Health Services Research最新文献

Objective: To examine changes in children's Medicaid and CHIP enrollment during the Families First Coronavirus Response Act unwinding and assess whether CHIP enrollment offsets Medicaid declines.

Study setting and design: We used linear probability models with monthly indicators to estimate changes in enrollment from April 2023 to September 2024 overall and by CHIP structure type.

Data sources and analytic sample: We used monthly children's enrollment data from the U.S. Centers for Medicare & Medicaid Services for 32 states and the District of Columbia.

Principal findings: During the unwinding, Medicaid enrollment declined from 48.1% to 41.2% of children, while CHIP enrollment remained stable (8.7% to 8.6%). We found average declines of 62,032 (95% confidence interval [CI]: -108,018 to -16,045) Medicaid-enrolled children per state (6.5 percentage points [pp], 95% CI: -8.1 to -5.0). Medicaid declines were larger in states with combination CHIP (-8.7 pp, 95% CI: -10.3 to -7.2) than Medicaid expansion CHIP (-4.5 pp, 95% CI: -6.0 to -3.1). We found no evidence of significant changes in CHIP enrollment overall or by CHIP structure.

Conclusions: Children's Medicaid enrollment fell sharply without offsetting CHIP gains during the unwinding, underscoring the need for policies that prevent administrative disenrollment and ensure seamless coverage transitions.

Objective: To determine if hospital bypass (use of the non-closest hospital) and/or hospital quality are associated with the probability of a patient receiving a timely follow-up visit following discharge from an Emergency Department (ED) visit for heart failure.

Study setting and design: Our sample consisted of all ED visits for heart failure in a population of Medicare beneficiaries. Our outcome was an outpatient visit within 7 days of discharge. Our primary independent variables consisted of an indicator of hospital bypass and four hospital quality measures: Overall Star Rating, Hospital Consumer Assessment of Health Providers and Services (HCAHPS) Summary Star Rating, Hospital-Wide Readmission Rate, and Heart Failure Readmission Rate. We used propensity score weighted-logistic regression models to predict the probability of follow-up within 7 days. Propensity score weighting accounted for clinical and demographic differences between those who bypassed and those who did not. Separate models were generated for each quality measure.

Data sources and analytic sample: We used data from a 2015-2019 20% Sample of Medicare Fee-for-Service claims, hospital quality measures from the Centers for Medicare and Medicaid Services' Hospital Compare, and data from the Healthcare Cost Reporting Information System.

Principal findings: 76,949 visits met the eligibility criteria. We found that patients who used the nearest hospital were more likely to have a follow-up visit than those who bypassed (average marginal effect [AME]: 0.010, p < 0.05). Better performance on each quality measure was also associated with a higher probability of follow-up, with HCAHPS having the strongest (AME: 0.015, p < 0.001) association.

Conclusions: Using the nearest hospital (i.e., not bypassing it) and using higher quality hospitals was associated with a higher probability of timely follow-up, which may be important in preventing hospital readmissions. There may be benefits to rural patients' use of their nearest hospital, such as proximity to support and lower travel burden.

Objective: Accurate, efficient identification of housing-related needs, including homelessness or housing instability, is crucial for health systems addressing health-related social needs (HRSN). We developed and validated a novel, pragmatic electronic health record (EHR)-based algorithm to identify patients with housing-related needs.

Study design and setting: We retrospectively evaluated sensitivity and specificity of the housing-related needs algorithm within our safety-net hospital, Boston Medical Center (BMC).

Data sources and analytic sample: The algorithm included six EHR structured data elements tailored to BMC operations, including HRSN screening results. We assessed each element's performance, alone and combined, using 12 months of BMC EHR data among two reference groups: (1) 433 patients with verified housing-related needs at housing program enrollment (2019-2023), and (2) a stratified random sample of 400 patients (200 adult, 200 pediatric) with ≥ 1 primary care medical visit (2022), whose charts we manually reviewed to verify housing status. We calculated algorithm sensitivity in both groups and specificity in the primary care group.

Principal findings: With all data elements included, algorithm sensitivity was 62% (95% CI: 57%-66%) among housing program enrollees and 81% (95% CI: 68%-91%) among primary care patients. Among primary care patients (13% with chart review-verified housing-related needs), specificity was 97% (95% CI: 95%-98%). HRSN screening yielded the highest single-element sensitivity, but screening alone remained limited: 57%-62% of those with verified housing-related needs were detected via screening. Patient address information and diagnostic codes had low single-element sensitivities.

Conclusion: Pragmatic EHR algorithms leveraging structured data elements tailored to local context present an accessible, efficient method for health systems to identify patients with housing-related needs. This is the first study to validate such an algorithm in a safety-net setting; we found it had moderate sensitivity and high specificity. The algorithm identified more housing-related needs than diagnostic codes alone, demonstrating the value of integrated clinical and administrative data. Further algorithm improvements require changes to HRSN screening and EHR documentation.

Objective: To assess how private equity ownership affects prices, service mix, and Medicaid participation in dentistry at the practice level.

Study setting and design: We utilize a proprietary dental office database linked to administrative dental commercial claims data to estimate the effects of private equity ownership on the financial and operational outcomes of dental offices, employing a staggered difference-in-differences panel design that addresses the nonrandom acquisitions of facilities by private equity firms.

Data sources: We rely on private equity transaction data from 2015 to 2021, longitudinal dental office data from the 2015 to 2017 and 2019 to 2021 American Dental Association dental office database, and aggregated commercial dental insurance price and utilization data from 2015 to 2021.

Principle findings: Following acquisition, private equity-owned dental offices increased charges for dental care services by 3.3% (95% CI: 2.3%-4.4%), although allowed prices for these services remained statistically unchanged. Dental offices acquired by private equity firms tended to shift from diagnostic and preventive procedures to generally higher reimbursement restorative, specialty, and surgical procedures. Dental offices were more likely to become multispecialty practices after being acquired by a private equity firm.

Conclusions: Allowed or negotiated prices between dentists and payers did not change in dental offices after being acquired by private equity. Nevertheless, list prices for dental services increased in private equity-owned practices, meaning higher prices can still be passed on to patients. Private equity firms can enhance dental practice revenue by shifting from preventive procedures to higher-cost restorative procedures while not reimbursing providers at a higher amount. In other words, financial enhancement of dental practices under private equity may not translate into benefits for providers or patients. Policymakers should be aware of the effects private equity acquisition has on provider and patient welfare.