Trends in overweight and obesity among reproductive-age women in Bangladesh: Analysis of nationally representative surveys over a decade

孟加拉国育龄妇女超重和肥胖趋势:基于十年全国代表性调查的分析

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Abstract

BACKGROUND: Overweight and obesity are emerging public health concerns in Bangladesh, contributing to the rising burden of non-communicable diseases. We aimed to examine trends in prevalence of overweight and obesity among reproductive-age women in Bangladesh over the past decade and assess socioeconomic inequalities in their distribution. METHODS: We analyzed nationally representative data from four rounds of the Bangladesh Demographic and Health Survey (BDHS) conducted in 2011, 2014, 2017-18, and 2022, including a total of 60,921 women aged 15-49 years. Body mass index (BMI) was classified using Asian-specific cut-offs. Prevalence estimates of overweight and obesity were calculated for each survey year and stratified by age, residence, educational attainment, and wealth quintile. Log-linear regression was used to estimate annual percentage changes (APC) in overweight and obesity. Socioeconomic inequality was quantified using concentration curves and Erreygers-corrected concentration indices (CIX). RESULTS: Overall prevalence of overweight and obesity increased from 13.1% in 2011 to 18.7% in 2022 (APC 3.1%), and 17.7% to 36.6% (APC 6.8%), respectively. Rural women experienced faster relative increases in both overweight (APCs 4.4% vs 1.1%).and obesity (APCs 9.2% vs 4.3%) compared with urban women. Women in the poorest and poorer wealth quintiles showed the largest APCs for obesity (14.2% and 14.1%, respectively). The CIX for overweight declined from 0.098 to 0.031, and for obesity from 0.200 to 0.141, indicating a modest reduction in inequality concentrated among wealthier groups over time. CONCLUSIONS: Overweight and obesity are increasing rapidly among reproductive-age women of Bangladesh, with faster rises among rural and lower-income groups. Policies and interventions should address both overall prevalence and shifting socioeconomic patterns to reduce the future burden of non-communicable diseases.

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