Urban-rural differences in BMI in low- and middle-income countries: the role of socioeconomic status

Abstract

Background: Urbanization is often cited as a main cause of increasing BMIs in low- and middle-income countries (LMICs), and urban residents in LMICs tend to have higher BMIs than do rural residents. However, urban-rural differences may be driven by differences in socioeconomic status (SES).

Objective: Using nationally representative data collected at 2 time points in 38 LMICs, we assessed the association between urban residence and BMI before and after adjustment for measures of individual- and household-level SES.

Design: We conducted a cross-sectional analysis of nationally representative samples of 678,471 nonpregnant women aged 15-49 y, with 225,312 women in the earlier round of surveys conducted between 1991 and 2004 and 453,159 women in the later round conducted between 1998 and 2010. We used linear and ordered multinomial analysis with a country fixed effect to obtain a pooled estimate and a country-stratified analysis.

Results: We found that mean BMI (kg/m²) in less-developed countries was generally higher within urban areas (excess BMI associated with urban residence before wealth index adjustment: 1.55; 95% CI: 1.52, 1.57). However, the urban association was attenuated after SES was accounted for (association after adjustment: 0.44; 95% CI: 0.41, 0.47). Individual- and household-level SES measures were independently and positively associated with BMI.

Conclusion: The association between urban residence and obesity in LMICs is driven largely by higher individual- and community-level SES in urban areas, which suggests that urban residence alone may not cause increased body weight in developing countries.

FIGURE 1.

Average annual change in percentage underweight (A) and overweight (B) in urban and rural areas in 38 countries. n = 697,573. National income groups were taken from the World Bank (46).

FIGURE 2.

Urban association with BMI before and after adjustment for education and assets indexes. Associations were calculated by using multilevel linear models and were additionally adjusted for age (10-y groups; fixed effect), marital status (fixed effect), country (random effect), region within country (random effect), and primary sampling unit (random effect). Error bars represent 95% CIs. n = 697,573 for analyses that excluded housing amenities and household assets and 678,471 for analyses that included housing amenities and household assets.