Medical Care最新文献

Objective: Fragmented readmissions, when admission and readmission occur at different hospitals, are associated with increased charges compared with nonfragmented readmissions. We assessed if hospital participation in health information exchange (HIE) was associated with differences in total charges in fragmented readmissions.

Data source: Medicare Fee-for-Service Data, 2018.

Study design: We used generalized linear models with hospital referral region and readmission month fixed effects to assess relationships between information sharing (same HIE, different HIEs, and no HIE available) and total charges of 30-day readmissions among fragmented readmissions; analyses were adjusted for patient-level clinical/demographic characteristics and hospital-level characteristics.

Data extraction methods: We included beneficiaries with a hospitalization for acute myocardial infarction, congestive heart failure, chronic obstructive pulmonary disease, syncope, urinary tract infection, dehydration, or behavioral issues with a 30-day readmission for any reason.

Principal findings: In all, 279,729 admission-readmission pairs were included, 27% of which were fragmented (n=75,438); average charges of fragmented readmissions were $64,897-$71,606. Compared with fragmented readmissions where no HIE was available, the average marginal effects of same-HIE and different-HIE admission-readmission pairs were -$2329.55 (95% CI: -7333.73, 2674.62) and -$3905.20 (95% CI: -7592.85, -307.54), respectively. While the average marginal effects of different-HIE pairs were lower than those for no-HIE fragmented readmissions, the average marginal effects of same-HIE and different-HIE pairs were not significantly different from each other.

Conclusions: There were no statistical differences in charges between fragmented readmissions to hospitals that share an HIE or that do not share an HIE compared with hospitals with no HIE available.

Background: Housing is a critical social determinant of health that can be addressed through hospital-supported community benefit programming.

Objectives: To explore the prevalence of hospital-based programs that address housing-related needs, categorize the specific actions taken to address housing, and determine organizational and community-level factors associated with investing in housing.

Research design: This retrospective, cross-sectional study examined a nationally representative dataset of administrative documents from nonprofit hospitals that addressed social determinants of health in their federally mandated community benefit implementation plans. We conducted descriptive statistics and bivariate analyses to examine hospital and community characteristics associated with whether a hospital invested in housing programs. Using an inductive approach, we categorized housing investments into distinct categories.

Measures: The main outcome measure was a dichotomous variable representing whether a hospital invested in one or more housing programs in their community.

Results: Twenty percent of hospitals invested in one or more housing programs. Hospitals that addressed housing in their implementation strategies were larger on average, less likely to be in rural communities, and more likely to be serving populations with greater housing needs. Housing programs fell into 1 of 7 categories: community partner collaboration (34%), social determinants of health screening (9%), medical respite centers (4%), community social determinants of health liaison (11%), addressing specific needs of homeless populations (16%), financial assistance (21%), and targeting high-risk populations (5%).

Conclusions: Currently, a small subset of hospitals nationally are addressing housing. Hospitals may need additional policy support, external partnerships, and technical assistance to address housing in their communities.

Background: Individuals with limited English proficiency (LEP) have long faced barriers in navigating the health care system. More information is needed to understand whether their care was limited further during the early period of the COVID-19 pandemic.

Objective: To assess the impact of English proficiency on delayed and forgone health care during the early COVID-19 pandemic.

Research design: Multivariate logistic regression analysis of National Health Interview Survey data (July-December 2020; n=16,941). Outcomes were self-reported delayed and forgone health care because of cost or the COVID-19 pandemic. Delayed health care included medical, dental, mental health, and pharmacy care. Forgone health care also included care at home from a health professional.

Results: A greater percentage of LEP adults reported delayed (49%) and forgone (41%) health care than English-proficient adults (40% and 30%, respectively). However, English proficiency was not significantly associated with delayed or forgone health care, after adjusting for demographic, socioeconomic, and health factors. Among LEP adults, multivariate models showed that being uninsured, having a disability, and having chronic conditions increased the risk of delaying and forgoing health care. LEP adults of Asian race and Hispanic ethnicity were also more likely to forgo health care while those with 65+ years were less likely to forgo health care.

Conclusions: Adults with LEP were more likely to experience challenges accessing health care early in the pandemic. Delayed and forgone health care were explained by low socioeconomic status and poor health. These findings highlight how during a period of limited health resources, deficiencies in the health care system resulted in an already disadvantaged group being at greater risk of inequitable access to care.

Background: Although federal legislation made COVID-19 vaccines free, inequities in access to medical care may affect vaccine uptake.

Objective: To assess whether health care access was associated with uptake and timeliness of COVID-19 vaccination in the United States.

Design: A cross-sectional study.

Setting: 2021 National Health Interview Survey (Q2-Q4).

Subjects: In all, 21,532 adults aged≥18 were included in the study.

Measures: Exposures included 4 metrics of health care access: health insurance, having an established place for medical care, having a physician visit within the past year, and medical care affordability. Outcomes included receipt of 1 or more COVID-19 vaccines and receipt of a first vaccine within 6 months of vaccine availability. We examined the association between each health care access metric and outcome using logistic regression, unadjusted and adjusted for demographic, geographic, and socioeconomic covariates.

Results: In unadjusted analyses, each metric of health care access was associated with the uptake of COVID-19 vaccination and (among those vaccinated) early vaccination. In adjusted analyses, having health coverage (adjusted odds ratio [AOR] 1.60; 95% CI: 1.39, 1.84), a usual place of care (AOR 1.58; 95% CI: 1.42, 1.75), and a doctor visit within the past year (AOR 1.45, 95% CI: 1.31, 1.62) remained associated with higher rates of COVID-19 vaccination. Only having a usual place of care was associated with early vaccine uptake in adjusted analyses.

Limitations: Receipt of COVID-19 vaccination was self-reported.

Conclusions: Several metrics of health care access are associated with the uptake of COVID-19 vaccines. Policies that achieve universal coverage, and facilitate long-term relationships with trusted providers, may be an important component of pandemic responses.

Background: Direct and indirect standardization are well-established approaches to performing risk adjustment when comparing outcomes between healthcare providers. However, it is an open question whether they work well when there is an association between the center effects and the distributions of the patient characteristics in these centers.

Objectives and methods: We try to shed further light on the impact of such an association. We construct an artificial case study with a single covariate, in which centers can be classified as performing above, on, or below average, and the center effects correlate with center-specific mean values of a patient characteristic, as a consequence of differential quality improvement. Based on this case study, direct standardization and indirect standardization-based on marginal as well as conditional models-are compared with respect to systematic differences between their results.

Results: Systematic differences between the methods were observed. All methods produced results that partially reflect differences in mean age across the centers. This may mask the classification as above, on, or below average. The differences could be explained by an inspection of the parameter estimates in the models fitted.

Conclusions: In case of correlations of center effects with center-specific mean values of a covariate, different risk adjustment methods can produce systematically differing results. This suggests the routine use of sensitivity analyses. Center effects in a conditional model need not reflect the position of a center above or below average, questioning its use in defining the truth. Further empirical investigations are necessary to judge the practical relevance of these findings.

Background: Many older women are screened for breast cancer beyond guideline-recommended thresholds. One contributor is pro-screening messaging from health care professionals, media, and family/friends. In this project, we developed and evaluated messages for reducing overscreening in older women.

Methods: We surveyed women ages 65+ who were members of a nationally representative online panel. We constructed 8 messages describing reasons to consider stopping mammograms, including guideline recommendations, false positives, overdiagnosis, and diminishing benefits from screening due to competing risks. Messages varied in their format; some presented statistical evidence, and some described short anecdotes. Each participant was randomized to read 4 of 8 messages. We also randomized participants to one of 3 message sources (clinician, family member, and news story). We assessed whether the message would make participants "want to find out more information" and "think carefully" about mammograms.

Results: Participants (N=790) had a mean age of 73.5 years; 25.8% were non-White. Across all messages, 73.0% of the time, participants agreed that the messages would make them seek more information (range among different messages=64.2%-78.2%); 46.5% of the time participants agreed that the messages would make them think carefully about getting mammograms (range =36.7%-50.7%). Top-rated messages mentioned false-positive anecdotes and overdiagnosis evidence. Ratings were similar for messages from clinicians and news sources, but lower from the family member source.

Conclusions: Overall, participants positively evaluated messages designed to reduce breast cancer overscreening regarding perceived effects on information seeking and deliberation. Combining the top-rated messages into messaging interventions may be a novel approach to reduce overscreening.

Background: Workplace injuries adversely affect worker well-being and may worsen staffing shortages and turnover in nursing homes. A better understanding of the trends in injuries in nursing homes including organizational factors associated with injuries can help improve our efforts in addressing worker injuries.

Objective: To summarize the trends in injuries and organizational correlates of injuries in US nursing homes.

Research design: We combine national injury tracking data from the Occupational Safety and Health Administration (2016-2019) with nursing home characteristics from Nursing Home Compare. Our outcomes include the proportion of nursing homes reporting any injuries, the mean number of injuries, and the mean number of injuries or illnesses with days away from work, or job transfer or restriction, or both (DART). We descriptively summarize trends in injuries over time. We also estimate the association between nursing home characteristics and injuries using multivariable regressions.

Results: We find that approximately 93% of nursing homes reported at least 1 occupational injury in any given year. Injuries had a substantial impact on productivity with 4.1 DART injuries per 100 full-time employees in 2019. Higher bed size, occupancy, RN staffing, and chain ownership are associated with increased DART rates whereas higher overall nursing home star ratings and for-profit status are associated with decreased DART rates.

Conclusions: A high proportion of nursing homes report occupational injuries that can affect staff well-being, productivity, and quality of care. Injury prevention policies should target the types of injuries occurring in nursing homes and OSHA should monitor nursing homes reporting high and repeated injuries.

Background: Frailty is an aging-related syndrome of reduced physiological reserve to maintain homeostasis. The Faurot frailty index has been validated as a Medicare claims-based proxy for predicting frailty using billing information from a user-specified ascertainment window.

Objectives: We assessed the validity of the Faurot frailty index as a predictor of the frailty phenotype and 1-year mortality using varying frailty ascertainment windows.

Research design: We identified older adults (66+ y) in Round 5 (2015) of the National Health and Aging Trends Study with Medicare claims linkage. Gold standard frailty was assessed using the frailty phenotype. We calculated the Faurot frailty index using 3, 6, 8, and 12 months of claims prior to the survey or all-available lookback. Model performance for each window in predicting the frailty phenotype was assessed by quantifying calibration and discrimination. Predictive performance for 1-year mortality was assessed by estimating risk differences across claims-based frailty strata.

Results: Among 4253 older adults, the 6 and 8-month windows had the best frailty phenotype calibration (calibration slopes: 0.88 and 0.87). All-available lookback had the best discrimination (C-statistic=0.780), but poor calibration. Mortality associations were strongest using a 3-month window and monotonically decreased with longer windows. Subgroup analyses revealed worse performance in Black and Hispanic individuals than counterparts.

Conclusions: The optimal ascertainment window for the Faurot frailty index may depend on the clinical context, and researchers should consider tradeoffs between discrimination, calibration, and mortality. Sensitivity analyses using different durations can enhance the robustness of inferences. Research is needed to improve prediction across racial and ethnic groups.