JAMA Health Forum最新文献

{"title":"The Supreme Court Disempowers Public Health Agencies and Devalues Science.","authors":"Lawrence O Gostin, Adi Radhakrishnan","doi":"10.1001/jamahealthforum.2024.3790","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.3790","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e243790"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300716","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":"Evaluating Policy Changes for Adjusting Payment to Address Health Disparities.","authors":"W Ryan Powell, Luke Chamberlain, William R Buckingham, Ying Cao, Mariétou H Ouayogodé, Robin L Lankton, Amy J H Kind","doi":"10.1001/jamahealthforum.2024.2905","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.2905","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e242905"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11400218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300711","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":"Understanding Variation in Negotiated Rates Using Novel Health Plan Price Transparency Data.","authors":"Allison H Oakes, Matthew Ikard, Catherine Patton, Jim Browne, Nancy Organ, Matthew O'Neill, Sanjula Jain","doi":"10.1001/jamahealthforum.2024.3020","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.3020","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e243020"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11415782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300717","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":"Health Care-Related Savings Accounts, Health Care Expenditures, and Tax Expenditures.","authors":"Dong Ding, Sherry Glied","doi":"10.1001/jamahealthforum.2024.2896","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.2896","url":null,"abstract":"<p><strong>Importance: </strong>Approximately 30% of US families with employer-sponsored health insurance, disproportionately drawn from high-income groups, benefit from flexible spending accounts (FSAs) or health savings accounts (HSAs). The combined association through both out-of-pocket spending and premiums of these tax-favored accounts with health care expenditures and tax expenditures remain uncertain.</p><p><strong>Objective: </strong>To compare the health care and health-related tax expenditures among families holding FSAs, HSAs, or neither type of account.</p><p><strong>Design, setting, and participants: </strong>This cross-sectional study used family-level data from the Medical Expenditure Panel Survey from January 1, 2011, to December 31, 2019, and conducted regression models, controlling for demographic and socioeconomic characteristics, chronic conditions, prior health care expenditures, and marginal tax rates to analyze how holding tax-favored accounts is associated with families' health care spending and tax expenditures. The sample was restricted to families included in the survey for 2 years, with no members 65 years or older, and with at least 1 policyholder covered (only) by full-year employer-sponsored insurance. Data were analyzed from December 1, 2023, to April 30, 2024.</p><p><strong>Exposures: </strong>Holding FSAs or HSAs.</p><p><strong>Main outcomes and measures: </strong>Out-of-pocket and insurance-paid health expenditures overall and by service were measured. Health-related tax expenditures were based on tax-excluded insurance premiums and tax-sheltered out-of-pocket expenses.</p><p><strong>Results: </strong>Of the 17 038 families included in the study sample, 2628 held FSAs (weighted 17%) and 1845 (weighted 13%) held HSAs. In regression-adjusted models, families with FSAs spent a mean of 20% or $2033 (95% CI, $789-$3276) more on health care annually than non-account holding families, largely due to increased insurer-paid expenses. Families with HSAs spent a mean of 44% or $697 (95% CI, $521-$873) more on out-of-pocket expenditures and had insignificantly higher insurance-paid expenditures than families without accounts, resulting in overall expenditures comparable to those of non-account holders. The additional tax expenditures associated with FSAs were a mean of $1306 (95% CI, $536-$2076) annually per family. Both types of funds were associated with significant increases in tax expenditures from additional office-based visits ($445 [95% CI, $244-$645] for FSAs and $174 [95% CI, $11-$336] for HSAs), outpatient visits ($330 [95% CI, $132-$528] for FSAs and $250 [95% CI, $15-$485] for HSAs), dental visits ($180 [95% CI, $126-$233] for FSAs and $165 [95% CI, $104-$226] for HSAs), and vision care ($36 [95% CI, $28-$45] for FSAs and $52 [95% CI, $40-$64] for HSAs).</p><p><strong>Conclusions and relevance: </strong>Participation in FSAs is associated with higher health care expenditures and tax expenditures, while HSAs are ","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e242896"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11415789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300712","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":"Health Policy in an Era of Universal Coverage.","authors":"Sherry Glied","doi":"10.1001/jamahealthforum.2024.3904","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.3904","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e243904"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332193","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":"Lessons From the Change Healthcare Ransomware Attack.","authors":"Genevieve P Kanter, James R Rekowski, Joseph T Kannarkat","doi":"10.1001/jamahealthforum.2024.2764","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.2764","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e242764"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300714","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":"Private Equity Acquisition of Physician Practices-Looking for Ethical Guidance From Professional Societies.","authors":"Peter A Ubel","doi":"10.1001/jamahealthforum.2024.2767","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.2767","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e242767"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300715","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":"Ethical and Operational Strategies for Scarce Resource Allocation.","authors":"Jonathon Leider, Debra DeBruin, Sarah Lim","doi":"10.1001/jamahealthforum.2024.2940","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.2940","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e242940"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300710","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":"A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies.","authors":"Mengli Xiao, Kyle C Molina, Neil R Aggarwal, Laurel E Beaty, Tellen D Bennett, Nichole E Carlson, Lindsey E Fish, Mika K Hamer, Bethany M Kwan, David A Mayer, Jennifer L Peers, Matthew K Wynia, Adit A Ginde","doi":"10.1001/jamahealthforum.2024.2884","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2024.2884","url":null,"abstract":"<p><strong>Importance: </strong>During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (PLTs), to optimize the distribution of scarce therapeutics.</p><p><strong>Objective: </strong>To evaluate whether a machine learning PLT-based method of scarce resource allocation optimizes the treatment benefit of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint.</p><p><strong>Design, setting, and participants: </strong>This retrospective cohort study used electronic health record data from October 1, 2021, to December 11, 2021, for the training cohort and data from June 1, 2021, to October 1, 2021, for the testing cohort. The cohorts included patients who had positive test results for SARS-CoV-2 and qualified for COVID-19 mAb therapy based on the US Food and Drug Administration's emergency use authorization criteria, ascertained from the patient electronic health record. Only some of the qualifying candidates received treatment with mAbs. Data were analyzed between from January 2023 to May 2024.</p><p><strong>Main outcomes and measures: </strong>The primary outcome was overall expected hospitalization, assessed as the potential reduction in overall expected hospitalization if the PLT-based allocation system was used. This was compared to observed allocation using risk differences.</p><p><strong>Results: </strong>Among 9542 eligible patients in the training cohort (5418 female [56.8%]; age distribution: 18-44 years, 4151 [43.5%]; 45-64 years, 3146 [33.0%]; and ≥65 years, 2245 [23.5%]), a total of 3862 (40.5%) received mAbs. Among 6248 eligible patients in the testing cohort (3416 female [54.7%]; age distribution: 18-44 years, 2827 [45.2%]; 45-64 years, 1927 [30.8%]; and ≥65 years, 1494 [23.9%]), a total of 1329 (21.3%) received mAbs. Treatment allocation using the trained PLT model led to an estimated 1.6% reduction (95% CI, -2.0% to -1.2%) in overall expected hospitalization compared to observed treatment allocation in the testing cohort. The visual assessment showed that the PLT-based point system had a larger reduction in 28-day hospitalization compared with the Monoclonal Antibody Screening Score (maximum overall hospitalization difference, -1.0% [95% CI, -1.3% to -0.7%]) in the testing cohort.</p><p><strong>Conclusions and relevance: </strong>This retrospective cohort study proposes and tests a PLT method, which can be linked to a electronic health record data platform to improve real-time allocation of scarce treatments. Use of this PLT-based allocation method would have likely resulted in fewer hospitalizations across a population than were observed in usual care, with greater expected reductions than a commonly used point system.</p>","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e242884"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11400220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300709","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":"Disparities in Medication Use for Criminal Justice System-Referred Opioid Use Disorder Treatment.","authors":"J Travis Donahoe, Julie M Donohue, Brendan K Saloner","doi":"10.1001/jamahealthforum.2024.2807","DOIUrl":"10.1001/jamahealthforum.2024.2807","url":null,"abstract":"<p><strong>Importance: </strong>Individuals with opioid use disorder (OUD) and criminal justice system involvement experience high rates of overdose death. Historical data point to limited use of medications for opioid use disorder (MOUD) in criminal justice system-referred treatment for OUD as playing a role. However, how MOUD use among those referred to treatment by the criminal justice system has changed relative to other referral sources over time is still unclear, as well as how it varies across states.</p><p><strong>Objective: </strong>To examine disparities in the use of MOUD between individuals referred to treatment by the criminal justice system compared to other referral sources over time.</p><p><strong>Design, setting, and participants: </strong>This cross-sectional study included admissions to specialty substance use treatment facilities for OUD in the national Treatment Episodes Dataset-Admissions from 2014 to 2021. Logistic regression models were used to examine trends in the probability of MOUD use among individuals with and without criminal justice referrals for OUD treatment, as well as any differential trends by state. The data were analyzed from September 2023 to August 2024.</p><p><strong>Main outcome and measure: </strong>The main outcome was the probability that treatment for individuals with OUD included MOUD.</p><p><strong>Results: </strong>A total of 3 235 445 admissions were analyzed in the study data. Among individuals referred to OUD treatment by the criminal justice system, the probability that treatment included MOUD increased by 3.42 percentage points (pp) (95% CI, 3.37 pp to 3.47 pp) annually from 2014 to 2021. This was faster than the increase in the probability of MOUD use for noncriminal justice-referred admissions (2.49 pp [95% CI, 2.46 pp to 2.51 pp) and reduced, but did not eliminate, disparities in MOUD use between individuals with and without criminal justice system-referred treatment. In 2021, only 33.6% of individuals in criminal justice system-referred treatment received MOUD, 15.6 pp lower than for individuals referred to treatment by other sources. Trends in the probability of MOUD use varied substantially for individuals in criminal justice system-referred treatment across states, but very few experienced enough growth to eliminate this disparity.</p><p><strong>Conclusions and relevance: </strong>The results of this cross-sectional study suggest that targeted efforts to address persistent disparities in MOUD use among those with OUD and criminal justice system involvement are needed to address the poor health outcomes experienced by this population.</p>","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"5 9","pages":"e242807"},"PeriodicalIF":9.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141750","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}