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

机译:在美国的肥胖率偏差效应:空间马尔可夫链条方法

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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.
机译:在本文中,我们通过空间马尔可夫链条进行调查,在1990 - 2011年期间的肥胖率上存在邻近效应的邻近效应。 我们发现邻近效应确实在肥胖症的空间扩散中发挥着重要作用(肥胖的流行病'),以及附近国家的实际健康地理在高比较低肥胖率方面是对未来的重要差异 随着时间的推移,州自身肥胖率的演变。 特别地,这意味着,这种状态的特征在于均匀高度肥胖率,因为它发生在美国南部宏观区域中,可能遭受越野的“地理锁定”效果,可以呼吁协调的动作 各国实施有效的反补贴政策。

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