IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i15p9577-d879939.html
   My bibliography  Save this article

An N-Shaped Association between Population Density and Abdominal Obesity

Author

Listed:
  • Bindong Sun

    (The Center for Modern Chinese City Studies, East China Normal University, Shanghai 200241, China
    Research Center for China Administrative Division, East China Normal University, Shanghai 200241, China
    Institute of Eco-Chongming, 20 Cuiniao Rd., Chenjia Zhen, Chongming, Shanghai 202162, China
    School of Urban and Regional Science, East China Normal University, Shanghai 200241, China)

  • Xiajie Yao

    (Research Center for China Administrative Division, East China Normal University, Shanghai 200241, China
    Institute of Eco-Chongming, 20 Cuiniao Rd., Chenjia Zhen, Chongming, Shanghai 202162, China
    School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
    Future City Laboratory, East China Normal University, Shanghai 200241, China)

  • Chun Yin

    (Research Center for China Administrative Division, East China Normal University, Shanghai 200241, China
    Institute of Eco-Chongming, 20 Cuiniao Rd., Chenjia Zhen, Chongming, Shanghai 202162, China
    School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
    Future City Laboratory, East China Normal University, Shanghai 200241, China)

Abstract

Abdominal obesity is a threat to public health and healthy cities. Densification may reduce abdominal obesity, but current evidence of the relationship between population density and abdominal obesity is not conclusive. The aim of this study was to disentangle the nonlinear association between population density and abdominal obesity. Data came from the 2004–2015 China Health and Nutrition Survey, which included 36,422 adults aged between 18 and 65 years. Generalized additive models (GAMs) were applied to explore how population density was associated with objectively measured waist circumference (WC) and waist-to-height ratio (WHtR), after controlling for other built environmental attributes, socioeconomic characteristics, and regional and year fixed effects. We found that population density had N-shaped associations with both WC and WHtR, and the two turning points were 12,000 and 50,000 people/km 2 . In particular, population density was positively correlated with abdominal obesity when it was below 12,000 people/km 2 . Population density was negatively associated with abdominal obesity when it was between 12,000 and 50,000 people/km 2 . Population density was also positively related to abdominal obesity when it was greater than 50,000 people/km 2 . Therefore, densification is not always useful to reduce abdominal obesity. Policy-makers need to pay more attention to local density contexts before adopting densification strategies.

Suggested Citation

  • Bindong Sun & Xiajie Yao & Chun Yin, 2022. "An N-Shaped Association between Population Density and Abdominal Obesity," IJERPH, MDPI, vol. 19(15), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9577-:d:879939
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/15/9577/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/15/9577/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pengxiang Zhao & Mei-Po Kwan & Suhong Zhou, 2018. "The Uncertain Geographic Context Problem in the Analysis of the Relationships between Obesity and the Built Environment in Guangzhou," IJERPH, MDPI, vol. 15(2), pages 1-20, February.
    2. Kristen Cooksey-Stowers & Marlene B. Schwartz & Kelly D. Brownell, 2017. "Food Swamps Predict Obesity Rates Better Than Food Deserts in the United States," IJERPH, MDPI, vol. 14(11), pages 1-20, November.
    3. Ao, Yibin & Yang, Dujuan & Chen, Chuan & Wang, Yan, 2019. "Exploring the effects of the rural built environment on household car ownership after controlling for preference and attitude: Evidence from Sichuan, China," Journal of Transport Geography, Elsevier, vol. 74(C), pages 24-36.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, November.
    5. Daisuke Fukuda & Tetsuo Yai, 2010. "Semiparametric specification of the utility function in a travel mode choice model," Transportation, Springer, vol. 37(2), pages 221-238, March.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, November.
    7. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    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. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    2. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    3. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    4. Hyunju Son & Youyi Fong, 2021. "Fast grid search and bootstrap‐based inference for continuous two‐phase polynomial regression models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    5. Wei Huang & Oliver Linton & Zheng Zhang, 2022. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1817-1830, October.
    6. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2017. "Generalized partially linear regression with misclassified data and an application to labour market transitions," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 145-159.
    7. Zi Ye & Giles Hooker & Stephen P. Ellner, 2021. "Generalized Single Index Models and Jensen Effects on Reproduction and Survival," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 492-512, September.
    8. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    9. Akdeniz Duran, Esra & Härdle, Wolfgang Karl & Osipenko, Maria, 2012. "Difference based ridge and Liu type estimators in semiparametric regression models," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 164-175.
    10. Nagler Thomas & Schellhase Christian & Czado Claudia, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
    11. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.
    12. Morteza Amini & Mahdi Roozbeh & Nur Anisah Mohamed, 2024. "Separation of the Linear and Nonlinear Covariates in the Sparse Semi-Parametric Regression Model in the Presence of Outliers," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    13. Chen, Haiqiang & Fang, Ying & Li, Yingxing, 2015. "Estimation And Inference For Varying-Coefficient Models With Nonstationary Regressors Using Penalized Splines," Econometric Theory, Cambridge University Press, vol. 31(4), pages 753-777, August.
    14. Wahba, Jackline & Schluter, Christian, 2009. "Illegal migration, wages and remittances- semi-parametric estimation of illegality effects," Discussion Paper Series In Economics And Econometrics 913, Economics Division, School of Social Sciences, University of Southampton.
    15. Feng, Yuanhua & Härdle, Wolfgang Karl, 2020. "A data-driven P-spline smoother and the P-Spline-GARCH models," IRTG 1792 Discussion Papers 2020-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    17. Clark, Andrew E. & Etilé, Fabrice, 2011. "Happy house: Spousal weight and individual well-being," Journal of Health Economics, Elsevier, vol. 30(5), pages 1124-1136.
    18. Hannes Matuschek & Reinhold Kliegl & Matthias Holschneider, 2015. "Smoothing Spline ANOVA Decomposition of Arbitrary Splines: An Application to Eye Movements in Reading," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-15, March.
    19. Shirun Shen & Huiya Zhou & Kejun He & Lan Zhou, 2024. "Principal Component Analysis of Two-dimensional Functional Data with Serial Correlation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 601-620, September.
    20. Michaelides, Michael & Spanos, Aris, 2020. "On modeling heterogeneity in linear models using trend polynomials," Economic Modelling, Elsevier, vol. 85(C), pages 74-86.

    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:gam:jijerp:v:19:y:2022:i:15:p:9577-:d:879939. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.