Health Services Research最新文献

{"title":"Conceptual and methodological recommendations for assessing the empirical validity of process measures of health care quality","authors":"Alex H. S. Harris PhD, MS, David R. Nerenz PhD","doi":"10.1111/1475-6773.14356","DOIUrl":"10.1111/1475-6773.14356","url":null,"abstract":"","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735702","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":"Lifetime abortion incidence when abortion care is covered by Medicaid: Maryland versus five comparison states","authors":"Heide M. Jackson PhD,&nbsp;Michael S. Rendall PhD","doi":"10.1111/1475-6773.14358","DOIUrl":"10.1111/1475-6773.14358","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To estimate the association of Medicaid coverage of abortion care with cumulative lifetime abortion incidence among women insured by Medicaid.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We use 2016–2019 (Pre-Dobbs) data from the Survey of Women studies that represent women aged 18–44 living in six U.S. states. One state, Maryland, has a Medicaid program that has long covered the cost of abortion care. The other five states, Alabama, Delaware, Iowa, Ohio, and South Carolina, have Medicaid programs that do not cover the cost of abortion care. Our sample includes 8972 women residing in the study states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>Our outcome, cumulative lifetime abortion incidence, is identified using an indirect survey method, the double list experiment. We use a multivariate regression of cumulative lifetime abortion on variables including whether women were Medicaid-insured and whether they were residing in Maryland versus in one of the other five states.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>This study used secondary survey data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>We estimate that Medicaid coverage of abortion care in Maryland is associated with a 37.0 percentage-point (95% CI: 12.3–61.4) higher cumulative lifetime abortion incidence among Medicaid-insured women relative to women not insured by Medicaid compared with those differences by insurance status in states whose Medicaid programs do not cover the cost of abortion care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We found that Medicaid coverage of abortion care is associated with a much higher lifetime incidence of abortion among individuals insured by Medicaid. We infer that Medicaid coverage of abortion care costs may have a very large impact on the accessibility of abortion care for low-income women.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1475-6773.14358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141621807","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":"Barriers to quality healthcare among transgender and gender nonconforming adults.","authors":"Kedryn Berrian, Marci D Exsted, Nik M Lampe, Sayer L Pease, Ellesse-Roselee L Akré","doi":"10.1111/1475-6773.14362","DOIUrl":"https://doi.org/10.1111/1475-6773.14362","url":null,"abstract":"<p><strong>Objective: </strong>To determine the barriers transgender and gender nonconforming (TGNC) adults face when accessing or receiving healthcare in the United States.</p><p><strong>Data sources and study setting: </strong>Primary data were collected between September 2022 and March 2023 from a purposive sample of TGNC adults (N = 116 participants) using an online survey with a series of open-ended and closed-ended questions.</p><p><strong>Study design: </strong>Thematic analysis was employed to extract and analyze participants' responses to an open-ended question about challenges they experienced when accessing or receiving healthcare. Two members of the research team conducted qualitative data analyses using Dedoose. The quality of each analysis was subsequently reviewed by a third research team member.</p><p><strong>Data collection/extraction methods: </strong>Data were collected from responses to one open-ended question that asked about participants' healthcare barriers as a TGNC individual.</p><p><strong>Principal findings: </strong>Five main themes surrounding healthcare barriers emerged from the content analysis: (1) acceptability, (2) accommodation, (3) affordability, (4) availability, and (5) accessibility. First, participants who noted acceptability issues reported explicit discriminatory treatment from providers, providers not using their chosen names and pronouns (e.g., misgendering), and provider refusal to provide gender-affirming care. Second, participants who experienced accommodation challenges identified provider medical training gaps on TGNC patient needs and administrative barriers to care. Third, participants explained affordability issues due to a lack of adequate health insurance coverage. Fourth, participants described availability challenges with accessing hormone therapy prescriptions. Finally, participants noted accessibility issues with obtaining TGNC-specific care at LGBTQ+-affirming clinics.</p><p><strong>Conclusions: </strong>There is a growing interest in the needs of TGNC adults within healthcare settings. This requires that health policies are enacted to ensure that TGNC adults have access to healthcare that is accommodating and accepting/affirming. Study findings may provide insight into the potential impact of current legislation on transgender access and availability.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581564","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":"Using social risks to predict unplanned hospital readmission and emergency care among hospitalized Veterans.","authors":"Portia Y Cornell, Cassandra L Hua, Zachary M Buchalksi, Gina R Chmelka, Alicia J Cohen, Marguerite M Daus, Christopher W Halladay, Alita Harmon, Jennifer W Silva, James L Rudolph","doi":"10.1111/1475-6773.14353","DOIUrl":"10.1111/1475-6773.14353","url":null,"abstract":"<p><strong>Objectives: </strong>(1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index.</p><p><strong>Data sources and setting: </strong>We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program.</p><p><strong>Study design: </strong>We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans' health-related social risk.</p><p><strong>Data extraction: </strong>We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022.</p><p><strong>Principal findings: </strong>The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA.</p><p><strong>Conclusions: </strong>Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141556001","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":"Differences across race and ethnicity in the quality of antidepressant medication management","authors":"Alex H. S. Harris PhD, MS,&nbsp;Pingyang Liu PhD, MS,&nbsp;Jessica Y. Breland PhD,&nbsp;Kenneth J. Nieser PhD,&nbsp;Eric M. Schmidt PhD","doi":"10.1111/1475-6773.14347","DOIUrl":"10.1111/1475-6773.14347","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To illustrate the importance of a multidimensional view of disparities in quality of antidepressant medication management (AMM), as well as discriminating “within-facility” disparities from disparities that exist between facilities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We used data from the Veterans Health Administration's (VA) Corporate Data Warehouse (CDW) which contains clinical and administrative data from VA facilities nationally.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>CDW data were used to measure five indicators of AMM quality, including the HEDIS Effective Acute-Phase and Effective Continuation-Phase measures. Mixed effects regression models were used to examine differences in quality indicators between racial/ethnic groups, controlling for other demographic and clinical factors. An adaptation of the Kitagawa-Blinder-Oaxaca (KBO) method was used to decompose mean differences in treatment quality between racial and ethnic groups into within- and between-facility effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Extraction Methods</h3>\u0000 \u0000 <p>Demographic, clinical, and health service utilization data were extracted for patients in fiscal year 2017 with a diagnosis of depression and a new start of an antidepressant medication.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>The decomposition of the overall differences between White and Black patients on receiving an initial 90-day prescription (46.7% vs. 32.7%), Effective Acute-Phase (79.7% vs. 66.8%), and Effective Continuation-Phase (64.0% vs. 49.6%) HEDIS measures revealed that most of the overall effects were “within-facility,” meaning that Black patients are less likely to meet these measures regardless of where they are treated. Although the overall magnitude of disparities between White and Hispanic patients on these three measures was very similar (46.7% vs. 32.7%; 79.7% vs. 69.2%; 64.0% vs. 53.6%), the differences were more attributable to Hispanic patients being treated in facilities with overall lower performance on these measures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Discriminating within- and between-facility disparities and taking a multidimensional view of quality are essential to informing efforts to address disparities in AMM quality.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536027","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":"Processing and validation of inpatient Medicare Advantage data for use in hospital outcome measures.","authors":"Kelly A Kyanko, Kashika M Sahay, Yongfei Wang, Michelle Schreiber, Melissa Hager, Raquel Myers, Wanda Johnson, Jing Zhang, Bing-Jie Yen, Lisa G Suter, Elizabeth W Triche, Shu-Xia Li","doi":"10.1111/1475-6773.14350","DOIUrl":"https://doi.org/10.1111/1475-6773.14350","url":null,"abstract":"<p><strong>Objective: </strong>To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter- and hospital-submitted inpatient claims.</p><p><strong>Data sources and study setting: </strong>Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges.</p><p><strong>Study design: </strong>We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim.</p><p><strong>Data collection/extraction methods: </strong>We combined hospital- and MAO-submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO-submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures.</p><p><strong>Principal findings: </strong>After removing void and duplicate claims, identifying overlapped claims between the hospital- and MAO-submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital-submitted claims were submitted within 3 months after a one-year performance period, versus 85.2% of MAO-submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code.</p><p><strong>Conclusions: </strong>Inpatient MA data are feasible for use in CMS claims-based hospital outcome measures. We recommend prioritizing hospital-submitted over MAO-submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499686","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":"Effects of Medicaid managed care on early detection of cancer: Evidence from mandatory Medicaid managed care program in Pennsylvania","authors":"Youngmin Kwon PhD,&nbsp;Eric T. Roberts PhD,&nbsp;Evan S. Cole PhD,&nbsp;Howard B. Degenholtz PhD,&nbsp;Bruce L. Jacobs MD, MPH,&nbsp;Lindsay M. Sabik PhD","doi":"10.1111/1475-6773.14348","DOIUrl":"10.1111/1475-6773.14348","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To examine changes in late- versus early-stage diagnosis of cancer associated with the introduction of mandatory Medicaid managed care (MMC) in Pennsylvania.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Sources and Study Setting</h3>\u0000 \u0000 <p>We analyzed data from the Pennsylvania cancer registry (2010–2018) for adult Medicaid beneficiaries aged 21–64 newly diagnosed with a solid tumor. To ascertain Medicaid and managed care status around diagnosis, we linked the cancer registry to statewide hospital-based facility records collected by an independent state agency (Pennsylvania Health Care Cost Containment Council).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Study Design</h3>\u0000 \u0000 <p>We leveraged a natural experiment arising from county-level variation in mandatory MMC in Pennsylvania. Using a stacked difference-in-differences design, we compared changes in the probability of late-stage cancer diagnosis among those residing in counties that newly transitioned to mandatory managed care to contemporaneous changes among those in counties with mature MMC programs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Collection/Extraction Methods</h3>\u0000 \u0000 <p>N/A.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Principal Findings</h3>\u0000 \u0000 <p>Mandatory MMC was associated with a reduced probability of late-stage cancer diagnosis (−3.9 percentage points; 95% CI: −7.2, −0.5; <i>p</i> = 0.02), particularly for screening-amenable cancers (−5.5 percentage points; 95% CI: −10.4, −0.6; <i>p</i> = 0.03). We found no significant changes in late-stage diagnosis among non-screening amenable cancers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In Pennsylvania, the implementation of mandatory MMC for adult Medicaid beneficiaries was associated with earlier stage of diagnosis among newly diagnosed cancer patients with Medicaid, especially those diagnosed with screening-amenable cancers. Considering that over half of the sample was diagnosed with late-stage cancer even after the transition to mandatory MMC, Medicaid programs and managed care organizations should continue to carefully monitor receipt of cancer screening and design strategies to reduce barriers to guideline-concordant screening or diagnostic procedures.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494238","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":"Hospitals' collection and use of data to address social needs and social determinants of health.","authors":"Chelsea Richwine, Samantha Meklir","doi":"10.1111/1475-6773.14341","DOIUrl":"https://doi.org/10.1111/1475-6773.14341","url":null,"abstract":"<p><strong>Objective: </strong>To assess differences in hospitals' collection and use of data on patients' health-related social needs (HRSN) by availability of programs or strategies in place to address patients' HRSN and social determinants of health (SDOH) of communities.</p><p><strong>Data sources: </strong>The 2021 American Hospital Association Annual Survey and 2022 Information Technology (IT) Supplement.</p><p><strong>Study design: </strong>This cross-sectional study described hospitals' engagement in screening and the availability of programs or strategies to address nine different HRSN. We assessed differences in screening rates and uses of data collected through screening among hospitals with and without programs or strategies in place to address HRSN or SDOH using Chi-squared tests of independence.</p><p><strong>Data collection/extraction methods: </strong>Analyses were restricted to IT Supplement respondents with complete data for social needs questions asked in the Annual Survey (N = 1997).</p><p><strong>Principal findings: </strong>In 2022, hospitals used social needs data collected through screening for various purposes including discharge planning and clinical decision-making at their hospital as well as to refer patients to needed resources and assess community-level needs. Hospitals with a program or strategy in place had higher rates of screening across all domains and higher rates of using of data collected through screening for uses involving exchange or coordination with external entities.</p><p><strong>Conclusions: </strong>Collection of social needs data may help inform the development of programs or strategies to address HRSN and SDOH, which in turn can enable providers to screen for these needs and use the data in the near term for care delivery and in the long term to address community and population needs.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141478016","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":"Skilled nursing facility staffing shortages: Sources, strategies, and impacts on staff who stayed.","authors":"Natalie E Leland, Rachel A Prusynski, Amanda D Shore, Michael P Cary, Jason Falvey, Tracy Mroz, Debra Saliba","doi":"10.1111/1475-6773.14355","DOIUrl":"https://doi.org/10.1111/1475-6773.14355","url":null,"abstract":"<p><strong>Objective: </strong>To examine skilled nursing facility (SNF) staffing shortages across job roles during the COVID-19 pandemic. We aimed to capture the perspectives of leaders on the breadth of staffing shortages and their implications on staff that stayed throughout the pandemic in order to provide recommendations for policies and practices used to strengthen the SNF workforce moving forward.</p><p><strong>Study setting and design: </strong>For this qualitative study, we engaged a purposive national sample of SNF leaders (n = 94) in one-on-one interviews between January 2021 and December 2022.</p><p><strong>Data source and analytic sample: </strong>Using purposive sampling (i.e., Centers for Medicare & Medicaid quality rating, region, ownership) to capture variation in SNF organizations, we conducted in-depth, semi-structured qualitative interviews, guided a priori by the Institute of Medicine's Model of Healthcare System Framework. Interviews were conducted via phone, audio-recorded, and transcribed. Rigorous rapid qualitative analysis was used to identify emergent themes, patterns, and relationships.</p><p><strong>Principal findings: </strong>SNF leaders consistently described staffing shortages spanning all job roles, including direct care (e.g., activities, nursing, social services), support services (e.g., laundry, food, environmental services), administrative staff, and leadership. Ascribed sources of shortages were multidimensional (e.g., competing salaries, family caregiving needs, burnout). The impact of shortages was felt by all staff that stayed. In addition to existing job duties, those remaining staff experienced re-distribution of essential day-to-day operational tasks (e.g., laundry) and allocation of new COVID-19 pandemic-related activities (e.g., screening). Cross-training was used to cover a wide range of job duties, including patient care.</p><p><strong>Conclusions: </strong>Policies are needed to support SNF staff across roles beyond direct care staff. These policies must address the system-wide drivers perpetuating staffing shortages (i.e., pay differentials, burnout) and leverage strategies (i.e., cross-training, job role flexibility) that emerged from the pandemic to ensure a sustainable SNF workforce that can meet patient needs.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460829","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":"Medicaid coverage for gender-affirming surgery: A state-by-state review.","authors":"Jonnby S LaGuardia, Madeline G Chin, Sarah Fadich, Katarina B J Morgan, Halena H Ngo, Meiwand Bedar, Shahrzad Moghadam, Kelly X Huang, Christy Mallory, Justine C Lee","doi":"10.1111/1475-6773.14338","DOIUrl":"https://doi.org/10.1111/1475-6773.14338","url":null,"abstract":"<p><strong>Objective: </strong>To systematically review Medicaid policies state-by-state for gender-affirming surgery coverage.</p><p><strong>Data sources and study setting: </strong>Primary data were collected for each US state utilizing the LexisNexis legal database, state legislature publications, and Medicaid manuals.</p><p><strong>Study design: </strong>A cross-sectional study evaluating Medicaid coverage for numerous gender-affirming surgeries.</p><p><strong>Data collection/extraction methods: </strong>We previously reported on state health policies that protect gender-affirming care under Medicaid coverage. Building upon our prior work, we systematically assessed the 27 states with protective policies to determine coverage for each type of gender-affirming surgery. We analyzed Medicaid coverage for gender-affirming surgeries in four domains: chest, genital, craniofacial and neck reconstruction, and miscellaneous procedures. Medicaid coverage for each type of surgery was categorized as explicitly covered, explicitly noncovered, or not described.</p><p><strong>Principal findings: </strong>Among the 27 states with protective Medicaid policies, 17 states (63.0%) provided explicit coverage for at least one gender-affirming chest procedure and at least one gender-affirming genital procedure, while only eight states (29.6%) provided explicit coverage for at least one craniofacial and neck procedure (p = 0.04). Coverage for specific surgical procedures within these three anatomical domains varied. The most common explicitly covered procedures were breast reduction/mastectomy and hysterectomy (n = 17, 63.0%). The most common explicitly noncovered surgery was reversal surgery (n = 12, 44.4%). Several states did not describe the specific surgical procedures covered; thus, final coverage rates are indeterminate.</p><p><strong>Conclusions: </strong>In 2022, 52.9% of states had health policies that protected gender-affirming care under Medicaid; however, coverage for various gender-affirming surgical procedures remains both variable and occasionally unspecified. When specified, craniofacial and neck reconstruction is the least covered anatomical area compared with chest and genital reconstruction.</p>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332462","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}