Medical Care最新文献

{"title":"Mixed Mode Substantially Increases Hospital Consumer Assessment of Healthcare Providers and Systems Response Rates Relative to Single-Mode Protocols.","authors":"Megan K Beckett, Marc N Elliott, Katrin Hambarsoomian, William G Lehrman, Elizabeth Goldstein, Laura A Giordano, Julie Brown","doi":"10.1097/MLR.0000000000002041","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002041","url":null,"abstract":"<p><strong>Background: </strong>Low response rates (RRs) can affect hospitals' data collection costs for patient experience surveys and value-based purchasing eligibility. Most hospitals use single-mode approaches, even though sequential mixed mode (MM) yields higher RRs and perhaps better patient representativeness. Some hospitals may be reluctant to incur MM's potential additional cost and complexity without knowing how much RRs would increase.</p><p><strong>Objective: </strong>The aim of this study was to estimate the differences in RR and patient representation between MM and single-mode approaches and to identify hospital characteristics associated with the largest RR differences from MM of single-mode protocols (mail-only, phone-only).</p><p><strong>Research design: </strong>Patients were randomized within hospitals to one of 3 modes (mail-only, phone-only, MM).</p><p><strong>Subjects: </strong>A total of 17,415 patients from the 51 nationally representative US hospitals participating in a randomized HCAHPS mode experiment.</p><p><strong>Results: </strong>Mail-only RRs were lowest for ages 18-24 (7%) and highest for ages 65+ (31%-35%). Phone-only RRs were 24% for ages 18-24, increasing to 37%-40% by ages 55+. MM RRs were 28% for ages 18-24, increasing to 50%-60% by ages 65-84. Lower hospital-level mail-only RRs strongly predicted greater gains from MM. For example, a hospital with a 15% mail-only RR has a predicted MM RR >40% (with >25% occurring in telephone follow-up).</p><p><strong>Conclusion: </strong>MM increased representation of hard-to-reach (especially young adult) patients and hospital RRs in all mode experiment hospitals, especially in hospitals with low mail-only RRs.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141909939","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":"Highly Stable Beneficiary Attribution in Medicare's Comprehensive Primary Care Plus Model.","authors":"Fang He, Ariella Hirsch, Chris Beadles, Yan Tang, Bridget Hagerty, Sarah Irie","doi":"10.1097/MLR.0000000000002027","DOIUrl":"10.1097/MLR.0000000000002027","url":null,"abstract":"<p><strong>Background: </strong>Advanced primary care models are key in moving primary care practices toward greater accountability for the quality and cost of a beneficiary's care. One critical but often overlooked detail in model design is the beneficiary attribution methodology. Attribution results are key inputs in calculating practice payments. Stable attribution yields predictable practice payments, fostering longer-term investments in advanced primary care.</p><p><strong>Objective: </strong>We examine attribution stability for Medicare fee-for-service beneficiaries in Medicare's Comprehensive Primary Care Plus (CPC+) Model.</p><p><strong>Design: </strong>To measure attribution stability, we calculate churn rates, which we define as the percentage of beneficiaries eligible for CPC+ who were not attributed to the same practice in a later period. Using 2017-2021 CPC+ program data and Medicare administrative data, we calculate churn rates for CPC+ overall and for beneficiary subgroups. To assess whether CPC+ attribution was responsive enough to changes in a beneficiary's practice, we calculate how long before attribution changes following a beneficiary's long-distance move.</p><p><strong>Results: </strong>We find that for every 100 beneficiaries attributed to a CPC+ practice, 88 were still attributed to the same practice a year later (ie, churn rate of 12%), 79 were attributed 2 years later, 74 three years later, and 70 four years later. However, some vulnerable subgroups, such as disabled beneficiaries, had higher churn rates. Our analysis of long-distance movers reveals that only after 5 quarters did attribution change for more than half of these movers.</p><p><strong>Conclusions: </strong>Overall, high attribution stability may have encouraged CPC+ practices to make longer-term investments in advanced primary care.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419715","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":"Development of Data Quality Indicators for Improving Hospital International Classification of Diseases-Coded Health Data Quality Globally.","authors":"Lucía Otero-Varela, Namneet Sandhu, Robin L Walker, Danielle A Southern, Hude Quan, Cathy A Eastwood","doi":"10.1097/MLR.0000000000002024","DOIUrl":"10.1097/MLR.0000000000002024","url":null,"abstract":"<p><strong>Background: </strong>Hospital inpatient data, coded using the International Classification of Diseases (ICD), is widely used to monitor diseases, allocate resources and funding, and evaluate patient outcomes. As such, hospital data quality should be measured before use; however, currently, there is no standard and international approach to assess ICD-coded data quality.</p><p><strong>Objective: </strong>To develop a standardized method for assessing hospital ICD-coded data quality that could be applied across countries: Data quality indicators (DQIs).</p><p><strong>Research design: </strong>To identify a set of candidate DQIs, we performed an environmental scan, reviewing gray and academic literature on data quality frameworks and existing methods to assess data quality. Indicators from the literature were then appraised and selected through a 3-round Delphi process. The first round involved face-to-face group and individual meetings for idea generation, while the second and third rounds were conducted remotely to collect online ratings. Final DQIs were selected based on the panelists' quantitative and qualitative feedback.</p><p><strong>Subjects: </strong>Participants included international experts with expertise in administrative health data, data quality, and ICD coding.</p><p><strong>Results: </strong>The resulting 24 DQIs encompass 5 dimensions of data quality: relevance, accuracy and reliability; comparability and coherence; timeliness; and Accessibility and clarity. These will help stakeholders (eg, World Health Organization) to assess hospital data quality using the same standard across countries and highlight areas in need of improvement.</p><p><strong>Conclusions: </strong>This novel area of research will facilitate international comparisons of ICD-coded data quality and be valuable to future studies and initiatives aimed at improving hospital administrative data quality.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141580200","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":"Behind the Curtain: Comparing Predictive Models Performance in 2 Publicly Insured Populations.","authors":"Ruichen Sun, Morgan Henderson, Leigh Goetschius, Fei Han, Ian Stockwell","doi":"10.1097/MLR.0000000000002050","DOIUrl":"https://doi.org/10.1097/MLR.0000000000002050","url":null,"abstract":"<p><strong>Introduction: </strong>Predictive models have proliferated in the health system in recent years and have been used to predict both health services utilization and medical outcomes. Less is known, however, on how these models function and how they might adapt to different contexts. The purpose of the current study is to shed light on the inner workings of a large-scale predictive model deployed in 2 distinct populations, with a particular emphasis on adaptability issues.</p><p><strong>Methods: </strong>We compared the performance and functioning of a predictive model of avoidable hospitalization in 2 very different populations: Medicaid and Medicare enrollees in Maryland. Specifically, we assessed characteristics of the risk scores from March 2022 for the 2 populations, the predictive ability of the scores, and the driving risk factors behind the scores. In addition, we created and assessed the performance of an \"unadapted\" model by applying coefficients from the Medicare model to the Medicaid population.</p><p><strong>Results: </strong>The model adapted to, and performed well in, both populations, despite demographic differences in these 2 groups. However, the most salient risk factors and their relative weightings differed, sometimes dramatically, across the 2 populations. The unadapted Medicaid model displayed poor performance relative to the adapted model.</p><p><strong>Conclusions: </strong>Our findings speak to the need to \"peek behind the curtain\" of predictive models that may be applied to different populations, and we caution that risk prediction is not \"one size fits all\": for optimal performance, models should be adapted to, and trained on, the target population.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893795","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":"Performance of the Elixhauser Comorbidity Index in Predicting Mortality Among a National US Sample of Hospitalized Homeless Adults.","authors":"Jack Tsai, Youngran Kim","doi":"10.1097/MLR.0000000000002019","DOIUrl":"10.1097/MLR.0000000000002019","url":null,"abstract":"<p><strong>Background: </strong>The Elixhauser Comorbidity Index (ECI) is widely used, but its performance in homeless populations has not been evaluated.</p><p><strong>Objectives: </strong>Using a national sample of inpatients, this study compared homeless and nonhomeless inpatients on common clinical diagnoses and evaluated ECI performance in predicting mortality among homeless inpatients.</p><p><strong>Research design: </strong>A retrospective study was conducted using 2019 National Inpatient Sample (NIS) data, the largest publicly available all-payer inpatient health care database in the United States.</p><p><strong>Subjects: </strong>Among 4,347,959 hospitalizations, 78,819 (weighted 1.8%) were identified as homeless.</p><p><strong>Measures: </strong>The ECI consists of 38 medical conditions; homelessness was defined using the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM) diagnostic code, and clinical conditions were based on the Clinical Classifications Software Refined (CCSR) for ICD-10-CM.</p><p><strong>Results: </strong>Leading clinical diagnoses for homeless inpatients included schizophrenia and other psychotic disorders (13.3%), depressive disorders (9.4%), and alcohol-related disorders (7.2%); leading diagnoses for nonhomeless inpatients were septicemia (10.2%), heart failure (5.2%), and acute myocardial infarction (3.0%). Metastatic cancer and liver disease were the most common ECI diagnoses for both homeless and nonhomeless inpatients. ECI indicators and summary scores were predictive of in-hospital mortality for homeless and nonhomeless inpatients, with all models yielding concordance statistics above 0.80, with better performance found among homeless inpatients.</p><p><strong>Conclusions: </strong>These findings underlie the high rates of behavioral health conditions among homeless inpatients and the strong performance of the ECI in predicting in-hospital mortality among homeless inpatients, supporting its continued use as a case-mix control method and predictor of hospital readmissions.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141262240","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":"Measuring Access to Mental Health Services Among Primary Care Patients.","authors":"J Lee Hargraves, Carol Cosenza, Paul D Cleary","doi":"10.1097/MLR.0000000000002029","DOIUrl":"10.1097/MLR.0000000000002029","url":null,"abstract":"<p><strong>Background: </strong>The lifetime risk of mental health disorders is almost 50% and, in any year, about 25% of the population have a psychiatric disorder. Many of those people are cared for in primary care settings.</p><p><strong>Research objective: </strong>Measure access to mental health services, such as getting counselling or prescription mental health medications, using new patient survey questions that can be added to Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys.</p><p><strong>Study design: </strong>Surveys were conducted with a stratified probability sample of patients receiving primary care services in a single state in 2018-2019. Medicaid and privately insured patients were surveyed by mail or telephone, respectively.</p><p><strong>Results: </strong>Approximately 14% of sampled patients responded to a survey. More than 10% of privately insured respondents and about 20% of Medicaid respondents got or tried to get appointments for mental health care. About 15% of privately insured respondents and 11% of Medicaid respondents reported problems getting appointments with counselors. Only 8%-9% of respondents seeking mental health medicines reported problems getting appointments for prescriptions. A composite measure combining access to counselors and prescribers of mental health medicines evidenced adequate internal consistency reliability. Group level reliability estimates were low.</p><p><strong>Conclusions: </strong>Many respondents got or tried to get mental health services and a substantial number reported problems getting appointments or getting mental health prescriptions. The tested questions can be combined into an Access to Mental Health Care measure, which can be included in patient experience surveys for ambulatory care to monitor access to behavioral health care.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11226347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141262230","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 Medical Costs of Determining Eligibility and Waiting for a Kidney Transplantation.","authors":"Kunyao Xu, Avi Dor, Suman Mohanty, Jialin Han, Gomathy Parvathinathan, Jennifer L Braggs-Gresham, Philip J Held, John P Roberts, William Vaughan, Jane C Tan, John D Scandling, Glenn M Chertow, Stephan Busque, Xingxing S Cheng","doi":"10.1097/MLR.0000000000002028","DOIUrl":"10.1097/MLR.0000000000002028","url":null,"abstract":"<p><strong>Background: </strong>Recent efforts to increase access to kidney transplant (KTx) in the United States include increasing referrals to transplant programs, leading to more pretransplant services. Transplant programs reconcile the costs of these services through the Organ Acquisition Cost Center (OACC).</p><p><strong>Objective: </strong>The aim of this study was to determine the costs associated with pretransplant services by applying microeconomic methods to OACC costs reported by transplant hospitals.</p><p><strong>Research design, subjects, and measures: </strong>For all US adult kidney transplant hospitals from 2013 through 2018 (n=193), we crosslinked the total OACC costs (at the hospital-fiscal year level) to proxy measures of volumes of pretransplant services. We used a multiple-output cost function, regressing total OACC costs against proxy measures for volumes of pretransplant services and adjusting for patient characteristics, to calculate the marginal cost of each pretransplant service.</p><p><strong>Results: </strong>Over 1015 adult hospital-years, median OACC costs attributable to the pretransplant services were $5 million. Marginal costs for the pretransplant services were: initial transplant evaluation, $9k per waitlist addition; waitlist management, $2k per patient-year on the waitlist; deceased donor offer management, $1k per offer; living donor evaluation, procurement and follow-up: $26k per living donor. Longer time on dialysis among patients added to the waitlist was associated with higher OACC costs at the transplant hospital.</p><p><strong>Conclusions: </strong>To achieve the policy goals of more access to KTx, sufficient funding is needed to support the increase in volume of pretransplant services. Future studies should assess the relative value of each service and explore ways to enhance efficiency.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11226385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419797","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":"Intensity, Characteristics, and Factors Associated With Receipt of Care Coordination Among High-Risk Veterans in the Veterans Health Administration.","authors":"Diana J Govier, Alex Hickok, Meike Niederhausen, Mazhgan Rowneki, Holly McCready, Elizabeth Mace, Kathryn M McDonald, Lisa Perla, Denise M Hynes","doi":"10.1097/MLR.0000000000002020","DOIUrl":"10.1097/MLR.0000000000002020","url":null,"abstract":"<p><strong>Background: </strong>The Veterans Health Administration (VHA) has initiatives underway to enhance the provision of care coordination (CC), particularly among high-risk Veterans. Yet, evidence detailing the characteristics of and who receives VHA CC is limited.</p><p><strong>Objectives: </strong>We examined intensity, timing, setting, and factors associated with VHA CC among high-risk Veterans.</p><p><strong>Research design: </strong>We conducted a retrospective observational cohort study, following Veterans for 1 year after being identified as high-risk for hospitalization or mortality, to characterize their CC. Demographic and clinical factors predictive of CC were identified via multivariate logistic regression.</p><p><strong>Subjects: </strong>A total of 1,843,272 VHA-enrolled high-risk Veterans in fiscal years 2019-2021.</p><p><strong>Measures: </strong>We measured 5 CC variables during the year after Veterans were identified as high risk: (1) receipt of any service, (2) number of services received, (3) number of days to first service, (4) number of days between services, and (5) type of visit during which services were received.</p><p><strong>Results: </strong>Overall, 31% of high-risk Veterans in the sample received CC during one-year follow-up. Among Veterans who received ≥1 service, a median of 2 [IQR (1, 6)] services were received. Among Veterans who received ≥2 services, there was a median of 26 [IQR (10, 57)] days between services. Most services were received during outpatient psychiatry (46%) or medicine (16%) visits. Veterans' sociodemographic and clinical characteristics were associated with receipt of CC.</p><p><strong>Conclusions: </strong>A minority of Veterans received CC in the year after being identified as high-risk, and there was variation in intensity, timing, and setting of CC. Research is needed to examine the fit between Veterans' CC needs and preferences and VHA CC delivery.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11219070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538074","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":"Geographic Variation of Antidementia and Antipsychotic Medication Use Among US Nursing Home Residents With Dementia.","authors":"Alison Rataj, Matthew Alcusky, Jonggyu Baek, Brian Ott, Kate L Lapane","doi":"10.1097/MLR.0000000000002016","DOIUrl":"10.1097/MLR.0000000000002016","url":null,"abstract":"<p><strong>Background: </strong>Several antidementia medications have been approved for symptomatic treatment of cognitive and functional impairment due to Alzheimer disease. Antipsychotics are often prescribed off-label for behavioral symptoms.</p><p><strong>Objective: </strong>The aim of this study was to describe the basis for regional variation in antidementia and antipsychotic medication use.</p><p><strong>Setting: </strong>US nursing homes (n=9735), hospital referral regions (HRR; n=289).</p><p><strong>Subjects: </strong>Long-stay residents with dementia (n=273,004).</p><p><strong>Methods: </strong>Using 2018 Minimum Data Set 3.0 linked to Medicare data, facility information, and Dartmouth Atlas files, we calculated prevalence of use and separate multilevel logistic models [outcomes: memantine, cholinesterase inhibitor (ChEI), antipsychotic use] estimated adjusted odds ratios (aOR) and 95% CIs for resident, facility, and HRR characteristics. We then fit a series of cross-classified multilevel logistic models to estimate the proportional change in cluster variance (PCV).</p><p><strong>Results: </strong>Overall, 20.9% used antipsychotics, 16.1% used memantine, and 23.3% used ChEIs. For antipsychotics, facility factors [eg, use of physical restraints (aOR: 1.08; 95% CI: 1.05-1.11) or poor staffing ratings (aOR: 1.10; 95% CI: 1.06-1.14)] were associated with more antipsychotic use. Nursing homes in HRRs with the highest health care utilization had greater antidementia drug use (aOR memantine: 1.68; 95% CI: 1.44-1.96). Resident/facility factors accounted for much regional variation in antipsychotics (PCV STATE : 27.80%; PCV HRR : 39.54%). For antidementia medications, HRR-level factors accounted for most regional variation (memantine PCV STATE : 37.44%; ChEI PCV STATE : 39.02%).</p><p><strong>Conclusion: </strong>Regional variations exist in antipsychotic and antidementia medication use among nursing home residents with dementia suggesting the need for evidence-based protocols to guide the use of these medications.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141248146","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":"The Effect of Medicare Annual Wellness Visits on Breast Cancer Screening and Diagnosis.","authors":"Mika K Hamer, Cathy J Bradley, Richard Lindrooth, Marcelo C Perraillon","doi":"10.1097/MLR.0000000000002023","DOIUrl":"10.1097/MLR.0000000000002023","url":null,"abstract":"<p><strong>Objective: </strong>The Medicare Annual Wellness Visit (AWV)-a prevention-focused annual check-up-has been available to beneficiaries with Part B coverage since 2011. The objective of this study was to estimate the effect of Medicare AWVs on breast cancer screening and diagnosis.</p><p><strong>Data sources and study setting: </strong>The National Cancer Institute's Surveillance, Epidemiology, and End Results cancer registry data linked to Medicare claims (SEER-Medicare), HRSA's Area Health Resources Files, the FDA's Mammography Facilities database, and CMS \"Mapping Medicare Disparities\" utilization data from 2013 to 2015.</p><p><strong>Study design: </strong>Using an instrumental variables approach, we estimated the effect of AWV utilization on breast cancer screening and diagnosis, using county Welcome to Medicare Visit (WMV) rates as the instrument.</p><p><strong>Data collection/extraction methods: </strong>66,088 person-year observations from 49,769 unique female beneficiaries.</p><p><strong>Principal findings: </strong>For every 1-percentage point increase in county WMV rate, the probability of AWV increased by 1.7 percentage points. Having an AWV was associated with a 22.4-percentage point increase in the probability of receiving a screening mammogram within 6 months ( P <0.001). There was no statistically significant increase in the probability of breast cancer diagnosis (overall or early stage) within 6 months of an AWV. Findings were robust to multiple model specifications.</p><p><strong>Conclusions: </strong>Performing routine cancer screening is an evidence-based practice for diagnosing earlier-stage, more treatable cancers. The AWV effectively increases breast cancer screening and may lead to more timely screening. Continued investment in Annual Wellness Visits supports breast cancer screening completion by women who are most likely to benefit, thus reducing the risk of overscreening and overdiagnosis.</p>","PeriodicalId":18364,"journal":{"name":"Medical Care","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11226348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419796","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}