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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
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    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, October.
    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, October.
    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.
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