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Small-area health comparisons using health-adjusted life expectancies: A Bayesian random-effects approach

机译：使用健康调整后的预期寿命进行小区域健康比较：贝叶斯随机效应方法

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Health-adjusted life expectancy (HALE) is one of the most attractive summary measures of population health. It provides balanced attention to fatal as well as non-fatal health outcomes, is sensitive to the severity of morbidity within the population, and can be readily compared between areas with very different population age structures. HALE, however, cannot be calculated at the small-area level using traditional life table methodology. Hence we propose a Bayesian random-effects modeling approach that recognizes correlations and pools strength between sexes, age-groups, geographical areas, and health outcomes. This approach allows for the calculation of HALE for areas as small as 2000 person years at risk and with relatively modest health state survey sample sizes. The feasibility of the Bayesian approach is illustrated in a real-life example, which also shows how differences in areas' health performances can be adequately quantified. Such information can be invaluable for the appropriate targetting and subsequent evaluation of urban regeneration, neighborhood renewal, and community-based initiatives aimed at improving health and reducing health inequalities.
机译：健康调整后的预期寿命（HALE）是最有吸引力的人口健康汇总指标之一。它平衡地关注致命和非致命的健康状况，对人群发病率的严重程度敏感，可以很容易地在具有不同人口年龄结构的地区之间进行比较。但是，无法使用传统的生命表方法在小区域级别上计算HALE。因此，我们提出了一种贝叶斯随机效应建模方法，该方法可以识别性别，年龄组，地理区域和健康结果之间的关联并集中力量。这种方法允许针对处于危险中的小至2000人年且健康状况调查样本量相对较小的区域计算HALE。在一个真实的例子中说明了贝叶斯方法的可行性，它还显示了如何充分量化各地区健康表现的差异。这样的信息对于适当地针对和随后评估城市更新，邻里更新以及旨在改善健康和减少健康不平等现象的基于社区的计划而言，具有无价的价值。

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