Abstract

Inequities in access to health services has negative consequences on individual well-being, and imposes financial and emotional burden on patients, families, health care systems, and the public. Inequities engendered from differences in socioeconomic status, health insurance coverage, race, and other characteristics can engender disparities. This study aimed to identify the potential predictors of unmet medical need among the civilian noninstitutionalized U.S. adults. Inability to receive needed medical care or receiving medical care after a delay, due to the associated costs, constructed unmet medical need. This study used a four-year (2014-2017) National Health Interview Survey (NHIS) data (sample size: 296,301 adults) and implemented a conceptual framework to study disparities in access to health services and estimate the relative importance of predisposing, enabling, and need factors as the predictors of unmet medical need. Findings from machine learning and logistics regression models highlight the importance of health insurance coverage as a key contributing factor of health disparities. About 60% of variation in unmet medical need was predictable, with over 90% accuracy, solely with health insurance coverage status. Self-rated health status, family structure, and family income to poverty ratio were other statistically significant predictors. Even after controlling for a wide variety of sociodemographic and health status variables such as age, gender, perceived health status, education, income, etc., health insurance remains significantly associated with unmet medical need (OR: 5.03 , 95%CI: 4.67-5.42). To ensure precise national estimates, proper survey data analysis methods were incorporated to account for the complex sampling method used by NHIS. Furthermore, the enabling factors (health insurance and income) exert much more weight on unmet medical need than predisposing factors and need factors. The findings raise the concerns about the existence and magnitude of disparities in health care access and provide a comprehensive framework to a target population for understanding the sources of health inequities with data-driven evidence. Results can be utilized to address potential areas for designing public policy and program interventions by identifying the relative vulnerability of different population groups for lacking access to affordable health services. Future studies using longitudinal panel data are necessary to establish a causal relationship between the predictors and unmet medical need.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2019

Semester

Spring

Advisor

Wan, Thomas

Degree

Doctor of Philosophy (Ph.D.)

College

College of Community Innovation and Education

Degree Program

Public Affairs; Health Services Management and Research

Format

application/pdf

Identifier

CFE0007477

URL

http://purl.fcla.edu/fcla/etd/CFE0007477

Language

English

Release Date

May 2020

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

Download

Share

COinS