas, also called urbanicity, and health in low- and middle- income countries. Previous studies have shown linkages between urbanicity and positive maternal and child health outcomes due to an increase in access to and utilization of health services. However, living in urban areas is also linked with poor health outcomes, especially in urban poor environments such as slums or peri-urban areas that may have less access to services.

Urbanicity Variables

Past studies have typically relied on a two-category i.e. urban-rural place of residence variable available in DHS datasets to measure urbanicity and its relationship to health. New analysis by DHS uses additional urbanicity variables to examine four health and service provision outcomes. The measure of urbanicity includes a variable which uses satellite data and population density to identify rural, peri-urban, and urban centers; nightlights, which measures the level of luminosity or lighting during nighttime hours; and a variable constructed from DHS data which further categorizes urban areas into urban poor and urban non-poor clusters. The health outcomes are women of reproductive age who use modern contraception, pregnant women who went to at least four antenatal care visits, children age 12-23 months who have completed three doses of the diphtheria, pertussis, and tetanus (DPT3) vaccine, and children age 6-23 months who have received the minimum acceptable diet (MAD). Data from 30 countries with a recent DHS were used for the analysis.

Photo credit: Kate Holt/MCSP

Findings

The findings were country-specific and showed few significant associations for the diphtheria, pertussis, and tetanus (DPT3) and minimum acceptable diet (MAD) outcomes. However, in some countries there were significant differences within urban areas. For example, in Benin and India women in urban poor areas were 30 percent less likely to attend four antenatal care visits compared to women in urban non-poor areas and this disparity was similar to the difference between rural and urban non-poor women. In Pakistan, women in urban poor and rural areas were 60 percent less likely to attend four ANC visits compared to women in urban non-poor areas. 

There was also a significantly lower likelihood of ANC visits between women in urban poor and urban non-poor areas in the DRC, Angola, Kenya, and Bangladesh (between 30-50 percent lower odds). This lower likelihood of utilizing ANC was lower in rural areas. In Haiti and Burundi, there were more than 80 percent lower odds of MAD for children who live in urban poor clusters compared to the urban non-poor, and this disparity was larger than the differences between rural and urban non-poor children. A few countries also indicated a lower likelihood of women attending four or more ANC visits in peri-urban areas compared to urban centers. This new approach to measure levels of urbanicity using DHS data helps us better identify health problems facing urban dwellers and better understand health disparities within urban areas. Better understanding these nuances will help us develop more responsive, effective policies to improve the health of women and children.

Limitations

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 displacement procedures in DHS of urban clusters by 2km and the non-standard definitions of urban areas between countries could have also further weakened the results. Further analyses have shown more disparities between urban poor and urban non-poor when examining health outcomes not related to service provision, such as stunting and anemia in children.


Variations in Health Outcomes with Alternative Measures of Urbanicity, Using Demographic and Health Surveys 2013-18 https://dhsprogram.com/pubs/pdf/AS73/AS73.pdf  

Variations in Health Outcomes with Alternative Measures of Urbanicity, Using Demographic and Health Surveys 2013-18 (AS73): An Analysis Brief from The DHS Program https://dhsprogram.com/pubs/pdf/AS73/AB14.pdf.pdf