Causal Structural Learning on MPHIA Individual Dataset

Abstract

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 constrained-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) data set 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.

Keywords: 90-90-90 targets; Causal structural learning; HIV; PHIA.

Fig. 1

90-90-90 Awareness graph in female. Vertices representing the Tri90 goals are biggest and marked by orange; vertices closer to goals have bigger sizes and darker colors than those farther away from goals. Widths of edges reflect the significance of the non-directional connection (conditional dependence) between vertices. Red and blue edges represent positive and negative relationships with Tri90 goals, respectively. Grey edge from Tri90Aware to PLWHSupportGroup represents an association between Tri90Aware and missingness in PLWHSupportGroup. Codebook can be found in Supplement S.7.2.