Socioeconomic and Clinical Correlates of Insurance Type and Avoidable Emergency Department Visits in New York State
Nickol Georgy, Circe Le CompteBackground
Avoidable emergency department (ED) utilization places considerable strain on US health systems, but the relationship between insurance type and avoidable ED visits remains contested in the literature. Recent statewide work in New York found, contrary to common assumption, that publicly insured and uninsured adults had lower adjusted odds of avoidable ED visits than privately insured adults.19 The socioeconomic and clinical factors that structure this pattern have not been fully characterized, and the policy implications across rural, urban, and structurally marginalized populations remain underdeveloped.
Objective
To examine the demographic, geographic, and clinical correlates of insurance type, and estimate the adjusted association between insurance type and avoidable ED visits in New York State during 2022, with attention to medically complex populations and structural drivers of care-seeking.
Methods
We conducted a retrospective cross-sectional analysis of de-identified 2022 inpatient discharge data from the New York State Statewide Planning and Research Cooperative System (SPARCS).1 The analytic sample comprised 1,258,855 ED-related discharges among adults age 18 and older. Avoidable ED visit was defined as an APR Severity of Illness classification of Minor.19 The primary exposure was insurance type (Private, Public, Uninsured). We estimated unadjusted and fully adjusted logistic regression models, then tested effect modification by mental health diagnosis using interaction terms and stratified models, and conducted four sensitivity analyses (alternative insurance groupings, age strata, geographic strata).
Results
Avoidable ED visits accounted for 18.3% of encounters overall, with rates of 28.3% among privately insured, 27.9% among uninsured, and 15.8% among publicly insured patients. In the unadjusted model, public insurance was associated with substantially lower odds of avoidable ED visits (OR = 0.48, 95% CI: 0.47, 0.48, p < .001). After full adjustment, public insurance remained protective (AOR = 0.82, 95% CI: 0.81, 0.83, p < .001), and uninsured status was associated with 7% lower adjusted odds (AOR = 0.93, 95% CI: 0.90, 0.96, p < .001). The relationship was strongly modified by mental health diagnosis (likelihood ratio chi-square = 408.2, df = 2, p < .001): among visits with a mental health diagnosis, the public-insurance protective effect more than doubled in magnitude (AOR = 0.65, 95% CI: 0.62, 0.68), and the uninsured association reversed direction (AOR = 1.17, 95% CI: 1.04, 1.32, p = .009). Geographic stratification showed parallel reversal among uninsured patients in NYC (AOR = 1.09, 95% CI: 1.03, 1.14) versus Non-NYC areas (AOR = 0.86, 95% CI: 0.82, 0.90). Disaggregation of the Uninsured category revealed that the apparent protective effect was driven by the Miscellaneous/Other group (AOR = 0.81), while Self-Pay alone was associated with higher odds of avoidable classification (AOR = 1.08).
Conclusion
Privately insured adults in New York State had the highest adjusted odds of avoidable ED visits, with publicly insured and uninsured adults showing lower odds even after adjustment for demographic, geographic, and rich clinical covariates. Mental health diagnosis and geographic context substantially modified this relationship, with the public-insurance protective effect doubling in magnitude among mental health visits and the uninsured association reversing direction among NYC patients. These findings argue against framing avoidable ED utilization as principally a public-insurance phenomenon and reframe it as a structurally patterned, often strategic response to fragmented primary care, specialty referral barriers, mental health parity gaps, and structural avoidance among medically complex populations. Policy and health system strategies should engage all insurance groups, address the mental health parity gap among privately insured patients, and leverage existing federal frameworks (EMTALA, ACA, ADA, Ryan White HIV/AIDS Treatment Extension Act, Older Americans Act) to address the underlying access architecture.