Introduction: Influenza is a preventable acute respiratory illness with a high potential to cause serious complications and is associated with high mortality and morbidity in the US. We aimed to determine the specific community-level vulnerabilities for different race/ethnic communities that are most predictive of influenza vaccination rates.
Methods: We conducted a machine learning analysis (XGBoost) to identify community-level social vulnerability features that are predictive of influenza vaccination rates among Medicare enrollees across counties in the US and by race/ethnicity.
Results: Population density per square mile in a county is the most important feature in predicting influenza vaccination in a county, followed by unemployment rates and the percentage of mobile homes. The gain relative importance of these features are 11.6%, 9.2%, and 9%, respectively. Among whites, population density (17% gain relative importance) was followed by the percentage of mobile homes (9%) and per capita income (8.7%). For Black/African Americans, the most important features were population density (12.8%), percentage of minorities in the county (8.0%), per capita income (6.9%), and percent of over-occupied housing units (6.8%). Finally, for Hispanics, the top features were per capita income (8.4%), percentage of mobile homes (8.0%), percentage of non-institutionalized persons with a disability (7.9%), and population density (7.6%).
Conclusions: Our study may have implications for the success of large vaccination programs in counties with high social vulnerabilities. Further, our findings suggest that policies and interventions seeking to increase rates of vaccination in race/ethnic minority communities may need to be tailored to address their specific socioeconomic vulnerabilities.
Keywords: Immunization; Infectious disease; Influenza; Machine learning; Social vulnerability; Vaccination rate.
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