Health Services Research最新文献

{"title":"Fitting in? Physician practice style after forced relocation","authors":"Alice J. Chen PhD,&nbsp;Michael R. Richards MD PhD MPH,&nbsp;Rachel Shriver PhD","doi":"10.1111/1475-6773.14340","DOIUrl":"10.1111/1475-6773.14340","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aims to examine how variation in physicians' treatment decisions for newborn deliveries responds to changes in the hospital-level norms for obstetric clinical decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>All hospital-based births in Florida from 2003 through 2017.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Difference-in-differences approach is adopted that leverages obstetric unit closures as the source of identifying variation to exogenously shift obstetricians to a new, nearby hospital with different propensities to approach newborn deliveries less intensively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Extraction</h3>\u0000 \u0000 <p>Births attributed to physicians continuously observed 2 years before the closure event and 2 years after the closure event (treatment group physicians) or for identical time periods around a randomly assigned placebo closure date (control group physicians).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>All of the physicians meeting our inclusion criteria shifted their births to a new hospital less than 20 miles from the hospital shuttering its obstetric unit. The new hospitals approached newborn births more conservatively, and treatment group physicians sharply became less aggressive in their newborn birth clinical management (e.g., use of C-section). The immediate 11-percentage point (33%) increase in delivering newborns without any procedure behavior change is statistically significant (p value &lt;0.01) and persistent after the closure event; however, the physicians' payer and patient mix are unchanged.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Obstetric physician behavior change appears highly malleable and sensitive to the practice patterns of other physicians delivering newborns at the same hospital. Incentives and policies that encourage more appropriate clinical care norms hospital-wide could sharply improve physician treatment decisions, with benefits for maternal and infant outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421913","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":"Perinatal care among Hispanic birthing people: Differences by primary language and state policy environment","authors":"Julia D. Interrante PhD, MPH,&nbsp;Cynthia Pando MA,&nbsp;Alyssa H. Fritz MPH, RD, CLC,&nbsp;Katy B. Kozhimannil PhD, MPA","doi":"10.1111/1475-6773.14339","DOIUrl":"10.1111/1475-6773.14339","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;The study aims to examine maternal care among Hispanic birthing people by primary language and state policy environment.&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;Pooled data from 2016 to 2020 Pregnancy Risk Assessment Monitoring System surveys from 44 states and two jurisdictions.&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;Using multivariable logistic regression, we calculated adjusted predicted probabilities of maternal care utilization (visit attendance, timeliness, adequacy) and quality (receipt of guideline-recommended care components). We examined outcomes by primary language (Spanish, English) and two binary measures of state policy environment: (1) expanded Medicaid eligibility to those &lt;133% Federal Poverty Level, (2) waived five-year waiting period for pregnant immigrants to access Medicaid.&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;Survey responses from 35,779 postpartum individuals with self-reported Hispanic ethnicity who gave birth during 2016–2020.&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;Compared to English-speaking Hispanic people, Spanish-speaking individuals reported lower preconception care attendance and worse timeliness and adequacy of prenatal care.&lt;/p&gt;\u0000 \u0000 &lt;p&gt;In states without Medicaid expansion and immigrant Medicaid coverage, Hispanic birthing people had, respectively, 2.3 (95% CI:0.6, 3.9) and 3.1 (95% CI:1.6, 4.6) percentage-point lower postpartum care attendance and 4.2 (95% CI:2.1, 6.3) and 9.2 (95% CI:7.2, 11.2) percentage-point lower prenatal care quality than people in states with these policies.&lt;/p&gt;\u0000 \u0000 &lt;p&gt;In states with these policies, Spanish-speaking Hispanic people had 3.3 (95% CI:1.3, 5.4) and 3.0 (95% CI:0.9, 5.1) percentage-point lower prenatal care adequacy, but 1.3 (95% CI:−1.1, 3.6) and 2.7 (95% CI:0.2, 5.1) percentage-point higher postpartum care quality than English-speaking Hispanic people. In states without these policies, those same comparisons were 7.3 (95% CI:3.8, 10.8) and 7.9 (95% CI:4.6, 11.1) percentage-points lower and 9.6 (95% CI:5.5, 13.7) and 5.3 (95% CI:1.8, 8.9) percentage-points higher.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Perinatal care utilization and quality vary among Hispanic birthing people by ","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332463","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":"Sampling coverage of the Arkansas all-payer claims database by County's persistent poverty designation","authors":"Chenghui Li PhD,&nbsp;Cheng Peng PhD,&nbsp;Peter DelNero PhD,&nbsp;Mahima Saini B.Pharm,&nbsp;Mario Schootman PhD","doi":"10.1111/1475-6773.14342","DOIUrl":"10.1111/1475-6773.14342","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>To evaluate the quality of Arkansas All-Payer Claims Database (APCD) for disparity research in persistent poverty areas by determining (1) its representativeness of Arkansas population, (2) variation by county, and (3) differences in coverage between persistent poverty and other counties.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>Cross-sectional study using 2019 Arkansas APCD member enrollment data and county-level data from various agencies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>An alias identifier linked persons across insurance plans. County FIPS codes were used to extract county-level variables.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Cohort 1 included individuals with ≥1 day of medical coverage in 2019. Cohort 2 included individuals with medical coverage in June, 2019. Cohort 3 included individuals with continuous medical coverage in 2019. Sampling proportions of a county's population in the three cohorts were compared between persistent poverty and other counties. Inverse-variance weighted linear regression was used to identify county-level socioeconomic and demographic characteristics associated with inclusion in each cohort.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>In 2019, 73.6% of Arkansans had medical coverage for ≥1 day (Cohort 1), 66.3% had coverage in June (Cohort 2), and 58.8% had continuous coverage (Cohort 3) in APCD. Sampling proportions varied by county (median[range]: Cohort 1, 78% [58%–95%]; Cohort 2, 71% [51%–88%]; and Cohort 3, 64% [44%–80%]), and were higher among persistent poverty counties than others for all three cohorts (mean [SD], persistent poverty vs. other: Cohort 1: 80.9% [6.4%] vs. 77.1% [6.3%], <i>p</i> = 0.04; Cohort 2: 74.0% [6.4%] vs. 70.1% [6.2%], <i>p</i> = 0.03; Cohort 3: 66.4% [6.1%] vs. 62.7% [6.0%], <i>p</i> = 0.03). In the 2019 APCD, larger counties and those with higher proportions of females or persons 65+ years had higher coverage, whereas counties with higher per capita household income, median home value, or disproportionately more persons of other races (non-White and non-Black) had lower coverage (<i>p</i> &lt; 0.05 for all three cohorts).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The Arkansas APCD had good coverage of Arkansas population. Coverage was higher in persistent poverty counties than others.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332464","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 Affordable Care Act on uninsured hospitalization: Evidence from Texas","authors":"Nima Khodakarami PhD,&nbsp;Benjamin Ukert PhD","doi":"10.1111/1475-6773.14334","DOIUrl":"10.1111/1475-6773.14334","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine the impact of the Affordable Care Act (ACA) health insurance exchanges (Marketplace) on the rate of uninsured discharges in Texas.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Source and Study Setting</h3>\u0000 \u0000 <p>Secondary discharge data from 2011 to 2019 from Texas.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We conducted a retrospective study estimating the effects of the ACA Marketplace using difference-in-difference regressions, with the main outcome being the uninsured discharge rate. We stratified our sample by patient's race, age, gender, urbanicity, major diagnostic categories (MDC), and emergent type of admissions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>We used Texas hospital discharge records for non-elderly adults collected by the state of Texas and included acute care hospitals who reported data from 2011 to 2019.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The expansion of insurance through ACA Marketplaces led to reductions in the uninsured discharge rate by 9.9% (95% CI, −17.5%, −2.3%) relative to the baseline mean. The effects of the ACA were felt strongest in counties with any share of Hispanic, in counties with a larger population of Black, and other racial groups, in counties with a significant share of female and older age individuals, in counties considered to be urban, in high-volume diagnoses, and emergent type of admissions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These findings indicate that the ACA facilitated a shift in hospital payor mix from uninsured to insured.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141236435","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":"Trends in Veteran hospitalizations and associated readmissions and emergency department visits during the MISSION Act era","authors":"R. Neal Axon MD,&nbsp;Ralph Ward PhD,&nbsp;Ahmed Mohamed PhD,&nbsp;Charlene Pope PhD,&nbsp;Michela Stephens MPH,&nbsp;Patrick D. Mauldin PhD,&nbsp;Mulugeta Gebregziabher PhD","doi":"10.1111/1475-6773.14332","DOIUrl":"10.1111/1475-6773.14332","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine changes in hospitalization trends and healthcare utilization among Veterans following Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>VA Corporate Data Warehouse and Centers for Medicare and Medicaid Services datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Retrospective cohort study to compare 7- and 30-day rates for unplanned readmission and emergency department visits following index hospital stays based on payor type (VHA facility stay, VA-funded stay in community facility [CC], or Medicare-funded community stay [CMS]). Segmented regression models were used to compare payors and estimate changes in outcome levels and slopes following MISSION Act implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Veterans with active VA primary care utilization and ≥1 acute hospitalization between January 1, 2016 and December 31, 2021.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Monthly index stays increased for all payors until MISSION Act implementation, when VHA and CMS admissions declined while CC admissions accelerated and overtook VHA admissions. In December 2021, CC admissions accounted for 54% of index admissions, up from 25% in January 2016. From adjusted models, just prior to implementation (May 2019), Veterans with CC admissions had 47% greater risk of 7-day readmission (risk ratio [RR]: 1.47, 95% confidence interval [CI]: 1.43, 1.51) and 20% greater risk of 30-day readmission (RR: 1.20, 95% CI: 1.19, 1.22) compared with those with VHA admissions; both effects persisted post-implementation. Pre-implementation CC admissions were also associated with higher 7- and 30-day ED visits, but both risks were substantially lower by study termination (RR: 0.90, 95% CI: 0.88, 0.91) and (RR: 0.89, 95% CI: 0.87, 0.90), respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>MISSION Act implementation was associated with substantial shifts in treatment site and federal payor for Veteran hospitalizations. Post-implementation readmission risk was estimated to be higher for those with CC and CMS index admissions, while post-implementation risk of ED utilization following CC admissions was estimated to be lower compared with VHA index admissions. Reasons for this divergence require further investigation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201560","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":"Requiem for odds ratios","authors":"Edward C. Norton PhD,&nbsp;Bryan E. Dowd PhD,&nbsp;Melissa M. Garrido PhD,&nbsp;Matthew L. Maciejewski PhD","doi":"10.1111/1475-6773.14337","DOIUrl":"10.1111/1475-6773.14337","url":null,"abstract":"<p><i>Health Services Research</i> encourages authors to report marginal effects instead of odds ratios for logistic regression with a binary outcome. Specifically, in the instructions for authors, Manuscript Formatting and Submission Requirements, section 2.4.2.2 Structured abstract and keywords, it reads “Reporting of odds ratios is discouraged (marginal effects preferred) except in case-control studies” (see the <i>HSR</i> website https://www.hsr.org/authors/manuscript-formatting-submission-requirements).</p><p>We applaud this decision. We also encourage other journals to make the same decision. It is time to end the reporting of odds ratios in the scientific literature for most research studies, except for case–control studies with matched samples.</p><p><i>HSR</i>'s decision is due to increasing recognition that odds ratios are not only confusing to non-researchers,<span>^{1, 2}</span> but that researchers themselves often misinterpret them.<span>^{3, 4}</span> Odds ratios are also of limited utility in meta-analyses. Marginal effects, which represent the difference in the probability of a binary outcome between comparison groups, are more straightforward to interpret and compare. Below, we illustrate the difficulties in interpreting odds ratios, outline the conditions that must be met for odds ratios to be compared directly, and explain how marginal effects overcome these difficulties.</p><p>Consider a hypothetical prospective cohort study of whether a new hospital-based discharge program affects the 30-day readmission rate, a binary outcome, observed for each patient who is discharged alive. The program's goal is to help eligible patients avoid unnecessary readmissions, and patients are randomized into participating in the program or not. Suppose that a carefully designed study estimates the logistic regression coefficient (the log odds) on the discharge program to be <span></span><math>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>0.2</mn>\u0000 </mrow></math>, indicating that readmission rates are lower for patients who participate in the discharge program than patients who do not. When writing about the results, the researcher must decide how to report the magnitude of the change and has several choices for how to do so.</p><p>One option is to report the odds ratio, which in this case is <span></span><math>\u0000 <mrow>\u0000 <mn>0.82</mn>\u0000 <mo>=</mo>\u0000 <mi>exp</mi>\u0000 <mfenced>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>0.2</mn>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow></math>, and then compare it with other published odds ratios in the literature. However, this estimated odds ratio of 0.82 depends on an unobservable scaling factor that makes its interpretation conditional on the data and on the model specification.<span><sup>3, 5","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187228","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":"Medicare Advantage plan characteristics associated with sorting their beneficiaries to providers that generate fewer avoidable hospital stays","authors":"Jianhui Xu PhD,&nbsp;Kelly E. Anderson PhD,&nbsp;Angela Liu PhD,&nbsp;Daniel Polsky PhD","doi":"10.1111/1475-6773.14335","DOIUrl":"10.1111/1475-6773.14335","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine whether certain Medicare Advantage (MA) plan characteristics are associated with driving beneficiaries to providers that generate fewer avoidable hospital stays.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>This paper primarily used 2018–2019 MA encounter data and traditional Medicare (TM) claims data for a nationally representative 20% sample of Medicare beneficiaries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>For each plan design aspect—plan type, carrier, star rating, and network breadth—we estimated two adjusted Poisson regressions of avoidable hospital stays: one without clinician fixed effects and the other with. We calculated the difference between the coefficients to evaluate the extent to which patient sorting affected avoidable hospital stays relative to TM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Extraction Methods</h3>\u0000 \u0000 <p>Our sample included Medicare beneficiaries 65 years and older who were continuously enrolled in either MA or TM during 2018–2019. Beneficiaries in our sample had one or more chronic, ambulatory care-sensitive conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Patient sorting can be attributed to certain characteristics of plan design aspects. For plan type, HMOs account for 86%, with PPOs accounting for only 14%. For carriers, Humana and smaller carriers account for 89%. For star ratings, high-star contracts account for 94%, with other stars only accounting for 6%. By network design, narrow network plan-counties explained 20% of the patient sorting effect.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>While MA plans were found to be associated with driving beneficiaries to providers that generate fewer avoidable hospital stays, the effect is not homogeneous across the characteristics of MA plans. HMOs and high-star contracts are drivers of this MA phenomenon.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177056","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 role of Medicaid home- and community-based services in use of Medicare post-acute care","authors":"Sijiu Wang PhD,&nbsp;Rachel M. Werner MD, PhD,&nbsp;Norma B. Coe PhD,&nbsp;Rhys Chua MPH, MscA,&nbsp;Mingyu Qi MS,&nbsp;R. Tamara Konetzka PhD","doi":"10.1111/1475-6773.14325","DOIUrl":"10.1111/1475-6773.14325","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Medicaid-funded long-term services and supports are increasingly provided through home- and community-based services (HCBS) to promote continued community living. While an emerging body of evidence examines the direct benefits and costs of HCBS, there may also be unexplored synergies with Medicare-funded post-acute care (PAC). This study aimed to provide empirical evidence on how the use of Medicaid HCBS influences Medicare PAC utilization among the dually enrolled.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources</h3>\u0000 \u0000 <p>National Medicare claims, Medicaid claims, nursing home assessment data, and home health assessment data from 2016 to 2018.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We estimated the relationship between prior Medicaid HCBS use and PAC (skilled nursing facilities [SNF] or home health) utilization in a national sample of duals with qualifying index hospitalizations. We used inverse probability weights to create balanced samples on observed characteristics and estimated multivariable regression with hospital fixed effects and extensive controls. We also conducted stratified analyses for key subgroups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Extraction Methods</h3>\u0000 \u0000 <p>The primary sample included 887,598 hospital discharges from community-dwelling duals who had an eligible index hospitalization between April 1, 2016, and September 30, 2018.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We found HCBS use was associated with a 9 percentage-point increase in the use of home health relative to SNF, conditional on using PAC, and a meaningful reduction in length of stay for those using SNF. In addition, in our primary sample, we found HCBS use to be associated with an overall increase in PAC use, given that the absolute increase in home health use was larger than the absolute decrease in SNF use. In other words, the use of Medicaid-funded HCBS was associated with a shift in Medicare-funded PAC use toward home-based settings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our findings indicate potential synergies between Medicaid-funded HCBS and increased use of home-based PAC, suggesting policymakers should cautiously consider these dynamics in HCBS expansion efforts.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141157970","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":"\"You want people to listen to you\": Patient experiences of women's healthcare within the Veterans Health Administration.","authors":"Ashley C Mog, Samantha K Benson, Vyshnika Sriskantharajah, P Adam Kelly, Kristen E Gray, Lisa S Callegari, Ernest M Moy, Jodie G Katon","doi":"10.1111/1475-6773.14324","DOIUrl":"https://doi.org/10.1111/1475-6773.14324","url":null,"abstract":"<p><strong>Objective: </strong>To identify constructs that are critical in shaping Veterans' experiences with Veterans Health Administration (VA) women's healthcare, including any which have been underexplored or are not included in current VA surveys of patient experience.</p><p><strong>Data sources and study setting: </strong>From June 2022 to January 2023, we conducted 28 semi-structured interviews with a diverse, national sample of Veterans who use VA women's healthcare.</p><p><strong>Study design: </strong>Using VA data, we divided Veteran VA-users identified as female into four groups stratified by age (dichotomized at age 45) and race/ethnicity (non-Hispanic White vs. all other). We enrolled Veterans continuously from each recruitment strata until thematic saturation was reached.</p><p><strong>Data collection/extraction methods: </strong>For this qualitative study, we asked Veterans about past VA healthcare experiences. Interview questions were guided by a priori domains identified from review of the literature, including trust, safety, respect, privacy, communication and discrimination. Analysis occurred concurrently with interviews, using inductive and deductive content analysis.</p><p><strong>Principal findings: </strong>We identified five themes influencing Veterans' experiences of VA women's healthcare: feeling valued and supported, bodily autonomy, discrimination, past military experiences and trauma, and accessible care. Each emergent theme was associated with multiple of the a priori domains we asked about in the interview guide.</p><p><strong>Conclusions: </strong>Our findings underscore the need for a measure of patient experience tailored to VA women's healthcare. Existing patient experience measures used within VA fail to address several aspects of experience highlighted by our study, including bodily autonomy, the influence of past military experiences and trauma on healthcare, and discrimination. Understanding distinct factors that influence women and gender-diverse Veterans' experiences with VA care is critical to advance efforts by VA to measure and improve the quality and equity of care for all Veterans.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158020","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 hospital price transparency after implementation of the CMS Final Rule","authors":"Aaron Brant MD,&nbsp;Patrick Lewicki MD,&nbsp;Stephen Rhodes PhD,&nbsp;Alec Zhu MD,&nbsp;Jonathan Shoag MD","doi":"10.1111/1475-6773.14329","DOIUrl":"10.1111/1475-6773.14329","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To assess trends in hospital price disclosures after the Centers for Medicare &amp; Medicaid Services (CMS) Final Rule went into effect.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>The Turquoise Health Price Transparency Dataset was used to identify all US hospitals that publicly displayed pricing from 2021 to 2023.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Price-disclosing versus nondisclosing hospitals were compared using Pearson's Chi-squared and Wilcoxon rank sum tests. Bayesian structural time-series modeling was used to determine if enforcement of increased penalties for nondisclosure was associated with a change in the trend of hospital disclosures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Not applicable.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>As of January 2023, 5162 of 6692 (77.1%) US hospitals disclosed pricing of their services, with the majority (2794 of 5162 [54.1%]) reporting their pricing within the first 6 months of the final rule going into effect in January 2021. An increase in hospital disclosures was observed after penalties for nondisclosure were enforced in January 2022 (relative effect size 20%, <i>p</i> = 0.002). Compared with nondisclosing hospitals, disclosing hospitals had higher annual revenue, bed number, and were more likely to be have nonprofit ownership, academic affiliation, provide emergency services, and be in highly concentrated markets (<i>p</i> &lt; 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Hospital pricing disclosures are continuously in flux and influenced by regulatory and market factors.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141157972","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}