PNS61 UNMET MEDICAL NEED AND HEALTH DISPARITIES AMONG U.S. ADULTS

Abstract

Inequities in access to health services have been known to contribute to negative consequences on individual wellbeing, along with financial and emotional burden on patients, families, healthcare systems, and the public. Inequities engendered from differences in socioeconomic status, health insurance coverage, race, etc. can increase the gap between the groups and strengthen disparities. This study aimed to identify the potential predictors of unmet medical need among the nationally representative sample of U.S. adults. This study used a four-year (2014-2017) National Health Interview Survey (NHIS) data (sample size: 296,301 adults), and implemented a conceptual framework built on the Andersen’s Behavioral Systems Model of Health Services Utilization, to study the potential predictors of unmet medical need. Findings from Classification and Regression Tree (CART) and Chi-square Automatic Interaction Detection (CHAID) models highlight the importance of health insurance coverage. About 60% of the variation in unmet medical need was explained, with over 90% accuracy, with incorporating health insurance status in the models. Self-rated health status, family structure, and family income to poverty ratio were other potential predictors. Additionally, results from logistic regression analyses indicate that self-reported unmet medical need is, predominantly, associated with health insurance status. Even after controlling for a wide variety of sociodemographic and health status variables such as age, gender, perceived health status, education, income, etc., yet, health insurance remains significantly associated with unmet medical needs (OR: 4:24, CI:.006). To ensure precise national estimates, we incorporated proper methods to account for the complex sampling method used by NHIS. The findings provide a comprehensive framework to understand national level inequities in access to health care with data driven evidences. It also can be utilized to address potential areas suitable for public policy and program interventions by identifying vulnerable groups with greater likelihood of experiencing unmet medical need.