Health Services Research最新文献

{"title":"Evaluating equity in a national virtual care management intervention: Delivery and outcomes by race/ethnicity among Veterans with hypertension and diabetes","authors":"Leah M. Marcotte MD, MS,&nbsp;Chelle L. Wheat PhD, MPH,&nbsp;Mayuree Rao MD, MS,&nbsp;Edwin S. Wong PhD,&nbsp;Paul Hebert PhD,&nbsp;Karin Nelson MD, MSHS,&nbsp;Jorge Rojas MS,&nbsp;Eric J. Gunnink MS,&nbsp;Ashok Reddy MD, MS","doi":"10.1111/1475-6773.14352","DOIUrl":"10.1111/1475-6773.14352","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To evaluate whether the Preventive Health Inventory (PHI)—a virtual care management intervention addressing hypertension and diabetes management implemented nationally in the Veterans Health Administration (VHA)—was delivered equitably among racial/ethnic groups and if existing inequities in hypertension and diabetes outcomes changed following PHI receipt.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We used data from the VHA Corporate Data Warehouse among Veterans enrolled in primary care nationally from February 28, 2021 to March 31, 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We used logistic regression to evaluate PHI receipt and hypertension and diabetes outcomes after PHI implementation among Veterans with hypertension and/or diabetes. We conducted unadjusted analyses and analyses adjusting for clinic fixed effects using dummy variables.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We identified Veterans engaged in primary care with documented race/ethnicity and hypertension and/or diabetes diagnoses in all months during the study period.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principle Findings</h3>\u0000 \u0000 <p>Prior to PHI, Non-Hispanic Black (NHB) (42.2%) and Hispanic (39.5%) Veterans were less likely to have controlled hypertension vs. Non-Hispanic White (NHW) Veterans (47.5%); NHB Veterans (32.9%) were more likely to have uncontrolled diabetes vs. NHW Veterans (25.1%). Among 1,805,658 Veterans, 5.7% NHW (<i>N</i> = 68,744), 5.6% NHB (<i>N</i> = 22,580), 10.2% Hispanic (<i>N</i> = 13,313), 6.2% Asian/Pacific Islander/Native Hawaiian (<i>N</i> = 1868), 5.1% American Indian/Native Alaskan (<i>N</i> = 744), and 5.6% multiple races or other race (<i>N</i> = 1647) Veterans received PHI. We found no significant racial inequities in PHI receipt in unadjusted and adjusted models. Hypertension and diabetes measures improved more in the intervention group compared with the group who did not receive the intervention. There were no new or worsened inequities after PHI, and in pre-/post-intervention analysis, among NHB Veterans, the inequity in uncontrolled diabetes improved by 1.9 percentage points (95% CI 0.2, 3.6).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings suggest the PHI intervention was equitably deployed across race/ethnicity groups without significantly impacting most existing inequities in diabetes and hypertension.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857168","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":"Information and resources VA health system leaders need to manage enrollment and retention for Post-9/11 veterans","authors":"Todd Brown MSc,&nbsp;Angela Fagerlin PhD,&nbsp;Matthew H. Samore MD,&nbsp;Alex H. S. Harris PhD,&nbsp;Patrick Galyean BS,&nbsp;Susan Zickmund PhD,&nbsp;Warren B. P. Pettey MPH, CPH,&nbsp;Megan E. Vanneman PhD, MPH","doi":"10.1111/1475-6773.14351","DOIUrl":"10.1111/1475-6773.14351","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post-9/11 Veterans' VA enrollment and retention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Interviews conducted from March–May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Interview questions were developed using Petersen et al.'s <i>Factors Influencing Choice of Healthcare System</i> framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Two coders analyzed 22% (<i>N</i> = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high-quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789951","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":"National rollout of a medication safety dashboard to improve testing for latent infections among biologic and targeted synthetic disease-modifying agent users within the Veterans Health Administration.","authors":"Gabriela Schmajuk, Anna Ware, Jing Li, Gary Tarasovsky, Stephen Shiboski, Jennifer L Barton, Karla L Miller, Holly A Mitchell, Jo Dana, Kimberly Reiter, Elizabeth Wahl, Karine Rozenberg-Ben-Dror, Ronald G Hauser, Mary A Whooley","doi":"10.1111/1475-6773.14363","DOIUrl":"https://doi.org/10.1111/1475-6773.14363","url":null,"abstract":"<p><strong>Objective: </strong>To develop, deploy, and evaluate a national, electronic health record (EHR)-based dashboard to support safe prescribing of biologic and targeted synthetic disease-modifying agents (b/tsDMARDs) in the United States Veterans Affairs Healthcare System (VA).</p><p><strong>Data sources and study setting: </strong>We extracted and displayed hepatitis B (HBV), hepatitis C (HCV), and tuberculosis (TB) screening data from the EHR for users of b/tsDMARDs using PowerBI (Microsoft) and deployed the dashboard to VA facilities across the United States in 2022; we observed facilities for 44 weeks post-deployment.</p><p><strong>Study design: </strong>We examined the association between dashboard engagement by healthcare personnel and the percentage of patients with all screenings complete (HBV, HCV, and TB) at the facility level using an interrupted time series. Based on frequency of sessions, facilities were grouped into high- and low/none-engagement categories. We modeled changes in complete screening pre- and post-deployment of the dashboard.</p><p><strong>Data collection methods: </strong>All VA facilities were eligible for inclusion; excluded facilities participated in design of the dashboard or had <20 patients receiving b/tsDMARDs. Session counts from facility personnel were captured using PowerBI audit log data. Outcomes were assessed weekly based on EHR data extracted via the dashboard itself.</p><p><strong>Principal findings: </strong>Totally 117 facilities (serving a total of 41,224 Veterans prescribed b/tsDMARDs) were included. Before dashboard deployment, across all facilities, 61.5% of patients had all screenings complete, which improved to 66.3% over the course of the study period. The largest improvement (15 percentage points, 60.3%-75.3%) occurred among facilities with high engagement (post-intervention difference in outcome between high and low/none-engagement groups was 0.17 percentage points (pp) per week, 95% confidence interval (0.04 pp, 0.30 pp); p = 0.01).</p><p><strong>Conclusions: </strong>We observed significant improvements in screening for latent infections among facilities with high engagement with the dashboard, compared with those with fewer sessions.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762751","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":"Wealth-related inequalities in self-reported health status in the United States and 14 high-income countries","authors":"Ilias Kyriopoulos PhD,&nbsp;Sara Machado PhD,&nbsp;Irene Papanicolas PhD","doi":"10.1111/1475-6773.14366","DOIUrl":"10.1111/1475-6773.14366","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine wealth-related inequalities in self-reported health status among older population in the United States and 14 European countries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We used secondary individual-level data from Health and Retirement Survey (HRS) and the Survey of Health, Ageing, and Retirement in Europe (SHARE) in 2011 and 2019.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>In this cross-sectional study, we used two waves from HRS (wave 10 and 14) and SHARE (wave 4 and 8) to compare wealth-related health inequality across countries, age groups, and birth cohorts. We estimated Wagstaff concentration indices to measure these inequalities across three age groups (50–59, 60–69, 70–79) and two birth cohorts (1942–1947, 1948–1953) in the US and 14 European countries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We performed secondary analysis of survey data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Focusing on older population, we found evidence of wealth-related inequalities in self-reported health status across several high-income countries, with the US demonstrating higher levels of inequality than its European counterparts. The magnitude of these inequalities with respect to wealth remained unchanged over the study period across all countries. Our findings also suggest that wealth-related health inequalities differ at different stages of workforce engagement, especially in the United States. This could be explained either by potential redistributive effects of retirement or by uneven survivor effect, as less wealthy may drop out of the observations at a greater rate partly due to their poorer health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Wealth-related inequalities in self-reported health status are strong and persistent across countries. Our results suggest that there is meaningful variation across high-income countries in health-wealth dynamics that merits further investigation to better understand whether certain health or welfare systems are more equitable. They also highlight the need to consider social policy and wealth redistribution mechanisms as strategies for improving population health among the less wealthy, in the United States and elsewhere.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762752","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":"Effectiveness of a virtual quality improvement training program to improve reach of weight management programs within a large health system","authors":"Laura J. Damschroder MPH, MSc,&nbsp;Richard Evans MS,&nbsp;H. Myra Kim ScD,&nbsp;Jeremy Sussman MD,&nbsp;Michelle B. Freitag MPH,&nbsp;Claire H. Robinson MPH,&nbsp;Jennifer A. Burns MHSA,&nbsp;Nicholas R. Yankey MPH, MSW,&nbsp;Julie C. Lowery PhD","doi":"10.1111/1475-6773.14344","DOIUrl":"10.1111/1475-6773.14344","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To test effectiveness of the LEAP (Learn Engage Act Process) Program on engaging frontline Veteran Health Administration (VHA) medical center teams in continuous quality improvement (QI), a core capability for learning health systems.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Sources and Study Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Data sources included VHA electronic health record (EHR) data, surveys, and LEAP coaching field notes.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A staggered difference-in-differences study was conducted. Fifty-five facilities participated in LEAP across eight randomly assigned clusters of 6–8 facilities per cluster over 2 years. Non-participating facilities were used as controls. A MOVE! weight management program team completed a Plan-Do-Study-Act cycle of change supported by learning curriculum, coaching, and virtual collaboratives in LEAP facilities. Primary outcome was program reach to Veterans. A mixed-effects model compared pre- versus post-LEAP periods for LEAP versus control facilities. LEAP adherence, satisfaction, and cost to deliver LEAP were evaluated.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Collection/Extraction Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Thirty months of facility-level EHR MOVE! enrollment data were included in analyses. LEAP Satisfaction and QI skills were elicited via surveys at baseline and 6-month post-LEAP.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Principal findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Fifty-five facilities were randomly assigned to eight time-period-based clusters to receive LEAP (71% completed LEAP) and 82 non-participating facilities were randomly assigned as controls. Reach in LEAP and control facilities was comparable in the 12-month pre-LEAP period (&lt;i&gt;p&lt;/i&gt; = 0.07). Though LEAP facilities experienced slower decline in reach in the 12-month post-LEAP period compared with controls (&lt;i&gt;p&lt;/i&gt; &lt; 0.001), this is likely due to unexplained fluctuations in controls. For LEAP facilities, satisfaction was high (all mean ratings &gt;4 on a 5-point scale), self-reported use of QI methods increased significantly (&lt;i&gt;p&lt;/i&gt;-values &lt;0.05) 6 months post-LEAP, and delivery cost was $4024 per facility-based team.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Control facilities experienced declining reach in the 12-month post-LEAP period, but LEAP facilities did not, plus they reported higher engagement in QI, an essential capab","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762774","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":"Linking implementation science and policy: Process and tools for congressionally mandated implementation, evaluation, and reporting","authors":"Monica M. Matthieu PhD, LCSW,&nbsp;David A. Adkins MHA,&nbsp;LaCinda Jones MSW, MJ, LISW-S,&nbsp;Ciara M. Oliver MS,&nbsp;Jack H. Suarez BS,&nbsp;Barbara Johnson BA,&nbsp;Mona J. Ritchie PhD, LCSW","doi":"10.1111/1475-6773.14357","DOIUrl":"10.1111/1475-6773.14357","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To describe a process model for assisting partners in addressing requirements of legislation and review policy analysis, planning, and evaluation design processes and tools. Throughout its 25-year history, the United States Department of Veterans Affairs (VA) Quality Enhancement Research Initiative (QUERI) program has been a forerunner in partnering with organizational leaders to improve health care. The Foundations of Evidence-based Policymaking Act of 2018 provided new opportunities for QUERI and other implementation scientists to support federal agency leaders in implementing, evaluating, and reporting on congressionally mandated programs. Although implementation scientists have the skills to support partnered implementation and evaluation, these skills must be adapted for congressionally mandated projects as many scientists have limited experience in policy analysis and the intersection of data informing organizational policy, programs, and practices (i.e., evidence-based policy).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>During the conduct of four congressionally mandated projects, our national VA QUERI team developed processes and tools to achieve the goals and aims of our VHA partners and to ensure our collective work and reporting met legislative requirements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Our process model, program planning, and analysis tools were informed by an iterative process of refining and adapting the tools over a period of six years, spanning the years 2017 to 2023.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Work to support our partners was conducted across three phases: preparation and planning, conducting implementation and evaluation, and developing the congressionally mandated report. The processes and tools we developed within the context of mutually respectful and honest partnerships have been critical to our QUERI center's success in this area.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Lessons we learned may help other scientists partnering in VA or other federal agencies to plan, conduct, and report on congressionally mandated projects.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753352","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":"Medical training program size and clinical staff productivity and turnover.","authors":"Kertu Tenso, Yufei Li, Aaron Legler, Izabela Sadej, Aigerim Kabdiyeva, Melissa M Garrido, Steven D Pizer","doi":"10.1111/1475-6773.14364","DOIUrl":"10.1111/1475-6773.14364","url":null,"abstract":"<p><strong>Objective: </strong>The objective of this analysis was to evaluate the effect of resident program training size on clinician productivity and turnover in the Veterans Health Administration (VHA), the largest education and training platform for medical professionals in the United States.</p><p><strong>Data sources: </strong>We retrieved administrative data on training programs and training facilities from the VA Office of Academic Affiliations and the VHA Corporate Data Warehouse. Data on primary care physician shortage areas were retrieved from the Health Resources and Services Administration.</p><p><strong>Study design: </strong>We used a quasi-experimental instrumental variables 2SLS design and constructed an exogenous predicted training allocation treatment variable as a function of the total national training program allocation. The outcome was clinical staff productivity and turnover. Secondary analyses stratified results using Health Professional Shortage Areas data (HPSA).</p><p><strong>Data collection/extraction methods: </strong>Data were obtained for a national dataset of 141 VHA medical facilities and 26 specialties that hosted training programs across 11 years from 2011 to 2021 (N = 132,177).</p><p><strong>Principal findings: </strong>Instrumental variables results showed that on average, an increase of one training slot in a specialty leads to a decrease of 0.039 visits per standardized clinic day (p < 0.001) and a 0.02 percentage point increase in turnover (p < 0.001). The direction of this association varied by specialty: while psychiatry and psychology specialties saw a decline in productivity, fields such as primary care and cardiology experienced an increase in productivity. HPSA stratified results indicate that negative effects on productivity and turnover are driven by areas with little to no primary care physician shortage, whereas shortage areas experienced a small increase in productivity and no effect on turnover.</p><p><strong>Conclusions: </strong>This quasi-experimental evaluation indicates that resident training program size is associated with reduced productivity and increased turnover in specialties such as psychiatry and in facilities with high baseline productivity. However, in specialties like primary care and cardiology, as well as areas with shortages of primary care, larger training programs are associated with increased productivity.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753353","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":"Conceptual and methodological recommendations for assessing the empirical validity of process measures of health care quality","authors":"Alex H. S. Harris PhD, MS,&nbsp;David R. Nerenz PhD","doi":"10.1111/1475-6773.14356","DOIUrl":"10.1111/1475-6773.14356","url":null,"abstract":"","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735702","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":"Lifetime abortion incidence when abortion care is covered by Medicaid: Maryland versus five comparison states","authors":"Heide M. Jackson PhD,&nbsp;Michael S. Rendall PhD","doi":"10.1111/1475-6773.14358","DOIUrl":"10.1111/1475-6773.14358","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To estimate the association of Medicaid coverage of abortion care with cumulative lifetime abortion incidence among women insured by Medicaid.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We use 2016–2019 (Pre-Dobbs) data from the Survey of Women studies that represent women aged 18–44 living in six U.S. states. One state, Maryland, has a Medicaid program that has long covered the cost of abortion care. The other five states, Alabama, Delaware, Iowa, Ohio, and South Carolina, have Medicaid programs that do not cover the cost of abortion care. Our sample includes 8972 women residing in the study states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Our outcome, cumulative lifetime abortion incidence, is identified using an indirect survey method, the double list experiment. We use a multivariate regression of cumulative lifetime abortion on variables including whether women were Medicaid-insured and whether they were residing in Maryland versus in one of the other five states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>This study used secondary survey data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We estimate that Medicaid coverage of abortion care in Maryland is associated with a 37.0 percentage-point (95% CI: 12.3–61.4) higher cumulative lifetime abortion incidence among Medicaid-insured women relative to women not insured by Medicaid compared with those differences by insurance status in states whose Medicaid programs do not cover the cost of abortion care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We found that Medicaid coverage of abortion care is associated with a much higher lifetime incidence of abortion among individuals insured by Medicaid. We infer that Medicaid coverage of abortion care costs may have a very large impact on the accessibility of abortion care for low-income women.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621807","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":"Barriers to quality healthcare among transgender and gender nonconforming adults.","authors":"Kedryn Berrian, Marci D Exsted, Nik M Lampe, Sayer L Pease, Ellesse-Roselee L Akré","doi":"10.1111/1475-6773.14362","DOIUrl":"https://doi.org/10.1111/1475-6773.14362","url":null,"abstract":"<p><strong>Objective: </strong>To determine the barriers transgender and gender nonconforming (TGNC) adults face when accessing or receiving healthcare in the United States.</p><p><strong>Data sources and study setting: </strong>Primary data were collected between September 2022 and March 2023 from a purposive sample of TGNC adults (N = 116 participants) using an online survey with a series of open-ended and closed-ended questions.</p><p><strong>Study design: </strong>Thematic analysis was employed to extract and analyze participants' responses to an open-ended question about challenges they experienced when accessing or receiving healthcare. Two members of the research team conducted qualitative data analyses using Dedoose. The quality of each analysis was subsequently reviewed by a third research team member.</p><p><strong>Data collection/extraction methods: </strong>Data were collected from responses to one open-ended question that asked about participants' healthcare barriers as a TGNC individual.</p><p><strong>Principal findings: </strong>Five main themes surrounding healthcare barriers emerged from the content analysis: (1) acceptability, (2) accommodation, (3) affordability, (4) availability, and (5) accessibility. First, participants who noted acceptability issues reported explicit discriminatory treatment from providers, providers not using their chosen names and pronouns (e.g., misgendering), and provider refusal to provide gender-affirming care. Second, participants who experienced accommodation challenges identified provider medical training gaps on TGNC patient needs and administrative barriers to care. Third, participants explained affordability issues due to a lack of adequate health insurance coverage. Fourth, participants described availability challenges with accessing hormone therapy prescriptions. Finally, participants noted accessibility issues with obtaining TGNC-specific care at LGBTQ+-affirming clinics.</p><p><strong>Conclusions: </strong>There is a growing interest in the needs of TGNC adults within healthcare settings. This requires that health policies are enacted to ensure that TGNC adults have access to healthcare that is accommodating and accepting/affirming. Study findings may provide insight into the potential impact of current legislation on transgender access and availability.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581564","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}