Medical Care最新文献

{"title":"Geographic Variation in Missing Race and Ethnicity Data in Minimum Data Set 3.0.","authors":"Jennifer Tjia, Francesca L Troiani, Anna Wyndham, Sruthi Tanikella, Joshua Rumbut, Maira A Castaneda-Avila","doi":"10.1097/MLR.0000000000002335","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002335","url":null,"abstract":"<p><strong>Background: </strong>Race and ethnicity measures in administrative data can vary geographically. The extent of this challenge in US nursing homes is not well described.</p><p><strong>Objectives: </strong>To describe geographic variation in missing race and ethnicity data in the Minimum Data Set (MDS) 3.0 and Medicare claims, and to compare discrepancies across data sources.</p><p><strong>Research design: </strong>Cross-sectional study.</p><p><strong>Subjects: </strong>Medicare beneficiaries with MDS 3.0 records between 2014 and 2018. The Medicare Beneficiary Summary File provided demographic information.</p><p><strong>Measures: </strong>Missingness of MDS race and ethnicity data by state, and misclassification of Medicare race and ethnicity enrollment database (EDB) and Research Triangle Institute (RTI) variables compared with MDS. We calculate the sensitivity, specificity, and positive predictive value of the EDB and RTI variables relative to the MDS.</p><p><strong>Results: </strong>Among 18.1 million nursing home residents pooled across 2014-2018, geographic variation in missing race and ethnicity in the MDS 3.0 ranged from 1.2% to 14.7%. Compared with MDS, misclassification of residents classified as Hispanic in MDS ranged from 48.1% to 89.2% for EDB and 0.5% to 44.8% for RTI. Misclassification of residents classified as Asian American/Pacific Islander in MDS ranged from 29.4% to 77.2% for EDB and 12.7% to 65.4% for RTI. Misclassification of residents classified as Black ranged from 0% to 14.2% for EDB and 0% to 16.2% for RTI. Overall, the RTI variables provided better sensitivity and specificity of race and ethnicity than the EDB.</p><p><strong>Conclusion: </strong>Missing race and ethnicity data in the MDS varies geographically, as do discrepancies between MDS and EDB and RTI variables. Thoughtful consideration of these issues is recommended when handling missing MDS race and ethnicity data.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839887","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 Home-Based and Community-Based Services Long-Term Care Expenditures: Evaluation of the Balancing Incentive Program.","authors":"Bonnie Ghosh-Dastidar, Michael W Robbins, Esther M Friedman, Nabeel Qureshi, Regina A Shih","doi":"10.1097/MLR.0000000000002333","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002333","url":null,"abstract":"<p><strong>Objective: </strong>The Balancing Incentive Program (BIP), legislated in the 2010 Affordable Care Act, offered states financial incentives to increase access to Medicaid home-based and community-based services (HCBS). Despite the major infrastructure changes required by BIP, no evaluation to date has quantified the increase in spending attributable to BIP, which is of concern to Medicaid HCBS policymakers, providers, and consumers. This is the first causal estimate of BIP's effects, including the timing of implementation in each state, compared with a counterfactual.</p><p><strong>Design: </strong>Using state-level expenditure data, we estimated the change in HCBS spending as a percentage of LTSS spending in 17 BIP participant states compared with a counterfactual or synthetic control calculated as a weighted average of the outcome in 17 BIP eligible, nonparticipant states. Synthetic control weights were estimated using pre-BIP characteristics. To assess how BIP effects evolved over time, we estimated cumulative change in the outcome in multiple post-BIP years (2013, 2016, and 2019).</p><p><strong>Results: </strong>Our primary analysis indicates that cumulatively from FY 2013 to 2019, BIP states increased their HCBS spending as a percentage of LTSS spending by an average of 5.2 percentage points (95% CI: 0.0, 9.8), compared with the synthetic control.</p><p><strong>Implications: </strong>Although many state-run programs have sought to increase HCBS access, our study's causal estimate of BIP effects in 17 states, compared with 17 states that did not, represents a more substantial growth than findings of prior studies.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147839942","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":"Validating ICD-10 Algorithms for Identifying Patient Safety Indicators Through 10,655 Charts Review.","authors":"Guosong Wu, Jie Pan, Danielle A Southern, Cheligeer Cheligeer, Yuan Xu, Cathy A Eastwood, Hude Quan","doi":"10.1097/MLR.0000000000002300","DOIUrl":"10.1097/MLR.0000000000002300","url":null,"abstract":"<p><strong>Background: </strong>Patient Safety Indicators (PSIs) derived from administrative data are widely used for monitoring and improving hospital care quality. However, the validity of ICD-10-based PSI algorithms remains uncertain, particularly in terms of their sensitivity and specificity.</p><p><strong>Objectives: </strong>To evaluate the diagnostic performance of ICD-10-CA-based algorithms for identifying fifteen PSIs using chart review as the reference standard.</p><p><strong>Research design: </strong>Multicenter retrospective cohort validation study.</p><p><strong>Subjects: </strong>A random sample of 10,665 adult patients admitted to 4 acute care hospitals in Calgary, Alberta, between January 1, 2017, and March 31, 2022.</p><p><strong>Measures: </strong>Fifteen PSIs were identified using ICD-10-CA codes and validated against detailed chart reviews. Diagnostic performance was measured using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy. Analyses were stratified by diagnosis code type and relevant patient characteristics.</p><p><strong>Results: </strong>Among 10,665 patients, 1688 had at least one PSI confirmed by chart review. ICD-10-CA coding detected any PSI with 67.0% sensitivity (95% CI, 64.7%-69.2%), 72.8% specificity (95% CI, 71.8%-73.7%), 31.6% PPV (95% CI, 30.1%-33.1%), 92.2% NPV (95% CI, 91.5%-92.8%), and 71.8% accuracy (95% CI, 71.0%-72.7%). Restricting PSIs to conditions that occurred after admission (limited diagnosis type II code) improved specificity (95.7%; 95% CI, 95.3%-96.1%) and PPV (56.5%; 95% CI, 53.2%-59.7%) but reduced sensitivity (29.6%; 95% CI, 27.4%-31.8%). Validity varied by PSI and patient characteristics, with higher sensitivity and PPV among older adults, males, and those with greater comorbidity, longer hospital and ICU stays, 30-day readmission, or in-hospital death.</p><p><strong>Conclusions: </strong>ICD-10 coded administrative data demonstrate high specificity and NPV but varied sensitivity and PPV in identifying PSIs. Restricting to type II codes improves PPV but reduces sensitivity. Tailoring coding strategies to specific surveillance or quality improvement goals is critical.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"310-317"},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147284367","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":"Award Winning Manuscripts From the American Public Health Association 2024.","authors":"Linda D Green, Katherine S Virgo","doi":"10.1097/MLR.0000000000002303","DOIUrl":"10.1097/MLR.0000000000002303","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 5","pages":"258-259"},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147627080","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 Qualitative Study of the Implementation of Referral Coordination for Specialty Care Referrals in the Veterans Health Administration.","authors":"Anna Zogas, Varsha G Vimalananda, Megan B McCullough, Amy M Linsky, Leslie J Chatelain, Kristin M Mattocks","doi":"10.1097/MLR.0000000000002297","DOIUrl":"10.1097/MLR.0000000000002297","url":null,"abstract":"<p><strong>Background: </strong>Veteran enrollees of the Veterans Health Administration (VA) have increasing options for where and how to access health care, including within VA in person or virtually and through VA-purchased community care. To promote Veterans' informed choice and streamline access to appointments, VA initiated referral coordination, which entails clinical review and conversations with Veterans before scheduling all specialty care referrals.</p><p><strong>Objective: </strong>Identify how VA facilities implemented referral coordination and local contextual factors influencing the implementation.</p><p><strong>Research design: </strong>Qualitative formative evaluation, using process maps to compare implementation approaches by hospital system and thematic analysis to identify contextual influences on implementation.</p><p><strong>Subjects: </strong>Between March and August 2022, we interviewed VA referral coordinators (n=27) for acupuncture, cardiology, endocrinology, and hematology/oncology at 8 VA hospital systems in a geographic region with urban and rural settings.</p><p><strong>Results: </strong>We identified 2 implementation approaches for referral coordination. Three facilities added clinical review to employees' existing responsibilities (\"expanders\"), and 5 created new roles dedicated to referral coordination (\"creators\"). \"Expander\" facilities relied minimally on VA-purchased care and received little implementation support from local leadership. \"Creator\" facilities relied heavily on VA-purchased care and local leadership was actively involved in implementation. The effort employees dedicated to referral coordination tasks varied according to other demands on their time.</p><p><strong>Conclusions: </strong>This work provides an empirically grounded way to identify different implementation approaches (ie, expanders and creators), which we conceptualize as a framework for interpreting the outcomes of referral coordination on waiting times, utilization of different types of care, and Veterans' experiences seeking care.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"326-334"},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147481047","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":"In Memory of Dr Julie M. Zito (1943-2025): Scientist, Advocate, Mentor, Colleague.","authors":"Jeroan J Allison, Catarina I Kiefe","doi":"10.1097/MLR.0000000000002304","DOIUrl":"10.1097/MLR.0000000000002304","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 5","pages":"257"},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147627036","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":"Long-Term Impact of Medicaid Expansion on Not-for-Profit Hospital Community Benefit Spending in the Southern United States.","authors":"Shelby Olin, Kelsey Owsley, Charles Stoecker, Tatiane Santos","doi":"10.1097/MLR.0000000000002250","DOIUrl":"10.1097/MLR.0000000000002250","url":null,"abstract":"<p><strong>Objective: </strong>To examine the long-term impact of Medicaid expansion on not-for-profit (NFP) hospital community benefit (CB) spending among hospitals located in Arkansas and Kentucky (9 y postexpansion), Louisiana (7 y postexpansion), and Alabama, Mississippi, Tennessee, and Texas (nonexpansion states).</p><p><strong>Background: </strong>To maintain tax-exemption status, NFP hospitals must provide CB, such as charity care and population health initiatives. In the short term, Medicaid expansion has led to decreased charity care and increased Medicaid shortfalls, but no change to other CB categories. Given these early findings and Medicaid expansion's impact on hospital finances, it is important to understand whether hospitals continued to adjust their CB spending.</p><p><strong>Methods: </strong>We used data on hospital CB spending (2011-2022), for NFP hospitals located in the West South Central and East South Central Census divisions. States that expanded Medicaid formed the treatment group (69 hospitals) and nonexpansion formed the control group (90 hospitals). We used staggered difference-in-differences and event study designs to examine changes in total CB, clinical, and population health spending as a share of operating expenses.</p><p><strong>Results: </strong>We found that Medicaid expansion was associated with a decrease in total CB spending by ∼$782,000 per hospital ( P =0.01). Clinical and population health spending decreased by ∼$759,000 ( P =0.01) and $92,000 per hospital ( P =0.009), respectively.</p><p><strong>Conclusion: </strong>Among southern hospitals, Medicaid expansion led to sustained long-term reductions in CB spending. Our findings suggest that states may need to implement CB laws to encourage hospitals to invest more in their communities.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"260-267"},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12854402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145715042","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":"Impacts of a Rural Hospital Global Budget Alternative Payment Model on Patterns of Cancer Surgery.","authors":"Lindsay M Sabik, Jacob Whitman, Donald S Bourne, Zhaojun Sun, Eric T Roberts, Jonathan G Yabes, Manisha Bhattacharya, David Bartlett, Jeremy M Kahn, Bruce L Jacobs","doi":"10.1097/MLR.0000000000002336","DOIUrl":"10.1097/MLR.0000000000002336","url":null,"abstract":"<p><strong>Background: </strong>Rural patients often experience barriers accessing high-quality surgical care. The Pennsylvania Rural Health Model (PARHM) aimed to improve rural health through all-payer hospital global budgets and transformation plans, which may influence hospitals' incentives and capacity to provide various surgical services, including cancer surgery.</p><p><strong>Objectives: </strong>Examine the association between PARHM and patterns of cancer surgery overall, by timing of entry into PARHM, and by cancer type.</p><p><strong>Research design: </strong>Stacked difference-in-differences (DID) models including hospital service area (HSA)-level propensity score weights, comparing patients living in HSAs with hospitals participating in PARHM to those in HSAs with eligible nonparticipating hospitals.</p><p><strong>Subjects: </strong>Patients in eligible HSAs who had surgery between 2016 and 2023 for one of 11 cancers with evidence of surgical volume-outcome relationships.</p><p><strong>Measures: </strong>Surgery at a high-volume, Commission on Cancer (CoC) accredited, or National Cancer Institute (NCI)-designated hospital, and travel distance to the surgical hospital.</p><p><strong>Results: </strong>The sample included 22,728 cancer surgeries for patients across 60 HSAs. Pooled estimates indicate no statistically significant differential changes in outcomes. In HSAs served by the 2019 cohort of PARHM hospitals (smaller and more remote facilities), PARHM was associated with a differential increase in surgery at CoC hospitals (DID estimate: 8.7 percentage points, 95% CI: 1.5- 16.0). We observed differential increases in surgery at CoC hospitals for colon and rectal cancers, and decreases in surgery at CoC and high-volume hospitals for liver cancer and at NCI centers for bladder cancer.</p><p><strong>Conclusion: </strong>PARHM had limited overall effects on surgical cancer care, with some variation across hospitals and cancer types.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147816775","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":"The Impact of the COVID-19 Pandemic on Registered Nurse Employment Across Settings.","authors":"Charlotte Ahr, Claudia Gates, Thuy D Nguyen, Christopher R Friese, Milisa Manojlovich, Matthew A Davis","doi":"10.1097/MLR.0000000000002294","DOIUrl":"10.1097/MLR.0000000000002294","url":null,"abstract":"<p><strong>Background: </strong>It is unknown whether the stress of the COVID-19 pandemic, which had a particular impact on inpatient and long-term care (LTC) nurses, had an effect on nurses' choice of employment settings.</p><p><strong>Objective: </strong>Determine whether the COVID-19 pandemic contributed to changes in nurses' choice of employment setting.</p><p><strong>Methods: </strong>This study used data from the 2018 and 2022 National Sample Survey of Registered Nurses to conduct a difference-in-difference analysis. We constructed a state-level measure of COVID-19 caseload, defined as COVID-19 cases per hospital bed; High versus Low COVID-19 states were defined as those above versus below the median, respectively. Logistic regression models were used to estimate the effect of exposure to High COVID-19 caseload (vs. Low) and time (2022 vs. 2018) on nurse employment choices across inpatient, LTC, outpatient, and nonclinical settings.</p><p><strong>Results: </strong>From 2018 to 2022, the size of the US nursing workforce grew from 3.27 to 3.57 million nurses; however, RN FTEs increased in outpatient settings and decreased in all other settings. In adjusted analyses, nurses were less likely to work in LTC settings in 2022 than in 2018; yet, those exposed to High COVID-19 caseloads were 0.9% (95% CI: 0.3-1.5) more likely to work in LTC than those exposed to Low COVID-19 caseloads. Differences between High versus Low COVID-19 caseload exposure were not statistically significant for the likelihood of working in inpatient, outpatient, and nonclinical settings.</p><p><strong>Conclusions: </strong>Our findings suggest that exposure to High COVID-19 caseload was not associated with changes in nurses' employment settings.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"274-282"},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146119522","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":"Spatiotemporal Disparities in Stroke Mortality From 1969 to 2020, by Race and Sex, in Tennessee.","authors":"Shongkour Roy, Fawaz Mzayek, Ashish Joshi, Xinhua Yu","doi":"10.1097/MLR.0000000000002277","DOIUrl":"10.1097/MLR.0000000000002277","url":null,"abstract":"<p><strong>Background: </strong>Tennessee ranks sixth in stroke mortality in the United States. Yet the patterns of stroke mortality vary significantly across counties and over time.</p><p><strong>Objectives: </strong>This study aims to examine spatiotemporal disparities of stroke mortality at the county level in Tennessee from 1969 to 2020.</p><p><strong>Research design: </strong>A population-based study using the national vital statistics system of stroke mortality data through the Surveillance, Epidemiology, and End Results and National Center for Health Statistics (SEER-NCHS) database.</p><p><strong>Subjects: </strong>Patients older than 35 years who died from stroke in Tennessee from 1969 to 2020.</p><p><strong>Methods: </strong>Data from the SEER-NCHS were aggregated into 4 periods (1969-1980, 1981-1992, 1993-2004, and 2005-2020), and age-adjusted stroke mortality rates were calculated by county and by race and sex for each time period.</p><p><strong>Results: </strong>The stroke mortality rates in Tennessee declined by 35.6%, 28.3%, 7.1%, and 24.4% in 1969-1980, 1981-1992, 1993-2004, 2005-2020, respectively. The degree of decline varied by race and sex groups. In the first 2 periods, the largest decline in stroke mortality was observed among Black women (43.2% and 31.5%). During 1993-2004, the largest decline was observed among Black men (22.4%), while the largest decline was observed among white women during 2005-2020 at 25.5%. There were urban-rural disparities in stroke mortality across counties and over the 4 periods. In general, urban and rural mortality rates were similar from 1969 to 1992; however, a substantial decline (24.1%) was observed in urban counties during 1993-2004, while a larger decline (34.6%) occurred later in rural counties during 2005-2020. County-level variations in stroke mortality were also evident across the 4 periods.</p><p><strong>Conclusion: </strong>Substantial disparities in stroke mortality by counties and race-sex subgroups persisted over the past 5 decades. The disease burden was clustered in a few counties and disproportionately higher among vulnerable populations.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"268-273"},"PeriodicalIF":2.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145857106","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}