OBJECTIVES: To estimate the potential effects of using precise social data to provide targeted interventions for hepatitis C.

METHODS: We developed a calculator to estimate the burden of hepatitis C (HCV) within the US. Using publicly available state-level CDC data we focused our analysis on Louisiana and Florida. We selected these states because they had geographic proximity and similar rates of HCV, but different rates of death associated with HCV. This indicated that there may be other social factors impacting the HCV populations in each state. We used Socially Determined social risk data to examine across 7 domains (economic climate, food landscape, housing environment, transportation network, health literacy, digital landscape and social connectedness) in order to identify very localised areas within those two states where social risk may affect chronic HCV infection rates.

Costs and health losses from HCV were sourced from published sofosbuvir cost-effectiveness papers. We then estimated the impact on QALYs, life years, and costs of targeted interventions for HCV prevention.

RESULTS: We estimate the current QALY loss from untreated diagnosed HCV is 9,100 and 4,500 QALYs (14,287 and 7,069 life years) for Florida and Louisiana respectively. The cost (US$) to treat this population is estimated to be approximately $113m and $56m with sofosbuvir ($82m and $41m with non-sofosbuvir treatment). The cost of not treating is $36m and 18m respectively. A public health intervention that could reduce new HCV cases by 2% would save society $1.6m and £0.8m while adding 122 and 60 QALYs (185 and 91 Life years).

CONCLUSIONS: Granular social risk data assets can directly inform public health policy. Socially Determined data can be used to understand variation in social risk to identify opportunities for effective public health interventions. Ultimately, this data-driven approach can improve outcomes, reduce costs, and drive health equity across the nation.