Racial, Income, and Marital Status Disparities in Hospital Readmissions Within a Veterans-Integrated Health Care Network

Moore, Crystal Dea; Gao, Kelly; Shulan, Mollie

doi:10.1177/0163278713492982

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…In the optimized model, based on a priori knowledge and clinical plausibility [33][34][35][36][37][38], we included social factor variables that are available in the MCBS, in addition to the variables used in the reference model above (i.e., the optimized model included patient's age, comorbidities, mechanical ventilation use, and all of the following variables). As the use of arbitrary statistical cutoffs for variable selection (e.g., univariate screening) has been criticized [39,40], we have selected the predictors in the final model based on a priori knowledge.…”

Section: Candidate Predictor Variables and Model Derivationmentioning

confidence: 99%

Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation

et al. 2020

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Background To investigate whether, in patients hospitalized for COPD, the addition of social factors improves the predictive ability for the risk of overall 30-day readmissions, early readmissions (within 7 days after discharge), and late readmissions (8–30 days after discharge). Methods Patients (aged ≥40 years) hospitalized for COPD were identified in the Medicare Current Beneficiary Survey from 2006 through 2012. With the use of 1000 bootstrap resampling from the original cohort (training-set), two prediction models were derived: 1) the reference model including age, comorbidities, and mechanical ventilation use, and 2) the optimized model including social factors (e.g., educational level, marital status) in addition to the covariates in the reference model. Prediction performance was examined separately for 30-day, early, and late readmissions. Results Following 905 index hospitalizations for COPD, 18.5% were readmitted within 30 days. In the test-set, for overall 30-day readmissions, the discrimination ability between reference and optimized models did not change materially (C-statistic, 0.57 vs. 0.58). By contrast, for early readmissions, the optimized model had significantly improved discrimination (C-statistic, 0.57 vs. 0.63; integrated discrimination improvement [IDI], 0.018 [95%CI, 0.003–0.032]) and reclassification (continuous net reclassification index [NRI], 0.298 [95%CI 0.060–0.537]). Likewise, for late readmissions, the optimized model also had significantly improved discrimination (C-statistic, 0.65 vs. 0.68; IDI, 0.026 [95%CI 0.009–0.042]) and reclassification (continuous NRI, 0.243 [95%CI 0.028–0.459]). Conclusions In a nationally-representative sample of Medicare beneficiaries hospitalized for COPD, we found that the addition of social factors improved the predictive ability for readmissions when early and late readmissions were examined separately.

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Section: Candidate Predictor Variables and Model Derivationmentioning

confidence: 99%

Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation

et al. 2020

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“…This lack of statistical control in the predictive model is problematic for hospitals that primarily serve diverse patients [12,13]. At the hospital level, previous research has consistently indicated hospitals serving minority populations experience higher readmissions for HF, AMI, PN, and diabetes [14,15]. County characteristics that correlate highly with readmission rates include minority and vulnerable populations, socioeconomic status (negative correlation), educational level (negative correlation), high unemployment rate, a large number of individuals who never married, and a high percentage of Medicare recipients per 100,000 residents [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…At the hospital level, previous research has consistently indicated hospitals serving minority populations experience higher readmissions for HF, AMI, PN, and diabetes [14,15]. County characteristics that correlate highly with readmission rates include minority and vulnerable populations, socioeconomic status (negative correlation), educational level (negative correlation), high unemployment rate, a large number of individuals who never married, and a high percentage of Medicare recipients per 100,000 residents [15][16][17]. At the individual level, Martsolf et al (2016) reported race, ethnicity, and low socioeconomic status affect hospitals' excess readmission ratios and significantly impact hospitals' risk for penalties.…”

Section: Introductionmentioning

confidence: 99%

Impact of hospitals’ Referral Region racial and ethnic diversity on 30-day readmission rates of older adults

Hamadi

Moody

Apatu

et al. 2019

Journal of Community Hospital Internal Medicine Perspectives

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Background: The Hospital Readmissions Reduction Program (HRRP) began decreasing Medicare payments to hospitals reporting high readmission rates for individuals over 65. Thus, financially incentivizing hospitals to improve quality performance on preventable readmissions. Well-established research indicates that minorities are more frequently readmitted to hospitals, but it is unknown if community diversity is associated with 30-day readmission rates. Objectives: To investigate the association between racial/ethnic diversity and hospitals' 30day readmission rates. Methods: We linked the 2017 HRRP, American Hospital Association (AHA) database, Area Health Resource File, US Census Bureau Current Population Survey, and the Dartmouth Atlas HRR dataset to examine 30-day readmission rate for heart failure (HF), pneumonia (PN), acute myocardial infarction (AMI), and hip replacement (HR) surgery of 4,299 hospitals across 306 HRRs. Results: Our findings indicate a statistically significant negative relationship between diversity and 30-day readmission rates for HF, PN, AMI, and HR with a hospital referral region (HRR). Thus, hospitals located in HRRs with diverse populations are more likely to have higher 30day readmission rates for all conditions under Medicare's HRRP Conclusion: Better discharge follow-up, interventions, and use of support staff aimed at meeting needs associated with differences in communities and cultures are likely to prove more fruitful than traditional one-size fits all approaches to care.

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“…Among the socio-demographic characteristics shown to impact readmission, patient age appears to have the most consistent relationship with risk of readmission [20242526]. Other demographic variables—such as gender, residential address, and marital status—are also potential predictors of readmission, however these are not avoidable or modifiable [817272829303132], as does the patient’s health insurance status [172930]. Coinsurance is a predetermined specific percentage of money that patient pays for healthcare services, typically is due at the time of service, and then the insurance agency pays the rest [33].…”

Section: Introductionmentioning

confidence: 99%

Integrating the Principles of Evidence Based Medicine and Evidence Based Public Health: Impact on the Quality of Patient Care and Hospital Readmission Rates in Jordan

et al. 2016

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Introduction:Hospital readmissions impose not only an extra burden on health care systems but impact patient health outcomes. Identifying modifiable behavioural risk factors that are possible causes of potentially avoidable readmissions can lower readmission rates and healthcare costs.Methods:Using the core principles of evidence based medicine and public health, the purpose of this study was to develop a heuristic guide that could identify what behavioural risk factors influence hospital readmissions through adopting various methods of analysis including regression models, t-tests, data mining, and logistic regression. This study was a retrospective cohort review of internal medicine patients admitted between December 1, 2012 and December 31, 2013 at King Abdullah University Hospital, in Jordan.Results:29% of all hospitalized patients were readmitted during the study period. Among all readmissions, 44% were identified as potentially avoidable. Behavioural factors including smoking, unclear follow-up and discharge planning, and being non-compliant with treatment regimen as well as discharge against medical advice were all associated with increased risk of avoidable readmissions.Conclusion:Implementing evidence based health programs that focus on modifiable behavioural risk factors for both patients and clinicians would yield a higher response in terms of reducing potentially avoidable readmissions, and could reduce direct medical costs.

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Racial, Income, and Marital Status Disparities in Hospital Readmissions Within a Veterans-Integrated Health Care Network

Cited by 8 publications

References 25 publications

Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation

Contribution of social factors to readmissions within 30 days after hospitalization for COPD exacerbation

Impact of hospitals’ Referral Region racial and ethnic diversity on 30-day readmission rates of older adults

Integrating the Principles of Evidence Based Medicine and Evidence Based Public Health: Impact on the Quality of Patient Care and Hospital Readmission Rates in Jordan

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