Incorporating lived experience into public health data science to improve contextualization and understanding of health inequalities for people with disability

The increased availability of big data has improved the capacity of epidemiologists to examine complex questions related to health equity for marginalized populations. Although data science methodologies enable reproducible and complex quantitative analyses, using these approaches without sufficient contextualization risks erroneous findings or interpretation, resulting in ineffective or potentially damaging public health recommendations that do not meet the needs of the population. Incorporating lived experience into the research process is one way to improve the quality and relevance of the research and its findings. Here we present a case study of a project that involved quantitative analysis of a large established population-based longitudinal survey that was informed by lived experience expertise. This research used the Household, Income and Labor Dynamics in Australia (HILDA) Survey to investigate the associations between loneliness, social isolation, and mental health in young people with disability. We show how we used lived experience to refine research questions, select outcome measures and covariates, develop statistical models, interpret results, contextualize findings, generate policy recommendations and future research ideas. We also discuss challenges and potential solutions. Our case study demonstrates a new way for undertaking quantitative health equity research that promotes the voice of the population of interest.