BMC public health · 2026
Identifying communities at elevated risk of hepatitis C infection in Los Angeles County: an ecological study using machine learning.
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Gizamba Jacob Mugoya, Ruddell Darren, Klausner Jeffrey D
Аннотация
BACKGROUND: Identifying 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.
METHODS: This 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.
RESULTS: Forty-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.
CONCLUSION: Integrating 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.
METHODS: This 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.
RESULTS: Forty-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.
CONCLUSION: Integrating 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.