Diabetes is a public health burden that disproportionately affects military veterans and racial minorities. Studies of racial disparities are inherently observational, and thus may require the use of methods such as Propensity Score Analysis (PSA). While traditional PSA accounts for patient-level factors, this may not be sufficient when patients are clustered at the geographic level and thus important confounders, whether observed or unobserved, vary by geographic location. We employ a spatial propensity score matching method to account for “geographic confounding”, which occurs when the confounding factors, whether observed or unobserved, vary by geographic region. We augment the propensity score and outcome models with spatial random effects, which are assigned scaled Besag-York-Mollié priors to address spatial clustering and improve inferences by borrowing information across neighboring geographic regions. We apply this approach to a study exploring racial disparities in diabetes specialty care between non-Hispanic black and non-Hispanic white veterans. We construct multiple global estimates of the risk difference in diabetes care: a crude unadjusted estimate, an estimate based solely on patient-level matching, and an estimate that incorporates both patient and spatial information. In simulation we show that in the presence of an unmeasured geographic confounder, ignoring spatial heterogeneity results in increased relative bias and mean squared error, whereas incorporating spatial random effects improves inferences. In our study of racial disparities in diabetes specialty care, the crude unadjusted estimate suggests that specialty care is more prevalent among non-Hispanic blacks, while patient-level matching indicates that it is less prevalent. Hierarchical spatial matching supports the latter conclusion, with a further increase in the magnitude of the disparity. These results highlight the importance of accounting for spatial heterogeneity in propensity score analysis, and suggest the need for clinical care and management strategies that are culturally sensitive and racially inclusive.

中文翻译：

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多级空间数据的倾向得分匹配：考虑健康差异研究中的地理混杂

糖尿病是一种公共卫生负担，严重影响退伍军人和少数族裔。种族差异研究本质上是观察性的，因此可能需要使用倾向评分分析 (PSA) 等方法。虽然传统的 PSA 考虑了患者层面的因素，但当患者聚集在地理层面时，这可能还不够，因此重要的混杂因素（无论是观察到的还是未观察到的）因地理位置而异。我们采用空间倾向得分匹配方法来解释“地理混杂”，当混杂因素（无论是观察到的还是未观察到的）因地理区域而异时，就会发生这种情况。我们用空间随机效应增加倾向得分和结果模型，它们被分配了按比例缩放的 Besag-York-Mollié 先验，以解决空间聚类问题并通过借用相邻地理区域的信息来改进推理。我们将这种方法应用于一项研究，探索非西班牙裔黑人和非西班牙裔白人退伍军人在糖尿病专科护理方面的种族差异。我们构建了糖尿病护理风险差异的多个全局估计：未经调整的粗略估计、仅基于患者级别匹配的估计，以及包含患者和空间信息的估计。在模拟中，我们表明，在存在未测量的地理混杂因素的情况下，忽略空间异质性会导致相对偏差和均方误差增加，而结合空间随机效应会改善推理。在我们对糖尿病专科护理中种族差异的研究中，未经调整的粗略估计表明，专科护理在非西班牙裔黑人中更为普遍，而患者水平匹配表明它不那么普遍。分层空间匹配支持后一个结论，差异的幅度进一步增加。这些结果强调了在倾向评分分析中考虑空间异质性的重要性，并表明需要具有文化敏感性和种族包容性的临床护理和管理策略。