Sunita M. Desai, Prianca Padmanabhan, Kyle Smith, Jessica Chang, J. Michael McWilliams
Objective
To examine whether hospital eligibility for the 340b drug pricing program reduces prices for clinician-administered drugs in commercial insurance markets and whether effects vary by market competition.
Study Setting and Design
We conducted a quasi-experimental study using a regression discontinuity design that leverages the federal eligibility threshold for 340B participation (disproportionate share hospital [DSH] percentage > 11.75%). The study included non-profit and public acute care hospitals that billed for clinician-administered drugs in the outpatient setting between 2012 and 2014. The primary outcome was hospital-insurer negotiated unit prices for high-spending outpatient drugs. Secondary outcomes included drug volume and revenue.
Data Sources and Analytic Sample
We analyzed secondary data from the Health Care Cost Institute (HCCI), which includes claims from three national commercial insurers linked to hospital-level characteristics from the Hospital Cost Report Information System (HCRIS) and provider identifiers from the National Plan and Provider Enumeration System (NPPES). The analytic sample comprised 637 hospitals billing 148,037 clinician-administered drug claims for the five drugs with highest total spending.
Principal Findings
Hospital 340B eligibility was associated with a $605 reduction (95% CI: −934 to −276) in median unit drug prices, a 25% decrease relative to the mean price among ineligible hospitals at the threshold ($2387). Effects were concentrated in competitive markets (Herfindahl–Hirschman Index [HHI] ≤ 1800), where eligibility was associated with a $793 reduction (95% CI: −1197 to −388), a 32% decrease. In highly concentrated markets, effects were small and statistically insignificant. Price reductions were offset by non-significant increases in drug volume (25%) and neutral effects on drug revenue.
Conclusions
Hospital 340B eligibility reduced commercial drug prices only in competitive markets. These findings suggest that market competition is critical for ensuring that policy-driven hospital cost savings are shared with payers and patients.
{"title":"The 340B Drug Pricing Program, Hospital Prices, and Competition in Commercial Markets","authors":"Sunita M. Desai, Prianca Padmanabhan, Kyle Smith, Jessica Chang, J. Michael McWilliams","doi":"10.1111/1475-6773.70085","DOIUrl":"10.1111/1475-6773.70085","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine whether hospital eligibility for the 340b drug pricing program reduces prices for clinician-administered drugs in commercial insurance markets and whether effects vary by market competition.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>We conducted a quasi-experimental study using a regression discontinuity design that leverages the federal eligibility threshold for 340B participation (disproportionate share hospital [DSH] percentage > 11.75%). The study included non-profit and public acute care hospitals that billed for clinician-administered drugs in the outpatient setting between 2012 and 2014. The primary outcome was hospital-insurer negotiated unit prices for high-spending outpatient drugs. Secondary outcomes included drug volume and revenue.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We analyzed secondary data from the Health Care Cost Institute (HCCI), which includes claims from three national commercial insurers linked to hospital-level characteristics from the Hospital Cost Report Information System (HCRIS) and provider identifiers from the National Plan and Provider Enumeration System (NPPES). The analytic sample comprised 637 hospitals billing 148,037 clinician-administered drug claims for the five drugs with highest total spending.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Hospital 340B eligibility was associated with a $605 reduction (95% CI: −934 to −276) in median unit drug prices, a 25% decrease relative to the mean price among ineligible hospitals at the threshold ($2387). Effects were concentrated in competitive markets (Herfindahl–Hirschman Index [HHI] ≤ 1800), where eligibility was associated with a $793 reduction (95% CI: −1197 to −388), a 32% decrease. In highly concentrated markets, effects were small and statistically insignificant. Price reductions were offset by non-significant increases in drug volume (25%) and neutral effects on drug revenue.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Hospital 340B eligibility reduced commercial drug prices only in competitive markets. These findings suggest that market competition is critical for ensuring that policy-driven hospital cost savings are shared with payers and patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101131","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}
Alina Denham, Michael Chen, Matthew L. Maciejewski, Bruce Friedman, Bryan E. Dowd
{"title":"In Memoriam: Professor Peter J. Veazie (1963–2025)","authors":"Alina Denham, Michael Chen, Matthew L. Maciejewski, Bruce Friedman, Bryan E. Dowd","doi":"10.1111/1475-6773.70069","DOIUrl":"10.1111/1475-6773.70069","url":null,"abstract":"","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145716674","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}
Ryan Sterling, Seppo Rinne, Megan Moldestad, Christian D. Helfrich, George Sayre, Sarah Keithly, Christine Sulc, Jessica Young, Emmi Obara, Sarah Shirley, Ekaterina Cole, Edwin Wong
Objective
To measure the impact of electronic health record (EHR) transition on burnout among Veterans Health Administration (VA) frontline clinicians using pseudorandom variation from staggered EHR implementations across VA sites.
Study Setting and Design
Employing a quasi-experimental design, we studied 140 VA medical center sites nationwide (including five sites that implemented the new EHR from 2019 to 2023). Explanatory measures included year, VA transition site (grouped into three cohorts by transition timing), and their interaction. Our outcome measure encapsulated two dimensions of burnout—emotional exhaustion and depersonalization (symptoms > once per week indicated burnout).
Data Sources and Analytic Sample
Using secondary data from the 2019 to 2023 VA All Employee Survey, we aggregated survey responses on the medical-site level by year and respondent characteristics. Our analytic sample included 12,155 aggregated observations. We used a difference-in-difference approach to compare pre–post changes in burnout between VA sites implementing and not implementing the new EHR. Where available, we reported post-transition treatment effects in the short term, medium term, and long term, relative to EHR implementation.
Principal Findings
Unadjusted burnout from 2019 to 2023 was 36.9% for Cohort 1, 33.0% for Cohort 2, 37.0% for Cohort 3, and 33.2% for non-transition sites. In adjusted analyses, burnout for Cohort 1 increased 4.8 percentage points (p < 0.001) in the medium term; differences in burnout dissipated in the long term. For Cohort 2, we detected a 1 percentage point increase in burnout (p = 0.004) in the short term and a 1.5 percentage point decrease (p = 0.013) in the medium term. For Cohort 3, burnout increased 3.3 percentage points (p < 0.001) in the medium term.
Conclusions
The impact of EHR transition on burnout differed across deployment sites and post-transition periods but was mild overall. Future research is needed to understand contextual and implementation process differences between sites that may explain differential effects and offer learnings to ensure a high-functioning health workforce during EHR transition.
{"title":"Effect of Electronic Health Record Modernization on Burnout Among VA Frontline Clinicians: A Quasi-Experimental Study","authors":"Ryan Sterling, Seppo Rinne, Megan Moldestad, Christian D. Helfrich, George Sayre, Sarah Keithly, Christine Sulc, Jessica Young, Emmi Obara, Sarah Shirley, Ekaterina Cole, Edwin Wong","doi":"10.1111/1475-6773.70074","DOIUrl":"10.1111/1475-6773.70074","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To measure the impact of electronic health record (EHR) transition on burnout among Veterans Health Administration (VA) frontline clinicians using pseudorandom variation from staggered EHR implementations across VA sites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>Employing a quasi-experimental design, we studied 140 VA medical center sites nationwide (including five sites that implemented the new EHR from 2019 to 2023). Explanatory measures included year, VA transition site (grouped into three cohorts by transition timing), and their interaction. Our outcome measure encapsulated two dimensions of burnout—emotional exhaustion and depersonalization (symptoms > once per week indicated burnout).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>Using secondary data from the 2019 to 2023 VA All Employee Survey, we aggregated survey responses on the medical-site level by year and respondent characteristics. Our analytic sample included 12,155 aggregated observations. We used a difference-in-difference approach to compare pre–post changes in burnout between VA sites implementing and not implementing the new EHR. Where available, we reported post-transition treatment effects in the short term, medium term, and long term, relative to EHR implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Unadjusted burnout from 2019 to 2023 was 36.9% for Cohort 1, 33.0% for Cohort 2, 37.0% for Cohort 3, and 33.2% for non-transition sites. In adjusted analyses, burnout for Cohort 1 increased 4.8 percentage points (<i>p</i> < 0.001) in the medium term; differences in burnout dissipated in the long term. For Cohort 2, we detected a 1 percentage point increase in burnout (<i>p</i> = 0.004) in the short term and a 1.5 percentage point decrease (<i>p</i> = 0.013) in the medium term. For Cohort 3, burnout increased 3.3 percentage points (<i>p</i> < 0.001) in the medium term.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The impact of EHR transition on burnout differed across deployment sites and post-transition periods but was mild overall. Future research is needed to understand contextual and implementation process differences between sites that may explain differential effects and offer learnings to ensure a high-functioning health workforce during EHR transition.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679721","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}
<p>Traumatic injury is a leading cause of death and disability across all age groups in the United States, yet major gaps persist in the provision and coordination of trauma care [<span>1</span>]. The National Safety Council estimates an economic burden of US $1.3 trillion per year in direct medical costs, work-loss, and quality-of-life decrements, while CDC modeling places the broader societal cost of injury at US $4.2 trillion [<span>2, 3</span>].</p><p>Survival after severe injury can depend on where the patient is located. County-level analyses show the risk of prehospital trauma death is 25% higher in small fringe-metropolitan counties and 69% higher in rural non-core counties than in large metropolitan cores [<span>4</span>], and trauma patients from rural communities are 14% more likely to die from their injuries compared to urban residents [<span>5</span>]. Rural communities already face recorded disparities in access to care and poorer health outcomes; for example, the natural-cause mortality (NCM), defined as stemming from disease-related deaths, for the working age population (25–54 years) in rural areas is 43% higher than their urban/metropolitan counterparts [<span>6</span>]. National estimates indicate that under-triage remains widespread: almost one-half of trauma patients who ultimately die in the emergency department arrive at non-trauma centers, including 86% of rural cases and 36% of urban cases, showcasing some of the stark geographic gaps in access to definitive care [<span>7</span>]. Contemporary registry data corroborate the problem, showing that one in five injured patients whose injuries truly warrant a full trauma team activation still only receive a limited response, with under-triage rates differing across trauma center levels and patient demographics [<span>8</span>].</p><p>Nearly 20 years since trauma centers were established as the best source of care for critically injured patients [<span>9</span>], and 10 years after the National Academies of Science, Engineering, and Medicine called for a robust national trauma system, disparities in trauma outcomes continue to plague many states and vulnerable populations [<span>10</span>]. These disparities are exacerbated by uneven legislation, variable funding for local emergency medical services (EMS), and persistently high rates of under- and over-triage [<span>11, 12</span>]. A health systems approach, combined with responsive legislation at all levels of government, is essential to address these deficiencies. State-level legislation, in particular, has the potential to play an important role in complementing federal legislation, especially at times when federal priorities may be structurally misaligned with the needs of different regions. In this commentary, we (i) synthesize where US trauma systems underperform, (ii) situate those gaps against international experience and US trends, and (iii) propose concrete, state-led legislative levers, paired with federal catalysts a
{"title":"Advancing Trauma Systems in the United States: Bridging Disparities Through State-Level Legislation and a Health Systems Approach","authors":"Bilal Irfan, Zain Hashmi, Tatiana Ramos, Molly Jarman","doi":"10.1111/1475-6773.70073","DOIUrl":"10.1111/1475-6773.70073","url":null,"abstract":"<p>Traumatic injury is a leading cause of death and disability across all age groups in the United States, yet major gaps persist in the provision and coordination of trauma care [<span>1</span>]. The National Safety Council estimates an economic burden of US $1.3 trillion per year in direct medical costs, work-loss, and quality-of-life decrements, while CDC modeling places the broader societal cost of injury at US $4.2 trillion [<span>2, 3</span>].</p><p>Survival after severe injury can depend on where the patient is located. County-level analyses show the risk of prehospital trauma death is 25% higher in small fringe-metropolitan counties and 69% higher in rural non-core counties than in large metropolitan cores [<span>4</span>], and trauma patients from rural communities are 14% more likely to die from their injuries compared to urban residents [<span>5</span>]. Rural communities already face recorded disparities in access to care and poorer health outcomes; for example, the natural-cause mortality (NCM), defined as stemming from disease-related deaths, for the working age population (25–54 years) in rural areas is 43% higher than their urban/metropolitan counterparts [<span>6</span>]. National estimates indicate that under-triage remains widespread: almost one-half of trauma patients who ultimately die in the emergency department arrive at non-trauma centers, including 86% of rural cases and 36% of urban cases, showcasing some of the stark geographic gaps in access to definitive care [<span>7</span>]. Contemporary registry data corroborate the problem, showing that one in five injured patients whose injuries truly warrant a full trauma team activation still only receive a limited response, with under-triage rates differing across trauma center levels and patient demographics [<span>8</span>].</p><p>Nearly 20 years since trauma centers were established as the best source of care for critically injured patients [<span>9</span>], and 10 years after the National Academies of Science, Engineering, and Medicine called for a robust national trauma system, disparities in trauma outcomes continue to plague many states and vulnerable populations [<span>10</span>]. These disparities are exacerbated by uneven legislation, variable funding for local emergency medical services (EMS), and persistently high rates of under- and over-triage [<span>11, 12</span>]. A health systems approach, combined with responsive legislation at all levels of government, is essential to address these deficiencies. State-level legislation, in particular, has the potential to play an important role in complementing federal legislation, especially at times when federal priorities may be structurally misaligned with the needs of different regions. In this commentary, we (i) synthesize where US trauma systems underperform, (ii) situate those gaps against international experience and US trends, and (iii) propose concrete, state-led legislative levers, paired with federal catalysts a","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145656312","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":"Financialization and the Fragility of Maternal Health Access","authors":"Yashaswini Singh","doi":"10.1111/1475-6773.70072","DOIUrl":"10.1111/1475-6773.70072","url":null,"abstract":"","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145642909","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}
Eileen Yang, Stephen Salerno, Claudia Dahlerus, Richard A. Hirth, Tao Xu, Ashley Eckard, Wilfred Agbenyikey, Golden M. Horton, Stephanie Clark, Joseph M. Messana, Yi Li
Objective
To evaluate how adding kidney transplantation waitlisting measures—the Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) and Percentage of Prevalent Patients Waitlisted (PPPW)—affects Dialysis Facility Care Compare Star Ratings.
Study Setting and Design
In this observational, cross-sectional study, we calculated the difference between facilities' published (with waitlisting measures) and counterfactual (without waitlisting measures) Star Ratings. We used multinomial regression to examine associations between Star Rating changes after waitlisting measure inclusion and facility characteristics and calculated corresponding average risk differences.
Data Sources and Analytic Sample
We used comprehensive clinical and administrative data from the Centers for Medicare/Medicaid Services from 2021 to investigate the impact of waitlisting measure addition on Star Ratings. Facility characteristics included demographic and patient mix, area deprivation index (ADI), dialysis organization affiliation, and urbanicity.
Principal Findings
36.5% of facilities' ratings changed after waitlisting measures were added. Facility characteristics associated with a higher average risk of Star increase included location in low-ADI (0.091; 95% CI: 0.072, 0.109) or urban areas (0.061; 95% CI: 0.034, 0.087), independent/small dialysis organization affiliation (0.062; 95% CI: 0.041, 0.083), and having more PD patients (0.115; 95% CI: 0.093, 0.138). Characteristics associated with a higher average risk of Star decrease included high-ADI (0.075; 95% CI: 0.054, 0.095) or rural (0.056; 95% CI: 0.028, 0.083) location, large dialysis organization affiliation (0.058; 95% CI: 0.039, 0.078), having more patients with dual Medicare/Medicaid eligibility (0.052; 95% CI: 0.032, 0.071), and having fewer peritoneal dialysis patients (0.100; 95% CI: 0.081, 0.120).
Conclusions
Including waitlisting measures significantly impacts the Star Ratings and captures a new dimension of care quality. Worse socioeconomic status-related facility characteristics were strongly associated with worse Star Rating outcomes. These findings can inform future discussions about risk adjustment among the developers of the SWR and PPPW measures.
{"title":"The Impact of Transplant Waitlisting Measures on Dialysis Facilities' Star Ratings","authors":"Eileen Yang, Stephen Salerno, Claudia Dahlerus, Richard A. Hirth, Tao Xu, Ashley Eckard, Wilfred Agbenyikey, Golden M. Horton, Stephanie Clark, Joseph M. Messana, Yi Li","doi":"10.1111/1475-6773.70071","DOIUrl":"10.1111/1475-6773.70071","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To evaluate how adding kidney transplantation waitlisting measures—the Standardized First Kidney Transplant Waitlist Ratio for Incident Dialysis Patients (SWR) and Percentage of Prevalent Patients Waitlisted (PPPW)—affects Dialysis Facility Care Compare Star Ratings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>In this observational, cross-sectional study, we calculated the difference between facilities' published (with waitlisting measures) and counterfactual (without waitlisting measures) Star Ratings. We used multinomial regression to examine associations between Star Rating changes after waitlisting measure inclusion and facility characteristics and calculated corresponding average risk differences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We used comprehensive clinical and administrative data from the Centers for Medicare/Medicaid Services from 2021 to investigate the impact of waitlisting measure addition on Star Ratings. Facility characteristics included demographic and patient mix, area deprivation index (ADI), dialysis organization affiliation, and urbanicity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>36.5% of facilities' ratings changed after waitlisting measures were added. Facility characteristics associated with a higher average risk of Star increase included location in low-ADI (0.091; 95% CI: 0.072, 0.109) or urban areas (0.061; 95% CI: 0.034, 0.087), independent/small dialysis organization affiliation (0.062; 95% CI: 0.041, 0.083), and having more PD patients (0.115; 95% CI: 0.093, 0.138). Characteristics associated with a higher average risk of Star decrease included high-ADI (0.075; 95% CI: 0.054, 0.095) or rural (0.056; 95% CI: 0.028, 0.083) location, large dialysis organization affiliation (0.058; 95% CI: 0.039, 0.078), having more patients with dual Medicare/Medicaid eligibility (0.052; 95% CI: 0.032, 0.071), and having fewer peritoneal dialysis patients (0.100; 95% CI: 0.081, 0.120).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Including waitlisting measures significantly impacts the Star Ratings and captures a new dimension of care quality. Worse socioeconomic status-related facility characteristics were strongly associated with worse Star Rating outcomes. These findings can inform future discussions about risk adjustment among the developers of the SWR and PPPW measures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589705","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}
Ashlee A. Korsberg, Gabriel A. Brooks, A. James O'Malley, Tracy Onega, Andrew P. Schaefer, Erika L. Moen
Objective
The objectives of this study were to evaluate associations of social deprivation with acute care utilization among patients with cancer, and to examine potential effect modification by physician network vulnerability.
Study Setting and Design
For this retrospective cohort study, the primary exposure variable was neighborhood-level socioeconomic disadvantage, operationalized through the social deprivation index (SDI). We assembled physician patient-sharing networks and calculated a measure of network vulnerability for each referral region to capture specialist scarcity. The two outcomes of interest were counts of emergency department (ED) visits and non-elective hospitalizations during the 12 months following cancer diagnosis. We conducted hurdle regressions, with logistic and negative binomial mixed-effects models for the zero and positive, non-zero parts of the outcome distribution, respectively, and stratified by physician network vulnerability.
Data Sources and Analytic Sample
We analyzed 2016–2020 Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data for Medicare beneficiaries diagnosed with breast, colorectal, or lung cancer.
Principal Findings
The study cohort comprised 47,756 patients with breast, colorectal or lung cancer. Patients in high SDI neighborhoods (vs. low) had a higher probability of at least one ED visit across all physician network vulnerability strata (low network vulnerability—average marginal effect (AME) [95% CI]: 0.03 [0.01–0.05]; medium network vulnerability—AME [95% CI]: 0.03 [0.01–0.04]; high network vulnerability—AME [95% CI]: 0.05 [0.02–0.08]). Conditional on at least one ED visit, patients in high SDI neighborhoods (vs. low) had a greater relative risk of additional ED visits when their region was characterized by low physician network vulnerability (RR [95% CI]: 1.25 [1.09–1.43]).
Conclusions
Our findings suggest that SDI and physician network vulnerability interact to increase the probability and likelihood of ED visits, but the interaction was minimal for non-elective hospitalizations. More research is needed to better understand how social drivers of health and oncology workforce scarcity affect care utilization and outcomes in patients with cancer.
{"title":"Associations of Social Deprivation and Oncology Physician Network Vulnerability With Acute Care Utilization in the SEER-Medicare Population","authors":"Ashlee A. Korsberg, Gabriel A. Brooks, A. James O'Malley, Tracy Onega, Andrew P. Schaefer, Erika L. Moen","doi":"10.1111/1475-6773.70070","DOIUrl":"10.1111/1475-6773.70070","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The objectives of this study were to evaluate associations of social deprivation with acute care utilization among patients with cancer, and to examine potential effect modification by physician network vulnerability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>For this retrospective cohort study, the primary exposure variable was neighborhood-level socioeconomic disadvantage, operationalized through the social deprivation index (SDI). We assembled physician patient-sharing networks and calculated a measure of network vulnerability for each referral region to capture specialist scarcity. The two outcomes of interest were counts of emergency department (ED) visits and non-elective hospitalizations during the 12 months following cancer diagnosis. We conducted hurdle regressions, with logistic and negative binomial mixed-effects models for the zero and positive, non-zero parts of the outcome distribution, respectively, and stratified by physician network vulnerability.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We analyzed 2016–2020 Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data for Medicare beneficiaries diagnosed with breast, colorectal, or lung cancer.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The study cohort comprised 47,756 patients with breast, colorectal or lung cancer. Patients in high SDI neighborhoods (vs. low) had a higher probability of at least one ED visit across all physician network vulnerability strata (low network vulnerability—average marginal effect (AME) [95% CI]: 0.03 [0.01–0.05]; medium network vulnerability—AME [95% CI]: 0.03 [0.01–0.04]; high network vulnerability—AME [95% CI]: 0.05 [0.02–0.08]). Conditional on at least one ED visit, patients in high SDI neighborhoods (vs. low) had a greater relative risk of additional ED visits when their region was characterized by low physician network vulnerability (RR [95% CI]: 1.25 [1.09–1.43]).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings suggest that SDI and physician network vulnerability interact to increase the probability and likelihood of ED visits, but the interaction was minimal for non-elective hospitalizations. More research is needed to better understand how social drivers of health and oncology workforce scarcity affect care utilization and outcomes in patients with cancer.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12807442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145551892","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}
Fredric Blavin, Breno Braga, Michael Karpman, Dulce Gonzalez, Maanasa Kona
Objective
To test if state consumer protection policies reduce the share of consumers with medical debt in collections on their credit reports.
Study Setting and Design
This study uses a quasi-experimental research design to estimate the impact of consumer protection laws implemented between 2020 and 2022 in Illinois, Maryland, New Mexico, and Oregon on the share of consumers with medical debt in collections. These laws primarily aim to protect consumers against medical debt by expanding access to hospital financial assistance. We use a synthetic control approach to estimate changes in medical debt following the implementation of policies in treatment states relative to changes in select control states. We also assess the effects of earlier policies implemented between 2013 and 2019 in Washington, Utah, and North Carolina.
Data Sources and Analytic Sample
This analysis relies on two extracts of credit bureau data from one of the country's three main credit bureau agencies. The first extract consists of random samples from June 2017 to June 2024 of approximately 125,000 consumers in each treatment state and 500,000 residents from the pool of 14 selected comparison states in each year. The second extract is based on a 2%–4% random sample of consumers in each year from 2011 to 2022.
Principal Findings
We did not observe a statistically significant reduction in medical debt associated with policies implemented in these states within the study timeframe. In most states in our primary analysis, point estimates of the treatment effects are near zero, and in nearly all state-years, we can only rule out declines in medical debt larger than 1–3 percentage points following policy implementation.
Conclusions
Though we did not detect statistically significant effects of recent consumer protection policies on medical debt in collections, additional research is needed on whether these policies benefited consumers in ways that are not measured in this analysis and whether states that continue to move forward with similar laws can improve their effectiveness by extending consumer protections to a wider group of patients and providers and addressing implementation and enforcement challenges.
{"title":"Exploring the Early Effects of State Consumer Protection Policies on Medical Debt in Collections","authors":"Fredric Blavin, Breno Braga, Michael Karpman, Dulce Gonzalez, Maanasa Kona","doi":"10.1111/1475-6773.70068","DOIUrl":"10.1111/1475-6773.70068","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To test if state consumer protection policies reduce the share of consumers with medical debt in collections on their credit reports.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>This study uses a quasi-experimental research design to estimate the impact of consumer protection laws implemented between 2020 and 2022 in Illinois, Maryland, New Mexico, and Oregon on the share of consumers with medical debt in collections. These laws primarily aim to protect consumers against medical debt by expanding access to hospital financial assistance. We use a synthetic control approach to estimate changes in medical debt following the implementation of policies in treatment states relative to changes in select control states. We also assess the effects of earlier policies implemented between 2013 and 2019 in Washington, Utah, and North Carolina.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>This analysis relies on two extracts of credit bureau data from one of the country's three main credit bureau agencies. The first extract consists of random samples from June 2017 to June 2024 of approximately 125,000 consumers in each treatment state and 500,000 residents from the pool of 14 selected comparison states in each year. The second extract is based on a 2%–4% random sample of consumers in each year from 2011 to 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We did not observe a statistically significant reduction in medical debt associated with policies implemented in these states within the study timeframe. In most states in our primary analysis, point estimates of the treatment effects are near zero, and in nearly all state-years, we can only rule out declines in medical debt larger than 1–3 percentage points following policy implementation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Though we did not detect statistically significant effects of recent consumer protection policies on medical debt in collections, additional research is needed on whether these policies benefited consumers in ways that are not measured in this analysis and whether states that continue to move forward with similar laws can improve their effectiveness by extending consumer protections to a wider group of patients and providers and addressing implementation and enforcement challenges.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145544279","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}
Amanda C. Chen, J. Michael McWilliams, Mary Beth Landrum, David C. Grabowski
Objective
To estimate the effect of starting a provider-led Institutional Special Needs Plan (I-SNP) arrangement on facility-level enrollment, utilization, and quality.
Study Setting and Design
I-SNPs are a type of Medicare Advantage (MA) plan that allows insurers to differentiate their benefits exclusively for long-term residents in nursing homes. Since I-SNPs first became available in 2006, there has been growth in provider-led I-SNPs where nursing homes are financially integrated or partnered with an insurer to operate a plan for their own residents. We used a difference-in-differences design to estimate the effect of starting a provider-led I-SNP arrangement on several facility-level outcomes, including the share of a facility's long-stay residents who were enrolled in an I-SNP, hospitalizations, medication use, pressure ulcers, physical restraints, falls, and mortality.
Data Sources and Analytic Sample
We used Medicare claims and nursing home resident assessments (2004–2021) to identify Medicare long-stay nursing home residents.
Principal Findings
The start of a provider-led I-SNP arrangement led to a 17.0 percentage point (pp) increase (standard error [SE]: 0.006) in I-SNP enrollment among facility residents within 4 years relative to control nursing homes. We also estimate that the start of a provider-led I-SNP arrangement significantly decreased hospitalizations (−1.0 pp, SE: 0.002), increased the use of antipsychotic (0.4 pp, SE: 0.002) and hypnotic drugs (0.3 pp, SE: 0.001), and reporting of pressure ulcers (0.4 pp, SE: 0.002).
Conclusions
Provider-led I-SNPs allow nursing homes to bear financial risk for their residents. These results suggest that this form of risk bearing may successfully reduce utilization (e.g., hospitalizations), but with unclear implications for quality as increased use of sedating drugs and rates of pressure ulcers could either reflect poorer care or retention of sicker patients due to lower hospitalization rates.
{"title":"Nursing Homes as Insurers? The Effect of Provider-Led Institutional Special Needs Plans","authors":"Amanda C. Chen, J. Michael McWilliams, Mary Beth Landrum, David C. Grabowski","doi":"10.1111/1475-6773.70067","DOIUrl":"10.1111/1475-6773.70067","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To estimate the effect of starting a provider-led Institutional Special Needs Plan (I-SNP) arrangement on facility-level enrollment, utilization, and quality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>I-SNPs are a type of Medicare Advantage (MA) plan that allows insurers to differentiate their benefits exclusively for long-term residents in nursing homes. Since I-SNPs first became available in 2006, there has been growth in provider-led I-SNPs where nursing homes are financially integrated or partnered with an insurer to operate a plan for their own residents. We used a difference-in-differences design to estimate the effect of starting a provider-led I-SNP arrangement on several facility-level outcomes, including the share of a facility's long-stay residents who were enrolled in an I-SNP, hospitalizations, medication use, pressure ulcers, physical restraints, falls, and mortality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We used Medicare claims and nursing home resident assessments (2004–2021) to identify Medicare long-stay nursing home residents.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The start of a provider-led I-SNP arrangement led to a 17.0 percentage point (pp) increase (standard error [SE]: 0.006) in I-SNP enrollment among facility residents within 4 years relative to control nursing homes. We also estimate that the start of a provider-led I-SNP arrangement significantly decreased hospitalizations (−1.0 pp, SE: 0.002), increased the use of antipsychotic (0.4 pp, SE: 0.002) and hypnotic drugs (0.3 pp, SE: 0.001), and reporting of pressure ulcers (0.4 pp, SE: 0.002).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Provider-led I-SNPs allow nursing homes to bear financial risk for their residents. These results suggest that this form of risk bearing may successfully reduce utilization (e.g., hospitalizations), but with unclear implications for quality as increased use of sedating drugs and rates of pressure ulcers could either reflect poorer care or retention of sicker patients due to lower hospitalization rates.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483933","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}
Thomas G. McGuire, Oana M. Enache, Michael Chernew, J. Michael McWilliams, Tram Nham, Sherri Rose
Objective
To define measures of Medicare diagnosis coding intensity that capture the dynamics of changes in coding practices.
Study Setting and Design
Retrospective analysis of coding for risk adjustment using observational claims data from Medicare beneficiaries.
Data Sources
Enrollment and claims data from 2017 and 2018 of a random 20% sample of Medicare beneficiaries were subset to those assigned to an Accountable Care Organization in 2018.
Principal Findings
We decompose the prevalence of a diagnosis code into incidence (proportion of beneficiaries that newly have the code) and persistence (proportion of beneficiaries who previously had the code and continue to do so). Together these define steady-state prevalence, the hypothetical long-run prevalence implied by no changes in current rates of incidence and persistence of coding. Steady-state prevalence can help explain why observed prevalence tends to grow over time without continued behavioral change. For example, our measures suggest that the prevalence of the Specified Heart Arrhythmias diagnosis would continue to rise from 18.7% in 2018 to 28.0% without changes in coding practices.
Conclusions
Researchers and policymakers can better understand why changes in coding practices can take years to be fully reflected in data and monitor coding behavior by using our proposed measures.
{"title":"Incidence, Persistence, and Steady-State Prevalence in Coding Intensity for Health Plan Payment","authors":"Thomas G. McGuire, Oana M. Enache, Michael Chernew, J. Michael McWilliams, Tram Nham, Sherri Rose","doi":"10.1111/1475-6773.70065","DOIUrl":"10.1111/1475-6773.70065","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To define measures of Medicare diagnosis coding intensity that capture the dynamics of changes in coding practices.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>Retrospective analysis of coding for risk adjustment using observational claims data from Medicare beneficiaries.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources</h3>\u0000 <p>Enrollment and claims data from 2017 and 2018 of a random 20% sample of Medicare beneficiaries were subset to those assigned to an Accountable Care Organization in 2018.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>We decompose the prevalence of a diagnosis code into incidence (proportion of beneficiaries that newly have the code) and persistence (proportion of beneficiaries who previously had the code and continue to do so). Together these define steady-state prevalence, the hypothetical long-run prevalence implied by no changes in current rates of incidence and persistence of coding. Steady-state prevalence can help explain why observed prevalence tends to grow over time without continued behavioral change. For example, our measures suggest that the prevalence of the Specified Heart Arrhythmias diagnosis would continue to rise from 18.7% in 2018 to 28.0% without changes in coding practices.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>Researchers and policymakers can better understand why changes in coding practices can take years to be fully reflected in data and monitor coding behavior by using our proposed measures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145472503","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}
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