ABSTRACT
BACKGROUND: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. As a case study, we consider recent findings that suggest human papillomavirus (HPV) vaccine is less effective against HPV-associated disease among girls living with HIV compared to girls without HIV. OBJECTIVES: To understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. METHODS: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions before data analysis and exemplify the process for designing causal graphs to inform an etiologic study. CONCLUSION: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research before undergoing data collection and/or analysis.
Subject(s)
HIV Infections , Papillomavirus Infections , Papillomavirus Vaccines , Female , HIV Infections/complications , Humans , Papillomavirus Infections/complications , Papillomavirus Infections/prevention & control , Papillomavirus Vaccines/therapeutic use , PublishingABSTRACT
Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.