Evidence map›Paper›PMID 42474698›Full record

ArticleJournal of racial and ethnic health disparities2026

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

Prashant Kumar Singh, Lucky Singh, Shashi Kala Saroj, Shalini Singh

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Article in Journal of racial and ethnic health disparities, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Prashant Kumar Singh *WHO FCTC Knowledge Hub on Smokeless Tobacco & Division of Preventive Oncology & Population Health, ICMR - National Institute of Cancer Prevention and Research (NICPR), Uttar Pradesh, 201303, Noida, India.ORCID http://orcid.org/0000-0003-1742-5534
Lucky Singh *Indian Council of Medical Research (ICMR)-HQ, 110029, New Delhi, India. drluckyicmr@gmail.com.ORCID http://orcid.org/0000-0003-2590-6135
Shashi Kala Saroj *Indian Council of Medical Research (ICMR)-HQ, 110029, New Delhi, India.ORCID http://orcid.org/0000-0002-2829-7446
Shalini Singh *WHO FCTC Knowledge Hub on Smokeless Tobacco & Division of Preventive Oncology & Population Health, ICMR - National Institute of Cancer Prevention and Research (NICPR), Uttar Pradesh, 201303, Noida, India.ORCID http://orcid.org/0000-0002-8271-1062

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6 · The paper itself

Abstract

objectivesObesity 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/m STUDY

designCross-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.

methodsMultilevel 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.

resultsBy 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.

conclusionIn 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.

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Asian BMI criteriaGeographic variationIndiaLocal environmentsMultilevel analysisObesity

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