Abstract

Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model’s offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as “offset endogeneity.” We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.

摘要

卫生部门评估健康不公平，以确定受疾病负担过重的社会群体，学术研究人员评估健康不公平，以了解社会、经济和环境不公平如何表现为健康不公平。为了描述不公平现象，特定群体的小区域健康数据通常使用具有随机截距的对数线性广义线性模型 (GLM) 或广义线性混合模型 (GLMM) 进行建模。这些方法估计了在标准假设下比较各组疾病发生率的相同边际比率。在这里，我们探讨居住隔离与疾病风险中的社会群体差异如何导致 GLM 和 GLMM 的矛盾发现。我们表明发生这种情况是因为在这些条件下收集的小区域疾病发生率数据由于模型的偏移和随机效应之间的相关性导致 GLMM 中的内生性。这导致 GLMM 估计代表条件关联而不是边际关联。我们将由抵消引起的内生性称为“抵消内生性”，据我们所知，之前没有注意到这一点。我们在模拟数据和真实的过早死亡数据中说明了这种现象，并且我们提出了替代建模方法来解决它。我们还向统计受众介绍了用于构建健康不公平现象的社会流行病学术语，从而能够对结果进行负责任的解释。这导致 GLMM 估计代表条件关联而不是边际关联。我们将由抵消引起的内生性称为“抵消内生性”，据我们所知，之前没有注意到这一点。我们在模拟数据和真实的过早死亡数据中说明了这种现象，并且我们提出了替代建模方法来解决它。我们还向统计受众介绍了用于构建健康不公平现象的社会流行病学术语，从而能够对结果进行负责任的解释。这导致 GLMM 估计代表条件关联而不是边际关联。我们将由抵消引起的内生性称为“抵消内生性”，据我们所知，之前没有注意到这一点。我们在模拟数据和真实的过早死亡数据中说明了这种现象，并且我们提出了替代建模方法来解决它。我们还向统计受众介绍了用于构建健康不公平现象的社会流行病学术语，从而能够对结果进行负责任的解释。