Uninsured Primary Care Visit Disparities Under the Affordable Care Act.

PURPOSE: Health insurance coverage affects a patient's ability to access optimal care, the percentage of insured patients on a clinic's panel has an impact on the clinic's ability to provide needed health care services, and there are racial and ethnic disparities in coverage in the United States. Thus, we aimed to assess changes in insurance coverage at community health center (CHC) visits after the Patient Protection and Affordable Care Act (ACA) Medicaid expansion by race and ethnicity. METHODS: We undertook a retrospective, observational study of visit payment type for CHC patients aged 19 to 64 years. We used electronic health record data from 10 states that expanded Medicaid and 6 states that did not, 359 CHCs, and 870,319 patients with more than 4 million visits. Our analyses included difference-in-difference (DD) and difference-in-difference-in-difference (DDD) estimates via generalized estimating equation models. The primary outcome was health insurance type at each visit (Medicaid-insured, uninsured, or privately insured). RESULTS: After the ACA was implemented, uninsured visit rates decreased for all racial and ethnic groups. Hispanic patients experienced the greatest increases in Medicaid-insured visit rates after ACA implementation in expansion states (rate ratio [RR] = 1.77; 95% CI, 1.56-2.02) and the largest gains in privately insured visit rates in nonexpansion states (RR = 3.63; 95% CI, 2.73-4.83). In expansion states, non-Hispanic white patients had twice the magnitude of decrease in uninsured visits compared with Hispanic patients (DD = 2.03; 95% CI, 1.53-2.70), and this relative change was more than 2 times greater in expansion states compared with nonexpansion states (DDD = 2.06; 95% CI, 1.52-2.78). CONCLUSION: The lower rates of uninsured visits for all racial and ethnic groups after ACA implementation suggest progress in expanding coverage to CHC patients; this progress, however, was not uniform when comparing expansion with nonexpansion states and among all racial and ethnic minority subgroups. These findings suggest the need for continued and more equitable insurance expansion efforts to eliminate health insurance disparities.

County Health Rankings: Relationships Between Determinant Factors and Health Outcomes.

INTRODUCTION: The County Health Rankings (CHR) provides data for nearly every county in the U.S. on four modifiable groups of health factors, including healthy behaviors, clinical care, physical environment, and socioeconomic conditions, and on health outcomes such as length and quality of life. The purpose of this study was to empirically estimate the strength of association between these health factors and health outcomes and to describe the performance of the CHR model factor weightings by state. METHODS: Data for the current study were from the 2015 CHR. Thirty-five measures for 45 states were compiled into four health factors composite scores and one health outcomes composite score. The relative contributions of health factors to health outcomes were estimated using hierarchical linear regression modeling in March 2015. County population size; rural/urban status; and gender, race, and age distributions were included as control variables. RESULTS: Overall, the relative contributions of socioeconomic factors, health behaviors, clinical care, and the physical environment to the health outcomes composite score were 47%, 34%, 16%, and 3%, respectively. Although the CHR model performed better in some states than others, these results provide broad empirical support for the CHR model and weightings. CONCLUSIONS: This paper further provides a framework by which to prioritize health-related investments, and a call to action for healthcare providers and the schools that educate them. Realizing the greatest improvements in population health will require addressing the social and economic determinants of health.