The Population-based HIV Impact Assessment (PHIA) is an ongoing project that conducts nationally representative HIV-focused surveys for measuring national and regional progress toward UNAIDS’ 90-90-90 targets, the primary strategy to end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to better understand the key factors that drive the HIV epidemics in the most affected countries in sub-Saharan Africa. In this article, we propose a novel causal structural learning algorithm to discover important covariates and potential causal pathways for 90-90-90 targets. Existing constraint-based causal structural learning algorithms are quite aggressive in edge removal. The proposed algorithm preserves more information about important features and potential causal pathways. It is applied to the Malawi PHIA (MPHIA) dataset and leads to interesting results. For example, it discovers age and condom usage to be important for female HIV awareness; the number of sexual partners to be important for male HIV awareness; and knowing the travel time to HIV care facilities leads to a higher chance of being treated for both females and males. We further compare and validate the proposed algorithm using BIC and using Monte Carlo simulations, and show that the proposed algorithm achieves improvement in true positive rates in important feature discovery over existing algorithms. Supplementary materials for this article are available online.

On the ground, how can research initiatives unfold to make meaningful contributions to real-world practice and real-time policy? This article draws on a case study evaluating an innovative program to alleviate toxic stress, boost resilience, and promote social inclusion among Syrian refugee and Jordanian nonrefugee youth. I describe the kind of project design and community engagement that animates research on stress biology and lived experiences, connecting people with humanitarian practice and policy. I highlight why and how biocultural work generates fluency in multiple forms of evidence to guide mental health interventions, reflecting on ways to anchor research in shared humanity and shared scientific purpose. I clarify what types of added value, pursued during intersectoral collaborations, help achieve plural, sustained, and inclusive contributions. This article shows how “creative relationality” can energize research-to-policy initiatives to bring about transformational change.