Scholarlink

Objectives <p>Obesity is rising rapidly in low- and middle-income countries; however, evidence on the geographic determinants of obesity remains limited. Using Asian-specific Body Mass Index (BMI) thresholds (overweight:23.0-24.9&#xa0;kg/m²; obesity: ≥25.0&#xa0;kg/m²; overweight/ obesity: ≥23.0 kg/m<sup>2</sup>), this study examined the contribution of geographic contexts to obesity among Indian adults and identified the most relevant level for intervention.</p> Study Design <p>Cross-sectional data used from fifth round of the National Family Health Survey (NFHS), a nationally representative survey conducted between 2019-21, and analysed data approximately 750,000 adults aged 15–49 years from 707 districts across 36 states and Union Territories of India.</p> Methods <p>Multilevel logistic regression models estimated the geographic variation in overweight, obesity and overweight/obesity across states, districts, and Primary Sampling Units (PSUs), (representing villages in rural areas and Census Enumeration Blocks in urban areas). All the models were adjusted for demographic and socioeconomic characteristics. Further, state-specific analyses were estimated to show within-state attribution at district and PSU-levels.</p> Results <p>By using the Asian-specific BMI criteria, results showed that 38.0% women and 40.1% men were found under overweight/ obese category. After adjustment, PSU-level factors attributed the largest share of geographic variation (51.1% among women and 62.4% among men), followed by state-level factors (34.7% and 27.2%, respectively). District-level factors accounted the least (14.2% among women and 10.4% among men). State-specific analyses showed that community-level variation predominated across most states, with PSU-level factors accounting for over 90% of geographic variation in some states.</p> Conclusion <p>In conclusion, overweight/obesity disparities in India are primarily driven by local community factors. Asian-specific BMI thresholds evident to reveal a substantially higher burden of overweight/obesity than conventional criteria. Hence, from policy persepective, community-focused interventions will be more effective for obesity prevention in India and other low- and middle-income countries which are undergoing through a rapid epidemiological transitions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Local Community Environment Drive India’s Obesity Epidemic: A Multilevel Analysis of Geographic Variations Using Asian-Specific BMI Criteria

  • 期刊论文

  • 发表日期：

    2026年7月20日

查看全文
  • Prashant Kumar Singh,
  • Lucky Singh,
  • Shashi Kala Saroj,
  • Shalini Singh

摘要

Objectives

Obesity is rising rapidly in low- and middle-income countries; however, evidence on the geographic determinants of obesity remains limited. Using Asian-specific Body Mass Index (BMI) thresholds (overweight:23.0-24.9 kg/m²; obesity: ≥25.0 kg/m²; overweight/ obesity: ≥23.0 kg/m2), this study examined the contribution of geographic contexts to obesity among Indian adults and identified the most relevant level for intervention.

Study Design

Cross-sectional data used from fifth round of the National Family Health Survey (NFHS), a nationally representative survey conducted between 2019-21, and analysed data approximately 750,000 adults aged 15–49 years from 707 districts across 36 states and Union Territories of India.

Methods

Multilevel logistic regression models estimated the geographic variation in overweight, obesity and overweight/obesity across states, districts, and Primary Sampling Units (PSUs), (representing villages in rural areas and Census Enumeration Blocks in urban areas). All the models were adjusted for demographic and socioeconomic characteristics. Further, state-specific analyses were estimated to show within-state attribution at district and PSU-levels.

Results

By using the Asian-specific BMI criteria, results showed that 38.0% women and 40.1% men were found under overweight/ obese category. After adjustment, PSU-level factors attributed the largest share of geographic variation (51.1% among women and 62.4% among men), followed by state-level factors (34.7% and 27.2%, respectively). District-level factors accounted the least (14.2% among women and 10.4% among men). State-specific analyses showed that community-level variation predominated across most states, with PSU-level factors accounting for over 90% of geographic variation in some states.

Conclusion

In conclusion, overweight/obesity disparities in India are primarily driven by local community factors. Asian-specific BMI thresholds evident to reveal a substantially higher burden of overweight/obesity than conventional criteria. Hence, from policy persepective, community-focused interventions will be more effective for obesity prevention in India and other low- and middle-income countries which are undergoing through a rapid epidemiological transitions.