Health Services Research最新文献

{"title":"Veterans' Behavioral Health Hospitalizations and Outcomes in VA Versus Non-VA Hospitals.","authors":"Megan E Vanneman, Ciaran S Phibbs, Michael K Ong, Yue Zhang, Adam Chow, Jean Yoon","doi":"10.1111/1475-6773.70013","DOIUrl":"10.1111/1475-6773.70013","url":null,"abstract":"<p><strong>Objective: </strong>To compare outcomes for Department of Veterans Affairs (VA) enrollees' behavioral health (BH) hospitalizations by source (VA-direct, VA-purchased community care (CC), Medicaid, Medicare, private insurance, and other payers).</p><p><strong>Study setting and design: </strong>We conducted a retrospective, longitudinal study with VA enrollees from 2015 to 2017 to examine differences in BH hospitalization outcomes by source. We used generalized linear models with clustered standard errors to predict length of stay (LOS), cost, and 30-day readmission.</p><p><strong>Data sources and analytic sample: </strong>We studied 124,609 BH hospitalizations of 77,299 VA enrollees in 11 geographically diverse states.</p><p><strong>Principal findings: </strong>Predicted mean LOS (9.03 days, 95% CI 8.92-9.14 days; p < 0.001) and cost ($17,608, 95% CI $17,347-$17,870; p < 0.001) were highest for VA-direct hospitalizations, while the mean readmission rate was lowest for VA-direct hospitalizations (17.36%, 95% CI 17.03%-17.69%; p < 0.001). Average marginal effects for each non-VA hospitalization source were statistically significantly different from VA-direct hospitalizations (p < 0.001): between 2.13 and 2.90 days less for LOS, $11,141 to $12,144 less for cost, and 2.71% to 5.18% higher for readmission rate.</p><p><strong>Conclusions: </strong>The majority of BH hospitalizations were in VA-direct care (56%), with 44% provided in locations outside VA hospitals: Medicare (19%), CC (7%), private insurance (7%), other payers (6%), and Medicaid (5%). There are trade-offs between BH hospitalizations provided in VA-direct care (lowest readmission rate, highest LOS and costs) and other sources.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70013"},"PeriodicalIF":3.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692505","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":"Drivers of Patient Experiences With Healthcare-Based Social Care.","authors":"Kameswari Potharaju, Laura M Gottlieb, Holly E Wing, Alejandra Gonzalez-Rocha, Amanda L Brewster, Danielle Hessler Jones, Andrea Quiñones-Rivera","doi":"10.1111/1475-6773.70020","DOIUrl":"https://doi.org/10.1111/1475-6773.70020","url":null,"abstract":"<p><strong>Objective: </strong>To identify key factors that define patient experiences of social care in healthcare settings.</p><p><strong>Study setting and design: </strong>This is a qualitative study using interviews from participants recruited by collaborators of a social care research group from across the United States.</p><p><strong>Data sources and analytic sample: </strong>We conducted 30 semi-structured interviews between September 2023 and February 2024. Participants were 18 or older, English- or Spanish-speaking, and had received social care in a healthcare setting within the last 12 months. Interview transcripts were dually coded and analyzed using a mixed inductive-deductive approach.</p><p><strong>Principal findings: </strong>Patient experience was defined by elements of social care delivery that fell into two categories: the functional and relational domains of social care. Participants reported that operational or \"functional\" aspects of social care, including screening, resource connections, and other forms of follow-up, represented an important part of their experiences of social care. Experiences of social care were also defined by relational factors, for example, demonstrations of empathy, positive perceptions of screening intentions, linguistic concordance, and longitudinal relationships with the care team. Many participants felt that these functional and relational factors were inextricably linked.</p><p><strong>Conclusions: </strong>The impressive role that relational factors-that is, interactions and relationships with social care providers-play in defining patient experiences highlights the need to include these factors in efforts to evaluate social care interventions. Discussions about social needs may retain value even in the absence of available resources if healthcare teams attend to the relational factors that drive patients' social care experiences. In the future, measures of social care quality should account for both the functional and relational dimensions of social care.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70020"},"PeriodicalIF":3.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692504","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":"Early Examination of Hospital-Level Performance on Unplanned, Potentially Avoidable Hospital Visits After Chemotherapy, 2018-2022.","authors":"Arthur S Hong, Lesi He, Pranathi Pilla, Joshua M Liao, D Mark Courtney, Navid Sadeghi, Ethan A Halm","doi":"10.1111/1475-6773.70014","DOIUrl":"https://doi.org/10.1111/1475-6773.70014","url":null,"abstract":"<p><strong>Objective: </strong>To assess changes in publicly reported, potentially avoidable hospital visits after chemotherapy since the introduction of a Medicare quality measure.</p><p><strong>Study setting and design: </strong>Retrospective analysis of avoidable emergency department (ED) and inpatient admission (ADM) rates after chemotherapy between 2018 and 2022, across absolute visit rates and relative hospital performance (\"better than\", \"no different than\", \"worse than\" the national rate). We stratified hospitals into quartiles of visit rates in 2018 and used this to model the change in visit rates from 2018 to 2022 with generalized linear regression.</p><p><strong>Data sources and analytic sample: </strong>A longitudinal cohort of hospitals from the Medicare Outpatient Quality Reporting Program.</p><p><strong>Principal findings: </strong>We analyzed 1179 hospitals (94.3% non-profit, 22.9% teaching). National avoidable ED visit rates were 6.0% in 2018, 5.4% in 2022; ADM rates were 12.5% in 2018, 10.3% in 2022. Nearly all hospitals were deemed to have performed \"no different\" than the national rate each year in ED (≥ 95.3%) and ADM (≥ 91.1%). In adjusted analyses, visit rates for hospitals in the lowest 2018 visit rate quartiles declined the least by 2022 (ED: -0.44% 95% CI: -0.58 to -2.94; ADM: -0.91%, 95% CI: -1.14 to -0.69), and declined the most for hospitals in the highest 2018 quartiles (ED: -1.72%, 95% CI: -1.85 to -7.73; ADM: -3.03%, 95% CI: -3.27 to -2.81). We estimated that the tendency for extreme baseline values to approach the average over time accounted for up to one-tenth of the decline among the worst-performing 2018 quartiles (ED: 10.6% of rate change, 95% CI: 9.8 to 11.5; ADM: 9.0%, 95% CI: 8.2 to 9.8).</p><p><strong>Conclusion: </strong>Hospitals reduced their potentially avoidable hospital visit rates, though Medicare deemed that nearly all hospitals performed \"no different\" than the national average each year. It remains unclear if the reductions were driven by this quality measure.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70014"},"PeriodicalIF":3.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683622","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 Clinical Implementation of Risk Prediction Based Interventions Using Difference-In-Differences.","authors":"Maricela Cruz, Susan M Shortreed, Gregory E Simon, Yates Coley","doi":"10.1111/1475-6773.70015","DOIUrl":"https://doi.org/10.1111/1475-6773.70015","url":null,"abstract":"<p><strong>Objective: </strong>To compare alternative Difference-in-Differences (DID) methods for evaluating the effect of risk-stratified interventions, or interventions targeting at-risk groups, on binary outcomes.</p><p><strong>Study setting and design: </strong>In simulations, we compared operating characteristics of recycled prediction estimators for common average treatment effect on the treated (ATT) estimands across three DID models: the traditional two groups and two periods model, a risk score adjusted model, and a model adjusting for risk score and its interactions with risk group and period. We compared DID ATT estimates to randomized evaluation estimates of a risk-stratified intervention implemented at Kaiser Permanente Washington (KPWA), delivering additional text-message reminders to reduce missed clinic visits.</p><p><strong>Data sources and analytic sample: </strong>Our study included 588,503 KPWA visits, with 285,814 (49%) visits pre-evaluation (05/01/2018-10/30/2018) and 302,689 (51%) visits during the evaluation (02/01/2019-09/30/2019). Pre-evaluation, 120,350 visits were classified as high-risk. During the evaluation, 125,076 visits were labeled as high-risk, with 62,557 (50%) randomized to the intervention. We generated data in simulations based on this setting.</p><p><strong>Principal findings: </strong>In simulations, the traditional DID and risk score adjusted models had smaller bias and standard errors, and better coverage probabilities. DID estimates closest to randomized evaluation estimates (-0.007, 95% CI [-0.010, -0.004]) were from the traditional DID model assuming the identity link (-0.008, 95% CI [-0.011, -0.005]) or the risk adjusted model with any link (-0.006, 95% CI [-0.008, -0.003] identity; -0.007, 95% CI [-0.011, -0.003] logit; -0.007, 95% CI [-0.012, -0.003] log) for the ATT on the absolute difference scale (usual DID ATT estimand), and the risk score adjusted model with log or logit links for all other estimands.</p><p><strong>Conclusions: </strong>Compared with randomized evaluation results, the traditional DID model is appropriate for the ATT on the absolute difference scale, while the risk score adjusted model with log or logit links is appropriate for all ATT estimands considered.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70015"},"PeriodicalIF":3.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676613","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":"Machine Learning Risk Stratification for Older Breast Cancer Survivors: Clinical Care Implications.","authors":"Stephanie B Wheeler, Jason Rotter, Lisa P Spees, Caitlin B Biddell, Justin G Trogdon, Catherine M Alfano, Deborah K Mayer, Michaela A Dinan, Larissa Nekhlyudov, Sarah A Birken","doi":"10.1111/1475-6773.70005","DOIUrl":"https://doi.org/10.1111/1475-6773.70005","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate a clinical risk prediction algorithm to identify breast cancer survivors at high risk for adverse outcomes.</p><p><strong>Study setting and design: </strong>Our national retrospective analysis used cross-validated random forest machine learning models to separately predict the risk of all-cause death, cancer-specific death, claims-derived risk of recurrence, and other adverse health outcomes within 3 and 5 years following treatment completion.</p><p><strong>Data sources and analytic sample: </strong>Our study used the Surveillance and Epidemiology End Results (SEER) registry-Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey (SEER-CAHPS) linked data for survivors diagnosed between 2003 and 2011, with follow-up claims data to 2017.</p><p><strong>Principal findings: </strong>Within the 3-year follow-up period, 372/4516 survivors (mean age 75.1; 81.7% white) in the primary cohort (8.2%) died, 111 from cancer (2.5%), 665 (14.7%) experienced cancer recurrence, and 488 (10.8%) were hospitalized for adverse health outcomes. The algorithm's prediction resulted in 91.9% out-of-sample accuracy (the percent of observations classified correctly) and a 37.6% Cohen's Kappa (i.e., improvement over an uninformed model). Out-of-sample accuracy was 97.5% (44% improvement) for predicting cancer-specific death, 85% (26% improvement) for recurrence, and 89% (28% improvement) for other adverse health outcomes. Important predictors across outcomes included geographic region, age, frailty, comorbidity, time since diagnosis, and out-of-pocket cost responsibility.</p><p><strong>Conclusions: </strong>Machine learning models accurately predicted relevant adverse survivorship outcomes, driven primarily by non-cancer specific factors. Breast cancer survivors at high risk for adverse outcomes may benefit from more intensive care, whereas those at low risk may be more appropriately managed by primary care.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70005"},"PeriodicalIF":3.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651272","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":"Share of Sales Subject to Medicare Inflation Rebates and Price Increases of Top-Selling Drugs.","authors":"Alexander C Egilman, Aaron S Kesselheim, Benjamin N Rome","doi":"10.1111/1475-6773.70012","DOIUrl":"https://doi.org/10.1111/1475-6773.70012","url":null,"abstract":"<p><strong>Objective: </strong>To examine whether the new Medicare inflation rebate policy was associated with changes in manufacturer pricing behavior.</p><p><strong>Study setting and design: </strong>In this cross-sectional study of 156 top-selling brand-name drugs, we used linear regression to evaluate whether there was an association between drugs' exposure to the policy (i.e., Medicare's share of net US sales) and differences in year-over-year price changes before (2021-2022) versus after (2022-2023, 2023-2024) the policy took effect.</p><p><strong>Data sources and analytic sample: </strong>The study used Medicare spending data and average sales prices from the Centers for Medicare and Medicaid Services, wholesale acquisition costs from Eversana NAVLIN's Price & Access database, and sales revenue and estimated rebates from SSR Health. Vaccines, biosimilars, drugs approved after 2020, and those with generic or biosimilar competition before 2023 were excluded. Drugs were stratified by whether they derived most sales from Medicare Part B or Part D.</p><p><strong>Principal findings: </strong>The median Medicare share of net sales was 28% (IQR: 18%-37%) for 50 Part B drugs and 32% (IQR: 16%-49%) for 106 Part D drugs. Median year-over-year price changes in 2021-2022, 2022-2023, and 2023-2024 were 3.2%, 2.9%, and 3.4% for Part B drugs and 5.0%, 5.9%, and 4.9% for Part D drugs. There was no association between drugs' Medicare share of net sales and differences in price changes pre- vs. post-policy for Part B drugs (2023: p = 0.99; 2024: p = 0.09). For Part D drugs, each 10% increase in drugs' share of Medicare sales was associated with a 0.18% (95% CI, 0.01%-0.35%, p = 0.04) higher price change in the first year after policy implementation; there was no significant association in the second year (p = 0.17).</p><p><strong>Conclusions: </strong>Medicare inflation rebates were not associated with smaller price increases among the top-selling drugs most affected by the policy. Additional measures are needed to prevent drug manufacturers from raising prices each year, such as extending inflation rebates to commercially insured patients.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70012"},"PeriodicalIF":3.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627795","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 Provider Productivity on Suicide-Related Events Among Veterans.","authors":"Kiersten L Strombotne, Daniel Lipsey, Fernando Mattar, Kathleen Carey, Samantha G Auty, Brian W Stanley, Steven D Pizer","doi":"10.1111/1475-6773.70008","DOIUrl":"https://doi.org/10.1111/1475-6773.70008","url":null,"abstract":"<p><strong>Objective: </strong>To examine the relationship between mental health provider productivity, staffing levels, and suicide-related events (SREs) among U.S. Veterans receiving care within the Veterans Health Administration (VHA), focusing on therapy and medication management providers.</p><p><strong>Data sources/setting: </strong>We analyzed administrative data from the Department of Defense and VHA (2014-2018), encompassing 109,376 Veterans who separated from active duty between 2010 and 2017.</p><p><strong>Design: </strong>A longitudinal design estimated the effects of facility-level provider work rate and staffing on SREs, adjusting for patient and facility characteristics. An instrumental variables (IV) approach addressed potential endogeneity.</p><p><strong>Data collection/extraction methods: </strong>Data were obtained from the VHA Corporate Data Warehouse and the VHA Survey of Enrollees.</p><p><strong>Principal findings: </strong>A 1% increase in therapy provider work rate led to a 12.1% increase in SRE probability, regardless of staffing levels. Conversely, a 1% increase in staffing levels led to a 1.6% reduction in SREs, with the largest effect in low-staffed facilities. For medication management providers, work rate had no overall impact on SREs, except in medium-staffed facilities. A 1% increase in staffing levels for medication management providers led to a 1.7% reduction in SREs.</p><p><strong>Conclusions: </strong>Increased work rates, particularly in low-staffed VHA facilities, may elevate suicide-related risks. In contrast, staffing increases simultaneously improve access and reduce adverse outcomes. Where possible, policymakers should prioritize staffing growth over productivity gains to improve access to mental health clinics and ensure Veteran safety and care quality.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70008"},"PeriodicalIF":3.1,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602296","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":"Association of Electronic Health Records Access and Coordination Between Primary Care Providers and Public Health Nurse Home Visitors in the United States.","authors":"Venice Ng Williams, Michael D Knudtson, Mandy A Allison, Gregory J Tung","doi":"10.1111/1475-6773.70006","DOIUrl":"https://doi.org/10.1111/1475-6773.70006","url":null,"abstract":"<p><strong>Objective: </strong>To measure nurse home visiting teams' access to electronic health records (EHR) and determine if access to EHR is associated with increased nurse home visitor collaboration with primary care providers in the United States.</p><p><strong>Study setting and design: </strong>Nurse-Family Partnership (NFP) is an evidence-based home visiting program for first-time parents experiencing adversities. We conducted an observational study using data from 265 local NFP agencies in the United States. We used multivariate regression models to estimate the association between home visitors' EHR access and relational coordination with primary care providers.</p><p><strong>Data sources and analytic sample: </strong>We linked data from the 2021 NFP Collaboration with Community Providers Survey to 2021 NFP program implementation data and 2010 Rural-Urban Commuting Area Codes. We matched 265 survey respondents to their NFP teams' implementation data, including those with client visits between September 1, 2021, and December 31, 2021.</p><p><strong>Principal findings: </strong>Thirty-four percent of NFP teams (91/265) had access to their patients' EHR, with variation by agency type, where more NFP programs implemented by healthcare systems had EHR access (56%) compared to other agency types (X₃ ²=19.44, p < 0.01). Most NFP teams with EHR access reported read access (91%), ability to document (64%), and receiving program referrals (53%). EHR access was significantly associated with increased relational coordination with women's care providers (0.36-point difference, 95% CI 0.17 to 0.55, p < 0.01) and pediatric care providers (0.39-point difference, 95% CI 0.18 to 0.61, p < 0.01).</p><p><strong>Conclusions: </strong>Access to EHRs varies by NFP team and agency type and is associated with greater relational coordination with primary care providers. Increasing home visitors' access to EHRs may help to facilitate collaboration with primary care providers.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70006"},"PeriodicalIF":3.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577005","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":"VA-Purchased Community Care and Risk of Potentially Unsafe Concurrent Medication Use Among Veterans Receiving Opioids: A Regression Discontinuity Analysis.","authors":"Eric T Roberts, Florentina E Sileanu, Yaming Li, Timothy S Anderson, Carolyn T Thorpe, John Cashy, Katie J Suda, Thomas R Radomski, Maria K Mor, Utibe R Essien, Megan E Vanneman, Michael J Fine, Walid F Gellad","doi":"10.1111/1475-6773.70001","DOIUrl":"10.1111/1475-6773.70001","url":null,"abstract":"<p><strong>Objective: </strong>To examine whether eligibility for Veterans Health Administration (VA) community care, which expanded Veterans' access to VA-funded care outside VA, increased the likelihood of Veterans concurrently filling prescriptions for opioids and central nervous system (CNS)-active medications.</p><p><strong>Study setting and design: </strong>We used a regression discontinuity design to analyze Veterans across a distance threshold for community care eligibility in the Veterans Choice Program, under which Veterans residing > 40 miles from the closest VA medical facility staffed by ≥ 1 full-time primary care physician qualified for community care. We used local linear regression to test whether exceeding this 40-mile threshold was associated with discontinuities in the probability of receiving overlapping supplies of opioids and another CNS medication (benzodiazepine, muscle relaxant, antiepileptic, or sleep aid) for ≥ 30 days per year.</p><p><strong>Data sources and analytic sample: </strong>We used VA pharmacy data for prescriptions filled at VA facilities, VA Program Integrity Tool files for prescriptions paid by VA and filled in community pharmacies, and Medicare and Medicaid data for prescriptions covered by those programs. Our analysis included annual cross-sectional samples of Veterans who filled ≥ 1 opioid prescription through VA, community care, Medicare, or Medicaid and lived 36-39 or 41-44 miles from the nearest VA facility during federal FYs 2016-2019 (n = 180,903 Veteran-year observations).</p><p><strong>Principal findings: </strong>Among Veterans who filled an opioid prescription, 34.1% concurrently received another CNS medication for ≥ 30 days. Exceeding the threshold for community care eligibility was associated with a 1.14 percentage point (pp) increase (95% CI: 0.08, 2.20) in the probability of concurrently receiving an opioid and another CNS drug during 2016-2019. Discontinuities in overlap were larger among Veterans with a serious mental illness (2.7 pp.; 95% CI: 0.6, 4.9) during 2016-2019. During 2018-2019, discontinuities were larger in the overall sample (1.6 pp.; 0.0, 3.1) and among non-Hispanic Black Veterans (5.4 pp.; 95% CI: 0.5, 10.4).</p><p><strong>Conclusions: </strong>Overall, VA community care eligibility was associated with a small increase in medication overlap involving opioids and other CNS-active medications. Increases in overlap were larger in certain Veteran subgroups and later study years, underscoring a need for continued monitoring of higher-risk co-prescribing in VA community care.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70001"},"PeriodicalIF":3.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568084","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":"Network Analysis to Define Pediatric Acute Care Regions in Wisconsin.","authors":"Kenneth A Michelson, Katherine E Remick, Emily M Bucholz, Patrick D McMullen, Naveen Singamsetty, Andrew D Skol, Danielle K Cory, John A Graves","doi":"10.1111/1475-6773.70000","DOIUrl":"10.1111/1475-6773.70000","url":null,"abstract":"<p><strong>Objective: </strong>To pilot a system for deriving borders of pediatric regions, and to compare these to adult markets based on fit with pediatric utilization data.</p><p><strong>Study setting and design: </strong>In this cross-sectional study, we studied all acute care encounters (emergency department visits and hospitalizations) for children less than 16 years old in Wisconsin 2021-2022.</p><p><strong>Data sources and analytic sample: </strong>We used the Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases. We first counted how many patients from each ZIP code visited each hospital and mapped ZIP-hospital connections. Using a network analysis technique called community detection that clustered hospitals by their common connections, we grouped ZIP codes to form pediatric emergency service areas (PESAs). We counted patient referrals within and between PESAs and repeated the community detection procedure, resulting in pediatric emergency referral regions (PERRs). The primary outcome was modularity, a common network fit measure ranging from -1 to 1 (1 represents perfect clustering). We also compared demographics and network quality measures between PERRs, hospital referral regions (HRRs), core-based statistical areas, and Pittsburgh Trauma Atlas regions.</p><p><strong>Principal findings: </strong>We analyzed 587,886 encounters, from which ZIP codes grouped into 24 PESAs. Based on referral patterns, there were 4 PERRs. PERRs had modestly higher modularity for interhospital referral patterns than all other systems (0.53, 95% confidence interval [CI] 0.52, 0.54 compared to 0.46, 95% CI 0.46, 0.47 for HRRs). PERRs were larger (median 11,361 mile² vs. 3957 for HRRs), contained more children (median 265,222 vs. 49,667 for HRRs), and contained more hospitals (median 35 vs. 7 for HRRs) than all other systems.</p><p><strong>Conclusions: </strong>Using Wisconsin HCUP data, we derived pediatric acute care regions with a strong fit for pediatric utilization data. Future work should test this approach across the whole US, which would allow between-region cost and outcomes comparison.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e70000"},"PeriodicalIF":3.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546256","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}