The Journal of allergy and clinical immunology · 2026
Predicting asthma prevalence across US communities by integrating social vulnerability and environmental risk with machine learning.
СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Nguyen Thinh H, Alsulami Saleh, Pham Teresia T, Story Diane, Hauptman Marissa, Phipatanakul Wanda
Аннотация
BACKGROUND: Asthma is one of the most common chronic conditions in the United States, disproportionately affecting racial and ethnic minority populations and low-income communities. Although both social and environmental factors are known contributors to asthma prevalence, the extent to which they interact to shape geographic disparities remains insufficiently understood.
OBJECTIVE: This study aimed to explore how these factors vary across regions with differing asthma prevalence and thereby identify patterns that may inform targeted public health responses.
METHODS: We used the US Centers for Disease Control and Prevention's 2022 Environmental Justice Index, which includes standardized social and environmental measures at the census tract level. K-means clustering identified geographic clusters with varying asthma prevalence based on asthma rates and related health outcomes. A random forest model was trained to predict asthma rates by using social and environmental variables, and feature importance was analyzed.
RESULTS: We identified 9 clusters, with 3 showing the highest asthma prevalence. High-prevalence areas had greater social vulnerability, including higher poverty rate, minority population rate, limited English proficiency, unemployment, and housing cost burden. They also experienced greater environmental exposures, such as elevated level of fine particulate matter with an aerodynamic diameter of 2.5 μm or less, ozone, diesel pollution, more land near hazardous waste sites, more chemical facilities, and older housing. The random forest model predicted asthma rates with strong predictive performance (R2 = 0.81). Socioeconomic variables consistently ranked as stronger predictors than environmental exposures.
CONCLUSIONS: Geographic areas with high asthma prevalence are shaped by overlapping social and environmental vulnerabilities. Socioeconomic disparities emerged as particularly influential. These findings support integrated, data-driven public health strategies to address asthma inequities.
OBJECTIVE: This study aimed to explore how these factors vary across regions with differing asthma prevalence and thereby identify patterns that may inform targeted public health responses.
METHODS: We used the US Centers for Disease Control and Prevention's 2022 Environmental Justice Index, which includes standardized social and environmental measures at the census tract level. K-means clustering identified geographic clusters with varying asthma prevalence based on asthma rates and related health outcomes. A random forest model was trained to predict asthma rates by using social and environmental variables, and feature importance was analyzed.
RESULTS: We identified 9 clusters, with 3 showing the highest asthma prevalence. High-prevalence areas had greater social vulnerability, including higher poverty rate, minority population rate, limited English proficiency, unemployment, and housing cost burden. They also experienced greater environmental exposures, such as elevated level of fine particulate matter with an aerodynamic diameter of 2.5 μm or less, ozone, diesel pollution, more land near hazardous waste sites, more chemical facilities, and older housing. The random forest model predicted asthma rates with strong predictive performance (R2 = 0.81). Socioeconomic variables consistently ranked as stronger predictors than environmental exposures.
CONCLUSIONS: Geographic areas with high asthma prevalence are shaped by overlapping social and environmental vulnerabilities. Socioeconomic disparities emerged as particularly influential. These findings support integrated, data-driven public health strategies to address asthma inequities.