Medical Care最新文献

{"title":"Defining Pharmacy Access: A Review of Pharmacy-Level Research in the United States.","authors":"Emeka Elvis Duru, Kenechukwu C Ben-Umeh, Kelly E Anderson, T Joseph Mattingly","doi":"10.1097/MLR.0000000000002196","DOIUrl":"10.1097/MLR.0000000000002196","url":null,"abstract":"<p><strong>Background: </strong>Access to pharmacy services is a critical determinant of health care equity, as it directly impacts medication adherence, chronic disease management, and overall health outcomes. Despite the important role of community pharmacies in the United States, disparities in access persist, particularly among rural, minority, and low-income populations. However, there is no consensus on how pharmacy access should be defined or measured, and how these definitions relate to health outcomes.</p><p><strong>Objective: </strong>This review evaluates how pharmacy access is defined and measured in US-based studies and examines its implications on health outcomes, quality of care, and health care costs.</p><p><strong>Methods: </strong>We conducted a scoping review of US-based studies published over the past 20 years, identifying patterns in definitions and measurements of pharmacy access, as well as associated health outcomes. The review followed Arksey and O'Malley's framework and PRISMA-ScR guidelines.</p><p><strong>Results: </strong>Sixteen studies met the inclusion criteria, most of which used cross-sectional designs. Definitions of pharmacy access varied, with metrics including distance to the nearest pharmacy, pharmacy density, and the concept of pharmacy deserts. Limited pharmacy access was associated with lower medication adherence, poorer chronic disease management, increased health care costs, and higher hospitalization rates.</p><p><strong>Conclusions: </strong>Efforts to improve pharmacy access should focus on standardizing measurement approaches and implementing targeted interventions to sustain pharmacies in underserved areas. These strategies have the potential to enhance medication adherence, reduce health care costs, and address health disparities across vulnerable communities.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"63 10","pages":"758-763"},"PeriodicalIF":2.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069506","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":"Florida's \"Live Healthy\" Legislation: Implications for Financing \"Nonemergent\" Emergency Care.","authors":"Theodore A Lee, Martin Wegman, Arjun K Venkatesh, Ryan Koski-Vacirca, Kristen Panthagani, Craig Rothenberg, Alexander Janke, Ula Hwang, Cameron J Gettel","doi":"10.1097/MLR.0000000000002225","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002225","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199851","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":"Comparing the Clinical Work of Advanced Practice Professionals Working Within and Outside of Accountable Care Organizations.","authors":"Hannah T Neprash, John F Mulcahy","doi":"10.1097/MLR.0000000000002224","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002224","url":null,"abstract":"<p><strong>Background: </strong>Health care delivery organizations increasingly employ advanced practice professionals (APPs) and participate in alternative payment models such as accountable care organizations (ACOs). Given the former's incentive to constrain spending, APPs' practice patterns may vary in ACO-participating versus non-ACO practices.</p><p><strong>Objectives: </strong>To compare outpatient care provided by APPs and physicians through ACO participation.</p><p><strong>Research design: </strong>We used multivariate linear regression to compare measures of workload allocation and billing across ACO-participating and non-ACO practices in 2022, controlling for practice size and market.</p><p><strong>Subjects: </strong>A total of 91,149 practices, 12,072 in a Medicare Shared Savings Program ACO in 2022.</p><p><strong>Measures: </strong>We used 100% fee-for-service Medicare claims to identify ACO-participating and non-ACO practices. For every practice, we calculated the share of outpatient encounters provided by APPs rather than physicians and the share of APP-provided encounters billed indirectly to Medicare. We also calculated the share of annual wellness visits, chronic condition care management services, transitional care management services, and postoperative visits provided by APPs.</p><p><strong>Results: </strong>APPs provided a smaller share of outpatient encounters at ACO-participating versus non-ACO practices, but were more likely to bill indirectly. Among most categories of routine services (eg, annual wellness visits and chronic condition management), APPs provided a smaller share of services at ACO-participating versus non-ACO practices. In the largest quartile of practices, APP practice patterns were more similar across ACO-participation status, and indirect billing was less likely within ACOs.</p><p><strong>Conclusions: </strong>Findings provide little evidence that ACOs deploy their APP workforce in a more cost-conscious manner than non-ACOs.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199836","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":"Using Self-Identified Gender Identity Data to Advance Health Equity Among Transgender and Gender Diverse Veterans in the Veterans Health Administration.","authors":"Guneet K Jasuja, Mark S Zocchi, Joel I Reisman, Julianne E Brady, Nicholas A Livingston, John R Blosnich, Varsha G Vimalananda, Rajinder S Singh, Michael Goodman, Michael J Silverberg, Jolie B Wormwood, Jillian C Shipherd","doi":"10.1097/MLR.0000000000002195","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002195","url":null,"abstract":"<p><strong>Background: </strong>Identification of transgender and gender diverse (TGD) people has been limited to diagnoses and text rather than self-identified gender identity (SIGI), representing a subset of TGD people. In 2017, the Veterans Health Administration (VHA) implemented SIGI, allowing for precise identification of TGD veterans, including subgroups (transgender man, transgender woman, and nonbinary).</p><p><strong>Objectives: </strong>Health conditions, adverse social determinants of health (SDOH), and health care utilization were compared among veterans (1) identified by SIGI only, both SIGI and diagnosis/text, diagnosis/text only (ie, without SIGI), and (2) SIGI subgroups.</p><p><strong>Research design: </strong>Cross-sectional.</p><p><strong>Subjects: </strong>Twenty thousand seventy-nine TGD VHA patients from 2019 to 2023; SIGI only (n=5523), both SIGI and diagnosis/text (n=4066), and without SIGI (n=10,490).</p><p><strong>Measures: </strong>Health conditions, adverse SODH and health care utilization.</p><p><strong>Results: </strong>In adjusted models, SIGI only veterans were less likely to have documentation of depression (32.4% vs. 60.7% vs. 54.8%), post-traumatic stress disorder (PTSD; 23.5% vs. 41.4% vs. 37.5%), housing instability (8.8% vs. 21.5% vs. 16.1%), unemployment/financial problems (10.5% vs. 23.8% vs. 19.0%), and mental health visits (72.5% vs. 97.7% vs. 95.2%) compared with those with both SIGI and diagnosis/text and without SIGI. Health conditions were more similar across the diagnosis groups (i.e. both SIGI and diagnosis/text and without SIGI). Among veterans with SIGI data, we identified 49% transgender women, 38% transgender men, and 14% nonbinary veterans without many differences across subgroups. In adjusted models, more nonbinary veterans than transgender women and transgender men had documentation of alcohol use disorder (10.1% vs. 6.1% vs. 7.5%), depression (62.3% vs. 42.6% vs. 47.0%), PTSD (45.9% vs. 27.4% vs. 33.5%), mental health visits (96.7% vs. 89.1% vs. 91.9%), and experienced unemployment/financial problems (21.3% vs. 16.9% vs. 14.7%).</p><p><strong>Conclusions: </strong>Without diagnosis, SIGI enables the identification of healthier TGD veterans. Regardless of SIGI, diagnosis signals much higher rates of health concerns. SIGI data facilitates understanding veteran subgroups, informing TGD policy and practice.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145149778","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":"Commentary on: Postoperative Complications and Readmission Rates in Robotic-Assisted and Manual Total Hip Arthroplasty: A Large, Multi-Hospital Study.","authors":"David C Ayers","doi":"10.1097/MLR.0000000000002171","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002171","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131215","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":"Empowering Patients to Make Goal-Aligned Decisions in Unhealthy Information Environments.","authors":"Laura J Faherty, David A Scales","doi":"10.1097/MLR.0000000000002215","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002215","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124775","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":"Impact of VA's Clinical Resource Hub Primary Care Telehealth Program on Health Care Use and Costs.","authors":"Kritee Gujral, Jennifer Y Scott, Clara E Dismuke-Greer, Hao Jiang, Samantha Illarmo, Emily Wong, Adam Chow, Jean Yoon","doi":"10.1097/MLR.0000000000002206","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002206","url":null,"abstract":"<p><strong>Background: </strong>Telehealth can improve health care access in underserved areas. Hub-and-spoke-models, wherein providers in regional hubs deliver care through telehealth to patients visiting local \"spoke\" clinics, can improve access. However, cost impacts of this model are unknown.</p><p><strong>Objective: </strong>Evaluate the utilization and cost impacts of VA's Clinical Resource Hub program for primary care (CRH-PC), a hub-and-spoke-model.</p><p><strong>Design: </strong>Adjusted difference-in-difference and event study analyses comparing patients at program-sites who used CRH-PC services with patients who never used CRH-PC services, prepost program adoption, fiscal years 2018-2021. We also compared all patients at CRH-PC sites versus at non-CRH-PC sites to assess site-wide impacts.</p><p><strong>Participants: </strong>CRH-PC sites: 164 sites and 1,546,892 patients; Non-CRH-PC sites: 704 sites and 4,062,797 patients.</p><p><strong>Measures: </strong>Costs and number of VA-provided and VA-purchased primary, emergency, and acute inpatient care visits.</p><p><strong>Results: </strong>At CRH-PC sites, 64,973 patients (4%) used CRH-PC services. Rural patients, African-American patients, and patients with greater comorbidities had higher odds of receiving program services. Program exposure was associated with an 18% increase in primary care visits (+0.7) and $612 per program-user per year. Comparing all patients (users and nonusers) at program-sites versus nonprogram sites, we found no impact, except video-based care more often replaced in-person services at program-sites.</p><p><strong>Conclusions: </strong>Among program-users, VA's CRH-PC increased mean primary care visits and VA costs, but as only 4% of patients at program-clinics were program-users, there were no differences in overall cost or utilization between program and nonprogram clinics. Findings suggest clinics can offer primary care telehealth services to high-need populations without affecting clinic-level costs, but costs should be monitored upon wider adoption.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145113517","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":"Prescriber-Level Responses to the 2018-2019 Valsartan, Irbesartan, and Losartan Recalls and Drug Shortages: A National Study.","authors":"Katherine Callaway Kim, Julie M Donohue, Eric T Roberts, Chester B Good, Lindsay M Sabik, Katie J Suda","doi":"10.1097/MLR.0000000000002209","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002209","url":null,"abstract":"<p><strong>Background: </strong>Global shortages for 3 angiotensin receptor-II blockers (ARBs)-valsartan, losartan, and irbesartan-occurred in 2018-2019 after recalls due to ingredient impurities. Provider-level responses to the ARB shortages in the United States and spillovers to other antihypertensive classes are unknown.</p><p><strong>Objective: </strong>To estimate changes in provider-level prescribing for ARBs and non-ARB antihypertensives up to 18 months after the 2018-2019 recalls and shortages.</p><p><strong>Research design: </strong>National cohort study of prescribers using all-payer pharmacy claims. Mixed interrupted time series models quantified changes in prescribing postshortages and heterogeneous changes by specialty, region, medical school graduation cohort, sex, and level of prerecall prescribing.</p><p><strong>Patients and methods: </strong>Active providers exposed to the 2018-2019 valsartan, irbesartan, and losartan shortages (defined as top-25th percentile for these drugs in 2017).</p><p><strong>Measures: </strong>Within-class changes in prescribing for ARBs (recalled and nonrecalled). Between-class substitutions to non-ARB antihypertensives (ACE-Is, alpha- and beta-adrenergic blockers, calcium channel blockers, diuretics, and other agents).</p><p><strong>Results: </strong>Among 138,032 prescribers who met the inclusion criteria, per-prescriber fills for valsartan decreased by 57%-59% after it was recalled in July 2018. We observed concurrent increases for losartan and irbesartan fills and no change in overall ARB prescribing. There were no significant changes in fills for ACE-Is or for other antihypertensives. Absolute decreases in valsartan fills were greatest among providers with higher levels of prescribing at baseline. However, relative changes did not differ by prescriber characteristics.</p><p><strong>Conclusions: </strong>In this prescriber level, national study, substitutions to other ARBs mitigated decreases in valsartan fills after it was recalled. There were no spillovers to non-ARB anti-hypertensives. The availability of close substitutes during drug shortages may mitigate gaps in access for prescribers and their patients.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092089","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":"Compliance With Recommendations of the Surveillance, Epidemiology, and End Results (SEER) Treatment Data Use Agreement: A Review of Published Studies.","authors":"Yoon Duk Hong, Angela B Mariotto, Denise R Lewis, Anne-Michelle Noone, Nadia Howlader, Steve Scoppa, Eric J Feuer","doi":"10.1097/MLR.0000000000002218","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002218","url":null,"abstract":"<p><strong>Introduction: </strong>The Surveillance, Epidemiology, and End Results (SEER) Program collects data on the first course of cancer treatment, but no and unknown receipt of treatment cannot be distinguished for radiation therapy (RT) and chemotherapy. As part of the Data Use Agreement (DUA), users must acknowledge that they understand the data limitations and agree to include a description of the limitations in any analyses published using the data. The objective of this review was to evaluate users' compliance with the recommendations of the DUA.</p><p><strong>Methods: </strong>Publications from a PubMed search were matched with the names of SEER treatment data users, and keywords were applied to identify relevant studies. Five reviewers (with 2 per publication) independently assessed if the authors (a) conducted analyses supported by these data, (b) correctly labelled no/unknown treatment as \"no/unknown\", and (c) described the limitations of their use. Publications were classified as \"followed recommendations\", \"partially followed recommendations\", or \"did not follow recommendations\" of the DUA.</p><p><strong>Results: </strong>Among a total of 120 studies included in the review, 106 (88.3%) studies did not follow recommendations, 11 (9.2%) partially followed recommendations, and 3 studies (2.5%) followed recommendations. Only 11.7% of publications correctly labelled the \"no/unknown\" category as \"no/unknown\", and described the limitations associated with the no/unknown issue.</p><p><strong>Conclusions: </strong>In this review, we found substantial misuse of the SEER treatment data and limited acknowledgement of the limitations of the SEER treatment data in publications. Such findings highlight the need to think of effective ways of encouraging appropriate use of the treatment data.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124790","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":"Grading Hospitals Using Multivariate Matching.","authors":"Jeffrey H Silber, Paul R Rosenbaum, Joseph G Reiter, Omar I Ramadan, Siddharth Jain, Alexander S Hill, Katherine Brumberg, Lee A Fleisher","doi":"10.1097/MLR.0000000000002188","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002188","url":null,"abstract":"<p><strong>Background and objectives: </strong>To improve upon existing hospital grading systems, we developed a new report card based on multivariate matching.</p><p><strong>Research design: </strong>Matched cohorts. For each focal hospital patient, we match 10 control patients treated at \"well-resourced\" hospitals with excellent hospital characteristics from across the nation, and 10 control patients treated at \"typical\" hospitals, on over 300 patient characteristics from Medicare Claims. Grades were based on outcome differences between patients at the focal hospital and their matched controls. We also create an \"Analogous\" match that is comprised of multiple control patients matched to each focal hospital patient with similar patient characteristics who were treated at hospitals with similar characteristics to the focal hospital, answering the question, \"How would patients who looked like my patients and who were treated at hospitals like my hospital fare, compared to how my patients fared.\" We also report outcomes by multimorbidity status.</p><p><strong>Subjects: </strong>Medicare admissions from 2017 to 2019 for heart attack, heart failure and pneumonia. To illustrate our methods, we report on 4 hospitals in the same region: a well-known \"Flagship\" teaching Hospital, an Affiliated Hospital within the same flagship system, a Poor-Performing Hospital that is not part of the flagship system, and a Small Hospital with unstable estimates.</p><p><strong>Measures: </strong>Thirty-day mortality and revisit rates.</p><p><strong>Results: </strong>Report cards for each example hospital.</p><p><strong>Conclusions: </strong>Matched report cards allow users to better benchmark hospitals and see those types of patients where a specific hospital is performing poorly compared to other hospitals treating very similar patients.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145091918","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}