JAMA Health Forum最新文献

{"title":"Recognizing and Strengthening the 4 Pillars of US Childhood Vaccine Policy.","authors":"Joshua M Sharfstein, Sarah Despres","doi":"10.1001/jamahealthforum.2025.0363","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.0363","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e250363"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drivers of Variation in Health Care Spending Across US Counties.","authors":"Joseph L Dieleman, Maxwell Weil, Meera Beauchamp, Catherine Bisignano, Sawyer W Crosby, Drew DeJarnatt, Haley Lescinsky, Ali H Mokdad, Samuel Ostroff, Hilary Paul, Ian Pollock, Maitreyi Sahu, John W Scott, Kayla V Taylor, Azalea Thomson, Marcia R Weaver, Lauren B Wilner, Christopher J L Murray","doi":"10.1001/jamahealthforum.2024.5220","DOIUrl":"10.1001/jamahealthforum.2024.5220","url":null,"abstract":"<p><strong>Importance: </strong>Understanding the drivers of health care spending across US counties is important for developing policies and assessing the allocation of health care services.</p><p><strong>Objective: </strong>To estimate the amount of cross-county health care spending variation explained by (1) population age, (2) health condition prevalence, (3) service utilization, and (4) service price and intensity.</p><p><strong>Design, setting, and participants: </strong>In this cross-sectional study, data for 4 key drivers of per capita spending were extracted for 3110 US counties, 148 health conditions, 38 age-sex groups, 4 payers, and 7 types of care for 2019. Service utilization was measured as service volume per prevalent case, while price and intensity was measured as spending per visit, admission, or prescription. Das Gupta and Shapley decomposition methods and linear regression were used to estimate the contribution of each factor. The data analysis was conducted between March 2024 and July 2024.</p><p><strong>Exposures: </strong>Age, disease prevalence, service utilization, or service price and intensity.</p><p><strong>Main outcomes and measures: </strong>Variation in health care spending across US counties.</p><p><strong>Results: </strong>In 2019, 76.6% of personal health care spending was included in this study. Overall, 64.8% of cross-county health care spending variation among 3110 US counties was explained by service utilization, while population age, disease prevalence, and price and intensity of services explained 4.1%, 7.0%, and 24.1%, respectively. The rate at which these factors contributed to variation in spending differed by payer, type of care, and health condition. Service utilization was associated with insurance coverage, median income, and education. An increase in each of these from the median to the 75th percentile was associated with a 7.8%, 4.4%, and 3.8% increase in ambulatory care utilization, respectively. The fraction of Medicare beneficiaries with Medicare Advantage was associated with less utilization. An increase in Medicare Advantage coverage from the median to the 75th percentile was associated with a 1.9% decrease in ambulatory care utilization. Differences in cross-state spending levels were also attributed to different factors. For Utah, the state with the least health care spending per capita, spending rates were lower for all types of care due principally to the young age profile. For New York, the state with the highest spending, spending rates were relatively high for hospital inpatient and prescribed pharmaceutical spending. For both types of care, high service price and intensity contributed to the above-average spending.</p><p><strong>Conclusions and relevance: </strong>In this cross-sectional study, variation in health care spending among US counties was largely related to variation in service utilization. Understanding the drivers of spending variation in the US may help policymakers assess","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e245220"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minnesota Law Brings Transparency to the 340B Drug Pricing Program.","authors":"Sayeh Nikpay, Claire McGlave, Stefan Gildemeister","doi":"10.1001/jamahealthforum.2024.5447","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.5447","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e245447"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fillings Needed for Gaps in Government Dental Coverage.","authors":"Hawazin W Elani, Benjamin D Sommers","doi":"10.1001/jamahealthforum.2025.0370","DOIUrl":"10.1001/jamahealthforum.2025.0370","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e250370"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11907204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Trademarks to Ensure Completion of Postapproval Trials.","authors":"Catherine S Hwang, S Sean Tu","doi":"10.1001/jamahealthforum.2025.0001","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.0001","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e250001"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Geographic Variation in Health Care Spending.","authors":"Melinda B Buntin, Angela Liu","doi":"10.1001/jamahealthforum.2024.5373","DOIUrl":"10.1001/jamahealthforum.2024.5373","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e245373"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Discrimination in Nonprofit Hospital Community Benefit Spending.","authors":"Aaron Hedquist, David Blumenthal, Dannie Dai, Jessica Phelan, E John Orav, Jose F Figueroa","doi":"10.1001/jamahealthforum.2024.5523","DOIUrl":"10.1001/jamahealthforum.2024.5523","url":null,"abstract":"<p><strong>Importance: </strong>Nonprofit hospitals receive substantial tax exemptions to provide a community benefit. However, little is known about the distribution of community benefit spending (CBS) across US communities with varying degrees of social vulnerability beyond the hospital's immediate geographic area.</p><p><strong>Objective: </strong>To assess associations of CBS per capita with community-level characteristics and social determinants of health.</p><p><strong>Design, setting, and participants: </strong>This cross-sectional study used Internal Revenue Service Series 990 Tax Forms from 2018 to 2023, to create a dataset of CBS for nonprofit hospitals in the US. Facility-level CBS allocation to counties was based on inpatient utilization to more accurately reflect a hospital's community. Data were analyzed from January to December 2024.</p><p><strong>Exposures: </strong>County-level race and ethnicity characteristics and socioeconomic factors, including educational attainment, proportion living below 138% of the federal poverty level (FPL), and the Social Vulnerability Index (SVI) score.</p><p><strong>Main outcomes and measures: </strong>The primary outcome was total CBS per capita. Generalized linear regression models with a γ log-link function were used to assess the association of CBS per capita with community-level social determinants of health characteristics.</p><p><strong>Results: </strong>A total of 2465 nonprofit hospitals across 3140 US counties were included. Allocation of CBS varied significantly across communities, with the counties in the highest quintile receiving a mean (SD) of $540 ($250) per capita compared with counties in the lowest quintile with $22 ($16) per capita. Communities in the highest quintile of CBS had a higher proportion of White residents, while communities in the lowest quintile had a higher proportion of residents who were non-Hispanic Black or Hispanic, had lower educational attainment, and were living with incomes below 138% of the FPL. For every 1% proportional increase in non-Hispanic Black or Hispanic residents in a community, there was 1.61% (95% CI, 1.38%-1.84%) and 0.88% (95% CI, 0.63%-1.14%) less CBS per capita, respectively. In addition, there was less allocation of CBS per capita among counties with a greater proportion of people with low educational attainment, greater levels of poverty, or higher SVI scores. These results were consistent before and during the COVID-19 pandemic.</p><p><strong>Conclusions and relevance: </strong>This cross-sectional study found that nonprofit hospitals' CBS was regressively allocated across US communities, with more socially vulnerable or racially and ethnically minoritized communities receiving less benefit than more affluent, non-Hispanic White communities, suggesting that the nonprofit tax system may be structurally discriminatory and contributing to health disparities.</p>","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e245523"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11871545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trump's Most Important Health Policy May Be at the Border.","authors":"David M Cutler","doi":"10.1001/jamahealthforum.2025.0617","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.0617","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e250617"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Difficult Social Issues-The Consequences of Abortion Bans.","authors":"Sandro Galea","doi":"10.1001/jamahealthforum.2025.0087","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.0087","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e250087"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Burden of Comorbid Conditions Among Individuals Screened for Lung Cancer.","authors":"Dejana Braithwaite, Shama Karanth, Christopher G Slatore, Jae Jeong Yang, Martin Tammemagi, Michael K Gould, Gerard A Silvestri","doi":"10.1001/jamahealthforum.2024.5581","DOIUrl":"10.1001/jamahealthforum.2024.5581","url":null,"abstract":"<p><strong>Importance: </strong>Screening for lung cancer with low-dose computed tomography (LDCT) has been shown to reduce lung cancer mortality in trials that included relatively younger, healthier, and predominantly White populations. The comorbidity profiles among patients undergoing lung cancer screening in practice settings are poorly understood.</p><p><strong>Objective: </strong>To evaluate the comorbidity profiles of patients in the Personalized Lung Cancer Screening (PLuS) cohort as a clinical setting vs the National Lung Screening Trial (NLST) participants in a clinical trial setting.</p><p><strong>Design, setting, and participants: </strong>This multicenter cohort study was conducted across 3 health care systems in California, Florida, and South Carolina and included patients who underwent LDCT lung cancer screening between 2016 and 2021. Data were analyzed between January 1, 2016, and December 31, 2021.</p><p><strong>Exposures: </strong>Receipt of the LDCT scan identified through Current Procedural Terminology and Healthcare Common Procedure Coding System codes.</p><p><strong>Main outcomes and measures: </strong>Detailed comorbidity data, measures of pulmonary function, and study data abstracted from electronic health records and institutional, Surveillance, Epidemiology, and End Results (SEER), and state registries were compared with self-reported comorbid conditions of participants in the LDCT arm of the NLST.</p><p><strong>Results: </strong>The PLuS cohort (n = 31 795) included 49.0% participants aged 65 years or older vs 26.6% in the NLST cohort (n = 26 723); 23.3% were individuals of racial and ethnic minority groups in the PLuS cohort compared with 8.5% in the NLST. The prevalence of comorbidity was substantially higher in the PLuS cohort than the NLST group, particularly chronic obstructive pulmonary disease (32.7% vs 17.5%), diabetes (24.6% vs 9.7%), and heart disease (15.9% vs 12.9%). Among those in the PLuS cohort, 19.3% had a Charlson Comorbidity Index score of 4 or higher, 18.0% had a frailty index greater than 0.20, 16.9% had a forced expiratory volume in 1 second (FEV-1) lower than 50% of predicted, and almost 5% had an ejection fraction lower than 40%. The prevalence of multimorbidity and frailty was especially high among those in the 75 years or older age group.</p><p><strong>Conclusions and relevance: </strong>This study found that the PLuS cohort members were older, had greater illness severity, and more racially and ethnically diverse than the NLST participants. Older patients and those with consequential comorbidity likely had different risk-benefit profiles, which may have affected screening outcomes. The high prevalence of multimorbidity, frailty, and impaired cardiopulmonary function in the PLuS cohort suggests that the balance of benefits and harms observed in the NLST group may not translate to the clinical setting.</p>","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 2","pages":"e245581"},"PeriodicalIF":9.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11846005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}