Health Services Research最新文献

{"title":"Hospital Accreditation and Geographic Disparities in Timely Cancer Care.","authors":"Jason T Semprini, Joshua W Devine, Ingrid M Lizarraga, Mary E Charlton","doi":"10.1111/1475-6773.14655","DOIUrl":"https://doi.org/10.1111/1475-6773.14655","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate whether the association between receiving care at an accredited hospital and timely treatment initiation varies by county income level.</p><p><strong>Study setting and design: </strong>This cross-sectional study compared days from diagnosis to treatment initiation among patients receiving care at CoC-accredited hospitals with patients receiving care at non-accredited hospitals. We estimated distributional effects with a quantile regression model. We stratified patients into low (median household-income < 80k) and high-income (median household-income ≥ 80k) counties.</p><p><strong>Data sources and analytic sample: </strong>We analyzed population-based Surveillance, Epidemiological, and End Results case data (2018-2021). We excluded cancer cases that did not receive treatment. All analyses were adjusted for tumor and patient characteristics, treatment received, and geographic factors.</p><p><strong>Principal findings: </strong>Among 2,107,188 patients receiving cancer treatment, 73.65% received care at an accredited hospital. Median time-to-treatment was 27 days (interquartile range = 1-52). Care at an accredited hospital was associated with longer median time-to-treatment (+2.6 days) in low-income counties but not high-income counties. Similarly, care at an accredited hospital was associated with widening the time-to-treatment interquartile range (+1.8 days) in low-income but not high-income counties. The magnitude of these associations was highest in patients aged 65+, unmarried, diagnosed at an early stage, and in less common cancers. Only among patients diagnosed with distant-stage cancer was accreditation associated with reduced median time-to-treatment in both low and high-income counties.</p><p><strong>Conclusions: </strong>Treatment at an accredited hospital appeared to increase time-to-treatment differences between high-and low-income counties and low-income counties. This heterogeneity may reflect access challenges facing low-income cancer patients. Health systems seeking to provide high quality, timely care must overcome these access challenges as they navigate patients through the cancer care continuum. While a 2.6-day delay in treatment may not impact outcomes, future research should understand why patients from lower-resource communities wait longer than patients in affluent communities.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14655"},"PeriodicalIF":3.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235978","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":"Veteran Mental Health Emergency Care Utilization Following SARS-CoV-2 Infection","authors":"Jason I. Chen,&nbsp;Meike Niederhausen,&nbsp;David P. Bui,&nbsp;Diana J. Govier,&nbsp;Mazhgan Rowneki,&nbsp;Alex Hickok,&nbsp;Troy A. Shahoumian,&nbsp;Megan Shepherd-Banigan,&nbsp;Anna Korpak,&nbsp;Eric Hawkins,&nbsp;Alan R. Teo,&nbsp;Jennifer Naylor,&nbsp;Thomas F. Osborne,&nbsp;Valerie A. Smith,&nbsp;C. Barrett Bowling,&nbsp;Edward J. Boyko,&nbsp;George N. Ioannou,&nbsp;Matthew L. Maciejewski,&nbsp;Ann M. O'Hare,&nbsp;Elizabeth M. Viglianti,&nbsp;Theodore J. Iwashyna,&nbsp;Amy S. B. Bohnert,&nbsp;Denise M. Hynes,&nbsp;VA HSR&D COVID-19 Observational Research Collaboratory (CORC)","doi":"10.1111/1475-6773.14622","DOIUrl":"10.1111/1475-6773.14622","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To evaluate whether Veterans infected with SARS-CoV-2 have an elevated risk for needing mental health emergency care (MHEC) relative to uninfected comparators, as measured by emergency department or urgent care clinic utilization for a mental health diagnosis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources/Extraction</h3>\u0000 \u0000 <p>Data from Veterans Health Administration (VHA), VHA-paid, and Centers for Medicare &amp; Medicaid-paid services were used to identify incident MHEC use within 1 year of infection for Veterans with a SARS-CoV-2 infection and matched comparators.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>This was a national, retrospective cohort study that leveraged a target trial emulation framework to examine long-term outcomes of SARS-CoV-2 infection among Veterans enrolled in VHA care. Uninfected comparators were matched based on month of infection, demographic, clinical, and health care utilization characteristics. We calculated cumulative incidence rates per 10,000 persons and utilized Cox regression models to estimate hazard ratios (HR) for MHEC up to one year post-infection.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The cohort included 207,968 Veterans with SARS-CoV-2 and 1,036,944 comparators. The 365-day incidence of MHEC use was greater among SARS-CoV-2 patients than comparators (HR = 1.48; 95% CI: [1.44, 1.52]). Patients with SARS-CoV-2 had a higher hazard for MHEC use than comparators in all timeframes analyzed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>SARS-CoV-2 infection was associated with increased MHEC use. Active care coordination with existing mental health treatment providers may help mitigate post-infection mental health distress. Future research should explore specific contextual factors contributing to MHEC, such as gaps in continuity of care.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235979","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":"Completeness and Quality of Data for Children in Medicaid Comprehensive Managed Care Compared to Fee-for-Service, 2001-2019.","authors":"Kristen Lloyd, Sanika Rege, Stephen Crystal, Mark Olfson, Daniel B Horton, Hillary Samples","doi":"10.1111/1475-6773.14651","DOIUrl":"10.1111/1475-6773.14651","url":null,"abstract":"<p><strong>Objective: </strong>To compare the data in national Medicaid research files for children enrolled in comprehensive managed care (CMC) vs. fee-for-service (FFS).</p><p><strong>Study setting and design: </strong>This observational study utilized inpatient, other services, and pharmacy files in national Medicaid data from 2001 to 2019. CMC-enrolled children in state-years with ≥ 10% CMC enrollment were compared on several measures to yearly FFS data across all available states. Completeness measures were the proportion with any claim and mean claims per enrollee. Quality measures were the proportion of inpatient and other services claims with primary diagnosis and procedure codes and the proportion of prescription claims with fill dates, National Drug Codes, days supplied, and quantity dispensed. The range of acceptable values for each measure was defined as overall FFS mean ± 2 standard deviations.</p><p><strong>Data sources and analytic sample: </strong>We analyzed secondary MAX/TAF data on 45 states from 2001 to 2013 and 50 states and DC from 2014 to 2019. The sample included children ages 0-17 with continuous calendar-year enrollment in Medicaid and/or Medicaid-expansion CHIP with full Medicaid benefits and not dually enrolled in Medicare.</p><p><strong>Principal findings: </strong>The sample included 368.7 million person-years across 888 state-years. Three hundred thirty-eight state-years (38.1%) had < 10% CMC enrollment. Of 550 remaining state-years, 70%, representing ~59% of all enrolled children, met criteria for both completeness and quality in all three files, increasing from 35.7% of states in 2001 to 83.8% of states in 2019. The percentages of state-years with comparable CMC/FFS data for completeness measures were 92.7% inpatient, 86.0% other services, and 87.3% prescription. For quality measures, these proportions were 88.5% inpatient, 95.6% other services, and 96.9% prescription.</p><p><strong>Conclusions: </strong>Growth in Medicaid-managed care over the last two decades, coupled with observed improvements in CMC data quality, presents opportunities to increase the sample size and scope of epidemiologic and health services research on publicly insured children.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":" ","pages":"e14651"},"PeriodicalIF":3.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217628","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":"Employment, Income, the ACA, and Health Insurance Coverage of Working-Age Adults During the First Year of the COVID-19 Pandemic: A Reassessment","authors":"José J. Escarce,&nbsp;Dennis Rünger,&nbsp;James M. Campbell,&nbsp;Peter J. Huckfeldt","doi":"10.1111/1475-6773.14646","DOIUrl":"10.1111/1475-6773.14646","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine the effects of income, income transitions, and the Affordable Care Act (ACA) Medicaid expansion on health insurance coverage for working-age adults who became unemployed during the first year of the COVID-19 pandemic and for those who remained employed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>We estimated panel-data regression models to assess the effects of employment, income and income transitions, and the Medicaid expansion on the type of insurance coverage and uninsurance among working-age adults in the United States during 2019 and 2020.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>Longitudinal data from the 2019–2020 Medical Expenditure Panel Survey and data on states' Medicaid expansion status. The study participants were 6435 adults aged 26–64.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Participants in all income groups who suffered spells of unemployment during the pandemic lost employer-sponsored insurance. In expansion states, the Medicaid expansion played a key role in preventing declines in insurance coverage for disadvantaged participants. The expansion was especially beneficial for participants with low pre-pandemic incomes who had unemployment spells during the pandemic (7.5% point increase in Medicaid coverage [95% CI, 1.2 to 13.8]) and for participants who transitioned from high pre-pandemic incomes to low pandemic incomes whether or not they lost their jobs (23.9% point increase in Medicaid coverage [95% CI, 7.8 to 40.0] during unemployment spells; 12.0% point increase [95% CI, 7.2 to 16.9] for those who remained employed). We found weaker evidence that private exchange coverage blunted increases in uninsurance in non-expansion states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our findings clarify findings from earlier research by demonstrating that not only employment status and pre-pandemic income, but also income transitions, played a key role in determining who received Medicaid coverage during the pandemic in Medicaid expansion states. All in all, the ACA acquitted itself relatively well during a very stressful period for the United States' system of health insurance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217629","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":"Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data","authors":"Sarah Conderino,&nbsp;Jasmin Divers,&nbsp;John A. Dodson,&nbsp;Lorna E. Thorpe,&nbsp;Mark G. Weiner,&nbsp;Samrachana Adhikari","doi":"10.1111/1475-6773.14649","DOIUrl":"10.1111/1475-6773.14649","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To compare anonymized and non-anonymized approaches for imputing race and ethnicity in descriptive studies of chronic disease burden using electronic health record (EHR)-based datasets.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Study Setting and Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In this New York City-based study, we first conducted simulation analyses under different missing data mechanisms to assess the performance of Bayesian Improved Surname Geocoding (BISG), single imputation using neighborhood majority information, random forest imputation, and multiple imputation with chained equations (MICE). Imputation performance was measured using sensitivity, precision, and overall accuracy; agreement with self-reported race and ethnicity was measured with Cohen's kappa (&lt;i&gt;κ&lt;/i&gt;). We then applied these methods to impute race and ethnicity in two EHR-based data sources and compared chronic disease burden (95% CIs) by race and ethnicity across imputation approaches.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Data Sources and Analytic Sample&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Our data sources included EHR data from NYU Langone Health and the INSIGHT Clinical Research Network from 3/6/2016 to 3/7/2020 extracted for a parent study on older adults in NYC with multiple chronic conditions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Principal Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Under simulation analyses, the non-anonymized BISG imputation provided the most accurate classification of race and ethnicity, ranging from 66% to 73% across missing data mechanisms. Anonymized imputation methods were more sensitive to the missing data mechanism, with agreement dropping when race and ethnicity was missing not at random (MNAR) (&lt;i&gt;κ&lt;/i&gt;\u0000 &lt;sub&gt;single&lt;/sub&gt; = 0.25, &lt;i&gt;κ&lt;/i&gt;\u0000 &lt;sub&gt;MICE&lt;/sub&gt; = 0.25, &lt;i&gt;κ&lt;/i&gt;\u0000 &lt;sub&gt;randomforest&lt;/sub&gt; = 0.33). When these methods were applied to the NYU and INSIGHT cohorts, however, racial and ethnic distributions and chronic disease burden were consistent across all imputation methods. Slight improvements in the precision of estimates were observed under all imputation approaches compared to a complete case analysis.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;BISG imputation may provide a more accurate racial and ethnic classification than single or multiple imputation using anonymized covariates, particularly if the missing data mechanism is MNAR. Descriptive studies of disease burden may not be sensitive to methods for imputing missing data.&lt;/p&gt;\u0000 ","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152730","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":"Time on Your Side: Aggregating Data in Difference-In-Differences Studies","authors":"Summer Rak,&nbsp;Laura A. Hatfield,&nbsp;Carrie E. Fry","doi":"10.1111/1475-6773.14636","DOIUrl":"10.1111/1475-6773.14636","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To compare the performance of difference-in-differences estimators fit to data aggregated to different time scales.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>In simulations, we generated monthly observations for 50–100 units over 6 years from both a parametric model and a resampling simulation. The simulation scenarios varied panel balance, treatment timing, and true treatment effects. Our target parameters were static and dynamic average effects of treatment on the treated (ATT) estimated via linear regression (for common timing scenarios) and Callaway and Sant'Anna (2021) estimators (for staggered timing scenarios). We compared estimates from monthly, quarterly, and yearly data using bias, standard error, root mean squared error (RMSE), power, and Type I error. We also conducted a case study to illustrate the real-world impacts of these decisions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We used data from a study of police retraining for the resampling simulations and case study. These data included counts of use-of-force incidents and dates of training enrollment for 8614 officers each month from 2011 to 2016.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Results from the simulation varied across performance metrics, estimation methods, target estimands, and data structures. In general, the choice of time aggregation was more consequential when estimating dynamic (versus static) treatment effects, in unbalanced (versus balanced) panel data, and in the resampling simulations (where data had less autocorrelation). Although time aggregation mattered little in many scenarios, coarser aggregation was preferable in resampling simulations of staggered timing scenarios. The re-analysis of police training data was sensitive to time aggregation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In many scenarios, time aggregation has little impact on difference-in-differences estimators. However, when estimating dynamic effects, especially in staggered timing settings and unbalanced data, we found a tradeoff between precision and power, with finer aggregations being more powerful but less precise. In addition, estimators that use a single reference time point are more sensitive to noise in data measured at finer time scales.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152792","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":"From the Editors' Desk: HSR's Report on the Demographics, Education, Employment, and Publication Experiences of Our Authors, Reviewers, and Editors","authors":"Austin Frakt,&nbsp;Chris Tachibana","doi":"10.1111/1475-6773.14644","DOIUrl":"10.1111/1475-6773.14644","url":null,"abstract":"","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133201","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":"Impact of Community Health Center Losses on County-Level Mortality: A Natural Experiment in the United States, 2011–2019","authors":"Sanjay Basu,&nbsp;Robert Phillips,&nbsp;Hank Hoang","doi":"10.1111/1475-6773.14648","DOIUrl":"10.1111/1475-6773.14648","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To estimate the effect of Community Health Center (CHC) site losses on county-level mortality rates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>We conducted a natural experiment study using difference-in-differences analysis of propensity score–matched US counties from 2011 through 2019.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>The study included 3142 US counties, with 177 counties experiencing CHC site losses in 2014, per data from the health resources and services administration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Loss of CHC sites was associated with an increase in age-adjusted all-cause mortality of 3.54 deaths per 100 000 population (95% CI: 1.19, 5.90; <i>p</i> = 0.003) in the year following the loss. The largest increase was observed for cancer mortality (2.61 per 100 000; 95% CI: 0.59, 4.62; <i>p</i> = 0.011). Primary care physician density and patient volume loss both mediated the relationship.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>CHC site losses were associated with increases in mortality. Preserving CHC access may be important for maintaining population health, particularly in underserved areas.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129546","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":"Telehealth Use by Home Health Agencies Before, During, and After COVID-19","authors":"Dana B. Mukamel,&nbsp;Debra Saliba,&nbsp;Heather Ladd,&nbsp;Melissa A. Clark,&nbsp;Michelle L. Rogers,&nbsp;Cheryl Meyer Nelson,&nbsp;Marisa L. Roczen,&nbsp;Dara H. Sorkin,&nbsp;Jacqueline S. Zinn,&nbsp;Peter Huckfeldt","doi":"10.1111/1475-6773.14645","DOIUrl":"10.1111/1475-6773.14645","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine telehealth adoption and discontinuation by home health agencies (HHAs) during the COVID-19 pandemic in the context of telehealth pre-pandemic diffusion into the industry and its continued use once the pandemic abated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>HHAs nationally, serving the most patients with dementia (averaging 33% of the agency's patients) were surveyed during October 2023 to November 2024. Key variables included the agency's adoption and discontinuation of specific telehealth technologies by year, the reasons for discontinuation, and the reasons for not adopting any telehealth technology, either before or during the pandemic.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>Data were collected via a web-based survey with telephone follow-ups. We received 791 responses (37% response-rate) and provide descriptive statistics of responses and graphics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>By 2019, prior to COVID-19, 183 (23%) of HHAs used telehealth, increasing to 446 (56%) by 2021. Growth occurred mainly in virtual visits. Of those HHAs adopting telehealth, 96 (19%) discontinued use later in the pandemic. Key concerns were about the appropriateness of the patient population and reimbursement.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Patterns of adoption and discontinuation suggest that COVID-19 interrupted the innovation diffusion process of telehealth into home health. Telehealth's future will depend on information about cost-effectiveness and Medicare reimbursement policies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121487","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":"The 2005 TennCare Disenrollments Increased Rates of Intimate Partner Violence: Insights for the Post-COVID Medicaid Unwinding","authors":"Wei Fu,&nbsp;Melissa B. Eggen,&nbsp;Qi Zheng","doi":"10.1111/1475-6773.14647","DOIUrl":"10.1111/1475-6773.14647","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To quantify the impact of 2005 TennCare mandatory disenrollment on intimate partner violence (IPV).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Setting and Design</h3>\u0000 \u0000 <p>Using 2005 TennCare mandatory disenrollment as a quasi-natural experiment, we classified counties with pre-reform TennCare enrollment rates above the median as high-exposure (treated group) and those below the median as low-exposure (control group). We employed a doubly robust Difference-in-Differences design that compares the high-exposure counties with the low-exposure counties before and after the TennCare disenrollment, adjusting for various county-level characteristics. We performed a dose–response DID model using the pre-reform TennCare enrollment rates as a continuous treatment for robustness checks.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Analytic Sample</h3>\u0000 \u0000 <p>We extracted IPV incidents from 2003 to 2008 National Incident-Based Reporting System, supplemented with data from Surveillance, Epidemiology, and End Results program and Area Health Resource Files. We aggregated IPV incidents by police agency, year, and quarter to create an aggregate-level count of IPV per 10,000 population. The analytic sample consists of 6780 agency-year-quarter observations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Our findings revealed that TennCare disenrollment led to an increase of 4.8 (95% CI: 0.90–8.6) IPV incidents per 10,000 population, approximately a 25.8% (95% CI: 4.9%–46.7%) increase. This impact was more salient among younger adults and women. Importantly, the disenrollment led to a significantly larger increase in drug-related IPV incidents. Our welfare calculation shows that the disenrollment resulted in a total economic burden of approximately $232.34 (95% CI: 43.95–420.94) million, with the government bearing $86.66 (95% CI: 16.39–157) million of this cost.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study underscores the importance of considering broader social costs associated with abrupt loss of insurance coverage. These findings support the need for Medicaid reforms that incorporate safety measures to prevent unintended social harms and support the well-being of economically vulnerable populations.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"60 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113072","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}