ArticleBMC public health2026
Identifying communities at elevated risk of hepatitis C infection in Los Angeles County: an ecological study using machine learning.
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIdentifying communities disproportionately affected by hepatitis C infection is essential for targeted prevention and resource allocation. This study aimed to identify zip code tabulation areas at elevated risk of hepatitis C infection in Los Angeles County and to identify area-level variables of importance in estimating hepatitis C infection rates.
methodsThis was an ecological cross-sectional study integrating Elastic Net and Random Forest machine learning approaches. The outcome was reported first-time RNA-positive hepatitis C infection among persons aged 18-39 years residing in Los Angeles County from January 2020 to December 2023. 31 area-level variables representing socioeconomic conditions, healthcare access, and opioid-related morbidity were included as model features. Model-predicted hepatitis C infection rates were used as proxies for underlying community-level risk. Zip code tabulation areas with model-predicted hepatitis C rates above the 80th percentile were classified as very high risk. Area-level variables of importance were identified through Elastic Net feature selection, and importance scores and rankings were derived from the Random Forest model.
resultsForty-nine of 245 zip code tabulation areas were classified as very high risk and were distributed across the county. Nine area-level variables of importance were identified. In descending order of importance, these included: proportions of families living below the federal poverty level, residents aged 18-39 years, and households without a vehicle; annual rates of opioid-related emergency department visits, newly diagnosed HIV infections, and opioid-related hospitalizations; distance to substance use treatment facilities; and proportions of Asian and male residents.
conclusionIntegrating machine learning methods with public health surveillance data enabled the identification of communities at elevated risk of hepatitis C infection and area-level variables of importance in estimating hepatitis C infection rates. The findings could help identify priority areas that warrant further assessment for targeted hepatitis C elimination interventions, including harm-reduction programs, testing, and linkage-to-care efforts.
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