Health Services Research最新文献

{"title":"Availability of behavioral health crisis care and associated changes in emergency department utilization.","authors":"Ashlyn Burns, Joshua R Vest, Nir Menachemi, Olena Mazurenko, Paul I Musey, Michelle P Salyers, Valerie A Yeager","doi":"10.1111/1475-6773.14368","DOIUrl":"10.1111/1475-6773.14368","url":null,"abstract":"<p><strong>Objective: </strong>To determine whether availability of behavioral health crisis care services is associated with changes in emergency department (ED) utilization.</p><p><strong>Data sources and study setting: </strong>We used longitudinal panel data (2016-2021) on ED utilization from the Healthcare Cost and Utilization Project's State ED Databases and a novel dataset on crisis care services compiled using information from the Substance Abuse and Mental Health Services Administration's National Directories of Mental Health Treatment Facilities. A total of 1002 unique zip codes from Arizona, Florida, Kentucky, Maryland, and Wisconsin were included in our analyses.</p><p><strong>Study design: </strong>To estimate the effect of crisis care availability on ED utilization, we used a linear regression model with zip code and year fixed effects and standard errors accounting for clustering at the zip code-level. ED utilization related to mental, behavioral, and neurodevelopmental (MBD) disorders served as our primary outcome. We also examined pregnancy-related ED utilization as a nonequivalent dependent variable to assess residual bias in effect estimates.</p><p><strong>Data collection/extraction methods: </strong>We extracted data on crisis care services offered by mental health treatment facilities (n = 14,726 facility-years) from the National Directories. MBD-related ED utilization was assessed by applying the Clinical Classification Software Refined from the Healthcare Cost and Utilization Project to the primary ICD-10-CM diagnosis code on each ED encounter (n = 101,360,483). All data were aggregated to the zip code-level (n = 6012 zip-years).</p><p><strong>Principal findings: </strong>The overall rate of MBD-related ED visits between 2016 and 2021 was 1610 annual visits per 100,000 population. Walk-in crisis stabilization services were associated with reduced MBD-related ED utilization (coefficient = -0.028, p = 0.009), but were not significantly associated with changes in pregnancy-related ED utilization.</p><p><strong>Conclusions: </strong>Walk-in crisis stabilization services were associated with reductions in MBD-related ED utilization. Decision-makers looking to reduce MBD-related ED utilization should consider increasing access to this promising alternative model.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908383","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":"Implementation outcomes from a multi-site stepped wedge cluster randomized family caregiver skills training trial.","authors":"Courtney Harold Van Houtven, Kasey Decosimo, Connor Drake, Rebecca Bruening, Nina R Sperber, Joshua Dadolf, Matthew Tucker, Cynthia J Coffman, Janet M Grubber, Karen M Stechuchak, Swetha Kota, Leah Christensen, Cathleen Colón-Emeric, George L Jackson, Emily Franzosa, Leah L Zullig, Kelli D Allen, Susan N Hastings, Virginia Wang","doi":"10.1111/1475-6773.14361","DOIUrl":"https://doi.org/10.1111/1475-6773.14361","url":null,"abstract":"<p><strong>Objective: </strong>To assess whether a team collaboration strategy (CONNECT) improves implementation outcomes of a family caregiver skills training program (iHI-FIVES).</p><p><strong>Data sources and study setting: </strong>iHI-FIVES was delivered to caregivers at eight Veterans Affairs (VA) medical centers. Data sources were electronic health records, staff surveys, and interviews.</p><p><strong>Study design: </strong>In a stepped wedge cluster randomized trial, sites were randomized to a 6-month time interval start date for iHI-FIVES launch. Sites were then randomized 1:1 to either (i) CONNECT, a team collaboration training strategy plus Replicating Effective Programs (REP), brief technical support training for staff, or (ii) REP only (non-CONNECT arm). Implementation outcomes included reach (proportion of eligible caregivers enrolled) and fidelity (proportion of expected trainings delivered). Staff interviews and surveys assessed team function including communication, implementation experience, and their relation to CONNECT and iHI-FIVES implementation outcomes.</p><p><strong>Data collection/extraction methods: </strong>The sample for assessing implementation outcomes included 571 Veterans referred to VA home- and community-based services and their family caregivers eligible for iHI-FIVES. Prior to iHI-FIVES launch, staff completed 65 surveys and 62 interviews. After the start of iHI-FIVES, staff completed 52 surveys and 38 interviews. Mixed methods evaluated reach and fidelity by arm.</p><p><strong>Principal findings: </strong>Fidelity was high overall with 88% of expected iHI-FIVES trainings delivered, and higher among REP only (non-CONNECT) compared with CONNECT sites (95% vs. 80%). Reach was 18% (average proportion of reach across eight sites) and higher among non-CONNECT compared with CONNECT sites (22% vs. 14%). Qualitative interviews revealed strong leadership support at high-reach sites. CONNECT did not influence self-reported team function.</p><p><strong>Conclusions: </strong>A team collaboration strategy (CONNECT), added to REP, required more resources to implement iHI-FIVES than REP only and did not substantially enhance reach or fidelity. Leadership support was a key condition of implementation success and may be an important factor for improving iHI-FIVES reach with national expansion.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908385","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 Medicaid Accountable Care Organizations on children's access to and utilization of health services","authors":"Joanne Constantin PhD, MPH,&nbsp;George L. Wehby PhD, MPH","doi":"10.1111/1475-6773.14370","DOIUrl":"10.1111/1475-6773.14370","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To evaluate the effects of Medicaid Accountable Care Organizations (ACOs) on children's access to and utilization of health services.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>This study employs difference-in-differences models comparing ACO and non-ACO states from 2018 through 2021. Access measures are indicators for preventive and sick care sources, unmet healthcare needs, and having a personal doctor or nurse. Utilization measures are preventive and dental care, mental healthcare, specialist visits, emergency department visits, and hospital admissions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>Secondary, de-identified data come from the 2016–2021 National Survey of Children's Health. The sample includes children with public insurance and ranges between 21,452 and 37,177 depending on the outcome.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Medicaid ACO implementation was associated with an increase in children's likelihood of having a personal doctor or nurse by about 4 percentage-points concentrated among states that implemented ACOs in 2018. Medicaid ACOs were also associated with an increase in specialist care use and decline in emergency visits by about 5 percentage-points (the latter being concentrated among states that implemented ACOs in 2020). There were no discernable or robust associations with other pediatric outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>There is mixed evidence on the associations of Medicaid ACOs with pediatric access and utilization outcomes. Examining effects over longer periods post-ACO implementation is important.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908384","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":"Organizational and patient factors associated with positive primary care experiences for veterans with current or recent homelessness.","authors":"Stefan G Kertesz, Aerin J deRussy, April E Hoge, Allyson L Varley, Sally K Holmes, Kevin R Riggs, Erika L Austin, Adam J Gordon, Sonya E Gabrielian, David E Pollio, Ann E Montgomery, Lillian Gelberg, Jocelyn L Steward, Audrey L Jones, Joshua R Richman","doi":"10.1111/1475-6773.14359","DOIUrl":"10.1111/1475-6773.14359","url":null,"abstract":"<p><strong>Objective: </strong>To identify organizational service features associated with positive patient ratings of primary care within primary care clinics tailored to accommodate persons with ongoing and recent experiences of homelessness (PEH).</p><p><strong>Data sources and study setting: </strong>PEH receiving primary care in 29 United States Veterans Health Administration homeless-tailored clinics were surveyed about their primary care experience using the validated Primary Care Quality-Homeless (PCQ-H) survey. Characteristics of the clinics were assessed through surveys of clinic staff using a new organizational survey developed through literature review, site visits, statistical analysis, and consensus deliberation.</p><p><strong>Study design: </strong>Cross-sectional examination of patients' ratings of care based on surveys of patients, and of clinic characteristics, analyzed with Classification and Regression Tree (CART) analysis, a form of machine learning.</p><p><strong>Data collection methods: </strong>Patient surveys (n = 3394) were obtained from a random sample of enrolled patients by both mail and telephone by an external survey contractor. Staff (n = 52 from 29 clinics) were interviewed by telephone.</p><p><strong>Principal findings: </strong>This analysis identified service features that impact patient experience favorably, including aspects of patient-centeredness, team identity, strong external leadership support, and service that reach beyond traditional primary care clinic confines. Results varied according to the patient experience scale analyzed. Individual characteristics of PEH, such as degree of social support, general health, and unsheltered status, were also correlated with how they rate care.</p><p><strong>Conclusions: </strong>Organizational characteristics correlate with ratings of primary care from patients with recent and ongoing homelessness. Primary care programs serving homeless individuals can assure better care based on who they hire, how they foster team identity, what services they provide, and the strength of leadership support to protect a homeless-focused mission.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898993","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":"Obstetric transport in rural settings: Referral and transport of pregnant patients in a state without a perinatal regionalized system of care","authors":"Kaitlin Fertaly PhD,&nbsp;McKenzie Javorka PhD,&nbsp;Diane Brown MPH,&nbsp;Carly Holman MS,&nbsp;Megan Nelson MSW,&nbsp;Annie Glover PhD, MPH, MPA","doi":"10.1111/1475-6773.14365","DOIUrl":"10.1111/1475-6773.14365","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To assess factors impacting obstetric transport and referral processes for pregnant patients experiencing an emergency in a rural state without a perinatal regionalized system of care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>Data is from Critical Access Hospitals (CAHs) without obstetric units and healthcare providers involved in obstetric care and transport at hospitals with varying levels of obstetric capacity in Montana.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>This mixed-methods study involved surveying CAHs without obstetric units about the hospitals' capacity for obstetric emergencies and transport policies. Survey data were collected from 32 of 34 CAHs without obstetric units (94% response rate) in the fall of 2021. Subsequent interviews were conducted in the fall and winter of 2022–2023 with 20 hospital and emergency medical services (EMS) personnel to provide further insights into the referral and transport process during obstetric emergencies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Survey data were collected using REDCap; interviews were conducted via videoconference. We performed descriptive statistics and Fisher's exact tests for quantitative data. We analyzed qualitative data using a three-phase pragmatic analytic approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The survey of CAHs found 12 of 32 facilities faced difficulties coordinating transport for pregnant patients. Qualitative data indicated this was often due to the state's decentralized transport system. Challenges identified through both quantitative and qualitative data included weather, securing a receiving facility/provider, and coordinating medical transport. Only 10 CAHs reported having written protocols for transporting pregnant patients; of those, four facilities had formal transfer agreements. Qualitative data emphasized variations in awareness and the utility of obstetric transport policies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A decentralized transport system in a rural state can exacerbate existing challenges faced by providers arranging transport for pregnant patients during an obstetric emergency. State and interfacility policies could enhance the transport process for increased regionalization as well as increased support for and coordination of existing EMS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894915","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 equity in a national virtual care management intervention: Delivery and outcomes by race/ethnicity among Veterans with hypertension and diabetes.","authors":"Leah M Marcotte, Chelle L Wheat, Mayuree Rao, Edwin S Wong, Paul Hebert, Karin Nelson, Jorge Rojas, Eric J Gunnink, Ashok Reddy","doi":"10.1111/1475-6773.14352","DOIUrl":"https://doi.org/10.1111/1475-6773.14352","url":null,"abstract":"<p><strong>Objective: </strong>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><p><strong>Data sources and study setting: </strong>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><p><strong>Study design: </strong>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><p><strong>Data collection/extraction methods: </strong>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><p><strong>Principle findings: </strong>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 (N = 68,744), 5.6% NHB (N = 22,580), 10.2% Hispanic (N = 13,313), 6.2% Asian/Pacific Islander/Native Hawaiian (N = 1868), 5.1% American Indian/Native Alaskan (N = 744), and 5.6% multiple races or other race (N = 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><p><strong>Conclusions: </strong>Our findings suggest the PHI intervention was equitably deployed across race/ethnicity groups without significantly impacting most existing inequities in diabetes and hypertension.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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, Sara Machado, Irene Papanicolas","doi":"10.1111/1475-6773.14366","DOIUrl":"https://doi.org/10.1111/1475-6773.14366","url":null,"abstract":"<p><strong>Objective: </strong>To examine wealth-related inequalities in self-reported health status among older population in the United States and 14 European countries.</p><p><strong>Data sources and study setting: </strong>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><p><strong>Study design: </strong>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><p><strong>Data collection/extraction methods: </strong>We performed secondary analysis of survey data.</p><p><strong>Principal findings: </strong>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><p><strong>Conclusions: </strong>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>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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":null,"pages":null},"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}