Health Services Research最新文献

Objective: To examine changes in colorectal cancer (CRC) screening rates over time and determine organizational-level factors influencing these shifts.

Study settign and design: This longitudinal study used mixed effects models to analyze data from Federally Qualified Health Centers (FQHCs) in the United States (US). Key organizational-level factors included Patient-Centered Medical Home (PCMH) recognition and duration, hypertension and diabetes management, and center-level characteristics such as racial composition, location, and center volume/size.

Data sources and analytic sample: This study used Uniform Data System (UDS) data from 2017 to 2022 for US-based FQHCs receiving full Public Health Service Section 330 grants and reporting CRC screening measures, excluding school-based centers, US territories, and look-alike centers.

Principal findings: Among the 1282 FQHCs analyzed, CRC screening rates were increasing before the COVID-19 pandemic but declined during and remain below pre-pandemic levels. FQHCs with consistent PCMH recognition reported significantly higher screening rates (β = 8.50, p < 0.001). Screening rates were also positively associated with a higher rate of controlled hypertension (β = 0.354, p < 0.0001) but lower in FQHCs with larger Black patient populations, Southern locations, and smaller center volume/size.

Conclusions: Consistent PCMH recognition and chronic disease management are essential for improving CRC screening rates in FQHCs. By integrating these population health management strategies, FQHCs can proactively address screening disparities. Prioritizing these organizational-level approaches may strengthen healthcare equity and expand CRC screening for historically marginalized communities.

Objective: Quantify racial and ethnic disparities in harm reduction and substance use disorder (SUD) treatment use among people who use drugs and compare estimates using a healthcare disparities measurement method aligned with the Institute of Medicine (IOM) definition of healthcare disparities against other regression approaches.

Study setting and design: 45-minute telephone survey of people who use drugs administered from January 2023 to August 2024 across four US locations (i.e., Milwaukee County, Wisconsin; Flint/Detroit, Michigan; statewide in New Jersey; and Bernalillo County, New Mexico). Service use disparities were estimated using propensity score models with rank-replace methods and compared against standard covariate-adjusted logistic regression models.

Data sources and analytic sample: Survey response data from 1651 respondents who identified as White non-Hispanic (N = 572), Black non-Hispanic (N = 479), Hispanic (N = 453), or American Indian/Alaska Native (N = 147) and had used drugs in the past 30 days. Exposures included respondent demographics, health status, social determinants of health (SDOH), and race/ethnicity. Outcomes included use of any harm reduction services, fentanyl test strip use, naloxone possession, any SUD treatment use, and receipt of buprenorphine, methadone, or naloxone in the past 30 days.

Principal findings: Compared to White non-Hispanic (NH) respondents, Black NH respondents were 17.8 (95% CI: -0.24, -0.12) percentage points less likely to use harm reduction services and 8.8 (95% CI: -0.15, -0.03) points less likely to use SUD treatment. Hispanic respondents were 12.8 percentage points less likely to use any SUD treatment (95% CI: -0.20, -0.06), while American Indian/Alaska Native respondents were 11 percentage points less likely (95% CI: 0.20, -0.02). Standard adjustment models tended to mask or overestimate healthcare disparities relative to rank and replace with propensity scores.

Conclusion: Racial and ethnic disparities in harm reduction and treatment necessitate policy reform. Social determinants adjustment should be performed carefully to prevent inaccurately estimating health disparities.

Objective: To evaluate the impact of California's restricted-scope Medi-Cal program on health insurance coverage and healthcare utilization among undocumented farmworkers.

Study setting and design: We use a difference-in-differences approach to compare undocumented farmworkers in California-where restricted-scope Medi-Cal was expanded in 2014-with those in other states that expanded Medicaid under the Affordable Care Act (ACA) but did not implement a similar program for undocumented workers. The analysis exploits nearly a decade of pretreatment data to assess parallel trends.

Data source and analytic sample: Restricted-access data from the National Agricultural Workers Survey (NAWS), covering farmworkers in the United States from 2001 to 2020, the last year for which data is available. We identify undocumented farmworkers in the NAWS and extract information on health insurance coverage and healthcare utilization, including use of hospitals/emergency rooms and private clinics.

Principal findings: The results show that following the expansion of restricted scope Medi-Cal, the use of institutional healthcare providers (community health centers, hospitals, and emergency rooms) increased by 8.0 percentage points (95% CI: 0.0044, 0.1564) while the use of private clinics decreased for undocumented farmworkers. This is consistent with an 11.6 percentage points (95% CI: 0.0755, 0.1572) increase in restricted Medi-Cal coverage. We also show that the parallel trend assumption holds, lending support to a causal interpretation.

Conclusions: Our results highlight that restricted Medi-Cal expansion increased access to care.

Objective: To examine the association between state Home- and Community-Based Services (HCBS) caregiver payment policies and timely follow-up visits (in-person and telehealth) within 14 days of hospital discharge among Medicare-Medicaid dual-eligible older adults with dementia.

Study setting and design: We categorized state HCBS caregiver payment policies into three groups: no caregiver payment, payment eligible for other friends/family, and payment eligible for two caregiver types (legally responsible relatives or other friends/family). The primary outcome was the mode of follow-up visit within 14 days post-hospital discharge (in-person, telehealth, or no visit). We used multinomial logistic regression with hospital random effects, adjusting for individual- and area-level and HCBS factors. Marginal effects were estimated.

Data sources and analytic sample: We analyzed 2021 Medicare claims data linked with publicly available datasets. The analytic cohort comprised 51,633 dual-eligible Medicare beneficiaries with dementia who were hospitalized and discharged to the community in 2021.

Principal findings: State HCBS caregiver payment policies were significantly associated with the mode of timely follow-up visits. Compared to states without providing caregiver payments, states providing payments to two caregiver types had a 6.8 percentage point higher probability (p < 0.01) of timely in-person visits but a 3.2 percentage point lower probability (p < 0.01) of timely telehealth visits. Similar, though smaller, significant differences were observed between states that provided payments to only other family or friends and those with no caregiver payments. Other HCBS generosity measures, as well as racial, ethnic, and geographic locations, were also associated with the mode of post-discharge visits.

Conclusion: Providing financial support to family caregivers through state HCBS policies may increase the rate of timely post-discharge visits, primarily driven by an increase in in-person visits. The effects were particularly prominent among states that allow payments to both types of caregivers.

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.

Objective: To measure the impact of electronic health record (EHR) transition on burnout among Veterans Health Administration (VA) frontline clinicians using pseudorandom variation from staggered EHR implementations across VA sites.

Study setting and design: Employing a quasi-experimental design, we studied 140 VA medical center sites nationwide (including five sites that implemented the new EHR from 2019 to 2023). Explanatory measures included year, VA transition site (grouped into three cohorts by transition timing), and their interaction. Our outcome measure encapsulated two dimensions of burnout-emotional exhaustion and depersonalization (symptoms > once per week indicated burnout).

Data sources and analytic sample: Using secondary data from the 2019 to 2023 VA All Employee Survey, we aggregated survey responses on the medical-site level by year and respondent characteristics. Our analytic sample included 12,155 aggregated observations. We used a difference-in-difference approach to compare pre-post changes in burnout between VA sites implementing and not implementing the new EHR. Where available, we reported post-transition treatment effects in the short term, medium term, and long term, relative to EHR implementation.

Principal findings: Unadjusted burnout from 2019 to 2023 was 36.9% for Cohort 1, 33.0% for Cohort 2, 37.0% for Cohort 3, and 33.2% for non-transition sites. In adjusted analyses, burnout for Cohort 1 increased 4.8 percentage points (p < 0.001) in the medium term; differences in burnout dissipated in the long term. For Cohort 2, we detected a 1 percentage point increase in burnout (p = 0.004) in the short term and a 1.5 percentage point decrease (p = 0.013) in the medium term. For Cohort 3, burnout increased 3.3 percentage points (p < 0.001) in the medium term.

Conclusions: The impact of EHR transition on burnout differed across deployment sites and post-transition periods but was mild overall. Future research is needed to understand contextual and implementation process differences between sites that may explain differential effects and offer learnings to ensure a high-functioning health workforce during EHR transition.