Nonprofit Hospital Community Benefit Spending and Readmission Rates.
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ABSTRACT:
SUBMITTER: Chaiyachati KH
PROVIDER: S-EPMC7041313 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
Publications
Nonprofit Hospital Community Benefit Spending and Readmission Rates.
Chaiyachati Krisda H KH Qi Mingyu M Werner Rachel M RM
Population health management 20190529 1
Addressing social determinants of health may help hospitals improve population health and outcomes that are tied to financial incentives, such as readmission rates. Whether this strategy is effective remains unknown. The authors measure the association between nonprofit hospital spending on their community's social needs and Medicare readmission rates. This is a retrospective cohort study (July 2013 to June 2014) that combines Internal Revenue Service tax reports of community benefit spending by ...[more]
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Project description:ObjectiveTo examine the relationship between community factors and hospital readmission rates.Data sources/study settingWe examined all hospitals with publicly reported 30-day readmission rates for patients discharged during July 1, 2007, to June 30, 2010, with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). We linked these to publicly available county data from the Area Resource File, the Census, Nursing Home Compare, and the Neilsen PopFacts datasets.Study designWe used hierarchical linear models to assess the effect of county demographic, access to care, and nursing home quality characteristics on the pooled 30-day risk-standardized readmission rate.Data collection/extraction methodsNot applicable.Principal findingsThe study sample included 4,073 hospitals. Fifty-eight percent of national variation in hospital readmission rates was explained by the county in which the hospital was located. In multivariable analysis, a number of county characteristics were found to be independently associated with higher readmission rates, the strongest associations being for measures of access to care. These county characteristics explained almost half of the total variation across counties.ConclusionsCommunity factors, as measured by county characteristics, explain a substantial amount of variation in hospital readmission rates.
| S-EPMC4319869 | biostudies-literature
Project description:Non-profit hospitals are facing greater pressure to address the social determinants of health. Since 2012, with new requirements for greater transparency and community health needs assessments, non-profit tax exemption requirements are believed to incentivize investments in the community, particularly for vulnerable populations. We conducted a cross-sectional analysis of community benefit spending by private, acute care, non-profit hospitals from 2012-2014 to measure if hospitals have begun to address local community needs. We measured total community benefit spending and two subsets of spending-health care-related expenditures and community-directed contributions-as the proportion of their total expenditure. We obtained sociodemographic characteristics for their community, defined by ZIP code. In unadjusted and adjusted analyses using hospital-level and community-level covariates, community benefit spending has not varied and community-directed contribution amounts did not reflect local needs. Stronger incentives-tax-based or otherwise-are needed to steer non-profit hospitals to invest in community health.
| S-EPMC6425942 | biostudies-other
Project description:Importance:Medicaid expansion was widely expected to alleviate the financial stresses faced by hospitals by providing additional revenue in the form of Medicaid reimbursements from patients previously receiving uncompensated care. Among nonprofit hospitals, which receive tax-exempt status in part because of their provision of uncompensated care, Medicaid expansion could have released hospital funds toward other community benefit activities. Objective:To examine changes in nonprofit hospital spending on community benefit activities after Medicaid expansion. Design, Setting, and Participants:This cohort study used difference-in-differences analysis of 1666 US nonprofit hospitals that filed Internal Revenue Service Form 990 Schedule H detailing their community benefit expenditures between 2011 and 2017. The analysis was conducted from February to September 2019. Exposures:State Medicaid expansion between 2011 and 2017. Main Outcomes and Measures:Percentage of hospital operating expenditures attributable to charity care and subsidized care, bad debt (ie, unreimbursed spending for care of patients who did not apply for charity care), unreimbursed Medicaid spending, noncare direct community spending, and total community benefit spending. Results:Of 1478 hospitals in the sample in 2011, nearly half (653 [44.2%]) were small hospitals with fewer than 100 beds, and nearly 70% of hospitals (1023 [69.2%]) were in urban areas. Among the 1666 nonprofit hospitals, Medicaid expansion was associated with a decrease in spending on charity care and subsidized care (-0.68 [95% CI, -0.99 to -0.37] percentage points from a baseline mean [SD] of 3.6% [4.0%] of total hospital expenditures; P?<?.001) and in bad debt (-0.17 [95% CI, -0.32 to -0.01] percentage points). There was an increase in unreimbursed spending attributable to caring for Medicaid patients (0.85 [95% CI, 0.60 to 1.10] percentage points; P?=?.04), which canceled out uncompensated care savings from the expansion. Noncare direct community expenditures decreased overall (-0.24 [95% CI, -0.48 to 0.00] percentage points; P?=?.049). Direct community expenditures remained more stable in small hospitals (-0.07 [95% CI, -0.20 to 0.05] percentage points; P?=.26) compared with large hospitals (-0.37 [95% CI, -0.86 to 0.12] percentage points; P?=?.14) and in nonurban hospitals (0.02 [95% CI, -0.09 to 0.14] percentage points; P?=?.70) compared with urban hospitals (-0.36 [95% CI, -0.73 to 0.01] percentage points; P?=?.06). Conclusions and Relevance:In this study, Medicaid expansion was associated with a decrease in nonprofit hospitals' burden of providing uncompensated care, but this financial relief was not redirected toward spending on other community benefits.
| S-EPMC7260619 | biostudies-literature
Project description:Background: U.S. nonprofit hospital community benefit recently underwent significant regulatory revisions. Starting in 2009, the Internal Revenue Service (IRS) required hospitals to submit a new Schedule H that provided greater detail on community benefit activities. In addition, the Affordable Care Act (ACA), which became law in 2010, requires hospitals to conduct community health needs assessments (CHNA) and develop community health implementation plans (CHIP) as a response to priority needs every 3 years. These new requirements have led to greater transparency and accountability and this scoping review considers what has been learned about community benefit from 2010 to 2019. Methods: This review identified peer-reviewed literature published from 2010 to 2019 using three methods. First, an OvidSP MEDLINE search using terms suggested previously by community benefit researchers. Second, a PubMed search using keywords frequently found in community benefit literature. Third, a SCOPUS search of the most frequently cited articles in this topic area. Articles were then selected based on their relevance to the research question. Articles were organized into topic areas using a qualitative strategy similar to axial coding. Results: Literature appeared around several topic areas: governance; CHNA and CHIP process, content, and impact; community programs and their evaluation; spending patterns and spending influences; population health; and policy recommendations. The plurality of literature centered on spending and needs assessments, likely because they can draw upon publicly available data. The vast majority of articles in these areas use spending data from 2009 to 2012 and the first cycle of CHNAs in 2013. Policy recommendations focus on accountability for impact, enhancing collaboration, and incentivizing action in areas other than clinical care. Discussion: There are several areas of community benefit in need of further study. Longitudinal studies on needs assessments and spending patterns would help inform whether organizations have changed and improved operations over time. Governance, program evaluation, and collaboration are some of the consequential areas about which relatively little is known. Gaps in knowledge also exist related to the operational realities that drive community benefit activities. Shaping organizational action and public policy would benefit from additional research in these and other areas.
| S-EPMC7078328 | biostudies-literature
Project description:ObjectiveTo determine if greater non-profit hospital spending for community benefits is associated with better health outcomes in the county where they are located.Data sources and study settingCommunity benefit data from IRS Form 990/Schedule H was linked to health outcome data from Area Health Resource Files, Map the Meal Gap, and Medicare claims from the Center for Medicare and Medicaid Services at the county level. Counties with at least one non-profit hospital in the United States from 2015 to 2019 (N = 5469 across the 5 years) were included.Study designWe ran multiple regressions on community benefit expenditures linked with the number of health professionals, food insecurity, and adherence to diabetes and hypertension medication for each county.Data collectionThe three outcomes were chosen based on prior studies of community benefit and a recent survey sent to 12 health care executives across four regions of the U.S. Data on community benefit expenditures and health outcomes were aggregated at the county level.Principal findingsAverage hospital community benefit spending in 2019 was $63.6 million per county ($255 per capita). Multivariable regression results did not demonstrate significant associations of total community benefit spending with food insecurity or medication adherence for diabetes. Statistically significant associations with the number of health professionals per 1000 (coefficient, 12.10; SE, 0.32; p < 0.001) and medication adherence for hypertension (marginal effect, 0.27; SE, 0.09; p = 0.003) were identified, but both would require very large increases in community benefit spending to meaningfully improve outcomes.ConclusionsDespite varying levels of non-profit hospital community benefit investment across counties, higher community benefit expenditures are not associated with an improvement in the selected health outcomes at the county level. Hospitals can use this information to reassess community benefit strategies, while federal, state, and local governments can use these findings to redefine the measures of community benefit they use to monitor and grant tax exemption.
| S-EPMC9836951 | biostudies-literature
Project description:BackgroundThe environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain.ObjectiveTo estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR)-a quality performance measure in the U.S.Research designWe assessed 71 community variables in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital.SubjectsMedicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n = 6,790,723). Patients were linked to community variables using their 5-digit zip code of residence.MethodsWe used a random forest algorithm to rank variables for their importance in predicting HWR scores. Variables were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Variables with P-values <0.10 were retained for a final model, after eliminating any that were collinear.ResultsAmong 71 community variables, 19 were retained in the 6 domain models and in the final model. Domains which explained the most to least variance in HWR were: physical environment (R2 = 15%); clinical care (R2 = 12%); demographics (R2 = 11%); social and economic environment (R2 = 7%); health behaviors (R2 = 9%); and social capital (R2 = 8%). In the final model, the 19 variables explained more than a quarter of the variance in readmission rates (R2 = 27%).ConclusionsReadmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital.
| S-EPMC7584172 | biostudies-literature
Project description:ImportanceThe Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.ObjectiveTo examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge.Design, setting, and participantsRetrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014.ExposureThirty-day risk-adjusted readmission rate (RARR).Main outcomes and measuresThirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition.ResultsIn 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality.Conclusions and relevanceAmong Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.
| S-EPMC5817448 | biostudies-literature
Project description:Safety-net and teaching hospitals are somewhat more likely to be penalized for excess readmissions, but the association of other hospital characteristics with readmission rates is uncertain and may have relevance for hospital-centered interventions.To examine the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR).This is a retrospective cross-sectional multivariable analysis.US hospitals.Centers for Medicare and Medicaid Services specification of hospital-wide RSRR from July 1, 2013 through June 30, 2014 with race and Medicaid dual-eligibility added.We included 6,789,839 admissions to 4474 hospitals of Medicare fee-for-service beneficiaries aged over 64 years. In multivariable analyses, there was regional variation: hospitals in the mid-Atlantic region had the highest RSRRs [0.98 percentage points higher than hospitals in the Mountain region; 95% confidence interval (CI), 0.84-1.12]. For-profit hospitals had an average RSRR 0.38 percentage points (95% CI, 0.24-0.53) higher than public hospitals. Both urban and rural hospitals had higher RSRRs than those in medium metropolitan areas. Hospitals without advanced cardiac surgery capability had an average RSRR 0.27 percentage points (95% CI, 0.18-0.36) higher than those with. The ratio of registered nurses per hospital bed was not associated with RSRR. Variability in RSRRs among hospitals of similar type was much larger than aggregate differences between types of hospitals.Overall, larger, urban, academic facilities had modestly higher RSRRs than smaller, suburban, community hospitals, although there was a wide range of performance. The strong regional effect suggests that local practice patterns are an important influence. Disproportionately high readmission rates at for-profit hospitals may highlight the role of financial incentives favoring utilization.
| S-EPMC5426655 | biostudies-literature
Project description:Medicare penalizes hospitals with higher than expected readmission rates by up to 3% of annual inpatient payments. Expected rates are adjusted only for patients' age, sex, discharge diagnosis, and recent diagnoses.To assess the extent to which a comprehensive set of patient characteristics accounts for differences in hospital readmission rates.Using survey data from the nationally representative Health and Retirement Study (HRS) and linked Medicare claims for HRS participants enrolled in Medicare who were hospitalized from 2009 to 2012 (n = 8067 admissions), we assessed 29 patient characteristics from survey data and claims as potential predictors of 30-day readmission when added to standard Medicare adjustments of hospital readmission rates. We then compared the distribution of these characteristics between participants admitted to hospitals with higher vs lower hospital-wide readmission rates reported by Medicare. Finally, we estimated differences in the probability of readmission between these groups of participants before vs after adjusting for the additional patient characteristics.All-cause readmission within 30 days of discharge.Of the additional 29 patient characteristics assessed, 22 significantly predicted readmission beyond standard adjustments, and 17 of these were distributed differently between hospitals in the highest vs lowest quintiles of publicly reported hospital-wide readmission rates (P ? .04 for all comparisons). Almost all of these differences (16 of 17) indicated that participants admitted to hospitals in the highest quintile of readmission rates were more likely to have characteristics that were associated with a higher probability of readmission. The difference in the probability of readmission between participants admitted to hospitals in the highest vs lowest quintile of hospital-wide readmission rates was reduced by 48% from 4.41 percentage points with standard adjustments used by Medicare to 2.29 percentage points after adjustment for all patient characteristics assessed (reduction in difference: -2.12; 95% CI, -3.33 to -0.67; P =?.003).Patient characteristics not included in Medicare's current risk-adjustment methods explained much of the difference in readmission risk between patients admitted to hospitals with higher vs lower readmission rates. Hospitals with high readmission rates may be penalized to a large extent based on the patients they serve.
| S-EPMC4991542 | biostudies-literature
Project description:BackgroundLowering hospital readmission rates has become a primary target for the Centers for Medicare & Medicaid Services, but studies of the relationship between adherence to the recommended hospital care processes and readmission rates have provided inconsistent and inconclusive results.ObjectiveTo examine the association between hospital performance on Medicare's Hospital Compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.Design, setting and participantsWe assessed hospital performance on process measures using the 2007 Hospital Inpatient Quality Reporting Program. The process measures for each condition were aggregated in two separate measures: Overall Measure (OM) and Appropriate Care Measure (ACM) scores. Readmission rates were calculated using Medicare claims.Main outcome measureRisk-standardized 30-day all-cause readmission rate was calculated as the ratio of predicted to expected rate standardized by the overall mean readmission rate. We calculated predicted readmission rate using hierarchical generalized linear models and adjusting for patient-level factors.ResultsAmong patients aged ≥ 66 years, the median OM score ranged from 79.4 % for abdominal surgery to 95.7 % for AMI, and the median ACM scores ranged from 45.8 % for abdominal surgery to 87.9 % for AMI. We observed a statistically significant, but weak, correlation between performance scores and readmission rates for pneumonia (correlation coefficient R = 0.07), AMI (R = 0.10), and orthopedic surgery (R = 0.06). The difference in the mean readmission rate between hospitals in the 1st and 4th quartiles of process measure performance was statistically significant only for AMI (0.25 percentage points) and pneumonia (0.31 percentage points). Performance on process measures explained less than 1 % of hospital-level variation in readmission rates.ConclusionsHospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.
| S-EPMC3579957 | biostudies-literature