Health Services Research最新文献

{"title":"A Bootstrap Method to Estimate Cost of Behavioral Intervention Implementation: A Proof of Concept.","authors":"Julia Mo, Daniel Maeng, Mark C Hornbrook, Virginia Sun, Ruth C McCorkle, Ronald S Weinstein, Robert S Krouse","doi":"10.1111/1475-6773.14608","DOIUrl":"https://doi.org/10.1111/1475-6773.14608","url":null,"abstract":"<p><strong>Objective: </strong>To develop a bootstrapping method to augment time-driven activity-based costing (TDABC) analysis intended to allow more realistic cost estimates.</p><p><strong>Data sources: </strong>Secondary data from a multisite clinical trial conducted from 2016 to 2018 on an ostomy self-management telehealth intervention for cancer survivors.</p><p><strong>Study design: </strong>The intervention cost was newly estimated by incorporating expected patient participation rates calculated via bootstrapping. This cost was compared against the cost estimate obtained via traditional TDABC.</p><p><strong>Data collection: </strong>Study personnel self-reported the time spent on each activity associated with the intervention. We also utilized patient participation data collected from the trial.</p><p><strong>Principal findings: </strong>The total cost of the telehealth intervention estimated via the bootstrapping method was $210,052.62 (95% CI: 208,652.13, 211,402.51), with an average cost per participant of $1981.63 (95% CI: 1968.42, 1994.36). Traditional TDABC analysis yielded $186,363 or $1758 per participant. Further adjusting assumptions about the cost of the postintervention monitoring phase, our approach yielded an alternative estimate of $176,362.56 (95% CI: 174,962.07, 177,712.45) and an average cost per participant of $1663.80 (95% CI: 1650.59, 1676.53) suggesting both methods yielded similar bottom-line results.</p><p><strong>Conclusions: </strong>Incorporating bootstrapping into traditional TDABC methodology is feasible and is likely to capture variance in clinical trial data.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14608"},"PeriodicalIF":3.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665404","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":"Risk Adjusted Continuous Monitoring of Postoperative Mortality After Cardiac Surgery.","authors":"Zahra Mobini, Ammer Saati, Turgay Ayer, Xiangqin Cui, Robert Krafty, Alex H S Harris, Nader N Massarweh","doi":"10.1111/1475-6773.14607","DOIUrl":"10.1111/1475-6773.14607","url":null,"abstract":"<p><strong>Objective: </strong>To compare continuous monitoring with a risk-adjusted cumulative sum (CUSUM) to standard episodic risk-adjusted evaluation for the detection of hospitals with higher-than-expected postoperative mortality after cardiac surgery.</p><p><strong>Study setting and design: </strong>In this national, observational, hospital-level study, the number of hospitals identified with higher-than-expected quarterly, risk-adjusted 30-day mortality and time to identification were compared using standard episodic evaluation (i.e., observed-to-expected [O-E] ratios) and the risk-adjusted CUSUM.</p><p><strong>Data sources and analytic sample: </strong>VA Surgical Quality Improvement Program (VASQIP) data (2016-2020) for patients 18 years and older who underwent a cardiac operation at a Veterans Affairs (VA) hospital.</p><p><strong>Principal findings: </strong>The cohort included 20,927 patients treated at 41 hospitals across 20 quarters of data. Overall, 1.8% of hospital quarters were identified using O-E compared to 3.8% with CUSUM. Hospitals concurrently identified using both CUSUM and O-E were identified a median of 17 days earlier with CUSUM (interquartile range [IQR] 7-51 days before quarter end). This translated to a median of 12 (IQR 8-37) surgical cases and 71 (IQR 34-331) postoperative inpatient days occurring after a CUSUM signal but before the quarter ended. At hospitals identified by CUSUM but not O-E, a median of 2 deaths (IQR 2-2) during a median of 22 days (IQR 12-38) triggered detection.</p><p><strong>Conclusions: </strong>CUSUM identifies hospitals with higher-than-expected mortality rates earlier than episodic analysis. Considering the time lag between data collection and report generation by national quality improvement (QI) programs, CUSUM represents a potentially useful tool that could facilitate more real-time recognition of performance concerns and encourage earlier implementation of interventions that can help avoid potentially preventable patient harm. Balancing sensitivity with the risk of false signaling will be essential for ensuring its effective application in national QI efforts.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14607"},"PeriodicalIF":3.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659767","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":"Decomposing Variations on Cluster Level for Binary Outcomes in Application to Cancer Care Disparity Studies.","authors":"Hajime Uno, Angela C Tramontano, Rinaa S Punglia, Michael J Hassett","doi":"10.1111/1475-6773.14599","DOIUrl":"https://doi.org/10.1111/1475-6773.14599","url":null,"abstract":"<p><strong>Objective: </strong>To develop a method to decompose the observed variance of binary outcomes (proportions) aggregated by regional clusters to determine targets for quality improvement efforts to reduce regional variations.</p><p><strong>Data sources and study setting: </strong>Data from the 2018 linkage of the Surveillance, Epidemiology, and End Results-Medicare database.</p><p><strong>Study design: </strong>We developed a method to decompose the observed regional-level variance into four attributions: random, patients' characteristics, regional cluster, and unexplained. To demonstrate the efficacy of the method, we conducted a series of numerical studies. We applied this method to our cohort to analyze endocrine therapy receipt 3-5 years after diagnosis, using health service area (HSA) as the regional cluster.</p><p><strong>Data extraction methods: </strong>Our cohort included Stages I-III breast cancer patients diagnosed at ages 66-79 between 2007 and 2013 who received cancer surgery and were enrolled in Medicare Parts A and B.</p><p><strong>Principal findings: </strong>After decomposition, 39% of the total variation was explained by HSAs, which was higher than that in some other breast cancer measures, such as the proportion of Stage I at diagnosis (4%), previously reported. This suggests geospatial efforts have a great potential to address the regional variation regarding this measure.</p><p><strong>Conclusions: </strong>Our variance decomposition method provides direct information about attributable variance in the proportions at a cluster level. This technique can help in the identification of intervention targets to improve regional variations in the quality of care and clinical outcomes.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14599"},"PeriodicalIF":3.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659766","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":"Understanding and Addressing Upcoding.","authors":"Bryan Dowd","doi":"10.1111/1475-6773.14606","DOIUrl":"https://doi.org/10.1111/1475-6773.14606","url":null,"abstract":"","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14606"},"PeriodicalIF":3.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652078","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":"Trends in Plan Offerings, Enrollment, and Premiums in Medicare Advantage and Medigap.","authors":"Andrew M Ryan, Anupama Warrier, Geronimo Bejarano, Christopher M Whaley, David J Meyers, Meehir N Dixit","doi":"10.1111/1475-6773.14456","DOIUrl":"https://doi.org/10.1111/1475-6773.14456","url":null,"abstract":"<p><strong>Objective: </strong>Examine trends in Medicare Advantage (MA) and Medigap plan offerings, enrollment, and premiums across state regulatory regimes.</p><p><strong>Study setting and design: </strong>We used national data between 2014 and 2021 on MA and Medigap plan offerings, enrollment, and premiums. Data on Medigap plan offerings and premiums were acquired from Weiss Ratings and matched with county-level data on the Medicare population from 2014 to 2021 Medicare Regional Variation and MA Landscape files. States were classified into three groups based on Medigap regulations: community rating and guaranteed issue states (Connecticut and New York); community rating-only states (Arkansas, Maine, Vermont, and Washington); and no additional Medigap regulation states (remaining states).</p><p><strong>Data collection/extraction methods: </strong>We considered only MA plans offering prescription drug coverage. Premiums for Traditional Medicare beneficiaries included Medigap and prescription drug premiums and were calculated using an inflation-adjusted Paasche price index to account for variation across plan types and market segments.</p><p><strong>Principal findings: </strong>Between 2014 and 2021, Medigap offerings and enrollment were relatively constant, while MA enrollment increased substantially. Medigap offerings were lower and MA offerings were higher in states with community rating and guaranteed issue. Between 2014 and 2021, Medigap premiums increased modestly from $4462 to $4745 in states with no additional Medigap regulations and from $6099 to $6612 in states with community rating and guaranteed issue. MA premiums (increased slightly from $2055 to $2121) in states with no additional Medigap regulations and were similar for other states.</p><p><strong>Conclusions: </strong>Despite modest changes in recent years, Medigap premiums were substantially higher than those of MA. Medigap offerings and enrollment are lower, and premiums are higher, in states with guaranteed issue and community ratings. Nuanced reforms are needed to reduce supplemental insurance costs in Traditional Medicare while preventing adverse selection in Medigap markets.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14456"},"PeriodicalIF":3.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634782","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":"From the Editor's Desk: HSR's Outstanding Reviewers in 2024.","authors":"Austin Frakt, Chris Tachibana","doi":"10.1111/1475-6773.14609","DOIUrl":"https://doi.org/10.1111/1475-6773.14609","url":null,"abstract":"","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14609"},"PeriodicalIF":3.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626868","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":"Enrollee, Plan, and State Characteristics Associated With Experience of Care Among Adults in Medicaid Managed Care.","authors":"Kevin H Nguyen, Kenneth Lim, Sarah H Gordon, Megan B Cole","doi":"10.1111/1475-6773.14605","DOIUrl":"https://doi.org/10.1111/1475-6773.14605","url":null,"abstract":"<p><strong>Objective: </strong>To examine five enrollee-reported experience of care metrics and assess enrollee, plan, and state characteristics associated with higher care ratings.</p><p><strong>Study setting and design: </strong>We conducted a repeated cross-sectional study using multivariable linear probability models and predictors that captured various enrollee, plan, and state characteristics. We evaluated five enrollee-reported experience of care measures: being \"always or usually\" easy to get needed care (yes/no), having a personal doctor (yes/no), having timely access to a checkup or routine care (yes/no), having timely access to specialty care (yes/no), and healthcare rating (0-10).</p><p><strong>Data sources and analytic sample: </strong>We used enrollee-level data for adults aged 18-64 from the National Committee on Quality Assurance (NCQA) Adult Medicaid Managed Care Member Experience Survey in 2018 and 2020.</p><p><strong>Principal findings: </strong>The study included 94,296 adults enrolled in 172 Medicaid managed care plans in 38 states and who responded to the member experience survey. Enrollees from racially and ethnically minoritized groups reported significantly worse experiences of care than non-Hispanic White enrollees on all outcomes. Larger plan size was associated with a lower likelihood of timely access to checkups (-5.44 percentage points [PP] difference) but a higher likelihood of having a personal doctor (4.52 PP). Plan for-profit status was associated with a lower likelihood of having access to needed care (-2.24 PP) or having a personal doctor (-4.07 PP). Enrollees in states with Medicaid managed care quality incentives for improving consumer experience of care were significantly more likely to report timely access to specialty care (5.16 PP).</p><p><strong>Conclusions: </strong>Enrollees from racially and ethnically minoritized groups with poor health status report worse access to care, with characteristics such as for-profit plan status and large plan size being associated with access to care. Strategies to improve care experiences may include targeted outreach, equity initiatives, and strengthening provider networks and availability.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14605"},"PeriodicalIF":3.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626855","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":"Expansion of 340B Disproportionate Share Hospitals in the United States From 2010 to 2022.","authors":"Benjamin Y Liu, Massimiliano Russo, Aaron S Kesselheim, Ryan Knox, Ameet Sarpatwari, William B Feldman","doi":"10.1111/1475-6773.14446","DOIUrl":"https://doi.org/10.1111/1475-6773.14446","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate how the communities of newly registered 340B Disproportionate Share Hospitals (DSHs), child sites, and contract pharmacies have changed over time on key socioeconomic measures.</p><p><strong>Study setting/design: </strong>Serial cross-sectional analysis from 2010 to 2022 examining yearly change in Social Deprivation Index (SDI).</p><p><strong>Data sources/analytic sample: </strong>340B DSHs, child sites, and contract pharmacies were identified in the Office of Pharmacy Affairs and Information System and linked to socioeconomic measures by ZIP code.</p><p><strong>Findings: </strong>Seven hundred and seventy five newly registered 340B DSHs, 29,475 child sites, and 48,214 contract pharmacies were included in the analysis. The SDI of communities with newly registered DSHs remained stable during the study period (median 62). By contrast, the SDI of communities with newly registered child sites decreased from a median of 59 in 2010 to 53 in 2022 (-0.18 centiles per year on linear regression, p < 0.001), and the SDI of communities with newly registered contract pharmacies decreased from a median of 72 in 2010 to 53 in 2022 (-0.69 centiles per year on linear regression, p < 0.001), suggesting expansion into areas with lower social deprivation over time.</p><p><strong>Conclusions: </strong>340B DSH hospitals have registered new child sites and contract pharmacies in increasingly wealthy areas.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14446"},"PeriodicalIF":3.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626857","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":"Understanding the Effect of Race on Medicare Advantage Enrollment.","authors":"Adam Atherly, Roger Feldman, Eline van den Broek-Altenburg, Bryan E Dowd","doi":"10.1111/1475-6773.14464","DOIUrl":"https://doi.org/10.1111/1475-6773.14464","url":null,"abstract":"<p><strong>Objective: </strong>To understand why Medicare Advantage (MA) has a relatively larger market share among racial minorities than traditional Medicare (TM).</p><p><strong>Study setting and design: </strong>We estimate Probit models for the choice of the MA sector versus TM by Black and Hispanic beneficiaries, as compared with White beneficiaries. We use a non-linear version of the Oaxaca-Blinder decomposition to decompose differences in the probability of MA enrollment by race into differences in explanatory variable values versus differences in the coefficients on those variables, which we identify as \"preferences\" for MA.</p><p><strong>Data sources and analytic sample: </strong>We combined 2020 Medicare Current Beneficiary Survey (MCBS) data with CMS data on MA plan payment levels aggregated to the county level, star ratings, and measures of market competition.</p><p><strong>Principal findings: </strong>In the Black/White beneficiary comparison, 83% of the 17% point difference in the probability of MA enrollment was explained by differences in preferences (p < 0.001) while only 17% was explained by differences in attributes (p < 0.05). In contrast, in the Hispanic/White beneficiary comparison, 72% of the difference was explained by differences in attributes (p < 0.001) and 28% was explained by differences in preferences (p < 0.01). Attributes associated with differing rates of MA enrollment by race included both market-level characteristics (e.g., payment levels) and personal characteristics (age, level of pain, and chronic disease count). Preferences associated with differing rates of MA enrollment included coefficients of sector characteristics such as payment rates and the number of four-star+ plans available and age.</p><p><strong>Conclusions: </strong>In this study, we find that the higher MA enrollment rate for Black versus White beneficiaries is largely associated with differences in preferences, while the higher enrollment rate for Hispanic beneficiaries is more associated with differences in attributes. Differences in preferences for MA sector characteristics were significant in explaining higher MA enrollment rates for both groups compared with White beneficiaries, suggesting that changes in payment rates will disproportionately impact racial minorities, particularly for Black beneficiaries. However, the reasons for different preferences for MA among racial groups remain somewhat of a puzzle, particularly given that we control for demographics, health, and market characteristics.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14464"},"PeriodicalIF":3.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626871","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 Effects of Pathways to Success on Utilization in Traditional Medicare.","authors":"Meiling Ying, Addison Shay, John M Hollingsworth, Vahakn B Shahinian, Brent K Hollenbeck","doi":"10.1111/1475-6773.14597","DOIUrl":"https://doi.org/10.1111/1475-6773.14597","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the early effects of Pathways to Success implementation on utilization, as measured by quarterly price-standardized Medicare spending per beneficiary.</p><p><strong>Study setting and design: </strong>This study was a nationwide retrospective cohort analysis of Traditional Medicare beneficiaries. The primary outcome was overall utilization, as measured by adjusted quarterly price standardized spending per beneficiary. Secondary outcomes included adjusted quarterly price standardized spending by component type (inpatient, outpatient institutional, Part B, and skilled nursing facility). The primary independent variable is Pathways to Success implementation on July 1, 2019.</p><p><strong>Data sources and analytic sample: </strong>A 20% sample of national Medicare data from January 1, 2018, through March 31, 2020, includes Traditional Medicare beneficiaries managed in ACOs (n = 1,368,523) and outside of ACOs (\"controls,\" n = 1,476,982) prior to Pathways implementation.</p><p><strong>Principal findings: </strong>Unadjusted quarterly spending among those in ACOs and controls decreased over the study period by $13.5 (from $2614.8 before Pathways implementation to $2601.3 after Pathways implementation) and $89.8 (from $2723.1 before Pathways implementation to $2633.3 after Pathways implementation), respectively. Adjusted quarterly spending per beneficiary decreased more slowly in ACOs compared to controls (differences-in-differences estimate +$46.8 (95% CI $19.2, $74.4) in ACOs vs. controls). This difference was largely driven by a more rapid decrease in the utilization of inpatient care. Adjusted quarterly spending per beneficiary for inpatient care decreased more slowly in ACOs compared to controls (differences-in-differences estimate +$43.6 [95% CI $27.2, $60.0] in ACOs vs. controls).</p><p><strong>Conclusions: </strong>After Pathways, reductions in utilization, as measured by price-standardized spending, by ACOs occurred less rapidly than for those managed outside of the Shared Savings Program. This effect was driven by a more rapid decrease in spending for inpatient care by nonparticipants.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14597"},"PeriodicalIF":3.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626833","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}