Medical Care最新文献

{"title":"Managed Care in Mental Health Care: How Do We Know When Cost Savings Is Cost-Effective?","authors":"Jennifer L Humensky, Michael C Freed, Agnes Rupp, Rachel Smith, Patricia A Areán","doi":"10.1097/MLR.0000000000002146","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002146","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Equity and Hospital Markets: Differences in the Association of Market Concentration and Quality of Care by Patient Race/Ethnicity and Payer.","authors":"Alexander C Adia, Charleen Hsuan, Hector P Rodriguez","doi":"10.1097/MLR.0000000000002123","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002123","url":null,"abstract":"<p><strong>Background: </strong>As hospital markets become increasingly consolidated, whether regulators should account for consolidation's impacts on health equity has become a key policy question. We assess the association of hospital market concentration with quality of care and examine differences by patient race/ethnicity and payer.</p><p><strong>Methods: </strong>We analyzed linked 2017 American Hospital Association Annual Survey and Healthcare Cost and Utilization Project State Inpatient Data from 14 US states. Market concentration was measured using the Herfindahl-Hirschman Index (HHI) at the county level, and quality was assessed using the Prevention Quality Indicators (PQI). We assessed the relationship of HHI, patient race/ethnicity, and payer with having any PQI admission, controlling for patient and hospital characteristics. We used interaction terms for race-HHI and payer-HHI to assess differential associations of concentration by race/ethnicity and payer using linear probability models.</p><p><strong>Results: </strong>In adjusted analyses, minoritized racial/ethnic group status and having a noncommercial primary payer were associated with a higher probability of having a PQI admission. Differences between Hispanic adults and White adults decreased in more competitive markets but increased for Asian/Pacific Islander adults versus White adults. Differences in the probability of a PQI admission between adults covered by Medicaid and self-pay/no-pay adults versus commercially insured adults increased, while differences for adults covered by Medicare decreased.</p><p><strong>Conclusions: </strong>Hospital market concentration may have heterogeneous effects on the quality of care by patient race/ethnicity and payer. Because market concentration may impact equity, regulators should consider accounting for health equity impacts in merger reviews.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Spatial Epidemiologic Analysis of Opioid Use Disorder Treatment in New York State.","authors":"Ashly E Jordan, Weihui Zhang, Sarah Gorry, Andrew Heck, Constance Burke, Chinazo O Cunningham","doi":"10.1097/MLR.0000000000002142","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002142","url":null,"abstract":"<p><strong>Background: </strong>Opioid agonist treatments (OAT; methadone and buprenorphine) for opioid use disorder (OUD) reduce overdose death by more than 50%. Low population-level rates of OAT are missed opportunities to reduce OUD-related mortality.</p><p><strong>Objective: </strong>We examined county-level OAT utilization patterns to guide state-level and county-level initiatives to improve equitable access and utilization in New York State (NYS).</p><p><strong>Research design: </strong>We calculated NYS county-level methadone and buprenorphine population utilization rates per 100,000 residents by county of patient residence using NYS Office of Addiction Services and Supports and public access datasets.</p><p><strong>Measures: </strong>We mapped rates onto counties and conducted analyses to assess if utilization varied by county, and to identify areas of high utilization (hot spots) and low utilization (cold spots). We used t tests and Fisher exact tests to compare county-level factors.</p><p><strong>Results: </strong>County-level buprenorphine and methadone utilization rates were 673.76 and 132.19 per 100,000 residents, respectively. Buprenorphine hot spot counties had significantly lower proportions of unemployed (-1.4, P-value<0.01), and higher proportions of non-Hispanic white residents (+50.1, P value<0.01) than counties identified as buprenorphine cold spots. Methadone hot spot counties had significantly higher proportions of unemployed (+1.0, P-value<0.01) and lower proportions of non-Hispanic white residents (-48.1, P- value<0.01) than counties identified as methadonecold spots. All buprenorphine cold spot counties were methadone hot spot counties.</p><p><strong>Conclusions: </strong>We found that OAT utilization rates differed by race/ethnicity and socioeconomic factors at the county level consistent with national and other state-level findings. Ensuring equitable OAT access must be part of a coordinated response to address the overdose crisis.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143605489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of the COVID-19 Pandemic on New Physician Job Market Outcomes.","authors":"Tarun Ramesh, David Armstrong, Gaetano J Forte, Marcela Horvitz-Lennon, Fang Zhang, Hao Yu","doi":"10.1097/MLR.0000000000002137","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002137","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the pandemic's impacts on new physician job market outcomes.</p><p><strong>Design: </strong>We conducted a retrospective, repeated cross-sectional analysis using an interrupted time series approach on graduating residents and fellows from New York. We estimated linear probability models to examine binary outcomes and a generalized estimating equations model to analyze the base salary (measured in 2022 dollars). Each model included the following covariates: sex, age, race/ethnicity, educational debt, primary care specialty, international medical graduate status, and citizenship. We also conducted stratification analyses.</p><p><strong>Setting: </strong>Annual data from the 2010 to 2022 Survey of Residents Completing Training in New York.</p><p><strong>Participants: </strong>In all, 31,925 graduating residents and fellows, with 16,612 accepting a job offer to enter the workforce as new physicians, participated in the study.</p><p><strong>Interventions: </strong>COVID-19.</p><p><strong>Results: </strong>Graduating residents and fellows had a 1.58% (95% confidence interval (CI), 0.000, 0.031) increase (86.60% vs. 88.18%) in their likelihood of receiving any job offers, but a 11.64% (95% CI, -0.139, -0.095) decrease (55.77% vs. 44.13%) in reporting a good job market outlook without a significant change in accepting a job offer from pre-pandemic to during the pandemic. New physicians experienced reductions in likelihood of entering rural practice(3.4% vs. 0.62% with a change of -3.38%, 95% CI, -0.046, -0.022), base salary ($288,257 vs. $264,687 with a change of -$23,569, 95% CI, -$28,703, -$18,435), likelihood of receiving additional job-related incentives (69.18% vs. 66.26% with a change of -2.92%, 95% CI,-0.054, -0.0043), and salary satisfaction (86.46% vs. 84.05% with a change of -2.4%, 95% CI, -0.005, -0.043) compared with the pre-pandemic trend.</p><p><strong>Conclusions: </strong>The pandemic significantly reduced new physicians' likelihood of entering rural practice and compensation, disproportionately affecting new primary care physicians. Such reductions may have adverse impacts on health care access, especially in rural areas.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143605493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medicaid Managed Care Penetration and Mental Health Service Use Among Adults.","authors":"Ju-Chen Hu, Janet R Cummings, Xu Ji, Adam S Wilk","doi":"10.1097/MLR.0000000000002105","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002105","url":null,"abstract":"<p><strong>Objective: </strong>To examine the association between Medicaid managed care (MMC) penetration and mental health (MH) service use among Medicaid-enrolled non-elderly adults, with a special focus on those with MH conditions.</p><p><strong>Background: </strong>Medicaid covers over 9 million adults living with MH conditions, with many enrolled in MMC. Despite increases in MMC enrollment over the past decade, nationwide evidence of MMC's association with MH service use during this period is lacking.</p><p><strong>Methods: </strong>Using 2015-2019 National Survey on Drug Use and Health data, we applied logistic and negative binomial regression models to examine the association between MMC penetration and MH service use among 35,500 non-elderly enrollees in 40 MMC states, and separately among 11,800 enrollees with MH conditions. Four dichotomous outcomes separately measured any MH service use in inpatient, outpatient, prescription medication, and any settings. Two additional count outcomes measured the number of inpatient MH stays and outpatient MH visits.</p><p><strong>Results: </strong>A 2-percentage point higher level of MMC penetration was associated with a 9% reduction (adjusted incidence rate ratio = 0.91, 95% CI = 0.87, 0.94) in days of inpatient MH stays among all enrollees and a 7% reduction (adjusted incidence rate ratio= 0.93, 95% CI = 0.87, 0.99) among enrollees with MH conditions. MMC penetration was not associated with significant changes in other outcomes.</p><p><strong>Conclusions: </strong>Among non-elderly adults and those with MH conditions, increased MMC enrollment was associated with reduced inpatient MH services with no significant changes in the use in other settings. Ongoing monitoring is crucial to assess the potential impact of shortened inpatient stays on MH outcomes.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Electronic Health Record Data to Identify Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ+) Veteran Participants in the Pride in All Who Served Program.","authors":"Guneet K Jasuja, Joel I Reisman, Christina Jefferson, Robert B Hall, Raymond G Van Cleve, Teddy Bishop, Heather A Sperry, Michelle C Wilcox, A M Racila, Michelle M Hilgeman","doi":"10.1097/MLR.0000000000002132","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002132","url":null,"abstract":"<p><strong>Background: </strong>Pride in All Who Served (PRIDE) is an intervention in the Veterans Health Administration (VHA) focused on enhancing Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ+) veterans' access to affirmative care services, social support, and engagement with VHA. Evaluation of PRIDE to date has focused on self-report data, missing critical opportunities to examine the impact of this program on health outcomes and utilization indicators detectable in the electronic health record (EHR).</p><p><strong>Objective: </strong>This study is the first to: (a) comprehensively identify a sample of LGBTQ+ veterans who attended PRIDE, and (b) describe the sample demographics, health conditions, and health care utilization.</p><p><strong>Research design: </strong>A retrospective cross-sectional study was conducted using EHR data and staff-reported PRIDE information (eg, site name, facilitator names, dates of delivery). PRIDE-related keywords and chart reviews were used to validate participation and determine the final sample.</p><p><strong>Subjects: </strong>We identified 588 PRIDE participants at 34 VHA sites from 2016 to 2022.</p><p><strong>Measures: </strong>Demographics (eg, age), health conditions (eg, depression), and health care utilization (eg, mental/behavioral health care visits).</p><p><strong>Results: </strong>Nearly half of the PRIDE participants (47%) were women, 75% were transgender and gender diverse, and 37% identified as lesbian or gay. A high proportion of the sample had stress-related health conditions, including depression (63%), hypertension (22%), and posttraumatic stress disorder (48%).</p><p><strong>Conclusions: </strong>PRIDE serves a disproportionate number of women and transgender and gender diverse veterans compared with general VHA users. In the absence of standardized EHR fields, time-intensive methods are required to leverage EHRs to evaluate programs addressing health equity for LGBTQ+ people.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143605503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation.","authors":"Jie Chen, Alice Shijia Yan","doi":"10.1097/MLR.0000000000002110","DOIUrl":"10.1097/MLR.0000000000002110","url":null,"abstract":"<p><strong>Objective: </strong>To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation.</p><p><strong>Background: </strong>AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI's impact on health equity.</p><p><strong>Methods: </strong>We used linked datasets from the 2022 American Hospital Association Annual Survey and the 2023 American Hospital Association Information Technology Supplement. The data were further linked to the 2022 Area Deprivation Index (ADI) for each hospital's service area. State fixed-effect regressions were employed. A decomposition model was also used to quantify predictors of AI/ML implementation, comparing hospitals in higher versus lower ADI areas.</p><p><strong>Results: </strong>Hospitals serving the most vulnerable areas (ADI Q4) were significantly less likely to apply ML or other predictive models (coef = -0.10, P = 0.01) and provided fewer AI/ML-related workforce applications (coef = -0.40, P = 0.01), compared with those in the least vulnerable areas. Decomposition results showed that our model specifications explained 79% of the variation in AI/ML adoption between hospitals in ADI Q4 versus ADI Q1-Q3. In addition, Accountable Care Organization affiliation accounted for 12%-25% of differences in AI/ML utilization across various measures.</p><p><strong>Conclusions: </strong>The underuse of AI/ML in economically disadvantaged and rural areas, particularly in workforce management and electronic health record implementation, suggests that these communities may not fully benefit from advancements in AI-enabled health care. Our results further indicate that value-based payment models could be strategically used to support AI integration.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"63 3","pages":"227-233"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11809723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Community Benefit and Tax-Exemption Levels at Non-Profit Hospitals Across US States.","authors":"D August Oddleifson, Huaying Dong, Rishi K Wadhera","doi":"10.1097/MLR.0000000000002064","DOIUrl":"10.1097/MLR.0000000000002064","url":null,"abstract":"<p><strong>Objective: </strong>To assess the association between state policies and sociodemographic characteristics and state mean fair share spending at non-profit hospitals. Fair share spending is a hospital's charity care and community investment less the estimated value of their tax-exempt status.</p><p><strong>Background: </strong>Hospitals with non-profit status in the United States are exempt from paying taxes. In return, they are expected to provide community benefits by subsidizing medical care for those who cannot pay and investing in the health and social needs of their community.</p><p><strong>Methods: </strong>We used a multivariable linear regression model to determine the association of state-level sociodemographics and policies with state-level mean fair share spending in 2019. Fair share spending data was obtained from the Lown Institute.</p><p><strong>Results: </strong>We found no association between the percentage of people living in poverty, in rural areas, or US region and fair share spending. Similarly, there was no association found for state minimum community benefit and reporting requirements. The state percentage of racial/ethnic minorities was associated with higher mean fair share spending [+$1.48 million for every 10% increase (95% CI: 0.01 to 2.96 million)]. Medicaid expansion status was associated with a 6.9-million-dollar decrease (95% CI: -10.4 to -3.3 million).</p><p><strong>Conclusions: </strong>State-level community benefit policies have been ineffective at raising community benefit spending to levels comparable to the value of non-profit hospital tax-exempt status.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"222-226"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142391711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Not-For-Profit Hospital Community Benefit Spending: Transparency, Accountability, and Fair Share Spending.","authors":"Kelsey Owsley, Tatiane Santos","doi":"10.1097/MLR.0000000000002118","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002118","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"63 3","pages":"189-192"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Alternative Approaches to Using Race-and-Ethnicity Data in Estimating Differences in Health Care and Social Determinants of Health.","authors":"Steven C Martino, Jacob W Dembosky, Katrin Hambarsoomian, Amelia M Haviland, Robert Weech-Maldonado, Megan K Beckett, Torrey Hill, Marc N Elliott","doi":"10.1097/MLR.0000000000002108","DOIUrl":"10.1097/MLR.0000000000002108","url":null,"abstract":"<p><strong>Objective: </strong>The objective of this study was to compare 2 approaches for representing self-reported race-and-ethnicity, additive modeling (AM), in which every race or ethnicity a person endorses counts toward measurement of that category, and a commonly used mutually exclusive categorization (MEC) approach. The benchmark was a gold-standard, but often impractical approach that analyzes all combinations of race-and-ethnicity as distinct groups.</p><p><strong>Methods: </strong>Data came from 313,739 respondents to the 2021 Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys who self-reported race-and-ethnicity. We used regression to estimate how accurately AM and MEC approaches predicted racial-and-ethnic differences in 5 CAHPS patient experience measures and 4 patient characteristics that we considered proxies for social determinants of health (SDOH): age, educational attainment, and self-reported general and mental health. We calculated average residual error proportions for AM and MEC estimates relative to all-combination estimates.</p><p><strong>Results: </strong>In predicting CAHPS scores by race-and-ethnicity, on average 0.9% of the variance across groups in the AM and MEC approaches represented a departure from the gold standard. In predicting proxy SDOH variables, on average 4.7% of the AM variance across groups and 7.1% of the MEC variance across groups represented departures from the gold standard.</p><p><strong>Conclusion: </strong>Researchers may want to consider AM over MEC when modeling outcomes by race-and-ethnicity given that AM outperforms MEC in predicting racial-and-ethnic differences in proxy SDOH characteristics and is comparably accurate in predicting differences in patient experience. Unlike MEC, AM does not assume that every multiracial person has similar outcomes and that Hispanic persons have similar outcomes irrespective of race.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"241-248"},"PeriodicalIF":3.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142910009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}