For many health conditions, there are highly efficacious treatment and prevention products. Maximizing their impact requires strategies that improve the reach of health screening in order to establish who could benefit. For example, HIV prevention strategies aim to expand risk screening and to improve uptake of pre-exposure prophylaxis (PrEP) among those experiencing risk. Often, these strategies induce changes at the group-level (e.g., health clinics or communities) and are evaluated through cluster randomized trials. This scenario creates a complex, multilevel-mediation-missing data problem for the following reasons. First, the strategy is delivered at the cluster-level, while health screening and outcomes are at the individual-level. Second, the strategy improves health outcomes directly and indirectly through improved health screening. Third, everyone has an underlying status, which is only observed among those screened. To formally define the total effect in such settings, we use Counterfactual Strata Effects: causal estimands where the outcome is only relevant for a group whose membership is subject to missingness and/ or impacted by the exposure of interest. To identify and estimate the corresponding statistical estimand, we propose a novel extension of Two-Stage targeted minimum loss-based estimation (TMLE). Simulations demonstrate the practical performance of our approach as well as the limitations of existing approaches.

翻译：对于许多健康状况，存在高效的治疗和预防手段。要最大化其影响，需要制定能提升健康筛查覆盖范围的策略，以确定哪些人群可能获益。例如，HIV预防策略旨在扩大风险筛查范围，并提高存在风险人群对暴露前预防（PrEP）的接受度。这类策略通常在群体层面（如卫生诊所或社区）引发改变，并通过整群随机试验进行评估。这种场景构成了一个复杂的多层级中介-缺失数据问题，原因如下：首先，策略在群体层面实施，而健康筛查与结局发生在个体层面；其次，策略既直接改善健康结局，也通过改进健康筛查间接发挥作用；第三，每个个体都具有潜在的状况，但该状况仅在接受筛查者中可被观测。为在此类情境中形式化定义总效应，我们采用反事实层效应：这是一种因果估计量，其结局仅对某个群体具有相关性，而该群体的成员身份存在缺失性和/或受目标暴露因素的影响。为识别并估计相应的统计估计量，我们提出了一种两阶段目标最小损失估计（TMLE）的新扩展方法。模拟实验验证了我们方法的实际性能，并揭示了现有方法的局限性。