Health Services Research最新文献

Objectives: (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.

Data sources and setting: 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.

Study design: 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.

Data extraction: We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022.

Principal findings: 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.

Conclusions: 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.

Objective: 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.

Data sources and study setting: Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges.

Study design: 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.

Data collection/extraction methods: 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.

Principal findings: 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.

Conclusions: 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.

Objective: 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.

Data sources: The 2021 American Hospital Association Annual Survey and 2022 Information Technology (IT) Supplement.

Study design: 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.

Data collection/extraction methods: Analyses were restricted to IT Supplement respondents with complete data for social needs questions asked in the Annual Survey (N = 1997).

Principal findings: 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.

Conclusions: 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.

Objective: 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.

Study setting and design: 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.

Data source and analytic sample: 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.

Principal findings: 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.

Conclusions: 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.

Objective: To systematically review Medicaid policies state-by-state for gender-affirming surgery coverage.

Data sources and study setting: Primary data were collected for each US state utilizing the LexisNexis legal database, state legislature publications, and Medicaid manuals.

Study design: A cross-sectional study evaluating Medicaid coverage for numerous gender-affirming surgeries.

Data collection/extraction methods: 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.

Principal findings: 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.

Conclusions: 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.