Using Population Data to Develop Precision Based Approaches to Hepatits C Prevention

OBJECTIVES: To estimate the costs and savings of using data to better identify and screen at risk populations for hepatitis C in the USA.

METHODS: An estimated 2,000,000 – 3,500,000 people living with hepatitis C Virus (HCV) in the USA and approximately 75% do not know they have the disease. The CDC reported 70,000 new cases in 2021 implying a crude secondary infection rate of 0.02 to 0.04 secondary infections per person per year in the US. We used published sources of population screening at 0.01 for general population, 0.2 for targeted populations and 0.6 for people who inject drugs. The cost of screening is $140.

We used the Socially Determined data set to examine social risk across 7 domains (economic climate, food landscape, housing environment, transportation network, health literacy, digital landscape and social connectedness) to generate heat maps of very specific locales of higher risk of undiagnosed HCV. We focused this analysis on Louisiana and Florida, which have similar rates of reported acute infection (6.7 and 7.1 per 100,000), but different death associated with HCV (5.34 and 2.89).

RESULTS: We estimated the number of people with HCV in both Louisiana and Florida (21,469 and 4,280 cases respectively). The cost of universal screening programs to identify these cases would cost US$300m and US$60m respectively. Using targeted data informed screening would cost US$15m and US$3m respectively to find the same number of expected chronic HCV cases.

This would prevent 519 and 188 deaths associated with HCV in the first year respectively. This would be at a cost of $29,000 per death avoided in Florida or $16,000 per death avoided in Louisiana.

CONCLUSIONS: Targeted screening programs have previously been very challenging to implement. The use of granular social risk data sets can enable a more informed approach to better address HCV screening, education, and prevention.