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Proximity effects in obesity rates in the US: A Spatial Markov Chains approach

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  • Agovino, Massimiliano
  • Crociata, Alessandro
  • Sacco, Pier Luigi

Abstract

In this paper, we investigate, by means of a Spatial Markov Chains approach, the existence of proximity effects at State level for US data on obesity rates in the period 1990–2011. We find that proximity effects do play an important role in the spatial diffusion of obesity (the obesity ‘epidemics’), and that the actual health geography of nearby States in terms of high vs. low obesity rates makes an important difference as to the future evolution of the State's own obesity rate over time. This means, in particular, that clusters of States characterized by uniformly high levels of obesity rates, as it happens for instance in the US Southern macro-region, may suffer from a perverse ‘geographical lock-in’ effect that calls for coordinated action across States to implement effective countervailing policies.

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  • Agovino, Massimiliano & Crociata, Alessandro & Sacco, Pier Luigi, 2019. "Proximity effects in obesity rates in the US: A Spatial Markov Chains approach," Social Science & Medicine, Elsevier, vol. 220(C), pages 301-311.
    • Handle: RePEc:eee:socmed:v:220:y:2019:i:c:p:301-311
    DOI: 10.1016/j.socscimed.2018.11.013
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    2. Zhongxun Zhang & Kaifang Shi & Zhiyong Zhu & Lu Tang & Kangchuan Su & Qingyuan Yang, 2022. "Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China," Land, MDPI, vol. 11(5), pages 1-29, May.
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