Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Center for Drug Safety and Effectiveness, Johns Hopkins University, Baltimore, Maryland, USA.
Popul Health Manag. 2021 Jun;24(3):403-411. doi: 10.1089/pop.2020.0135. Epub 2020 Sep 10.
Traditionally, risk-adjustment models do not address the characteristics of minority populations, such as race or socioeconomic status. This study aimed to evaluate the added value of place-based social determinants on risk-adjustment models in explaining health care costs and utilization. Statewide commercial claims from the Maryland Medical Care Database were used, including 1,150,984 Maryland residents aged 18 to 63 with ≥6 months enrollment in 2013 and 2014. Area Deprivation Index (ADI) was assigned to individuals through zip code. The authors examined the addition of ADI to predictive models of concurrent and prospective costs and utilization; linear regression was adopted for costs and logistic regression for utilization markers. Performance measures included R for costs (total, pharmacy, and medical costs) and the area under the curve (AUC) for utilization (being top 5% top users, having any hospitalization, having any emergency room [ER] visit, having any avoidable ER visit, and having any readmission). All performance measures were derived from the bootstrapping analysis with 200 iterations. Study subjects were ∼48% male with a mean age of ∼41 years. Adding ADI to the demographics or claims-based models generally did not improve performance except in predicting the probability of having any ER or any avoidable ER visit; for example, AUC of avoidable ER visits increased significantly from .610 to .613 when using ADI rank deciles in claims-based models. Future research should focus on patients with a higher need for social services, assess more granular place-based determinants (eg, Census block group), and evaluate the added value of individual social variables.
传统上，风险调整模型并不考虑少数族裔群体的特征，如种族或社会经济地位。本研究旨在评估基于地点的社会决定因素对风险调整模型在解释医疗保健成本和利用方面的附加价值。使用了来自马里兰州医疗保健数据库的全州商业索赔数据，包括 2013 年和 2014 年在马里兰州居住的 18 至 63 岁、连续参保 6 个月以上的 1150984 名居民。通过邮政编码为个人分配区域贫困指数（ADI）。作者检查了 ADI 对同期和前瞻性成本和利用预测模型的附加作用；采用线性回归分析成本，采用逻辑回归分析利用标记物。绩效衡量标准包括成本的 R 值（总费用、药房费用和医疗费用）和利用的曲线下面积（AUC）（前 5%的最高使用者、任何住院、任何急诊就诊、任何可避免的急诊就诊和任何再入院）。所有绩效指标均来自具有 200 次迭代的自举分析。研究对象中约有 48%为男性，平均年龄约为 41 岁。除了预测急诊或可避免急诊就诊的概率外，将 ADI 添加到人口统计学或基于索赔的模型中通常不会提高性能；例如，在基于索赔的模型中使用 ADI 等级的十分位数时，可避免急诊就诊的 AUC 从 0.610 显著增加到 0.613。未来的研究应集中在需要更多社会服务的患者上，评估更细粒度的基于地点的决定因素（例如，人口普查街区组），并评估单个社会变量的附加价值。
