IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0279840.html
   My bibliography  Save this article

Spatial inequality and explaining the urban-rural gap in obesity in India: Evidence from 2015–16 population-based survey

Author

Listed:
  • Somdutta Barua

Abstract

Objective: This study assessed the spatial dimension of urban-rural disparity in obesity prevalence and identified the determinants explaining the urban-rural gap in obesity prevalence in India. Methods: Using cross-sectional survey data from the 2015–16 National Family Health Survey, the prevalence rates of obesity were calculated for aged 15–49 years. Two multiscale geographically weighted regressions were performed separately from rural and urban spaces for Indian districts to examine the spatial relationship of the outcome variable and covariates at different geographical scales. Fairlie decomposition analysis was carried out to explore the contribution of each variable in the urban-rural gap. Results: The rural-urban obesity prevalence difference has increased in a decade time for India from 13.0 to 14.6. Urban counterparts tended to have more people with excess weight. 15 states had an urban-rural prevalence ratio of 2 or higher. The MGWR model showed that varying covariates operated at different scales, i.e. global, regional and local scales, and determined the spatial heterogeneity of obesity prevalence. The only variable, i.e. age (9.49 per cent), had contributed in reducing the gap. Conversely, the socioeconomic variables, i.e. income (96.39 per cent), education (4.95 per cent), caste (4.78 per cent) and occupation (3.11 per cent), had widened the gap. Conclusions: Even though this study evidenced the rural-urban gap in obesity prevalence, it indicated the gap’s closing shortly, as it was witnessed in a few states. It is urgent to address the obesity epidemic, especially in urban India, due to its higher prevalence and prevent the further increase of prevalence in rural India, mainly because it shelters nearly 70 per cent of the Indian population.

Suggested Citation

  • Somdutta Barua, 2023. "Spatial inequality and explaining the urban-rural gap in obesity in India: Evidence from 2015–16 population-based survey," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-17, January.
  • Handle: RePEc:plo:pone00:0279840
    DOI: 10.1371/journal.pone.0279840
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0279840
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0279840&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0279840?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hande Barlin & Murat Anil Mercan, 2016. "Occupation and Obesity: Effect of Working Hours on Obesity by Occupation Groups," Applied Economics and Finance, Redfame publishing, vol. 3(2), pages 179-185, May.
    2. Saad Siddiqui & Ngianga-Bakwin Kandala & Saverio Stranges, 2015. "Urbanisation and geographic variation of overweight and obesity in India: a cross-sectional analysis of the Indian Demographic Health Survey 2005–2006," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 60(6), pages 717-726, September.
    3. Balasubramanian, Sriram & Kumar, Rishabh & Loungani, Prakash, 2020. "Inequality and locational determinants of the distribution of living standards in India," SocArXiv rmcej, Center for Open Science.
    4. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    2. Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2024. "Effects of Urban Land-Use Planning on Housing Prices in Chiang Mai, Thailand," Land, MDPI, vol. 13(8), pages 1-13, July.
    3. Yang, Zhiwei & Liu, Han & Chen, Xiaohong & Zhou, Jun & Yuan, Quan, 2025. "Discovering the origins of freight demand: An empirical investigation of spatial heterogeneity in the generation of heavy-duty truck trips," Transport Policy, Elsevier, vol. 164(C), pages 60-79.
    4. Jack C. Yue & Ming-Huei Tu & Yin-Yee Leong, 2024. "A spatial analysis of the health and longevity of Taiwanese people," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 384-399, April.
    5. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    6. Ya-Di Dai & Hui-Guo Zhang, 2025. "Non-Iterative Estimation of Multiscale Geographically and Temporally Weighted Regression Model," Mathematics, MDPI, vol. 13(9), pages 1-16, April.
    7. Tao Wang & Kai Zhang & Keliang Liu & Keke Ding & Wenwen Qin, 2023. "Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    8. Junfeng Wang & Shaoyao Zhang & Wei Deng & Qianli Zhou, 2024. "Metropolitan Expansion and Migrant Population: Correlation Patterns and Influencing Factors in Chengdu, China," Land, MDPI, vol. 13(1), pages 1-19, January.
    9. Xin Lao & Hengyu Gu, 2020. "Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1860-1876, December.
    10. Zhenbao Wang & Jiarui Song & Yuchen Zhang & Shihao Li & Jianlin Jia & Chengcheng Song, 2022. "Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    11. Y Selvamani & Pushpendra Singh, 2018. "Socioeconomic patterns of underweight and its association with self-rated health, cognition and quality of life among older adults in India," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    12. Jiansheng Qu & Lina Liu & Jingjing Zeng & Tek Narayan Maraseni & Zhiqiang Zhang, 2022. "City-Level Determinants of Household CO 2 Emissions per Person: An Empirical Study Based on a Large Survey in China," Land, MDPI, vol. 11(6), pages 1-14, June.
    13. Li Yue & Hongbo Zhao & Xiaoman Xu & Tianshun Gu & Zeting Jia, 2022. "Quantifying the Spatial Fragmentation Pattern and Its Influencing Factors of Urban Land Use: A Case Study of Pingdingshan City, China," Land, MDPI, vol. 11(5), pages 1-15, May.
    14. Zhang, Wei & Li, Yuqing & Zheng, Caigui, 2023. "The distribution characteristics and driving mechanism of vacant land in Chengdu, China: A perspective of urban shrinkage and expansion," Land Use Policy, Elsevier, vol. 132(C).
    15. Cui, Pengfei & Abdel-Aty, Mohamed & Wang, Chenzhu & Yang, Xiaobao & Song, Dongdong, 2025. "Examining the impact of spatial inequality in socio-demographic and commute patterns on traffic crash rates: Insights from interpretable machine learning and spatial statistical models," Transport Policy, Elsevier, vol. 167(C), pages 222-245.
    16. Pengzhi Wei & Shaofeng Xie & Liangke Huang & Lilong Liu, 2021. "Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM 2.5 Concentration in Central and Southern China," IJERPH, MDPI, vol. 18(15), pages 1-26, July.
    17. Longjiang Zhang & Guoping Chen & Junsan Zhao & Yilin Lin & Haibo Yang & Jianhua He, 2025. "Spatiotemporal Characteristics and Scale Effects of Ecosystem Service Bundles in the Xijiang River Basin: Implications for Territorial Spatial Planning and Sustainable Land Management," Sustainability, MDPI, vol. 17(5), pages 1-23, February.
    18. Paul Harris & Bruno Lanfranco & Binbin Lu & Alexis Comber, 2020. "Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay," Agriculture, MDPI, vol. 10(7), pages 1-17, July.
    19. Aditya Kusuma & Bethanna Jackson & Ilan Noy, 2018. "A viable and cost-effective weather index insurance for rice in Indonesia," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 43(2), pages 186-218, September.
    20. Zhenbao Wang & Xin Gong & Yuchen Zhang & Shuyue Liu & Ning Chen, 2023. "Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership," Sustainability, MDPI, vol. 15(6), pages 1-22, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0279840. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.