Targeting Reduced Asthma Hospitalizations

Targeting Reduced Asthma Hospitalizations through Geospatial Statistics:

Secondary Research of Preventable Asthma Hospitalizations

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This research investigated correlations of asthma and uncontrolled-asthma prevalence in California controlling for demographic, health, health insurance and access, and socioeconomic, factors, in order to develop a predictive model and apply it to the California population for creation of a targeted outreach identification tool. The study objectives were for the tool to enable health care providers to develop targeted outreach that would improve health care and reduce preventable hospitalizations, while the overall project would generate a population health framework that could be applied to other diseases and effectively scale to more granular health data. Adult California Health Interview Survey public-use data from 2011 through 2016 were utilized for model generation, with those models being applied to the 2017 five-year American Community Survey population estimates for California to predict disease prevalence. Generated models were statistically significant and resulted in identification of asthma prevalence binomial logistic regression coefficients for sex, race, citizenship, health insurance status, and income level. The uncontrolled-asthma binomial regression model resulted in identification of coefficients for sex, race, and income model. After application of the model against population data, the Asthma Probability Model Dashboard was published to Tableau® Public. In addition to providing the asthma dashboard, this study resulted in development of a framework that may be readily applied to other disease conditions and scaled to confidential health care data for more precise reporting.