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

Massimiliano Agovino; Alessandro Crociata; Pier Luigi Sacco

doi:10.1016/j.socscimed.2018.11.013

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

Soc Sci Med. 2019 Jan:220:301-311. doi: 10.1016/j.socscimed.2018.11.013. Epub 2018 Nov 16.

Authors

Massimiliano Agovino¹, Alessandro Crociata², Pier Luigi Sacco³

Affiliations

¹ Parthenope University, Naples, Italy.
² Gran Sasso Science Institute - GSSI, L'Aquila, Italy.
³ IULM University, Via Carlo Bo, 1, 20143, Milan, Italy; Harvard University, Boylston Hall, Harvard Yard, Cambridge, MA, 20138, USA; metaLAB (at) Harvard, Cambridge, MA, USA; FBK-IRVAPP, Trento, Italy. Electronic address: pierluigi.sacco@iulm.it.

PMID: 30496953
DOI: 10.1016/j.socscimed.2018.11.013

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.

Keywords: Ergodic distribution; Obesity epidemics; Obesity rates; Proximity effects; Spatial Markov chain.

MeSH terms

Geography, Medical*
Humans
Markov Chains*
Obesity / epidemiology*
Spatial Analysis*
United States / epidemiology