Medical CarePub Date : 2024-11-01Epub Date: 2024-05-29DOI: 10.1097/MLR.0000000000002008
Werner Vach, Sonja Wehberg, George Luta
{"title":"Do Common Risk Adjustment Methods Do Their Job Well If Center Effects Are Correlated With the Center-Specific Mean Values of Patient Characteristics?","authors":"Werner Vach, Sonja Wehberg, George Luta","doi":"10.1097/MLR.0000000000002008","DOIUrl":"10.1097/MLR.0000000000002008","url":null,"abstract":"<p><strong>Background: </strong>Direct and indirect standardization are well-established approaches to performing risk adjustment when comparing outcomes between healthcare providers. However, it is an open question whether they work well when there is an association between the center effects and the distributions of the patient characteristics in these centers.</p><p><strong>Objectives and methods: </strong>We try to shed further light on the impact of such an association. We construct an artificial case study with a single covariate, in which centers can be classified as performing above, on, or below average, and the center effects correlate with center-specific mean values of a patient characteristic, as a consequence of differential quality improvement. Based on this case study, direct standardization and indirect standardization-based on marginal as well as conditional models-are compared with respect to systematic differences between their results.</p><p><strong>Results: </strong>Systematic differences between the methods were observed. All methods produced results that partially reflect differences in mean age across the centers. This may mask the classification as above, on, or below average. The differences could be explained by an inspection of the parameter estimates in the models fitted.</p><p><strong>Conclusions: </strong>In case of correlations of center effects with center-specific mean values of a covariate, different risk adjustment methods can produce systematically differing results. This suggests the routine use of sensitivity analyses. Center effects in a conditional model need not reflect the position of a center above or below average, questioning its use in defining the truth. Further empirical investigations are necessary to judge the practical relevance of these findings.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"773-781"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141248143","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}
Medical CarePub Date : 2024-11-01Epub Date: 2023-12-04DOI: 10.1097/MLR.0000000000001955
Richard C van Kleef, René C J A van Vliet, Michel Oskam
{"title":"Risk Adjustment in Health Insurance Markets: Do Not Overlook the \"Real\" Healthy.","authors":"Richard C van Kleef, René C J A van Vliet, Michel Oskam","doi":"10.1097/MLR.0000000000001955","DOIUrl":"10.1097/MLR.0000000000001955","url":null,"abstract":"<p><strong>Objectives: </strong>The goals of this paper are: (1) to identify groups of healthy people; and (2) to quantify the extent to which the Dutch risk adjustment (RA) model overpays insurers for these groups.</p><p><strong>Background: </strong>There have been strong signals that insurers in the Dutch regulated health insurance market engage in actions to attract healthy people. A potential explanation for this behavior is that the Dutch RA model overpays insurers for healthy people.</p><p><strong>Methods: </strong>We identify healthy groups using 3 types of ex-ante information (ie, information available before the start of the health insurance contract): administrative data on prior spending for specific health care services (N = 17 m), diagnoses from electronic patient records (N = 1.3 m), and health survey data (N = 457 k). In a second step, we calculate the under/overpayment for these groups under the Dutch RA model (version: 2021).</p><p><strong>Results: </strong>We distinguish eight groups of healthy people using various \"identifiers.\" Although the Dutch RA model substantially reduces the predictable profits that insurers face for these groups, significant profits remain. The mean per person overpayment ranges from 38 euros (people with hospital spending below the third quartile in each of 3 prior years) to 167 euros (those without any medical condition according to their electronic patient record).</p><p><strong>Conclusions: </strong>The Dutch RA model does not eliminate the profitability of healthy groups. The identifiers used for flagging these groups, however, seem inappropriate for serving as risk adjuster variables. An alternative way of exploiting these identifiers and eliminating the profitability of healthy groups is to estimate RA models with \"constrained regression.\"</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"767-772"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478121","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}
Medical CarePub Date : 2024-11-01Epub Date: 2024-08-02DOI: 10.1097/MLR.0000000000002050
Ruichen Sun, Morgan Henderson, Leigh Goetschius, Fei Han, Ian Stockwell
{"title":"Behind the Curtain: Comparing Predictive Models Performance in 2 Publicly Insured Populations.","authors":"Ruichen Sun, Morgan Henderson, Leigh Goetschius, Fei Han, Ian Stockwell","doi":"10.1097/MLR.0000000000002050","DOIUrl":"10.1097/MLR.0000000000002050","url":null,"abstract":"<p><strong>Introduction: </strong>Predictive models have proliferated in the health system in recent years and have been used to predict both health services utilization and medical outcomes. Less is known, however, on how these models function and how they might adapt to different contexts. The purpose of the current study is to shed light on the inner workings of a large-scale predictive model deployed in 2 distinct populations, with a particular emphasis on adaptability issues.</p><p><strong>Methods: </strong>We compared the performance and functioning of a predictive model of avoidable hospitalization in 2 very different populations: Medicaid and Medicare enrollees in Maryland. Specifically, we assessed characteristics of the risk scores from March 2022 for the 2 populations, the predictive ability of the scores, and the driving risk factors behind the scores. In addition, we created and assessed the performance of an \"unadapted\" model by applying coefficients from the Medicare model to the Medicaid population.</p><p><strong>Results: </strong>The model adapted to, and performed well in, both populations, despite demographic differences in these 2 groups. However, the most salient risk factors and their relative weightings differed, sometimes dramatically, across the 2 populations. The unadapted Medicaid model displayed poor performance relative to the adapted model.</p><p><strong>Conclusions: </strong>Our findings speak to the need to \"peek behind the curtain\" of predictive models that may be applied to different populations, and we caution that risk prediction is not \"one size fits all\": for optimal performance, models should be adapted to, and trained on, the target population.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"716-722"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893795","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}
Medical CarePub Date : 2024-11-01Epub Date: 2024-09-09DOI: 10.1097/MLR.0000000000002056
Rachel Patterson
{"title":"Alabama Embryo Ruling Threatened Access to In Vitro Fertilization Across the State and Possibly Nationwide.","authors":"Rachel Patterson","doi":"10.1097/MLR.0000000000002056","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002056","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"62 11","pages":"701-702"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895735","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}
Medical CarePub Date : 2024-11-01Epub Date: 2024-10-11DOI: 10.1097/MLR.0000000000002057
Hana Šinkovec, Walter Gall, Georg Heinze
{"title":"Cross-Sectoral Comparisons of Process Quality Indicators of Health Care Across Residential Regions Using Restricted Mean Survival Time.","authors":"Hana Šinkovec, Walter Gall, Georg Heinze","doi":"10.1097/MLR.0000000000002057","DOIUrl":"10.1097/MLR.0000000000002057","url":null,"abstract":"<p><strong>Background: </strong>Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time.</p><p><strong>Methods: </strong>For assessing process indicators where compliance to the recommended treatment can be assessed by evaluating a patient's trace in linked routine databases, we propose using restricted mean survival time or restricted mean time lost, which are applicable even in competing risk situations. We demonstrate their application by assessing the compliance of patients with acute myocardial infarction (AMI) to high-power statins over 12 months in Austria's political districts, using pseudo-observations and employing causal inference methods to achieve regional comparability.</p><p><strong>Results: </strong>We analyzed the compliance of 31,678 AMI patients from Austria's 116 political districts with index AMI between 2011 and 2015. The results revealed considerable compliance variations across districts but also plausible spatial similarities.</p><p><strong>Conclusions: </strong>Restricted mean survival time and restricted mean time lost provide interpretable estimates of patients' expected time in compliance (lost), well-suited for risk-adjusted entity comparisons in the presence of (measurable) confounding, censoring, and competing risks.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"62 11","pages":"748-756"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895767","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}
Medical CarePub Date : 2024-11-01Epub Date: 2024-10-11DOI: 10.1097/MLR.0000000000002078
{"title":"Statistical Methods for Risk Adjustment in Health Care.","authors":"","doi":"10.1097/MLR.0000000000002078","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002078","url":null,"abstract":"","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"62 11","pages":"723"},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895773","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}
Medical CarePub Date : 2024-10-28DOI: 10.1097/MLR.0000000000002087
Eric A Apaydin, Caroline K Yoo, Susan E Stockdale, Nicholas J Jackson, Elizabeth M Yano, Karin M Nelson, David C Mohr, Danielle E Rose
{"title":"Burnout and Turnover Among Veterans Health Administration Primary Care Providers From Fiscal Years 2017-2021.","authors":"Eric A Apaydin, Caroline K Yoo, Susan E Stockdale, Nicholas J Jackson, Elizabeth M Yano, Karin M Nelson, David C Mohr, Danielle E Rose","doi":"10.1097/MLR.0000000000002087","DOIUrl":"10.1097/MLR.0000000000002087","url":null,"abstract":"<p><strong>Objectives: </strong>We examined how individual-level turnover among Veterans Health Administration primary care providers (PCPs) from fiscal years 2017 to 2021 was associated with health care system-level burnout and turnover intent.</p><p><strong>Background: </strong>Burnout among PCPs has been well documented in recent studies, but less is known about the potential relationship between burnout and turnover.</p><p><strong>Methods: </strong>We identified a national cohort of 6444 PCPs (physicians, nurse practitioners, and physician assistants) in 129 Veterans Health Administration health care systems in the first quarter of fiscal year 2017 and tracked their employment status for 20 quarters. PCP employment data on turnover were linked to annual health care system-level employee survey data on burnout, turnover intent, and other covariates. We performed logistic regression to estimate the impact of health care system-level burnout and turnover intent on individual PCP turnover, controlling for individual and health care system-level covariates and adjusting for clustering at the health care system level.</p><p><strong>Results: </strong>Median health care system-level burnout ranged from 42.5% to 52.0% annually, and turnover among PCPs ranged from 6.3% to 8.4% (mean = 7.0%; SD = 0.9%). Separation from employment was higher among employees at health care systems with the highest burnout (odds ratio =1.14; 95% CI = 1.01-1.29) and turnover intent (OR = 1.18; 95% CI = 1.03-1.35).</p><p><strong>Conclusions: </strong>PCPs in health care systems with high burnout are more likely to separate from employment. Policymakers and administrators seeking to improve retention should consider system-level interventions to address organizational drivers of burnout.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142623594","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}
Medical CarePub Date : 2024-10-02DOI: 10.1097/MLR.0000000000002064
D August Oddleifson, Huaying Dong, Rishi K Wadhera
{"title":"Community Benefit and Tax-Exemption Levels at Non-Profit Hospitals Across U.S. States.","authors":"D August Oddleifson, Huaying Dong, Rishi K Wadhera","doi":"10.1097/MLR.0000000000002064","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002064","url":null,"abstract":"<p><strong>Objective: </strong>To assess the association between state policies and sociodemographic characteristics and state mean fair share spending at non-profit hospitals. Fair share spending is a hospital's charity care and community investment less the estimated value of their tax-exempt status.</p><p><strong>Background: </strong>Hospitals with non-profit status in the United States are exempt from paying taxes. In return, they are expected to provide community benefits by subsidizing medical care for those who cannot pay and investing in the health and social needs of their community.</p><p><strong>Methods: </strong>We used a multivariable linear regression model to determine the association of state-level sociodemographics and policies with state-level mean fair share spending in 2019. Fair share spending data was obtained from the Lown Institute.</p><p><strong>Results: </strong>We found no association between the percentage of people living in poverty, in rural areas, or U.S. region and fair share spending. Similarly, there was no association found for state minimum community benefit and reporting requirements. The state percentage of racial/ethnic minorities was associated with higher mean fair share spending [+$1.48 million for every 10% increase (95% CI: 0.01 to 2.96 million)]. Medicaid expansion status was associated with a 6.9-million-dollar decrease (95% CI: -10.4 to -3.3 million).</p><p><strong>Conclusions: </strong>State-level community benefit policies have been ineffective at raising community benefit spending to levels comparable to the value of non-profit hospital tax-exempt status.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142391711","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}
Medical CarePub Date : 2024-10-01Epub Date: 2024-07-18DOI: 10.1097/MLR.0000000000002043
Hayden B Bosworth, Uptal D Patel, Allison A Lewinski, Clemontina A Davenport, Jane Pendergast, Megan Oakes, Matthew J Crowley, Leah L Zullig, Sejal Patel, Jivan Moaddeb, Julie Miller, Shauna Malone, Huiman Barnhart, Clarissa J Diamantidis
{"title":"Clinical Outcomes Among High-Risk Primary Care Patients With Diabetic Kidney Disease: Methodological Challenges and Results From the STOP-DKD Study.","authors":"Hayden B Bosworth, Uptal D Patel, Allison A Lewinski, Clemontina A Davenport, Jane Pendergast, Megan Oakes, Matthew J Crowley, Leah L Zullig, Sejal Patel, Jivan Moaddeb, Julie Miller, Shauna Malone, Huiman Barnhart, Clarissa J Diamantidis","doi":"10.1097/MLR.0000000000002043","DOIUrl":"10.1097/MLR.0000000000002043","url":null,"abstract":"<p><strong>Background/objective: </strong>Slowing the progression of diabetic kidney disease (DKD) is critical. We conducted a randomized controlled trial to target risk factors for DKD progression.</p><p><strong>Methods: </strong>We evaluated the effect of a pharmacist-led intervention focused on supporting healthy behaviors, medication management, and self-monitoring on decline in estimated glomerular filtration rate (eGFR) for 36 months compared with an educational control.</p><p><strong>Results: </strong>We randomized 138 individuals to the intervention group and 143 to control. At baseline, mean (SD) eGFR was 80.7 (21.7) mL/min/1.73m 2 , 56% of participants had chronic kidney disease and a history of uncontrolled hypertension with a baseline SBP of 134.3 mm Hg. The mean (SD) decline in eGFR by cystatin C from baseline to 36 months was 5.0 (19.6) and 5.9 (18.6) mL/min/1.73m 2 for the control and intervention groups, respectively, with no significant between-group difference ( P =0.75).</p><p><strong>Conclusions: </strong>We did not observe a significant difference in clinical outcomes by study arm. However, we showed that individuals with DKD will engage in a pharmacist-led intervention. The potential explanations for a lack of change in DKD risk factors can be attributed to 5 broad issues, challenges: (1) associated with enrolling patients with low eGFR and poor BP control; (2) implementing the intervention; (3) limited duration during which to observe any clinical benefit from the intervention; (4) potential co-intervention or contamination; and (5) low statistical power.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"660-666"},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141748542","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}
Medical CarePub Date : 2024-10-01Epub Date: 2024-08-12DOI: 10.1097/MLR.0000000000002051
Erin D Bouldin, Ben J Brintz, Jared Hansen, Rand Rupper, Rachel Brenner, Orna Intrator, Bruce Kinosian, Mikayla Viny, Stuti Dang, Mary Jo Pugh
{"title":"Trajectories and Transitions in Service Use Among Older Veterans at High Risk of Long-Term Institutional Care.","authors":"Erin D Bouldin, Ben J Brintz, Jared Hansen, Rand Rupper, Rachel Brenner, Orna Intrator, Bruce Kinosian, Mikayla Viny, Stuti Dang, Mary Jo Pugh","doi":"10.1097/MLR.0000000000002051","DOIUrl":"10.1097/MLR.0000000000002051","url":null,"abstract":"<p><strong>Background: </strong>We aimed to identify combinations of long-term services and supports (LTSS) Veterans use, describe transitions between groups, and identify factors influencing transition.</p><p><strong>Methods: </strong>We explored LTSS across a continuum from home to institutional care. Analyses included 104,837 Veterans Health Administration (VHA) patients 66 years and older at high-risk of long-term institutional care (LTIC). We conduct latent class and latent transition analyses using VHA and Medicare data from fiscal years 2014 to 2017. We used logistic regression to identify variables associated with transition.</p><p><strong>Results: </strong>We identified 5 latent classes: (1) No Services (11% of sample in 2015); (2) Medicare Services (31%), characterized by using LTSS only in Medicare; (3) VHA-Medicare Care Continuum (19%), including LTSS use in various settings across VHA and Medicare; (4) Personal Care Services (21%), characterized by high probabilities of using VHA homemaker/home health aide or self-directed care; and (5) Home-Centered Interdisciplinary Care (18%), characterized by a high probability of using home-based primary care. Veterans frequently stayed in the same class over the three years (30% to 46% in each class). Having a hip fracture, self-care impairment, or severe ambulatory limitation increased the odds of leaving No Services, and incontinence and dementia increased the odds of entering VHA-Medicare Care Continuum. Results were similar when restricted to Veterans who survived during all 3 years of the study period.</p><p><strong>Conclusions: </strong>Veterans at high risk of LTIC use a combination of services from across the care continuum and a mix of VHA and Medicare services. Service patterns are relatively stable for 3 years.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":"650-659"},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11545584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141988239","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}