Mazzali Cristina, Andreano Anita, Magnoni Pietro, Salvatori Andrea, Testa Deborah, Milanese Alberto, Zanfino Adele, Russo Antonio Giampiero
Epidemiology Unit, Agency for Health Protection (ATS) of Milan, Via Conca del Naviglio, 45, 20123, Milan, Italy.
BMC Health Serv Res. 2025 Aug 19;25(1):1106. doi: 10.1186/s12913-025-13203-9.
The Epidemiological Unit of the Agency for Health Protection of Milan (ATS) calculates several indicators, at hospital and at patients' residence area level (group level of analysis), with monitoring and programming scopes. Outcome indicators are usually influenced by differences in subject case-mix; therefore, adjustment methods are applied to compare each group level with mean ATS values. Inverse probability weighting (IPW) is explored as an alternative to multivariable generalized linear model (GLM), to overcome some limitations of the latter method.
To implement IPW, a multinomial logistic model, with group level as dependent and subject characteristics as independent variables, was used to estimate patient weights, which were subsequently stabilized and truncated. Checks on IPW assumptions and covariates balance were implemented both in a quantitative and in a graphical way. Comparisons on adjustment performed with fixed effects and random intercept multivariable GLM were performed and exemplified using three outcome indicators.
IPW assumptions were satisfied for all the indicators, and covariate balance presented minor issues for group levels with the lowest number of cases/events. Case-mix adjustment performed with multivariable fixed effects GLM showed a tendency to overestimate raw values and was characterized by broad confidence intervals in the three case-example indicators. Adjusted values produced using random intercept multivariable GLM suffered from a shrinkage towards the mean effect, particularly evident in the indicators with the lowest number of cases. IPW-adjusted estimates differed from raw values only when substantial differences in covariates distribution were present.
Provided that appropriate checks are implemented, IPW adjustment is applicable in the context of healthcare quality evaluation and can be easily conveyed to healthcare managers for effective dissemination.
米兰卫生保护局（ATS）的流行病学部门在医院和患者居住地区层面（分析组层面）计算多个指标，用于监测和规划。结果指标通常受病例组合差异的影响；因此，采用调整方法将每个组层面与ATS的均值进行比较。探索使用逆概率加权（IPW）作为多变量广义线性模型（GLM）的替代方法，以克服后一种方法的一些局限性。
为实施IPW，使用一个以组层面为因变量、个体特征为自变量的多项逻辑模型来估计患者权重，随后对权重进行稳定化和截断处理。以定量和图形方式对IPW假设和协变量平衡进行检验。使用固定效应和随机截距多变量GLM进行调整的比较，并以三个结果指标为例进行说明。
所有指标均满足IPW假设，协变量平衡在病例/事件数量最少的组层面存在一些小问题。多变量固定效应GLM进行的病例组合调整显示出高估原始值的趋势，并且在三个案例指标中具有较宽的置信区间。使用随机截距多变量GLM产生的调整值向平均效应收缩，在病例数最少的指标中尤为明显。仅当协变量分布存在实质性差异时，IPW调整后的估计值才与原始值不同。
只要进行适当的检验，IPW调整适用于医疗质量评估背景，并且可以轻松传达给医疗管理人员以进行有效传播。
