A Modeling Framework for Evaluating the Synergistic Impact of Structural Interventions on Related Diseases: HIV and Cervical Cancer as Case Study

BackgroundWomen with HIV face elevated cervical cancer risks, compounded by social conditions that influence both disease outcomes. Current models fail to adequately capture the complex interactions between diseases and social determinants. MethodsWe enhanced a mixed agent and compartment model for HIV and cervical cancer (MAC-HIV-CC) to model disparities by social conditions. We analyzed the impact of hypothetical 100% efficacious interventions over 30 years (2018-2048): (1) an HIV care intervention that eliminates disparities in viral load suppression between social groups, (2) a sexual behavior intervention aligning behaviors of women who exchange sex with those who do not, and (3) a combination of both interventions. ResultsThe HIV care intervention reduced HIV incidence by 26.9% and cervical cancer cases by 14.5% among HIV-positive women. The sexual behavior intervention decreased HIV prevalence by 8.1% and HPV prevalence by 36.1% among HIV-positive women engaged in exchange sex. The combination intervention reduced HIV prevalence by 25.3%, HIV incidence by 34.3%, and cervical cancer cases by 37.5% in the target population. ConclusionsThe proposed framework provides a novel approach for health equity analyses by modeling social determinants that are common pathways to interrelated diseases and health disparities. Such a model is of significance for cost-effectiveness intervention analyses of interrelated diseases.