Dataset Information

Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates.

ABSTRACT:

Background

Reducing hospital readmissions, including preventable healthcare-associated infections, is a national priority. The proportion of readmissions due to infections is not well-understood. Better understanding of hospital risk factors for readmissions and infection-related readmissions may help optimize interventions to prevent readmissions.

Methods

Retrospective cohort study of California acute care hospitals and their patient populations discharged between 2009 and 2011. Demographics, comorbidities, and socioeconomic status were entered into a hierarchical generalized linear mixed model predicting all-cause and infection-related readmissions. Crude verses adjusted hospital rankings were compared using Cohen's kappa.

Results

We assessed 30-day readmission rates from 323 hospitals, accounting for 213 879 194 post-discharge person-days of follow-up. Infection-related readmissions represented 28% of all readmissions and were associated with discharging a high proportion of patients to skilled nursing facilities. Hospitals serving populations with high proportions of males, comorbidities, prolonged length of stay, and populations living in a federal poverty area, had higher all-cause and infection-related readmission rates. Academic hospitals had higher all-cause and infection-related readmission rates (odds ratio 1.24 and 1.15, respectively). When comparing adjusted vs crude hospital rankings for infection-related readmission rates, adjustment revealed 31% of hospitals changed performance category for infection-related readmissions.

Conclusions

Infection-related readmissions accounted for nearly 30% of all-cause readmissions. High hospital infection-related readmissions were associated with serving a high proportion of patients with comorbidities, long lengths of stay, discharge to skilled nursing facility, and those living in federal poverty areas. Preventability of these infections needs to be assessed.

SUBMITTER: Gohil SK

PROVIDER: S-EPMC4583583 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates.

Gohil Shruti K SK Datta Rupak R Cao Chenghua C Phelan Michael J MJ Nguyen Vinh V Rowther Armaan A AA Huang Susan S SS

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20150630 8

<h4>Background</h4>Reducing hospital readmissions, including preventable healthcare-associated infections, is a national priority. The proportion of readmissions due to infections is not well-understood. Better understanding of hospital risk factors for readmissions and infection-related readmissions may help optimize interventions to prevent readmissions.<h4>Methods</h4>Retrospective cohort study of California acute care hospitals and their patient populations discharged between 2009 and 2011. ...[more]

PMID: 26129752

Similar Datasets

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.

Project description:ImportanceThirty-day readmission rates have been suggested as a marker for quality of care. By investigating the factors associated with readmissions in all otolaryngology subspecialties we provide data relevant for the development of risk stratification systems to improve outcomes.ObjectiveTo establish the association of surgical and hospital volume and patient characteristics with 30-day readmission rates to guide the development of otolaryngology-specific risk stratification models.Design, setting, and participantsA retrospective cohort study including adult patients who underwent inpatient otolaryngology surgery in New York State between 1995 and 2015 was conducted using the Statewide Planning and Research Cooperative System (SPARCS). Regression techniques were used to describe relationships of patient-level factors, hospital, and surgeon volume to 30-day readmission rates in New York State.Main outcomes and measuresThe main outcome measures were patient-, surgeon-, and hospital-level risk factors for readmission. Secondary outcome measures were rate of readmissions by subspecialty procedure and by diagnosis on readmission.ResultsWe identified 254 257 cases of otolaryngology surgery (147 065 women [58%], mean [SD] age 50 [17] years). The 30-day readmission rate was 6%. In a multivariable model, odds ratios (ORs) identified Medicaid insurance (OR, 1.46; 99% CI, 1.36-1.57), Medicare insurance (OR, 1.32; 99% CI, 1.24-1.42), bottom quartile income (OR, 1.08; 99% CI, 1.01-1.15), patient comorbidities measured by the Charlson Comorbidity Index (CCI) (CCI >1; OR, 2.31; 99% CI, 2.16-2.47), length of stay (LOS) (LOS >10 days; OR, 2.29; 99% CI, 2.00-2.45), rhinology (OR, 1.37; 99% CI, 1.24-1.51), laryngology (OR, 1.98; 99% CI, 1.62-2.43), and head and neck cancer (OR, 1.27; 99% CI, 1.17-1.37) procedures as readmission predictors. High-volume surgeons were protective of 30-day readmission (OR, 0.67; 99% CI, 0.635-0.708) relative to low volume. Hospital volume was not significantly associated to readmissions. The most common causes of readmission included wound- (2682 patients, 18%), respiratory- (1776 patients, 12%), cardiovascular- (1210 patients, 8%), and volume- (1089 patients, 7%) related disorders.Conclusions and relevanceThis study evaluated the combined effects of patient-, surgeon-, and hospital-level factors on 30-day readmission after otolaryngology surgery. Socioeconomic factors, patient comorbidities, surgeon volumes, and procedure were significantly associated with 30-day readmission. Though the cause of 30-day readmission is multifactorial, a large portion is driven by socioeconomic factors. Addressing these disparities at the system level is necessary to address the described readmission disparities. The development of risk-stratification models based on patient-, procedure-, and surgeon-level factors may help facilitate resource distribution.

Project description:BackgroundSeveral care transition interventions propose that post-discharge phone calls can reduce adverse events and decrease costly return visits to the hospital. However, given the multi-faceted nature of most care transitions interventions, the true relationship between post-discharge phone calls and readmissions in a real world setting is uncertain.ObjectiveTo determine the effect of receiving a post-discharge telephone call on all-cause 30-day readmission in a general medicine population.DesignRetrospective observational study.ParticipantsPatients discharged home from the Medicine Service at a tertiary care academic medical center between November 2010 and May 2012.InterventionPatients received two telephone call attempts by a nurse within 72 h of discharge. Nurses followed a standard script to address issues associated with readmission.Main outcome and measuresBilling data captured readmissions. We used logistic regression-adjusted patient and clinical covariates as well as a propensity score representing likelihood of being called to determine the association between call receipt and risk for readmission.Key resultsThere were 5,507 eligible patients. In unadjusted analyses, patients who received a call and completed the intervention were significantly less likely to be readmitted compared to those who did not [155 (5.8 %) vs 123 (8.6 %), p < 0.01]. In multivariable models adjusting for socio-demographic and clinical covariates alone, completing a post-discharge telephone call intervention was associated with lower odds for readmission (AOR 0.71; 95 % CI: 0.55-0.91). However, when models adjusted for the likelihood of receiving the phone call using the propensity score, no association between call receipt and readmission was observed (AOR 0.91; 95%CI: 0.69-1.20).ConclusionsEffectiveness of post-discharge phone call programs may be more related to whether patients are able to answer a phone call than to the care delivered by the phone call. Programs would benefit from improving their ability to perform phone outreach while simultaneously improving on the care delivered during the calls.

Dataset Information

Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates.

Background

Methods

Results

Conclusions

Publications

Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-Cause and Infection-Related 30-Day Readmission Rates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets