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首页> 外文期刊>Annals of epidemiology >Accounting for context in studies of health inequalities: A review and comparison of analytic approaches
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Accounting for context in studies of health inequalities: A review and comparison of analytic approaches

机译：卫生不平等研究中的背景说明：分析方法的回顾和比较

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Background: A common epidemiologic objective is to evaluate the contribution of residential context to individual-level disparities by race or socioeconomic position. Purpose: We reviewed analytic strategies to account for the total (observed and unobserved factors) contribution of environmental context to health inequalities, including conventional fixed effects (FE) and hybrid FE implemented within a random effects (RE) or a marginal model. Methods: To illustrate results and limitations of the various analytic approaches of accounting for the total contextual component of health disparities, we used data on births nested within neighborhoods as an applied example of evaluating neighborhood confounding of racial disparities in gestational age at birth, including both a continuous and a binary outcome. Results: Ordinary and RE models provided disparity estimates that can be substantially biased in the presence of neighborhood confounding. Both FE and hybrid FE models can account for cluster level confounding and provide disparity estimates unconfounded by neighborhood, with the latter having greater flexibility in allowing estimation of neighborhood-level effects and intercept/slope variability when implemented in a RE specification. Conclusions: Given the range of models that can be implemented in a hybrid approach and the frequent goal of accounting for contextual confounding, this approach should be used more often.
机译：背景：流行病学的共同目标是通过种族或社会经济地位评估居住环境对个人水平差异的影响。目的：我们回顾了分析策略，以说明环境背景对健康不平等的总体（可观察和不可观察因素）贡献，包括常规固定效应（FE）和在随机效应（RE）或边际模型中实施的混合有限元。方法：为了说明各种考虑健康差异总背景因素的分析方法的结果和局限性，我们使用嵌套在社区中的出生数据作为评估出生时胎龄的种族差异在社区中混杂的应用示例，包括两者连续的和二进制的结果。结果：普通模型和RE模型提供的视差估计值在存在邻域混淆的情况下可能会出现明显偏差。有限元模型和混合有限元模型都可以解决群集级别的混淆问题，并提供邻域没有混淆的视差估计，后者在RE规范中实现时，在估计邻域级别影响和截距/斜率可变性方面具有更大的灵活性。结论：鉴于可以用混合方法实现的模型范围很广，并且考虑了上下文混淆的频繁目标，因此应该更频繁地使用这种方法。

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