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

被引：26

作者：

Agovino, Massimiliano ^{[4
]}

Crociata, Alessandro ^{[5
]}

Sacco, Pier Luigi ^{[1
,2
,3
,6
]}

机构：

[1] IULM Univ, Via Carlo Bo 1, I-20143 Milan, Italy

[2] Harvard Univ, Boylston Hall,Harvard Yard, Cambridge, MA 02138 USA

[3] Harvard, MetaLAB, Cambridge, MA USA

[4] Parthenope Univ, Naples, Italy

[5] GSSI, Laquila, Italy

[6] FBK IRVAPP, Trento, Italy

来源：

SOCIAL SCIENCE & MEDICINE | 2019年 / 220卷

关键词：

Obesity rates; Obesity epidemics; Proximity effects; Spatial Markov chain; Ergodic distribution; SPACE-TIME ANALYSIS; SOCIOECONOMIC-STATUS; PHYSICAL-ACTIVITY; UNITED-STATES; FOOD-PRICES; HEALTH; NEIGHBORHOOD; CONSUMPTION; INEQUALITY; EFFICACY;

D O I：

10.1016/j.socscimed.2018.11.013

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

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 States 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.

引用

页码：301 / 311

页数：11