Medical Care最新文献

{"title":"Bridging Data Systems to Develop Interventions for Older Adults Exposed to Extreme Heat.","authors":"Arnab K Ghosh, Nikhil Garg, Emma Pierson, Chethan Sarabu, Genevive Meredith, John P Pollak, Anthony Townsend, Deborah Estrin","doi":"10.1097/MLR.0000000000002320","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002320","url":null,"abstract":"<p><strong>Background: </strong>The development of clinical tools to combat extreme heat events (EHEs) is urgently needed. However, the collection, linkage, and application of data and technology required to address the health consequences of EHEs-through individualized decision-making, population-focused interventions, and health system planning-remain in its infancy despite the wealth of data infrastructure in health care systems.</p><p><strong>Methods: </strong>In this paper, we describe a use case for data-intensive system architecture that can enable best practices for addressing EHE-related health risks in older adults with cardiovascular disease (CVD), a population uniquely vulnerable to EHEs.</p><p><strong>Results: </strong>Descriptions of various data sources integrated into a modular approach are discussed that allows multilevel (ie, individual-level, population-level) evaluation of EHE-related risk. Individual data streams include batched data from personal digital health devices such as wearables, indoor temperature sensors, and electronic medical record data linked through unique identifiers. Data collection, processing, and analysis as well as related challenges (eg, data quality, processing requirements, and health care system attribution) are also discussed. How this data architecture can then address important preclinical, clinical, and related questions are then described, including: (1) which physiological signals (including cardiovascular and sleep measures) may best anticipate EHE-related health care utilization in older adults with CVD; (2) how do heat thresholds that increase EHE-related health care utilization differ by medication use and type, and comorbidities; and (3) how does indoor versus outdoor temperature measures influence these associations-all understudied aspects of EHE risk in older adults.</p><p><strong>Conclusions: </strong>With considered effort and expertise, a modular data architecture that allows the combination of different elements will enable the development of clinical tools to address EHE-related health risk among older adults with CVD at multilevel scales.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"644-652"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912912","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}

{"title":"What Might Happen If We Do Not Collect High-Quality Race and Ethnicity Data.","authors":"Judith A Long, Carmen E Guerra, Joni S Williams","doi":"10.1097/MLR.0000000000002338","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002338","url":null,"abstract":"<p><p>This perspective examines what is lost when race and ethnicity data are not adequately collected or analyzed. It highlights the importance of these data for identifying disparities, informing the design of effective interventions, and ensuring that clinical trials achieve meaningful and equitable impact. It also discusses how the absence of such data can limit the broader applicability and effectiveness of research findings. The conclusion is that when high-quality data are available and analyzed thoughtfully, they enable the identification of inequities, illuminate underlying mechanisms, guide targeted interventions, support accountability, and ultimately improve the health of all Americans.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"641-643"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912344","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}

{"title":"Postpartum Linkage to Primary Care Among Patients With Diabetes and Hypertensive Disorders of Pregnancy Following Medicaid ACO Implementation.","authors":"Kathryn D Thompson, Mallika Sabharwal, Karen E Lasser, Lois McCloskey, Sarah H Gordon, Kevin Nguyen, Elizabeth Patton, Megan B Cole","doi":"10.1097/MLR.0000000000002389","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002389","url":null,"abstract":"<p><strong>Background: </strong>Timely postpartum linkage to primary care is important for managing diabetes and hypertensive disorders of pregnancy (HDP), yet linkage remains suboptimal. Medicaid accountable care organizations (ACOs) aim to strengthen care coordination and improve postpartum linkage, although effects may vary by ACO design.</p><p><strong>Objective: </strong>Evaluate whether Massachusetts Medicaid ACO implementation was associated with changes in postpartum primary care linkage among enrollees with diabetes or HDP and whether effects differed by ACO model.</p><p><strong>Design: </strong>Retrospective cohort study using the 2016-2023 Massachusetts All-Payer Claims Database (APCD). Using a difference-in-differences approach, we compared outcomes before (2016-2017) versus after (2018-2023) implementation among health system/managed care partnership ACOs, primary care-led ACOs, and non-ACO enrollees.</p><p><strong>Subjects: </strong>In total, 38,340 Medicaid-covered deliveries among enrollees ages 18-44 with diabetes or HDP, continuously enrolled for at least 60 days before and after delivery.</p><p><strong>Main measures: </strong>A primary care visit within 3, 6, and 12 months postpartum.</p><p><strong>Results: </strong>Overall, 53.6%, 76.8%, and 90.3% were linked to primary care by 3, 6, and 12 months, respectively. Although positive, ACO implementation was not associated with statistically significant changes at any interval for either model. At 3 months, model 1 was associated with a 2.44 percentage point [PP] increase (95% CI: -1.7 to 6.6) and model 2 with a 3.73 PP increase (95% CI: -0.5 to 8.0) versus non-ACO enrollees.</p><p><strong>Conclusions: </strong>Massachusetts Medicaid ACO implementation was not associated with significant changes in postpartum primary care linkage. Explicit postpartum performance measures and stronger coordination between maternity and primary care clinicians may be needed to improve transitions to ongoing care.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149111785","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}

{"title":"Housing Wealth and Glycemic Control Among Patients With Type 2 Diabetes.","authors":"Tammy Leonard, Michael E Bowen, Jessica I Billig, Uma Gunasekaran, Joshua Liao, Andrew L Masica, Catherine Oliveros, Ferdinand Velasco, Amy Hughes","doi":"10.1097/MLR.0000000000002306","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002306","url":null,"abstract":"<p><strong>Background: </strong>Housing wealth can be leveraged to maintain economic security when expenses exceed income, allowing households flexibility when managing a new chronic disease diagnosis. We linked housing and electronic health record (EHR) data to examine how housing wealth and stability relate to type 2 diabetes (T2D) outcomes.</p><p><strong>Methods: </strong>EHR data from patients diagnosed with T2D were linked to housing data (tenure, foreclosure, home equity, reverse mortgage, and loan-to-value ratio) using residential address. Multinomial logistic regression models were used to estimate associations between housing variables measured in the year before T2D diagnosis and glycemic control a year or more after T2D diagnosis.</p><p><strong>Results: </strong>Among 5810 patients with T2D and complete housing data, patients with more home equity [relative risk ratio (rrr)=0.52, 95% CI: 0.323-0.838, P<0.001] were less likely to experience HbA1c >9% 1 year after diagnosis, and owners who were actively extracting home equity through a reverse mortgage (rrr=2.137, 95% CI: 0.008-1.511, P=0.048) were also more likely to have HbA1c >9% compared with patients with HbA1c<6.5%. Homeowners were less likely than renters to experience HbA1c >9% (rrr=0.798, 95% CI: 0.672-0.948, P=0.010).</p><p><strong>Conclusion: </strong>Patients diagnosed with T2D who had more housing wealth were less likely to experience challenges with glycemic control after diagnosis.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"670-677"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148911712","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}

{"title":"Hospital-Based Severe Maternal Morbidity Surveillance and Review: A Strategy for Improving Maternity Care in Maryland.","authors":"Andreea A Creanga, Carrie Wolfson, Elizabeth Stierman, Briana Kramer, Robert Atlas, Jeanne Sheffield, Irina Burd, Monica Jones, Elizabeth Zadzielski, Khyzer Aziz","doi":"10.1097/MLR.0000000000002309","DOIUrl":"10.1097/MLR.0000000000002309","url":null,"abstract":"<p><strong>Background: </strong>Learning from promising maternal health interventions is limited by the lack of standardized outcome measures needed for their evaluation. We describe the development and utilization of Maryland's severe maternal morbidity (SMM) surveillance by the Maryland Maternal Health Innovation (MDMOM) program.</p><p><strong>Methods: </strong>To ascertain the development, attributes, and data collected by SMM surveillance, we use 2020-2024 MDMOM program records and 2023 surveillance data. Hospital reports and MDMOM's 2023 maternal health care provider survey data are used to document policy and practice changes attributable to participation in SMM surveillance. Data collected from MDMOM's \"Learning from Adverse Events in Maryland\" trainees are analyzed to explore knowledge score changes (Wilcoxon signed-rank test) and training satisfaction.</p><p><strong>Results: </strong>SMM surveillance is a simple, flexible, stable, high-quality system for identifying areas of care in need of improvement. Before October 2024, when state legislation mandated all hospitals to participate, 24 of 32 hospitals (80% of the state's annual deliveries) engaged in SMM surveillance. The data are used to assess SMM levels and contributors, disparities (eg, intensity of care practices and outcome differences between patient groups), and preventability. Knowledge scores improved after the training on SMM surveillance findings (P<0.001). Data can be used for quality improvement and to generate PCOR hypotheses.</p><p><strong>Conclusions: </strong>Maryland established hospital-based SMM surveillance, demonstrated its value, and gained legislative support for statewide scale-up. This model surveillance system can be replicated in other states, aiming to better understand and address SMM and SMM disparities.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"715-723"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148911568","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}

{"title":"Improving Analysis of Medicaid Enrollee Race and Ethnicity Using Data From a State-Based Marketplace and Regional Health Information Exchange.","authors":"Parker James, Alexis Smirnow, David Idala, Leigh Goetschius, Alice Middleton","doi":"10.1097/MLR.0000000000002205","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002205","url":null,"abstract":"<p><strong>Background: </strong>Medicaid race and ethnicity data quality continues to pose challenges for patient-centered outcomes research. Collection practices and data systems vary; according to the Centers for Medicare & Medicaid Services Data Quality Atlas, many states have race and ethnicity data quality of concern (medium, high, or unusable), including Maryland.</p><p><strong>Objective: </strong>This study links data from 3 sources to reduce the percentage of Maryland Medicaid enrollees with an unknown race and ethnicity and improve the accuracy of population-level data.</p><p><strong>Research design: </strong>People enrolled in Maryland Medicaid (MMIS2) at any point during calendar year 2023 (N=1,898,041) were matched to data from Maryland's state health insurance marketplace (MHBE) and designated health information exchange (CRISP). Enrollees were assigned a single race and ethnicity value from the data sources in the following order: MHBE; CRISP; historic MMIS2; current MMIS2. If a participant had an unknown race and ethnicity, the next source was used.</p><p><strong>Results: </strong>Most Medicaid enrollees (97.8%) were found in MHBE and/or CRISP. The study methodology allowed for greater disaggregation of the population by race and ethnicity and reduced the percentage with an unknown race and ethnicity from 23.0% to 1.0%. The distribution of enrollees by race and ethnicity after applying the study method was better aligned with American Community Survey benchmarks than the original data.</p><p><strong>Conclusions: </strong>These results will help stakeholders in Maryland better identify disparities in patient-centered outcomes and advance health equity goals. This methodology can assist researchers and policymakers in other states with similar data quality issues, as part of a larger effort to improve Medicaid race and ethnicity data.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"678-684"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912385","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}

{"title":"Data Standards for Comprehensive Shared Care Planning for People Living With Multiple Chronic Conditions: Critical Data to Advance Health for All.","authors":"Jenna M Norton, Karen L Fortuna, Jose A Plascencia-Jimenez, Gay Dolin, Evelyn Gallego, Maria Michaels, Arlene S Bierman","doi":"10.1097/MLR.0000000000002322","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002322","url":null,"abstract":"<p><strong>Background: </strong>Uninteroperable health data impedes care and research-especially for people with multiple chronic conditions (MCC) or from disadvantaged communities. By enabling the sharing of person-centered data, standards-based care plans may improve health outcomes and reduce disparities.</p><p><strong>Objective: </strong>Develop and test interoperable electronic e-Care planning tools to collect, aggregate, and share person-centered data for MCC healthcare and research.</p><p><strong>Methods: </strong>Using participatory and agile design, we developed, implemented, and evaluated e-Care plan tools-including data standards and clinician- and patient/caregiver-facing e-Care plan applications (eCarePlanner and MyCarePlanner). The Consolidated Framework for Implementation Research Process Redesign and the Systems Engineering Initiative for Patient Safety model informed a mixed methods evaluation to gather feedback from patients, caregivers, and clinicians across formative, iterative, and summative stages.</p><p><strong>Results: </strong>The apps successfully connected with 4 electronic health records at 17 institutions. Evaluation participants included 57 patients/caregivers and 15 clinicians. Patients and caregivers were predominantly aged 65 years or older, White, and well-educated. Most participants were comfortable using the app (97%). Most felt app loading was timely (90%), and the app would support complex care coordination (63%). Usability scores were lowest for the ability to improve care team communication (48% agreed/strongly agreed) and for inconsistencies across app sections (37% agreed/strongly agreed). Key themes from interviews included challenges in moving health information across settings and the apps' potential to overcome these challenges, thereby improving the frequency and quality of care planning.</p><p><strong>Conclusion: </strong>This project provides a proof-of-concept for standards-based tools to collect, aggregate, and share patient-centered health and social data across health care and research settings.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"660-669"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912907","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}

{"title":"Enhancing the Observational Medical Outcomes Partnership Common Data Model to Support Health Equity Research: Expanding Race and Ethnicity Data Representation.","authors":"Melanie Philofsky, Atif Adam","doi":"10.1097/MLR.0000000000002308","DOIUrl":"10.1097/MLR.0000000000002308","url":null,"abstract":"<p><strong>Background: </strong>Common Data Models (CDMs) standardize health data from disparate observational sources, enabling the use of real-world data in more efficient, collaborative observational research studies. Until recently, the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) had limited infrastructure and ontology options to store a person's race and ethnicity data. This made using these data challenging for patient-centered outcomes research (PCOR) and health equity studies.</p><p><strong>Objective: </strong>The primary goals are to outline the Observational Health Data Sciences and Informatics (OHDSI) community's collaborative efforts to improve race and ethnicity representation in the OMOP CDM and to explain how these enhancements enable a more detailed and comprehensive representation of individuals' racial and ethnic identities. These improvements ensure that research findings are both relevant to everyday clinical practice and applicable across diverse demographic groups.</p><p><strong>Methods: </strong>The OHDSI community collaboratively addressed limitations in race and ethnicity representation within the OMOP CDM through a structured, inclusive process. Key workgroups worked together, engaging diverse stakeholders. Patient input played a pivotal role in shaping enhancements like multivalue storage. The multistep enhancement process included community-wide feedback at every step.</p><p><strong>Results: </strong>The OHDSI community significantly enhanced the OMOP CDM's ability to represent race and ethnicity data, improving its granularity, inclusivity, and flexibility. These updates expand the race and ethnicity value sets, address multiracial and multiethnic identities, and enable more accurate and granular PCOR and health equity studies.</p><p><strong>Conclusion: </strong>This enhancement to the OMOP CDM reduces the gap between data stored in source systems and data converted to the OMOP CDM. The enhanced data model enables more detailed, nuanced health equity research and eliminates biases previously associated with limited demographic representation.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"653-659"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148911350","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}

{"title":"Resources Available for Pulmonary Hypertension Care Across Veterans Health Administration Medical Centers: Results From a Multicenter Survey.","authors":"Kari R Gillmeyer, Varsha G Vimalananda, Lanxia Sun, Seppo T Rinne, Renda S Wiener","doi":"10.1097/MLR.0000000000002386","DOIUrl":"10.1097/MLR.0000000000002386","url":null,"abstract":"<p><strong>Background: </strong>Pulmonary hypertension (PH) care requires extensive resources and support, including expert multidisciplinary teams, advanced diagnostic modalities, and complex pharmacologic therapies. While this infrastructure is standardized within accredited PH centers, less is known about resource availability outside of PH centers, where most patients with PH receive care.</p><p><strong>Objective: </strong>To explore PH-specific resource availability across the Veterans Health Administration (VA), which cares for over 250,000 patients with PH.</p><p><strong>Research design, subjects, and measures: </strong>Between November 2024 and January 2025, we surveyed pulmonary and cardiology providers within 66 highly resourced VA medical centers, exploring PH-specific structures, resources, and programs through structured and open-ended questions. We summarized structured responses as counts (percentages) by site and analyzed free-text responses using conventional content analysis. We overlayed maps of VA sites with PH experts and non-VA PH centers and associated 60-minute drive times.</p><p><strong>Results: </strong>Providers at 61 sites responded (site-level response 92.4%). 57.4% of sites had a PH expert physician; few sites had other key personnel or PH-specific programs. Free-text responses highlighted (1) an overall lack of VA PH resources, (2) the value of PH expert physicians, (3) the need for PH support staff, and (4) challenges to providing PH-specific therapies. Geographic analyses revealed insufficient access to PH expertise in many U.S. regions.</p><p><strong>Conclusions: </strong>Within the largest U.S. integrated health care system, there is a paucity of resources vital for PH care. These findings highlight the need for strategies to build PH-specific resource infrastructure within VA and to better understand how these gaps affect PH care quality.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149111777","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}

{"title":"Innovative Data Linkage for Enhancing Identification and Understanding of Social Determinants of Health Among the Medicaid Population.","authors":"Victoria Udalova, Aubrey Limburg","doi":"10.1097/MLR.0000000000002324","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002324","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic led to economic and policy changes that increased Medicaid enrollment and reduced disenrollment. Tracking the impact of these changes on the composition of enrollees is crucial for resource allocation and program evaluation. However, Medicaid data currently lack the necessary demographic, social, and economic information about enrollees.</p><p><strong>Objective: </strong>Enhance Medicaid enrollment data by linking it with nationally representative survey data to compare the composition of enrollees across various social determinants of health characteristics before and after the COVID-19 pandemic.</p><p><strong>Research design: </strong>We utilize individual-level Medicaid enrollment records (TAF, 2018-2021) before and during the pandemic, linked to restricted American Community Survey (ACS, 2021) microdata.</p><p><strong>Results: </strong>Almost 95% of enrollees in our analytic sample received an anonymous identifier (Protected Identification Key), and nearly 1% were found in ACS data. By comparing Medicaid enrollees in different enrollment cohorts, we find that the pandemic caused significant compositional changes, particularly among the newly enrolled. Our findings indicate that those experiencing a major health or economic shock, either directly or through a family member, relied on Medicaid, likely as a temporary source of health insurance during the pandemic.</p><p><strong>Conclusions: </strong>Linking individual-level records between Medicaid and ACS data effectively addresses a crucial gap in current data capacity. The integrated data can be utilized, repurposed, and expanded by incorporating additional survey and administrative records to enhance the utility of Medicaid data for future research.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":"64 10","pages":"685-695"},"PeriodicalIF":1.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912323","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}