JAMA Health Forum最新文献

{"title":"Racial and Ethnic Disparities in Occupational Health.","authors":"Michael Dworsky, Leslie I Boden, Elizabeth C Chase, Yvette C Cozier, Sarah Edgington, Marc N Elliott, Seth A Seabury","doi":"10.1001/jamahealthforum.2025.3495","DOIUrl":"10.1001/jamahealthforum.2025.3495","url":null,"abstract":"<p><strong>Importance: </strong>Occupational health disparities affect the safety and well-being of workers across different racial and ethnic groups. Understanding these disparities is crucial for developing targeted interventions, but evidence on occupational injury rates by race and ethnicity has been scarce due to data limitations.</p><p><strong>Objective: </strong>To determine the incidence of lost-time workplace injuries among major racial and ethnic groups in California and to assess the contributions of occupational concentration and within-occupation disparities to these differences.</p><p><strong>Design, setting, and participants: </strong>A cross-sectional study analyzing 15 years (2005-2019) of California workers' compensation data was carried out. The study examined injury rates across 4 racial and ethnic groups: Asian/Pacific Islander (non-Hispanic), Black (non-Hispanic), Hispanic, and White (non-Hispanic). California residents employed in the private sector or state and local government, encompassing diverse industries and occupations, between 2005 and 2019 were included. Analysis was conducted between May 2024 and May 2025.</p><p><strong>Intervention: </strong>The study analyzed existing workers' compensation data without specific interventions.</p><p><strong>Main outcomes and measures: </strong>The primary outcome was the incidence of lost-time workplace injuries, with comparisons made between racial and ethnic groups. The analysis focused on the role of occupational concentration and within-occupation disparities in explaining injury rates.</p><p><strong>Results: </strong>The analysis included 2.6 million lost-time injuries among California workers (mean [SD] age, 42 [11]; 63% male). The overall injury rate was 1.32 lost-time injuries per 100 full-time equivalent workers (FTE). Lost-time injury rates were higher for Black (1.74 cases per 100 FTE) and Hispanic workers (1.90 cases per 100 FTE) than for White workers (1.00 cases per 100 FTE), whereas rates were lower for Asian/Pacific Islander workers (0.63 cases per 100 FTE). Black and Hispanic workers experienced injury rates that were 74% and 90% higher, respectively, compared with White workers, whereas Asian/Pacific Islander workers had injury rates that were 37% lower. Occupational concentration accounted for 53% of the disparity between Black and White workers and 71% of the disparity between Hispanic and White workers. Notably, 56% of the excess risk of injuries for Black women was attributed to within-occupation disparities.</p><p><strong>Conclusions and relevance: </strong>Disparities in workplace safety are a significant contributor to racial and ethnic health disparities. Addressing both occupational concentration and within-occupation disparities is essential for improving workplace safety and reducing health inequities among workers.</p>","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e253495"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151664","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":"State Investments in Primary Care-5 Early Leaders of a Potential Policy Trend.","authors":"Sarah H Brown, Dru A Ricci, Ananya Tadikonda, Zirui Song","doi":"10.1001/jamahealthforum.2025.3505","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.3505","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e253505"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088305","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":"Modeling the Impact of MMR Vaccination Strategies on Measles Outbreaks in Texas.","authors":"Kaiming Bi, Thuy Nguyen, Boya Peng, Trudy Krause, Cecilia Ganduglia Cazaban, Janelle Rios, Cici Bauer, Catherine Troisi, Eric Boerwinkle, Aanand D Naik","doi":"10.1001/jamahealthforum.2025.3992","DOIUrl":"10.1001/jamahealthforum.2025.3992","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e253992"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088312","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":"Stimulant Overdose Prediction Model for Medicaid-Insured Persons.","authors":"Tuhina Srivastava, Rebecca Arden Harris, Cheryl Bettigole, Hanxi Zhang, Colleen M Brensinger, Kacie Bogar, Fengge Wang, Elizabeth D Nesoff, Warren B Bilker, Sean Hennessy","doi":"10.1001/jamahealthforum.2025.3489","DOIUrl":"10.1001/jamahealthforum.2025.3489","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Importance: &lt;/strong&gt;Overdoses involving methamphetamines and cocaine have increased in recent years. Identification of individuals at highest risk could facilitate the implementation of evidence-based interventions to reduce overdose risk.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To develop and internally validate a model that predicts hospitalization or emergency department (ED) treatment for stimulant-involved overdose among the Medicaid-insured population.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design, setting, and participants: &lt;/strong&gt;This was a retrospective case-cohort study using Medicaid claims data from 2016 to 2019 (development) and 2020 (validation) for all Medicaid enrollees age 15 years or older with a cocaine- or other stimulant-involved overdose. A subcohort was created using a simple random sample of the full cohort of all cases. Within the full cohort, cases were identified as those having any inpatient or ED encounter for stimulant-involved overdose during the following year. A case-cohort sample was obtained for each calendar year from 2016 to 2020, each with a subcohort size of 100 000. Each individual contributed only 1 case event (for an individual with multiple overdoses, only the first eligible was selected). For each of the 4 overdose outcomes, a predictive weighted Cox model was first developed among enrollees of sampling years 2016 to 2019 (development set), and its performance was evaluated in our test set of 2020. The prediction models were first developed in November 2023, and the model fairness assessment was performed in April to May 2025.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interventions or exposures: &lt;/strong&gt;Individual-level candidate predictors were demographic characteristics, enrollment, health care utilization, and other clinical variables. Area-level variables included social, economic, housing, and demographic characteristics data from the American Community Survey, rural-urban classification, Social Deprivation Index, retail opioid dispensing rates, and health resources.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Main outcomes and measures: &lt;/strong&gt;Four types of stimulant-involved overdose associated with hospitalization or ED treatment: cocaine-involved overdose, (1) involving an opioid or (2) not involving an opioid; or methamphetamine-, ecstasy-, or other psychostimulant-involved overdose (hereafter, other stimulant), (3) involving an opioid or (4) not involving an opioid.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The analysis included 78 795 enrollees with cocaine- and other stimulant-involved overdose (mean [SD] age, 42.2 [13.7] years; 33 304 [42%] female and 45 491 [58%] male individuals). Weighted Cox regression prediction models showed good calibration and high discriminatory performance (Harrell C statistic): cocaine-involved overdose, with (0.923) or without (0.902) an opioid; other stimulant-involved overdose, with (0.909) or without (0.868) an opioid. For cocaine-involved overdose with opioids, previous individual opioid use disorder diagnosis or cocaine use disorder di","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e253489"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088352","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":"New Medicaid Enrollment Barriers and Lessons From Unwinding.","authors":"Adrianna McIntyre, Jinwoo Kim, Benjamin D Sommers","doi":"10.1001/jamahealthforum.2025.4849","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.4849","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e254849"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994433","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":"Potential Storylines From Trump-Era Health Care Cuts.","authors":"Larry Levitt","doi":"10.1001/jamahealthforum.2025.4944","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.4944","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e254944"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034365","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":"The Purpose and Value of Editorial Content at JAMA Health Forum.","authors":"Pragya Kakani, Melinda B Buntin, Sandro Galea","doi":"10.1001/jamahealthforum.2025.4020","DOIUrl":"https://doi.org/10.1001/jamahealthforum.2025.4020","url":null,"abstract":"","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e254020"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088302","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":"Benefit-Risk Reporting for FDA-Cleared Artificial Intelligence-Enabled Medical Devices.","authors":"John C Lin, Bhav Jain, Jay M Iyer, Ishan Rola, Anusha R Srinivasan, Chaerim Kang, Heta Patel, Ravi B Parikh","doi":"10.1001/jamahealthforum.2025.3351","DOIUrl":"10.1001/jamahealthforum.2025.3351","url":null,"abstract":"<p><strong>Importance: </strong>Devices enabled by artificial intelligence (AI) and machine learning (ML) are increasingly used in clinical settings, but there are concerns regarding benefit-risk assessment and surveillance by the US Food and Drug Administration (FDA).</p><p><strong>Objective: </strong>To characterize pre- and postmarket efficacy, safety, and risk assessment reporting for FDA-cleared AI/ML devices.</p><p><strong>Design and setting: </strong>This was a cross-sectional study using linked data from FDA decision summaries and approvals databases, the FDA Manufacturer and User Facility Device Experience Database, and the FDA Medical Device Recalls Database for all AI/ML devices cleared by the FDA from September 1995 to July 2023. Data were analyzed from October to November 2024.</p><p><strong>Main outcomes and measures: </strong>AI/ML reporting of study design, data availability, efficacy, safety, bias assessments, adverse events, device recalls, and risk classification.</p><p><strong>Results: </strong>The analysis included data for all 691 AI/ML devices that received FDA clearance through 2023, with 254 (36.8%) cleared in or after 2021. Device summaries often failed to report study designs (323 [46.7%]), training sample size (368 [53.3%]), and/or demographic information (660 [95.5%]). Only 6 devices (1.6%) reported data from randomized clinical trials and 53 (7.7%) from prospective studies. Few premarket summaries contained data published in peer-reviewed journals (272 [39.4%]) or provided statistical or clinical performance, including sensitivity (166 [24.0%]), specificity (152 [22.0%]), and/or patient outcomes (3 [<1%]). Some devices reported safety assessments (195 [28.2%]), adherence to international safety standards (344 [49.8%]), and/or risks to health (42 [6.1%]). In all, 489 adverse events were reported involving 36 (5.2%) devices, including 458 malfunctions, 30 injuries, and 1 death. A total of 40 devices (5.8%) were recalled 113 times, primarily due to software issues.</p><p><strong>Conclusions and relevance: </strong>This cross-sectional study suggests that despite increasing clearance of AI/ML devices, standardized efficacy, safety, and risk assessment by the FDA are lacking. Dedicated regulatory pathways and postmarket surveillance of AI/ML safety events may address these challenges.</p>","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e253351"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151644","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":"Measuring the Budget Impact of Nondiscriminatory Cost-Effectiveness.","authors":"Karen Mulligan, Drishti Baid, Maria-Alice Manetas, Darius N Lakdawalla","doi":"10.1001/jamahealthforum.2025.3076","DOIUrl":"10.1001/jamahealthforum.2025.3076","url":null,"abstract":"<p><strong>Importance: </strong>The US Inflation and Reduction Act (IRA) prohibits the Centers for Medicare & Medicaid Services (CMS) from using discriminatory methods such as cost-effectiveness analysis (CEA) that assign lower value to treating sicker and disabled persons. Generalized risk-adjusted cost- effectiveness (GRACE) provides a nondiscriminatory alternative, but the potential impact on health care budgets is unknown.</p><p><strong>Objective: </strong>To compare value-based drug prices based on traditional CEA with those based on IRA-compliant GRACE and assess the implications for health care budgets.</p><p><strong>Design and setting: </strong>In this economic evaluation, GRACE was implemented using the direct-utility method and estimated the resulting value-based prices and total budget impact. Model inputs were derived from CEAs published by the Institute for Clinical and Economic Review (ICER) between 2014 and 2024. Data from 302 CEA results for pharmaceuticals published across 72 studies were extracted. The final analysis sample consisted of 259 observations (219 treatment-comparator pairs) across 53 distinct diseases, some of which had subgroup results.</p><p><strong>Main outcomes and measures: </strong>Value-based prices under GRACE and CEA were estimated. A 1-year budget impact was calculated, measured as total drug expenditures using value-based prices assuming a willingness-to-pay threshold of $150 000. The data were analyzed from October 2024 to May 2025.</p><p><strong>Results: </strong>The mean value-based prices were 7.5% higher under GRACE than under CEA (IQR, -3.9% to 9.1%). Furthermore, compared with traditional CEA, GRACE increased value-based prices for more severe diseases and decreased them for milder diseases. Twenty-four drugs (8 from the top population size quartile) cost less under GRACE; total spending was 3.3% lower under GRACE for these drugs. The remaining 45 drugs (13 from the bottom population size quartile) cost more under GRACE, resulting in 14.7% higher spending for these drugs. Taken together, GRACE increased the total budget by 2%..</p><p><strong>Conclusions and relevance: </strong>This economic evaluation found that although GRACE does increase value-based prices on average, the net effect on total health care spent is minimal, in part because resources are redistributed toward more severe, less prevalent illnesses.</p>","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e253076"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001905","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":"Medicaid Primary Care Utilization and Area-Level Social Vulnerability.","authors":"Jordan Herring, Yoon Hong Park, Qian Luo, Anushree Vichare, Clese Erikson, Patricia Pittman","doi":"10.1001/jamahealthforum.2025.3020","DOIUrl":"10.1001/jamahealthforum.2025.3020","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Importance: &lt;/strong&gt;The concentration of poverty and multidimensional disadvantage has been shown to limit access to health care in these communities. There is a growing interest in using area-level socioeconomic indexes to address the unequal geographic distribution of health care resources. However, the association of area-level socioeconomic indexes with access to primary care-a key area in health policy-has not been determined.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To investigate the association of Medicaid primary care utilization with the concentration of poverty and multidimensional disadvantage at the zip code level.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design, settings, and participants: &lt;/strong&gt;This cross-sectional study used the 2019 Transformed-Medicaid Statistical Information System to identify variations in primary care utilization among Medicaid and the Children's Health Insurance Program beneficiaries (age &lt;65 years) by poverty and multidimensional disadvantage levels of their area of residence. Included beneficiaries were enrolled in Medicaid from January 1 to December 31, 2019, and were not dually eligible for Medicare. The zip code-level Social Vulnerability Index (SVI) was used to assess the likelihood of a beneficiary having an annual primary care visit, while controlling for individual beneficiary demographic and health characteristics. An activity-based approach was adopted to classify clinicians billing Medicaid for primary care and to identify primary care visits at federally qualified health centers (FQHCs). SVI results were compared with results using income-based poverty rates alone. Data analysis was performed from May 1, 2023, through February 28, 2025.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Exposure: &lt;/strong&gt;Zip code-level deciles of the SVI and poverty rates.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Main outcomes and measures: &lt;/strong&gt;Regression analysis was performed at the beneficiary level, using a binary indicator for having a primary care visit on a set of dummy variables for SVI deciles, controlling for age and sex interactions, disability status, and indicators for having been diagnosed with behavioral health or chronic physical health conditions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The total population analyzed comprised 34 890 932 Medicaid beneficiaries (&lt;65 years old; 54.2% female and 45.8% male), more than half of whom resided in the top 20% of socially vulnerable zip codes; approximately 33%, in the top 10%; and another 20%, in the ninth decile. Of the total, 68.1% had at least 1 primary care visit in 2019, at either a non-FQHC practice (61.1%) or a FQHC (12.7%). The probability of having a primary care visit was highest for children (age &lt;18 years) but varied substantially by age. Compared to those residing in the first decile of the SVI (least socially vulnerable), beneficiaries in the tenth decile (most socially vulnerable) were 8.9 (95% CI, -9.9 to -7.9) percentage points (pp) less likely to have a primary care visit when not counting FQHC visits, but this increased","PeriodicalId":53180,"journal":{"name":"JAMA Health Forum","volume":"6 9","pages":"e253020"},"PeriodicalIF":11.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001985","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}