Megan E. Vanneman, Ciaran S. Phibbs, Michael K. Ong, Yue Zhang, Adam Chow, Jean Yoon
Objective
To compare outcomes for Department of Veterans Affairs (VA) enrollees' behavioral health (BH) hospitalizations by source (VA-direct, VA-purchased community care (CC), Medicaid, Medicare, private insurance, and other payers).
Study Setting and Design
We conducted a retrospective, longitudinal study with VA enrollees from 2015 to 2017 to examine differences in BH hospitalization outcomes by source. We used generalized linear models with clustered standard errors to predict length of stay (LOS), cost, and 30-day readmission.
Data Sources and Analytic Sample
We studied 124,609 BH hospitalizations of 77,299 VA enrollees in 11 geographically diverse states.
Principal Findings
Predicted mean LOS (9.03 days, 95% CI 8.92–9.14 days; p < 0.001) and cost ($17,608, 95% CI $17,347–$17,870; p < 0.001) were highest for VA-direct hospitalizations, while the mean readmission rate was lowest for VA-direct hospitalizations (17.36%, 95% CI 17.03%–17.69%; p < 0.001). Average marginal effects for each non-VA hospitalization source were statistically significantly different from VA-direct hospitalizations (p < 0.001): between 2.13 and 2.90 days less for LOS, $11,141 to $12,144 less for cost, and 2.71% to 5.18% higher for readmission rate.
Conclusions
The majority of BH hospitalizations were in VA-direct care (56%), with 44% provided in locations outside VA hospitals: Medicare (19%), CC (7%), private insurance (7%), other payers (6%), and Medicaid (5%). There are trade-offs between BH hospitalizations provided in VA-direct care (lowest readmission rate, highest LOS and costs) and other sources.
目的：比较退伍军人事务部（VA）入选者按来源（VA直接、VA购买的社区护理（CC）、Medicaid、Medicare、私人保险和其他支付者）的行为健康（BH）住院治疗的结果。研究设置和设计：我们对2015年至2017年VA入组者进行了一项回顾性纵向研究，以检查不同来源的BH住院结果的差异。我们使用具有聚类标准误差的广义线性模型来预测住院时间（LOS）、费用和30天再入院。数据来源和分析样本：我们研究了11个地理位置不同的州77,299名VA注册者的124,609例BH住院。主要发现：预测平均LOS(9.03天，95% CI 8.92-9.14天；p结论：大多数BH住院是VA直接护理（56%），44%在VA医院以外的地方提供：医疗保险（19%），CC(7%)，私人保险（7%），其他付款人（6%）和医疗补助（5%）。在va直接护理中提供的BH住院治疗（再入院率最低，LOS和费用最高）与其他来源之间存在权衡。
{"title":"Veterans' Behavioral Health Hospitalizations and Outcomes in VA Versus Non-VA Hospitals","authors":"Megan E. Vanneman, Ciaran S. Phibbs, Michael K. Ong, Yue Zhang, Adam Chow, Jean Yoon","doi":"10.1111/1475-6773.70013","DOIUrl":"10.1111/1475-6773.70013","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To compare outcomes for Department of Veterans Affairs (VA) enrollees' behavioral health (BH) hospitalizations by source (VA-direct, VA-purchased community care (CC), Medicaid, Medicare, private insurance, and other payers).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>We conducted a retrospective, longitudinal study with VA enrollees from 2015 to 2017 to examine differences in BH hospitalization outcomes by source. We used generalized linear models with clustered standard errors to predict length of stay (LOS), cost, and 30-day readmission.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We studied 124,609 BH hospitalizations of 77,299 VA enrollees in 11 geographically diverse states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Predicted mean LOS (9.03 days, 95% CI 8.92–9.14 days; <i>p</i> < 0.001) and cost ($17,608, 95% CI $17,347–$17,870; <i>p</i> < 0.001) were highest for VA-direct hospitalizations, while the mean readmission rate was lowest for VA-direct hospitalizations (17.36%, 95% CI 17.03%–17.69%; <i>p</i> < 0.001). Average marginal effects for each non-VA hospitalization source were statistically significantly different from VA-direct hospitalizations (<i>p</i> < 0.001): between 2.13 and 2.90 days less for LOS, $11,141 to $12,144 less for cost, and 2.71% to 5.18% higher for readmission rate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The majority of BH hospitalizations were in VA-direct care (56%), with 44% provided in locations outside VA hospitals: Medicare (19%), CC (7%), private insurance (7%), other payers (6%), and Medicaid (5%). There are trade-offs between BH hospitalizations provided in VA-direct care (lowest readmission rate, highest LOS and costs) and other sources.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kameswari Potharaju, Laura M. Gottlieb, Holly E. Wing, Alejandra Gonzalez-Rocha, Amanda L. Brewster, Danielle Hessler Jones, Andrea Quiñones-Rivera
Objective
To identify key factors that define patient experiences of social care in healthcare settings.
Study Setting and Design
This is a qualitative study using interviews from participants recruited by collaborators of a social care research group from across the United States.
Data Sources and Analytic Sample
We conducted 30 semi-structured interviews between September 2023 and February 2024. Participants were 18 or older, English- or Spanish-speaking, and had received social care in a healthcare setting within the last 12 months. Interview transcripts were dually coded and analyzed using a mixed inductive-deductive approach.
Principal Findings
Patient experience was defined by elements of social care delivery that fell into two categories: the functional and relational domains of social care. Participants reported that operational or “functional” aspects of social care, including screening, resource connections, and other forms of follow-up, represented an important part of their experiences of social care. Experiences of social care were also defined by relational factors, for example, demonstrations of empathy, positive perceptions of screening intentions, linguistic concordance, and longitudinal relationships with the care team. Many participants felt that these functional and relational factors were inextricably linked.
Conclusions
The impressive role that relational factors—that is, interactions and relationships with social care providers—play in defining patient experiences highlights the need to include these factors in efforts to evaluate social care interventions. Discussions about social needs may retain value even in the absence of available resources if healthcare teams attend to the relational factors that drive patients' social care experiences. In the future, measures of social care quality should account for both the functional and relational dimensions of social care.
{"title":"Drivers of Patient Experiences With Healthcare-Based Social Care","authors":"Kameswari Potharaju, Laura M. Gottlieb, Holly E. Wing, Alejandra Gonzalez-Rocha, Amanda L. Brewster, Danielle Hessler Jones, Andrea Quiñones-Rivera","doi":"10.1111/1475-6773.70020","DOIUrl":"10.1111/1475-6773.70020","url":null,"abstract":"<div>\u0000 <section>\u0000 <h3> Objective</h3>\u0000 <p>To identify key factors that define patient experiences of social care in healthcare settings.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Study Setting and Design</h3>\u0000 <p>This is a qualitative study using interviews from participants recruited by collaborators of a social care research group from across the United States.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 <p>We conducted 30 semi-structured interviews between September 2023 and February 2024. Participants were 18 or older, English- or Spanish-speaking, and had received social care in a healthcare setting within the last 12 months. Interview transcripts were dually coded and analyzed using a mixed inductive-deductive approach.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Principal Findings</h3>\u0000 <p>Patient experience was defined by elements of social care delivery that fell into two categories: the functional and relational domains of social care. Participants reported that operational or “functional” aspects of social care, including screening, resource connections, and other forms of follow-up, represented an important part of their experiences of social care. Experiences of social care were also defined by relational factors, for example, demonstrations of empathy, positive perceptions of screening intentions, linguistic concordance, and longitudinal relationships with the care team. Many participants felt that these functional and relational factors were inextricably linked.</p>\u0000 </section>\u0000 <section>\u0000 <h3> Conclusions</h3>\u0000 <p>The impressive role that relational factors—that is, interactions and relationships with social care providers—play in defining patient experiences highlights the need to include these factors in efforts to evaluate social care interventions. Discussions about social needs may retain value even in the absence of available resources if healthcare teams attend to the relational factors that drive patients' social care experiences. In the future, measures of social care quality should account for both the functional and relational dimensions of social care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":"1-9"},"PeriodicalIF":3.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maricela Cruz, Susan M. Shortreed, Gregory E. Simon, Yates Coley
Objective
To compare alternative Difference-in-Differences (DID) methods for evaluating the effect of risk-stratified interventions, or interventions targeting at-risk groups, on binary outcomes.
Study Setting and Design
In simulations, we compared operating characteristics of recycled prediction estimators for common average treatment effect on the treated (ATT) estimands across three DID models: the traditional two groups and two periods model, a risk score adjusted model, and a model adjusting for risk score and its interactions with risk group and period. We compared DID ATT estimates to randomized evaluation estimates of a risk-stratified intervention implemented at Kaiser Permanente Washington (KPWA), delivering additional text-message reminders to reduce missed clinic visits.
Data Sources and Analytic Sample
Our study included 588,503 KPWA visits, with 285,814 (49%) visits pre-evaluation (05/01/2018–10/30/2018) and 302,689 (51%) visits during the evaluation (02/01/2019–09/30/2019). Pre-evaluation, 120,350 visits were classified as high-risk. During the evaluation, 125,076 visits were labeled as high-risk, with 62,557 (50%) randomized to the intervention. We generated data in simulations based on this setting.
Principal Findings
In simulations, the traditional DID and risk score adjusted models had smaller bias and standard errors, and better coverage probabilities. DID estimates closest to randomized evaluation estimates (−0.007, 95% CI [−0.010, −0.004]) were from the traditional DID model assuming the identity link (−0.008, 95% CI [−0.011, −0.005]) or the risk adjusted model with any link (−0.006, 95% CI [−0.008, −0.003] identity; −0.007, 95% CI [−0.011, −0.003] logit; −0.007, 95% CI [−0.012, −0.003] log) for the ATT on the absolute difference scale (usual DID ATT estimand), and the risk score adjusted model with log or logit links for all other estimands.
Conclusions
Compared with randomized evaluation results, the traditional DID model is appropriate for the ATT on the absolute difference scale, while the risk score adjusted model with log or logit links is appropriate for all ATT estimands considered.
目的：比较不同的差分法（DID）来评估风险分层干预或针对高危人群的干预对二元结果的影响。研究设置和设计：在模拟中，我们比较了三种DID模型（传统的两组两期模型、风险评分调整模型和风险评分及其与风险组和时期的相互作用调整模型）中回收预测估计器的共同平均处理效果（ATT）估计的运行特征。我们比较了DID ATT估计值与Kaiser Permanente Washington （KPWA）实施的风险分层干预的随机评估估计值，提供额外的短信提醒以减少错过的诊所就诊。数据来源及分析样本：我们的研究包括588,503次KPWA访问，其中评估前（2018/01/05 - 2018/10/30）访问285,814次（49%），评估期间（2019年1月02 - 2019年9月30日）访问302,689次（51%）。预评估中，120,350次就诊被归为高风险。在评估期间，125,076次就诊被标记为高风险，其中62,557次（50%）随机分配到干预组。我们基于这个设置在模拟中生成数据。主要发现：在模拟中，传统的DID和风险评分调整模型具有较小的偏差和标准误差，并且具有更好的覆盖概率。最接近随机评价估计的DID估计（-0.007,95% CI[-0.010, -0.004]）来自传统的DID模型，假设存在身份关联（-0.008,95% CI[-0.011, -0.005]）或具有任何联系的风险调整模型（-0.006,95% CI [-0.008, -0.003]）；-0.007, 95% CI [-0.011, -0.003] logit；-0.007, 95% CI [-0.012, -0.003] log)用于绝对差量表上的ATT（通常的DID ATT估计），风险评分调整模型与log或logit链接用于所有其他估计。结论：与随机评价结果相比，传统DID模型适用于绝对差量表上的ATT，而带有log或logit链接的风险评分调整模型适用于所有考虑的ATT估计值。
{"title":"Evaluating Clinical Implementation of Risk Prediction Based Interventions Using Difference-In-Differences","authors":"Maricela Cruz, Susan M. Shortreed, Gregory E. Simon, Yates Coley","doi":"10.1111/1475-6773.70015","DOIUrl":"10.1111/1475-6773.70015","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To compare alternative Difference-in-Differences (DID) methods for evaluating the effect of risk-stratified interventions, or interventions targeting at-risk groups, on binary outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>In simulations, we compared operating characteristics of recycled prediction estimators for common average treatment effect on the treated (ATT) estimands across three DID models: the traditional two groups and two periods model, a risk score adjusted model, and a model adjusting for risk score and its interactions with risk group and period. We compared DID ATT estimates to randomized evaluation estimates of a risk-stratified intervention implemented at Kaiser Permanente Washington (KPWA), delivering additional text-message reminders to reduce missed clinic visits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>Our study included 588,503 KPWA visits, with 285,814 (49%) visits pre-evaluation (05/01/2018–10/30/2018) and 302,689 (51%) visits during the evaluation (02/01/2019–09/30/2019). Pre-evaluation, 120,350 visits were classified as high-risk. During the evaluation, 125,076 visits were labeled as high-risk, with 62,557 (50%) randomized to the intervention. We generated data in simulations based on this setting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>In simulations, the traditional DID and risk score adjusted models had smaller bias and standard errors, and better coverage probabilities. DID estimates closest to randomized evaluation estimates (−0.007, 95% CI [−0.010, −0.004]) were from the traditional DID model assuming the identity link (−0.008, 95% CI [−0.011, −0.005]) or the risk adjusted model with any link (−0.006, 95% CI [−0.008, −0.003] identity; −0.007, 95% CI [−0.011, −0.003] logit; −0.007, 95% CI [−0.012, −0.003] log) for the ATT on the absolute difference scale (usual DID ATT estimand), and the risk score adjusted model with log or logit links for all other estimands.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Compared with randomized evaluation results, the traditional DID model is appropriate for the ATT on the absolute difference scale, while the risk score adjusted model with log or logit links is appropriate for all ATT estimands considered.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this study is to update estimates of comprehensive service availability among CCBHCs and compare them to other settings serving individuals with severe mental illness, including community mental health centers (CMHCs) and federally qualified health centers (FQHCs).
Study Design and Setting
This study is a cross-sectional secondary data analysis.
Data Sources and Analytic Sample
Using 2022 National Substance Use and Mental Health Services Survey (N-SUMHSS) data, logistic regression examined associations between service setting (CCBHC, CMHC, FQHC) and the availability of psychiatric, health management, and navigation, and social care services.
Principle Findings
Compared to CCBHCs, FQHC designation was associated with a decreased likelihood of offering psychiatric rehabilitation services, including ACT (marginal effect = −0.26, 95% CI: −0.33 to −0.19) and peer coaching (marginal effect = −0.36, 95% CI: −0.43 to −0.29), and psychiatric crisis intervention (marginal effect = −0.14, 95% CI: −0.22 to −0.07). Rates of health management services were comparable to those at CCBHCs. CMHCs were also less likely to offer health management services (marginal effect = −0.26, 95% CI: −0.32 to −0.21) and a range of psychiatric rehabilitation services relative to CCBHCs.
Conclusions
CCBHC certified clinics were more likely to offer psychiatric and social services than FQHC or CMHC clinics serving individuals with severe mental illness.
{"title":"Enhanced Service Capacity for Severe Mental Illness: A Comparative Analysis of Certified Community Behavioral Health Centers, Community Mental Health Centers, and Federally Qualified Health Centers","authors":"Elizabeth B. Matthews, Victoria Stanhope","doi":"10.1111/1475-6773.70010","DOIUrl":"10.1111/1475-6773.70010","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The objective of this study is to update estimates of comprehensive service availability among CCBHCs and compare them to other settings serving individuals with severe mental illness, including community mental health centers (CMHCs) and federally qualified health centers (FQHCs).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design and Setting</h3>\u0000 \u0000 <p>This study is a cross-sectional secondary data analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>Using 2022 National Substance Use and Mental Health Services Survey (N-SUMHSS) data, logistic regression examined associations between service setting (CCBHC, CMHC, FQHC) and the availability of psychiatric, health management, and navigation, and social care services.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principle Findings</h3>\u0000 \u0000 <p>Compared to CCBHCs, FQHC designation was associated with a decreased likelihood of offering psychiatric rehabilitation services, including ACT (marginal effect = −0.26, 95% CI: −0.33 to −0.19) and peer coaching (marginal effect = −0.36, 95% CI: −0.43 to −0.29), and psychiatric crisis intervention (marginal effect = −0.14, 95% CI: −0.22 to −0.07). Rates of health management services were comparable to those at CCBHCs. CMHCs were also less likely to offer health management services (marginal effect = −0.26, 95% CI: −0.32 to −0.21) and a range of psychiatric rehabilitation services relative to CCBHCs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>CCBHC certified clinics were more likely to offer psychiatric and social services than FQHC or CMHC clinics serving individuals with severe mental illness.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146088096","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}
Alexander C. Egilman, Aaron S. Kesselheim, Benjamin N. Rome
Objective
To examine whether the new Medicare inflation rebate policy was associated with changes in manufacturer pricing behavior.
Study Setting and Design
In this cross-sectional study of 156 top-selling brand-name drugs, we used linear regression to evaluate whether there was an association between drugs' exposure to the policy (i.e., Medicare's share of net US sales) and differences in year-over-year price changes before (2021–2022) versus after (2022–2023, 2023–2024) the policy took effect.
Data Sources and Analytic Sample
The study used Medicare spending data and average sales prices from the Centers for Medicare and Medicaid Services, wholesale acquisition costs from Eversana NAVLIN's Price & Access database, and sales revenue and estimated rebates from SSR Health. Vaccines, biosimilars, drugs approved after 2020, and those with generic or biosimilar competition before 2023 were excluded. Drugs were stratified by whether they derived most sales from Medicare Part B or Part D.
Principal Findings
The median Medicare share of net sales was 28% (IQR: 18%–37%) for 50 Part B drugs and 32% (IQR: 16%–49%) for 106 Part D drugs. Median year-over-year price changes in 2021–2022, 2022–2023, and 2023–2024 were 3.2%, 2.9%, and 3.4% for Part B drugs and 5.0%, 5.9%, and 4.9% for Part D drugs. There was no association between drugs' Medicare share of net sales and differences in price changes pre- vs. post-policy for Part B drugs (2023: p = 0.99; 2024: p = 0.09). For Part D drugs, each 10% increase in drugs' share of Medicare sales was associated with a 0.18% (95% CI, 0.01%–0.35%, p = 0.04) higher price change in the first year after policy implementation; there was no significant association in the second year (p = 0.17).
Conclusions
Medicare inflation rebates were not associated with smaller price increases among the top-selling drugs most affected by the policy. Additional measures are needed to prevent drug manufacturers from raising prices each year, such as extending inflation rebates to commercially insured patients.
{"title":"Share of Sales Subject to Medicare Inflation Rebates and Price Increases of Top-Selling Drugs","authors":"Alexander C. Egilman, Aaron S. Kesselheim, Benjamin N. Rome","doi":"10.1111/1475-6773.70012","DOIUrl":"10.1111/1475-6773.70012","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine whether the new Medicare inflation rebate policy was associated with changes in manufacturer pricing behavior.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>In this cross-sectional study of 156 top-selling brand-name drugs, we used linear regression to evaluate whether there was an association between drugs' exposure to the policy (i.e., Medicare's share of net US sales) and differences in year-over-year price changes before (2021–2022) versus after (2022–2023, 2023–2024) the policy took effect.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>The study used Medicare spending data and average sales prices from the Centers for Medicare and Medicaid Services, wholesale acquisition costs from Eversana NAVLIN's Price & Access database, and sales revenue and estimated rebates from SSR Health. Vaccines, biosimilars, drugs approved after 2020, and those with generic or biosimilar competition before 2023 were excluded. Drugs were stratified by whether they derived most sales from Medicare Part B or Part D.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The median Medicare share of net sales was 28% (IQR: 18%–37%) for 50 Part B drugs and 32% (IQR: 16%–49%) for 106 Part D drugs. Median year-over-year price changes in 2021–2022, 2022–2023, and 2023–2024 were 3.2%, 2.9%, and 3.4% for Part B drugs and 5.0%, 5.9%, and 4.9% for Part D drugs. There was no association between drugs' Medicare share of net sales and differences in price changes pre- vs. post-policy for Part B drugs (2023: <i>p</i> = 0.99; 2024: <i>p</i> = 0.09). For Part D drugs, each 10% increase in drugs' share of Medicare sales was associated with a 0.18% (95% CI, 0.01%–0.35%, <i>p</i> = 0.04) higher price change in the first year after policy implementation; there was no significant association in the second year (<i>p</i> = 0.17).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Medicare inflation rebates were not associated with smaller price increases among the top-selling drugs most affected by the policy. Additional measures are needed to prevent drug manufacturers from raising prices each year, such as extending inflation rebates to commercially insured patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kiersten L. Strombotne, Daniel Lipsey, Fernando Mattar, Kathleen Carey, Samantha G. Auty, Brian W. Stanley, Steven D. Pizer
Objective
To examine the relationship between mental health provider productivity, staffing levels, and suicide-related events (SREs) among U.S. Veterans receiving care within the Veterans Health Administration (VHA), focusing on therapy and medication management providers.
Data Sources/Setting
We analyzed administrative data from the Department of Defense and VHA (2014–2018), encompassing 109,376 Veterans who separated from active duty between 2010 and 2017.
Design
A longitudinal design estimated the effects of facility-level provider work rate and staffing on SREs, adjusting for patient and facility characteristics. An instrumental variables (IV) approach addressed potential endogeneity.
Data Collection/Extraction Methods
Data were obtained from the VHA Corporate Data Warehouse and the VHA Survey of Enrollees.
Principal Findings
A 1% increase in therapy provider work rate led to a 12.1% increase in SRE probability, regardless of staffing levels. Conversely, a 1% increase in staffing levels led to a 1.6% reduction in SREs, with the largest effect in low-staffed facilities. For medication management providers, work rate had no overall impact on SREs, except in medium-staffed facilities. A 1% increase in staffing levels for medication management providers led to a 1.7% reduction in SREs.
Conclusions
Increased work rates, particularly in low-staffed VHA facilities, may elevate suicide-related risks. In contrast, staffing increases simultaneously improve access and reduce adverse outcomes. Where possible, policymakers should prioritize staffing growth over productivity gains to improve access to mental health clinics and ensure Veteran safety and care quality.
{"title":"The Impact of Provider Productivity on Suicide-Related Events Among Veterans","authors":"Kiersten L. Strombotne, Daniel Lipsey, Fernando Mattar, Kathleen Carey, Samantha G. Auty, Brian W. Stanley, Steven D. Pizer","doi":"10.1111/1475-6773.70008","DOIUrl":"10.1111/1475-6773.70008","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine the relationship between mental health provider productivity, staffing levels, and suicide-related events (SREs) among U.S. Veterans receiving care within the Veterans Health Administration (VHA), focusing on therapy and medication management providers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources/Setting</h3>\u0000 \u0000 <p>We analyzed administrative data from the Department of Defense and VHA (2014–2018), encompassing 109,376 Veterans who separated from active duty between 2010 and 2017.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>A longitudinal design estimated the effects of facility-level provider work rate and staffing on SREs, adjusting for patient and facility characteristics. An instrumental variables (IV) approach addressed potential endogeneity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>Data were obtained from the VHA Corporate Data Warehouse and the VHA Survey of Enrollees.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>A 1% increase in therapy provider work rate led to a 12.1% increase in SRE probability, regardless of staffing levels. Conversely, a 1% increase in staffing levels led to a 1.6% reduction in SREs, with the largest effect in low-staffed facilities. For medication management providers, work rate had no overall impact on SREs, except in medium-staffed facilities. A 1% increase in staffing levels for medication management providers led to a 1.7% reduction in SREs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Increased work rates, particularly in low-staffed VHA facilities, may elevate suicide-related risks. In contrast, staffing increases simultaneously improve access and reduce adverse outcomes. Where possible, policymakers should prioritize staffing growth over productivity gains to improve access to mental health clinics and ensure Veteran safety and care quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"61 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Venice Ng Williams, Michael D. Knudtson, Mandy A. Allison, Gregory J. Tung
Objective
To measure nurse home visiting teams' access to electronic health records (EHR) and determine if access to EHR is associated with increased nurse home visitor collaboration with primary care providers in the United States.
Study Setting and Design
Nurse-Family Partnership (NFP) is an evidence-based home visiting program for first-time parents experiencing adversities. We conducted an observational study using data from 265 local NFP agencies in the United States. We used multivariate regression models to estimate the association between home visitors' EHR access and relational coordination with primary care providers.
Data Sources and Analytic Sample
We linked data from the 2021 NFP Collaboration with Community Providers Survey to 2021 NFP program implementation data and 2010 Rural–Urban Commuting Area Codes. We matched 265 survey respondents to their NFP teams' implementation data, including those with client visits between September 1, 2021, and December 31, 2021.
Principal Findings
Thirty-four percent of NFP teams (91/265) had access to their patients' EHR, with variation by agency type, where more NFP programs implemented by healthcare systems had EHR access (56%) compared to other agency types (X32=19.44, p < 0.01). Most NFP teams with EHR access reported read access (91%), ability to document (64%), and receiving program referrals (53%). EHR access was significantly associated with increased relational coordination with women's care providers (0.36-point difference, 95% CI 0.17 to 0.55, p < 0.01) and pediatric care providers (0.39-point difference, 95% CI 0.18 to 0.61, p < 0.01).
Conclusions
Access to EHRs varies by NFP team and agency type and is associated with greater relational coordination with primary care providers. Increasing home visitors' access to EHRs may help to facilitate collaboration with primary care providers.
{"title":"Association of Electronic Health Records Access and Coordination Between Primary Care Providers and Public Health Nurse Home Visitors in the United States","authors":"Venice Ng Williams, Michael D. Knudtson, Mandy A. Allison, Gregory J. Tung","doi":"10.1111/1475-6773.70006","DOIUrl":"10.1111/1475-6773.70006","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To measure nurse home visiting teams' access to electronic health records (EHR) and determine if access to EHR is associated with increased nurse home visitor collaboration with primary care providers in the United States.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>Nurse-Family Partnership (NFP) is an evidence-based home visiting program for first-time parents experiencing adversities. We conducted an observational study using data from 265 local NFP agencies in the United States. We used multivariate regression models to estimate the association between home visitors' EHR access and relational coordination with primary care providers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We linked data from the 2021 NFP Collaboration with Community Providers Survey to 2021 NFP program implementation data and 2010 Rural–Urban Commuting Area Codes. We matched 265 survey respondents to their NFP teams' implementation data, including those with client visits between September 1, 2021, and December 31, 2021.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Thirty-four percent of NFP teams (91/265) had access to their patients' EHR, with variation by agency type, where more NFP programs implemented by healthcare systems had EHR access (56%) compared to other agency types (<i>X</i><sub><i>3</i></sub><sup>2</sup>=19.44, <i>p</i> < 0.01). Most NFP teams with EHR access reported read access (91%), ability to document (64%), and receiving program referrals (53%). EHR access was significantly associated with increased relational coordination with women's care providers (0.36-point difference, 95% CI 0.17 to 0.55, <i>p</i> < 0.01) and pediatric care providers (0.39-point difference, 95% CI 0.18 to 0.61, <i>p</i> < 0.01).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Access to EHRs varies by NFP team and agency type and is associated with greater relational coordination with primary care providers. Increasing home visitors' access to EHRs may help to facilitate collaboration with primary care providers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 6","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eric T. Roberts, Florentina E. Sileanu, Yaming Li, Timothy S. Anderson, Carolyn T. Thorpe, John Cashy, Katie J. Suda, Thomas R. Radomski, Maria K. Mor, Utibe R. Essien, Megan E. Vanneman, Michael J. Fine, Walid F. Gellad
<div> <section> <h3> Objective</h3> <p>To examine whether eligibility for Veterans Health Administration (VA) community care, which expanded Veterans' access to VA-funded care outside VA, increased the likelihood of Veterans concurrently filling prescriptions for opioids and central nervous system (CNS)-active medications.</p> </section> <section> <h3> Study Setting and Design</h3> <p>We used a regression discontinuity design to analyze Veterans across a distance threshold for community care eligibility in the Veterans Choice Program, under which Veterans residing > 40 miles from the closest VA medical facility staffed by ≥ 1 full-time primary care physician qualified for community care. We used local linear regression to test whether exceeding this 40-mile threshold was associated with discontinuities in the probability of receiving overlapping supplies of opioids and another CNS medication (benzodiazepine, muscle relaxant, antiepileptic, or sleep aid) for ≥ 30 days per year.</p> </section> <section> <h3> Data Sources and Analytic Sample</h3> <p>We used VA pharmacy data for prescriptions filled at VA facilities, VA Program Integrity Tool files for prescriptions paid by VA and filled in community pharmacies, and Medicare and Medicaid data for prescriptions covered by those programs. Our analysis included annual cross-sectional samples of Veterans who filled ≥ 1 opioid prescription through VA, community care, Medicare, or Medicaid and lived 36–39 or 41–44 miles from the nearest VA facility during federal FYs 2016–2019 (<i>n</i> = 180,903 Veteran-year observations).</p> </section> <section> <h3> Principal Findings</h3> <p>Among Veterans who filled an opioid prescription, 34.1% concurrently received another CNS medication for ≥ 30 days. Exceeding the threshold for community care eligibility was associated with a 1.14 percentage point (pp) increase (95% CI: 0.08, 2.20) in the probability of concurrently receiving an opioid and another CNS drug during 2016–2019. Discontinuities in overlap were larger among Veterans with a serious mental illness (2.7 pp.; 95% CI: 0.6, 4.9) during 2016–2019. During 2018–2019, discontinuities were larger in the overall sample (1.6 pp.; 0.0, 3.1) and among non-Hispanic Black Veterans (5.4 pp.; 95% CI: 0.5, 10.4).</p> </section> <section> <h3> Conclusions</h3> <p>Overall, VA community care eligibility was associated with a small increase in medication overlap involving opioids and other CNS-active medications. Increases in overlap were larger in certain Veteran subgroups and later study years, underscoring a need for continu
{"title":"VA-Purchased Community Care and Risk of Potentially Unsafe Concurrent Medication Use Among Veterans Receiving Opioids: A Regression Discontinuity Analysis","authors":"Eric T. Roberts, Florentina E. Sileanu, Yaming Li, Timothy S. Anderson, Carolyn T. Thorpe, John Cashy, Katie J. Suda, Thomas R. Radomski, Maria K. Mor, Utibe R. Essien, Megan E. Vanneman, Michael J. Fine, Walid F. Gellad","doi":"10.1111/1475-6773.70001","DOIUrl":"10.1111/1475-6773.70001","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine whether eligibility for Veterans Health Administration (VA) community care, which expanded Veterans' access to VA-funded care outside VA, increased the likelihood of Veterans concurrently filling prescriptions for opioids and central nervous system (CNS)-active medications.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>We used a regression discontinuity design to analyze Veterans across a distance threshold for community care eligibility in the Veterans Choice Program, under which Veterans residing > 40 miles from the closest VA medical facility staffed by ≥ 1 full-time primary care physician qualified for community care. We used local linear regression to test whether exceeding this 40-mile threshold was associated with discontinuities in the probability of receiving overlapping supplies of opioids and another CNS medication (benzodiazepine, muscle relaxant, antiepileptic, or sleep aid) for ≥ 30 days per year.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We used VA pharmacy data for prescriptions filled at VA facilities, VA Program Integrity Tool files for prescriptions paid by VA and filled in community pharmacies, and Medicare and Medicaid data for prescriptions covered by those programs. Our analysis included annual cross-sectional samples of Veterans who filled ≥ 1 opioid prescription through VA, community care, Medicare, or Medicaid and lived 36–39 or 41–44 miles from the nearest VA facility during federal FYs 2016–2019 (<i>n</i> = 180,903 Veteran-year observations).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Among Veterans who filled an opioid prescription, 34.1% concurrently received another CNS medication for ≥ 30 days. Exceeding the threshold for community care eligibility was associated with a 1.14 percentage point (pp) increase (95% CI: 0.08, 2.20) in the probability of concurrently receiving an opioid and another CNS drug during 2016–2019. Discontinuities in overlap were larger among Veterans with a serious mental illness (2.7 pp.; 95% CI: 0.6, 4.9) during 2016–2019. During 2018–2019, discontinuities were larger in the overall sample (1.6 pp.; 0.0, 3.1) and among non-Hispanic Black Veterans (5.4 pp.; 95% CI: 0.5, 10.4).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Overall, VA community care eligibility was associated with a small increase in medication overlap involving opioids and other CNS-active medications. Increases in overlap were larger in certain Veteran subgroups and later study years, underscoring a need for continu","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 6","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kenneth A. Michelson, Katherine E. Remick, Emily M. Bucholz, Patrick D. McMullen, Naveen Singamsetty, Andrew D. Skol, Danielle K. Cory, John A. Graves
Objective
To pilot a system for deriving borders of pediatric regions, and to compare these to adult markets based on fit with pediatric utilization data.
Study Setting and Design
In this cross-sectional study, we studied all acute care encounters (emergency department visits and hospitalizations) for children less than 16 years old in Wisconsin 2021–2022.
Data Sources and Analytic Sample
We used the Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases. We first counted how many patients from each ZIP code visited each hospital and mapped ZIP-hospital connections. Using a network analysis technique called community detection that clustered hospitals by their common connections, we grouped ZIP codes to form pediatric emergency service areas (PESAs). We counted patient referrals within and between PESAs and repeated the community detection procedure, resulting in pediatric emergency referral regions (PERRs). The primary outcome was modularity, a common network fit measure ranging from −1 to 1 (1 represents perfect clustering). We also compared demographics and network quality measures between PERRs, hospital referral regions (HRRs), core-based statistical areas, and Pittsburgh Trauma Atlas regions.
Principal Findings
We analyzed 587,886 encounters, from which ZIP codes grouped into 24 PESAs. Based on referral patterns, there were 4 PERRs. PERRs had modestly higher modularity for interhospital referral patterns than all other systems (0.53, 95% confidence interval [CI] 0.52, 0.54 compared to 0.46, 95% CI 0.46, 0.47 for HRRs). PERRs were larger (median 11,361 mile2 vs. 3957 for HRRs), contained more children (median 265,222 vs. 49,667 for HRRs), and contained more hospitals (median 35 vs. 7 for HRRs) than all other systems.
Conclusions
Using Wisconsin HCUP data, we derived pediatric acute care regions with a strong fit for pediatric utilization data. Future work should test this approach across the whole US, which would allow between-region cost and outcomes comparison.
目的：试点儿科地区边界划分系统，并将其与成人市场进行比较，以符合儿童利用数据。研究设置和设计：在这项横断面研究中，我们研究了威斯康星州2021-2022年16岁以下儿童的所有急性护理遭遇（急诊科就诊和住院）。数据来源和分析样本：我们使用医疗成本和利用项目国家急诊科和住院病人数据库。我们首先统计了每个邮政编码有多少患者访问了每家医院，并绘制了邮政-医院之间的连接图。我们使用一种称为社区检测的网络分析技术，根据医院的共同联系对医院进行分组，将邮政编码分组，形成儿科急诊服务区（pesa）。我们统计了pesa内和pesa之间的患者转诊，并重复了社区检测程序，得出了儿科急诊转诊区域（perr）。主要结果是模块化，这是一种常见的网络拟合度量，范围从-1到1（1代表完美聚类）。我们还比较了perr、医院转诊区域（HRRs）、基于核心的统计区域和匹兹堡创伤地图集区域之间的人口统计学和网络质量测量。主要发现：我们分析了587,886次遭遇，其中邮政编码分为24个PESAs。根据转诊模式，有4个perr。PERRs对医院间转诊模式的模块化程度略高于其他所有系统（hrr为0.53,95%可信区间[CI] 0.52, 0.54，而hrr为0.46,95% CI 0.46, 0.47）。与所有其他系统相比，perr更大（中位数为11,361英里2，HRRs为3957英里2），包含更多儿童（中位数为265,222，HRRs为49,667），包含更多医院（中位数为35，HRRs为7）。结论：使用威斯康辛州HCUP数据，我们得出了与儿科利用数据非常吻合的儿科急症护理区域。未来的工作应该在整个美国测试这种方法，这将允许在地区之间进行成本和结果比较。
{"title":"Network Analysis to Define Pediatric Acute Care Regions in Wisconsin","authors":"Kenneth A. Michelson, Katherine E. Remick, Emily M. Bucholz, Patrick D. McMullen, Naveen Singamsetty, Andrew D. Skol, Danielle K. Cory, John A. Graves","doi":"10.1111/1475-6773.70000","DOIUrl":"10.1111/1475-6773.70000","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To pilot a system for deriving borders of pediatric regions, and to compare these to adult markets based on fit with pediatric utilization data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>In this cross-sectional study, we studied all acute care encounters (emergency department visits and hospitalizations) for children less than 16 years old in Wisconsin 2021–2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We used the Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases. We first counted how many patients from each ZIP code visited each hospital and mapped ZIP-hospital connections. Using a network analysis technique called community detection that clustered hospitals by their common connections, we grouped ZIP codes to form pediatric emergency service areas (PESAs). We counted patient referrals within and between PESAs and repeated the community detection procedure, resulting in pediatric emergency referral regions (PERRs). The primary outcome was modularity, a common network fit measure ranging from −1 to 1 (1 represents perfect clustering). We also compared demographics and network quality measures between PERRs, hospital referral regions (HRRs), core-based statistical areas, and Pittsburgh Trauma Atlas regions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We analyzed 587,886 encounters, from which ZIP codes grouped into 24 PESAs. Based on referral patterns, there were 4 PERRs. PERRs had modestly higher modularity for interhospital referral patterns than all other systems (0.53, 95% confidence interval [CI] 0.52, 0.54 compared to 0.46, 95% CI 0.46, 0.47 for HRRs). PERRs were larger (median 11,361 mile<sup>2</sup> vs. 3957 for HRRs), contained more children (median 265,222 vs. 49,667 for HRRs), and contained more hospitals (median 35 vs. 7 for HRRs) than all other systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Using Wisconsin HCUP data, we derived pediatric acute care regions with a strong fit for pediatric utilization data. Future work should test this approach across the whole US, which would allow between-region cost and outcomes comparison.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 6","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To test whether industry payments to clinicians are clustered at the level of the medical practice.
Study Setting and Design
We performed a cross-sectional study of clinicians who billed Medicare Part B in 2021 to test whether the receipt of an industry payment, log total value of industry payments, or log total number of industry payments to clinicians were clustered at the level of the medical practice. We used mixed effects linear regression to analyze practice-level clustering, controlling for clinician sex, age, urbanicity, state, and specialty, as well as practice size and specialty.
Data Source and Analytic Sample
We used the 2021 Medicare Data on Provider Practice and Specialty file to assign clinicians to medical practices, and the 2021 General Payment Data from the Open Payments Program to calculate the total value and number of industry payments to each clinician.
Principal Findings
We identified 996,982 clinicians who billed Medicare Part B in 2021, of whom 679,577 (68.2%) were physicians and 317,305 (31.8%) were advanced practice clinicians. These clinicians worked across 109,952 medical practices. In total, 474,312 (47.6%) clinicians received an industry payment in 2021. The average total value of industry payments was $1497 (SD $54,823), and the average total number of industry payments was 9.4 (SD 27.5). Regression analysis of each outcome identified significant clustering at the level of the medical practice, including 24.8% of the variation in the receipt of an industry payment, 36.8% in the log total value of industry payments, and 60.5% in the log total number of industry payments.
Conclusions
Industry payments to clinicians are strongly clustered by medical practice. Future research should examine the role of the medical practice in facilitating financial conflicts of interest between industry and clinicians.
{"title":"Practice-Level Clustering of Industry Payments to Clinicians","authors":"Max J. Hyman, Micah T. Prochaska, Parth K. Modi","doi":"10.1111/1475-6773.70004","DOIUrl":"10.1111/1475-6773.70004","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To test whether industry payments to clinicians are clustered at the level of the medical practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>We performed a cross-sectional study of clinicians who billed Medicare Part B in 2021 to test whether the receipt of an industry payment, log total value of industry payments, or log total number of industry payments to clinicians were clustered at the level of the medical practice. We used mixed effects linear regression to analyze practice-level clustering, controlling for clinician sex, age, urbanicity, state, and specialty, as well as practice size and specialty.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Source and Analytic Sample</h3>\u0000 \u0000 <p>We used the 2021 Medicare Data on Provider Practice and Specialty file to assign clinicians to medical practices, and the 2021 General Payment Data from the Open Payments Program to calculate the total value and number of industry payments to each clinician.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We identified 996,982 clinicians who billed Medicare Part B in 2021, of whom 679,577 (68.2%) were physicians and 317,305 (31.8%) were advanced practice clinicians. These clinicians worked across 109,952 medical practices. In total, 474,312 (47.6%) clinicians received an industry payment in 2021. The average total value of industry payments was $1497 (SD $54,823), and the average total number of industry payments was 9.4 (SD 27.5). Regression analysis of each outcome identified significant clustering at the level of the medical practice, including 24.8% of the variation in the receipt of an industry payment, 36.8% in the log total value of industry payments, and 60.5% in the log total number of industry payments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Industry payments to clinicians are strongly clustered by medical practice. Future research should examine the role of the medical practice in facilitating financial conflicts of interest between industry and clinicians.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 6","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144509517","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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