Adding socioeconomic data to hospital readmissions calculations may produce more useful results

Collection and Use of Social Determinants of Health Data in Inpatient General Internal Medicine Wards: A Scoping Review

BACKGROUND

There is growing interest in incorporating social determinants of health (SDoH) data collection in inpatient hospital settings to inform patient care. However, there is limited information on this data collection and its use in inpatient general internal medicine (GIM). This scoping review sought to describe the current state of the literature on SDoH data collection and its application to patient care in inpatient GIM settings.

METHODS

English-language searches on MedLine, Embase, Web of Science, CINAHL, Cochrane, and PsycINFO were conducted from 2000 to April 2021. Studies reporting systematic data collection or use of at least three SDoH, sociodemographic, or social needs variables in inpatient hospital GIM settings were included. Four independent reviewers screened abstracts, and two reviewers screened full-text articles.

RESULTS

A total of 8190 articles underwent abstract screening and eight were included. A range of SDoH tools were used, such as THRIVE, PRAPARE, WHO-Quality of Life, Measuring Health Equity, and a biopsychosocial framework. The most common SDoH were food security or malnutrition (n=7), followed by housing, transportation, employment, education, income, functional status and disability, and social support (n=5 each). Four of the eight studies applied the data to inform patient care, and three provided community resource referrals.

DISCUSSION

There is limited evidence to guide the collection and use of SDoH data in inpatient GIM settings. This review highlights the need for integrated care, the role of the electronic health record, and social history taking, all of which may benefit from more robust SDoH data collection. Future research should examine the feasibility and acceptability of SDoH integration in inpatient GIM settings.

Are Neighborhood Characteristics Associated With Outcomes After THA and TKA? Findings From a Large Healthcare System Database

BACKGROUND

Non-White patients have higher rates of discharge to an extended care facility, hospital readmission, and emergency department use after primary THA and TKA. The reasons for this are unknown. Place of residence, which can vary by race, has been linked to poorer healthcare outcomes for people with many health conditions. However, the potential relationship between place of residence and disparities in these joint arthroplasty outcomes is unclear.

QUESTIONS/PURPOSES

(1) Are neighborhood-level characteristics, including racial composition, marital proportions, residential vacancy, educational attainment, employment proportions, overall deprivation, access to medical care, and rurality associated with an increased risk of discharge to a facility, readmission, and emergency department use after elective THA and TKA? (2) Are the associations between neighborhood-level characteristics and discharge to a facility, readmission, and emergency department use the same among White and Black patients undergoing elective THA and TKA?

METHODS

Between 2007 and 2018, 34,008 records of elective primary THA or TKA for osteoarthritis, rheumatoid arthritis, or avascular necrosis in a regional healthcare system were identified. After exclusions for unicompartmental arthroplasty, bilateral surgery, concomitant procedures, inability to geocode a residential address, duplicate records, and deaths, 21,689 patients remained. Ninety-seven percent of patients in this cohort self-identified as either White or Black, so the remaining 659 patients were excluded due to small sample size. This left 21,030 total patients for analysis. Discharge destination, readmissions within 90 days of surgery, and emergency department visits within 90 days were identified. Each patient's street address was linked to neighborhood characteristics from the American Community Survey and Area Deprivation Index. A multilevel, multivariable logistic regression analysis was used to model each outcome of interest, controlling for clinical and individual sociodemographic factors and allowing for clustering at the neighborhood level. The models were then duplicated with the addition of neighborhood characteristics to determine the association between neighborhood-level factors and each outcome. The linear predictors from each of these models were used to determine the predicted risk of each outcome, with and without neighborhood characteristics, and divided into tenths. The change in predicted risk tenths based on the model containing neighborhood characteristics was compared to that without neighborhood characteristics.The change in predicted risk tenth for each outcome was stratified by race.

RESULTS

After controlling for age, sex, insurance type, surgery type, and comorbidities, we found that an increase of one SD of neighborhood unemployment (odds ratio 1.26 [95% confidence interval 1.17 to 1.36]; p < 0.001) was associated with an increased likelihood of discharge to a facility, whereas an increase of one SD in proportions of residents receiving public assistance (OR 0.92 [95% CI 0.86 to 0.98]; p = 0.008), living below the poverty level (OR 0.82 [95% CI 0.74 to 0.91]; p < 0.001), and being married (OR 0.80 [95% CI 0.71 to 0.89]; p < 0.001) was associated with a decreased likelihood of discharge to a facility. Residence in areas one SD above mean neighborhood unemployment (OR 1.12 [95% CI [1.04 to 1.21]; p = 0.002) was associated with increased rates of readmission. An increase of one SD in residents receiving food stamps (OR 0.83 [95% CI 0.75 to 093]; p = 0.001), being married (OR 0.89 [95% CI 0.80 to 0.99]; p = 0.03), and being older than 65 years (OR 0.93 [95% CI 0.88 to 0.98]; p = 0.01) was associated with a decreased likelihood of readmission. A one SD increase in the percentage of Black residents (OR 1.11 [95% CI 1.00 to 1.22]; p = 0.04) and unemployed residents (OR 1.15 [95% CI 1.05 to 1.26]; p = 0.003) was associated with a higher likelihood of emergency department use. Living in a medically underserved area (OR 0.82 [95% CI 0.68 to 0.97]; p = 0.02), a neighborhood one SD above the mean of individuals using food stamps (OR 0.81 [95% CI 0.70 to 0.93]; p = 0.004), and a neighborhood with an increasing percentage of individuals older than 65 years (OR 0.90 [95% CI 0.83 to 0.96]; p = 0.002) were associated with a lower likelihood of emergency department use. With the addition of neighborhood characteristics, the risk prediction tenths of the overall cohort remained the same in more than 50% of patients for all three outcomes of interest. When stratified by race, neighborhood characteristics increased the predicted risk for 55% of Black patients for readmission compared with 17% of White patients (p < 0.001). The predicted risk tenth increased for 60% of Black patients for emergency department use compared with 21% for White patients (p < 0.001).

CONCLUSION

These results can be used to identify high-risk patients who might benefit from preemptive interventions to avoid these particular outcomes and to create more realistic, comprehensive risk adjustment models for value-based care programs. Additionally, this study demonstrates that neighborhood characteristics are associated with greater risk for these outcomes among Black patients compared with White patients. Further studies should consider that race/ethnicity and neighborhood characteristics may not function independently from each other. Understanding this link between race and place of residence is essential for future racial disparities research.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Evaluating Social Determinants of Health in a Mobile Integrated Healthcare-Community Paramedicine Program

In 2018, the University of Maryland Medical Center and the Baltimore City Fire Department implemented a community paramedicine program to help medically or socially complex patients transition from hospital to home and avoid hospital utilization. This study describes how patients' social determinants of health (SDoH) needs were identified, and measures the association between needs and hospital utilization. SDoH needs were categorized into ten domains. Multinomial logistic regression was used to measure association between identified SDoH domains and predicted risk of readmission. Poisson regression was used to measure association between SDoH domains and actual 30-day hospital utilization. The most frequently identified SDoH needs were in the Coordination of Healthcare (37.7%), Durable Medical Equipment (18.8%), and Medication (16.3%) domains. Compared with low-risk patients, patients with an intermediate risk of readmission were more likely to have needs within the Coordination of Healthcare (RRR [95% CI] 1.12 [1.01, 1.24], p = 0.032) and Durable Medical Equipment (RRR = 1.13 [1.00, 1.27], p = 0.046) domains. Patients with the highest risk for readmission were more likely to have needs in the Utilities domain (RRR = 1.76 [0.97, 3.19], p = 0.063). Miscellaneous domain needs, such as requiring a social security card, were associated with increased 30-day hospital utilization (IRR = 1.23 [0.96, 1.57], p = 0.095). SDoH needs within the Coordination of Healthcare, Durable Medical Equipment, and Utilities domains were associated with higher predicted 30-day readmission, while identification documentation and social services needs were associated with actual readmission. These results suggest where to allocate resources to effectively diminish hospital utilization.

Influence of Social Determinants of Health on Heart Failure Outcomes: A Systematic Review

Background Prior research suggests an association between clinical outcomes in heart failure (HF) and social determinants of health (SDoH). Because providers should identify and address SDoH in care delivery, we evaluated how SDoH have been defined, measured, and evaluated in studies that examine HF outcomes. Methods and Results Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, databases were searched for observational or interventional studies published between 2009 and 2021 that assessed the influence of SDoH on outcomes. Selected articles were assessed for quality using a validated rating scheme. We identified 1373 unique articles for screening; 104 were selected for full-text review, and 59 met the inclusion criteria, including retrospective and prospective cohort, cross-sectional, and intervention studies. The majority examined readmissions and hospitalizations (k=33), mortality or survival (k=29), and success of medical devices and transplantation (k=8). SDoH examined most commonly included race, ethnicity, age, sex, socioeconomic status, and education or health literacy. Studies used a range of 1 to 9 SDoH as primary independent variables and 0 to 7 SDoH as controls. Multiple data sources were employed and frequently were electronic medical records linked with national surveys and disease registries. The effects of SDoH on HF outcomes were inconsistent because of the heterogeneity of data sources and SDoH constructs. Conclusions Our systematic review reveals shortcomings in measurement and deployment of SDoH variables in HF care. Validated measures need to be prospectively and intentionally collected to facilitate appropriate analysis, reporting, and replication of data across studies and inform the design of appropriate, evidence-based interventions that can ameliorate significant HF morbidity and societal costs.

Substance use disorders and social determinants of health from electronic medical records obtained during Kentucky's "triple wave"

Social determinants of health (SDOH) play a critical role in the risk of harmful drug use. Examining SDOH as a means of differentiating populations with multiple co-occurring substance use disorders (SUDs) is particularly salient in the era of prevalent opioid and stimulant use known as the "Third Wave". This study uses electronic medical records (EMRs) from a safety net hospital system from 14,032 patients in Kentucky from 2017 to 2019 in order to 1) define three types of SUD cohorts with shared/unique risk factors, 2) identify patients with unstable housing using novel methods for EMRs and 3) link patients to their residential neighborhood to obtain quantitative perspective on social vulnerability. We identified patients in three cohorts with statistically significant unique risk factors that included race, biological sex, insurance type, smoking status, and urban/rural residential location. Adjusting for these variables, we found a statistically significant, increasing risk gradient for patients experiencing unstable housing by cohort type: opioid-only (n = 7385, reference), stimulant-only (n = 4794, odds ratio (aOR) 1.86 95 % confidence interval (CI): 1.66-2.09), and co-diagnosed (n = 1853, aOR = 2.75, 95 % CI: 2.39 to 3.16). At the neighborhood-level, we used 8 different measures of social vulnerability and found that, for the most part, increasing proportions of patients with stimulant use living in a census tract was associated with more social vulnerability. Our study identifies potentially modifiable factors that can be tailored by substance type and demonstrates robust use of EMRs to meet national goals of enhancing research on social determinants of health.

Hospital value-based payment programs and disparity in the United States: A review of current evidence and future perspectives

Beginning in the early 2010s, an array of Value-Based Purchasing (VBP) programs has been developed in the United States (U.S.) to contain costs and improve health care quality. Despite documented successes in these efforts in some instances, there have been growing concerns about the programs' unintended consequences for health care disparities due to their built-in biases against health care organizations that serve a disproportionate share of disadvantaged patient populations. We explore the effects of three Medicare hospital VBP programs on health and health care disparities in the U.S. by reviewing their designs, implementation history, and evidence on health care disparities. The available empirical evidence thus far suggests varied impacts of hospital VBP programs on health care disparities. Most of the reviewed studies in this paper demonstrate that hospital VBP programs have the tendency to exacerbate health care disparities, while a few others found evidence of little or no worsening impacts on disparities. We discuss several policy options and recommendations which include various reform approaches and specific programs ranging from those addressing upstream structural barriers to health care access, to health care delivery strategies that target service utilization and health outcomes of vulnerable populations under the VBP programs. Future studies are needed to produce more explicit, conclusive, and consistent evidence on the impacts of hospital VBP programs on disparities.

A Machine Learning Model for Predicting the Risk of Readmission in Community-Acquired Pneumonia

Background

Pneumonia is a common respiratory infection that affects all ages, with a higher rate anticipated as age increases. It is a disease that impacts patient health and the economy of the healthcare institution. Therefore, machine learning methods have been used to guide clinical judgment in disease conditions and can recognize patterns based on patient data. This study aims to develop a prediction model for the readmission risk within 30 days of patient discharge after the management of community-acquired pneumonia (CAP).

Methodology

Univariate and multivariate logistic regression were used to identify the statistically significant factors that are associated with the readmission of patients with CAP. Multiple machine learning models were used to predict the readmission of CAP patients within 30 days by conducting a retrospective observational study on patient data. The dataset was obtained from the Hospital Information System of a tertiary healthcare organization across Saudi Arabia. The study included all patients diagnosed with CAP from 2016 until the end of 2018.

Results

The collected data included 8,690 admission records related to CAP for 5,776 patients (2,965 males, 2,811 females). The results of the analysis showed that patient age, heart rate, respiratory rate, medication count, and the number of comorbidities were significantly associated with the odds of being readmitted. All other variables showed no significant effect. We ran four algorithms to create the model on our data. The decision tree gave high accuracy of 83%, while support vector machine (SVM), random forest (RF), and logistic regression provided better accuracy of 90%. However, because the dataset was unbalanced, the precision and recall for readmission were zero for all models except the decision tree with 16% and 18%, respectively. By applying the Synthetic Minority Oversampling TEchnique technique to balance the training dataset, the results did not change significantly; the highest precision achieved was 16% in the SVM model. RF achieved the highest recall with 45%, but without any advantage to this model because the accuracy was reduced to 65%.

Conclusions

Pneumonia is an infectious disease with major health and economic complications. We identified that less than 10% of patients were readmitted for CAP after discharge; in addition, we identified significant predictors. However, our study did not have enough data to develop a proper machine learning prediction model for the risk of readmission.

Follow-up Post-discharge and Readmission Disparities Among Medicare Fee-for-Service Beneficiaries, 2018

BACKGROUND

Previous studies have identified disparities in readmissions among Medicare beneficiaries hospitalized for the Hospital Readmissions Reduction Program's (HRRP's) priority conditions. Evidence suggests timely follow-up is associated with reduced risk of readmission, but it is unknown whether timely follow-up reduces disparities in readmission.

OBJECTIVE

To assess whether follow-up within 7 days after discharge from a hospitalization reduces risk of readmission and mitigates identified readmission disparities.

DESIGN

A retrospective cohort study using Cox proportional hazards models to estimate the associations between sociodemographic characteristics (race and ethnicity, dual-eligibility status, rurality, and area social deprivation), follow-up, and readmission. Mediation analysis was used to examine if disparities in readmission were mitigated by follow-up.

PARTICIPANTS

We analyzed data from 749,402 Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, or pneumonia, and discharged home between January 1 and December 1, 2018.

MAIN MEASURE

All-cause unplanned readmission within 30 days after discharge.

KEY RESULTS

Post-discharge follow-up within 7 days of discharge was associated with a substantially lower risk of readmission (HR: 0.52, 95% CI: 0.52-0.53). Across all four HRRP conditions, beneficiaries with dual eligibility and beneficiaries living in areas with high social deprivation had a higher risk of readmission. Non-Hispanic Black beneficiaries had higher risk of readmission after hospitalization for pneumonia relative to non-Hispanic Whites. Mediation analysis suggested that 7-day follow-up mediated 21.2% of the disparity in the risk of readmission between dually and non-dually eligible beneficiaries and 50.7% of the disparity in the risk of readmission between beneficiaries living in areas with the highest and lowest social deprivation. Analysis suggested that after hospitalization for pneumonia, 7-day follow-up mediated nearly all (97.5%) of the increased risk of readmission between non-Hispanic Black and non-Hispanic White beneficiaries.

CONCLUSIONS

Improving rates of follow-up could be a strategy to reduce readmissions for all beneficiaries and reduce disparities in readmission based on sociodemographic characteristics.

Social Risk Adjustment In The Hospital Readmissions Reduction Program: A Systematic Review And Implications For Policy

Value-based payment programs adjust payments to providers based on spending, quality, or health outcomes. Concern that these programs penalize providers disproportionately serving vulnerable patients prompted calls to adjust performance measures for social risk factors. We reviewed fourteen studies of social risk adjustment in Medicare's Hospital Readmissions Reduction Program (HRRP), a value-based payment model that initially did not adjust for social risk factors but subsequently began to do so. Seven studies found that adding social risk factors to the program's base risk-adjustment model (which adjusts only for age, sex, and comorbidities) reduced differences in risk-adjusted readmissions and penalties between safety-net hospitals and other hospitals. Three studies found that peer grouping, the HRRP's current approach to social risk adjustment, reduced penalties among safety-net hospitals. Two studies found that differences in risk-adjusted readmissions and penalties were further narrowed when augmentation of the base model was combined with peer grouping. Two studies showed that it is possible to adjust for social risk factors without obscuring quality differences between hospitals. These findings support the use of social risk adjustment to improve provider payment equity and highlight opportunities to enhance social risk adjustment in value-based payment programs.

Racial and ethnic disparities in pediatric magnetic resonance imaging missed care opportunities

BACKGROUND

Imaging missed care opportunities (MCOs), previously referred to as "no shows," impact timely patient diagnosis and treatment and can exacerbate health care disparities. Understanding factors associated with imaging MCOs could help advance pediatric health equity.

OBJECTIVE

To assess racial/ethnic differences in pediatric MR imaging MCOs and whether health system and socioeconomic factors, represented by a geography-based Social Vulnerability Index (SVI), influence racial/ethnic differences.

MATERIALS AND METHODS

We conducted a retrospective analysis of MR imaging MCOs in patients younger than 21 years at a pediatric academic medical center (2015-2019). MR imaging MCOs were defined as: scheduled but appointment not attended, canceled within 24 h, and canceled but not rescheduled. Mixed effects multivariable logistic regression assessed the association between MCOs and race/ethnicity and community-level social factors, represented by the SVI.

RESULTS

Of 68,809 scheduled MRIs, 6,159 (9.0%) were MCOs. A higher proportion of MCOs were among Black/African-American and Hispanic/Latino children. Multivariable analysis demonstrated increased odds of MCOs among Black/African-American (adjusted odds ratio [aOR] 1.9, 95% confidence interval [CI] 1.7-2.3) and Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.7) children compared to White children. The addition of SVI >90th percentile to the adjusted model had no effect on adjusted OR for Black/African-American (aOR 1.9, 95% CI 1.7-2.2) or Hispanic/Latino (aOR 1.5, 95% CI 1.3-1.6) children. Living in a community with SVI >90th percentile was independently associated with MCOs.

CONCLUSION

Black/African-American and Hispanic/Latino children were almost twice as likely to experience MCOs, even when controlling for factors associated with MCOs. Independent of race/ethnicity, higher SVI was significantly associated with MCOs. Our study supports that pediatric health care providers must continue to identify systemic barriers to health care access for Black/African-American and Hispanic/Latino children and those from socially vulnerable areas.

Risk factors for hospital readmissions in pneumonia patients: A systematic review and meta-analysis

BACKGROUND

Factors that are associated with the short-term rehospitalization have been investigated previously in numerous studies. However, the majority of these studies have not produced any conclusive results because of their smaller sample sizes, differences in the definition of pneumonia, joint pooling of the in-hospital and post-discharge deaths and lower generalizability.

AIM

To estimate the effect of various risk factors on the rate of hospital readmissions in patients with pneumonia.

METHODS

Systematic search was conducted in PubMed Central, EMBASE, MEDLINE, Cochrane library, ScienceDirect and Google Scholar databases and search engines from inception until July 2021. We used the Newcastle Ottawa (NO) scale to assess the quality of published studies. A meta-analysis was carried out with random-effects model and reported pooled odds ratio (OR) with 95% confidence interval (CI).

RESULTS

In total, 17 studies with over 3 million participants were included. Majority of the studies had good to satisfactory quality as per NO scale. Male gender (pooled OR = 1.22; 95%CI: 1.16-1.27), cancer (pooled OR = 1.94; 95%CI: 1.61-2.34), heart failure (pooled OR = 1.28; 95%CI: 1.20-1.37), chronic respiratory disease (pooled OR = 1.37; 95%CI: 1.19-1.58), chronic kidney disease (pooled OR = 1.38; 95%CI: 1.23-1.54) and diabetes mellitus (pooled OR = 1.18; 95%CI: 1.08-1.28) had statistically significant association with the hospital readmission rate among pneumonia patients. Sensitivity analysis showed that there was no significant variation in the magnitude or direction of outcome, indicating lack of influence of a single study on the overall pooled estimate.

CONCLUSION

Male gender and specific chronic comorbid conditions were found to be significant risk factors for hospital readmission among pneumonia patients. These results may allow clinicians and policymakers to develop better intervention strategies for the patients.

Evaluating How Safety-Net Hospitals Are Identified: Systematic Review and Recommendations

Objective:

To systematically review how safety-net hospitals' status is identified and defined, discuss current definitions' limitations, and provide recommendations for a new classification and evaluation framework.

Data Sources:

Safety-net hospital-related studies in the MEDLINE database published before May 16, 2019.

Study Design:

Systematic review of the literature that adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Data Collection/Extraction Methods:

We followed standard selection protocol, whereby studies went through an abstract review followed by a full-text screening for eligibility. For each included study, we extracted information about the identification method itself, including the operational definition, the dimension(s) of disadvantage reflected, study objective, and how safety-net status was evaluated.

Principal Findings:

Our review identified 132 studies investigating safety-net hospitals. Analysis of identification methodologies revealed substantial heterogeneity in the ways disadvantage is defined, measured, and summarized at the hospital level, despite a 4.5-fold increase in studies investigating safety-net hospitals for the past decade. Definitions often exclusively used low-income proxies captured within existing health system data, rarely incorporated external social risk factor measures, and were commonly separated into distinct safety-net status categories when analyzed.

Conclusions:

Consistency in research and improvement in policy both require a standard definition for identifying safety-net hospitals. Yet no standardized definition of safety-net hospitals is endorsed and existing definitions have key limitations. Moving forward, approaches rooted in health equity theory can provide a more holistic framework for evaluating disadvantage at the hospital level. Furthermore, advancements in precision public health technologies make it easier to incorporate detailed neighborhood-level social determinants of health metrics into multidimensional definitions. Other countries, including the United Kingdom and New Zealand, have used similar methods of identifying social need to determine more accurate assessments of hospital performance and the development of policies and targeted programs for improving outcomes.

Factors Associated With Disparities in Hospital Readmission Rates Among US Adults Dually Eligible for Medicare and Medicaid

Importance

Low-income older adults who are dually eligible (DE) for Medicare and Medicaid often experience worse outcomes following hospitalization. Among other federal policies aimed at improving health for DE patients, Medicare has recently begun reporting disparities in within-hospital readmissions. The degree to which disparities for DE patients are owing to differences in community-level factors or, conversely, are amenable to hospital quality improvement, remains heavily debated.

Objective

To examine the extent to which within-hospital disparities in 30-day readmission rates for DE patients are ameliorated by state- and community-level factors.

Design Setting and Participants

In this retrospective cohort study, Centers for Medicare & Medicaid Services (CMS) Disparity Methods were used to calculate within-hospital disparities in 30-day risk-adjusted readmission rates for DE vs non-DE patients in US hospitals participating in Medicare. All analyses were performed in February and March 2019. The study included Medicare patients (aged ≥65 years) hospitalized for acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2014 to 2017.

Main Outcomes and Measures

Within-hospital disparities, as measured by the rate difference (RD) in 30-day readmission between DE vs non-DE patients following admission for AMI, HF, or pneumonia; variance across hospitals; and correlation of hospital RDs with and without adjustment for state Medicaid eligibility policies and community-level factors.

Results

The final sample included 475 444 patients admitted for AMI, 898 395 for HF, and 1 214 282 for pneumonia, of whom 13.2%, 17.4%, and 23.0% were DE patients, respectively. Dually eligible patients had higher 30-day readmission rates relative to non-DE patients (RD >0) in 99.0% (AMI), 99.4% (HF), and 97.5% (pneumonia) of US hospitals. Across hospitals, the mean (IQR) RD between DE vs non-DE was 1.00% (0.87%-1.10%) for AMI, 0.82% (0.73%-0.96%) for HF, and 0.53% (0.37%-0.71%) for pneumonia. The mean (IQR) RD after adjustment for community-level factors was 0.87% (0.73%-0.97%) for AMI, 0.67% (0.57%-0.80%) for HF, and 0.42% (0.29%-0.57%) for pneumonia. Relative hospital rankings of corresponding within-hospital disparities before and after community-level adjustment were highly correlated (Pearson coefficient, 0.98).

Conclusions and Relevance

In this cohort study, within-hospital disparities in 30-day readmission for DE patients were modestly associated with differences in state Medicaid policies and community-level factors. This suggests that remaining variation in these disparities should be the focus of hospital efforts to improve the quality of care transitions at discharge for DE patients in efforts to advance equity.

Elements of the care environment influence coronary artery bypass surgery readmission

Background

Coronary artery bypass grafting 30-day unplanned readmission is a focus for the CMS Hospital Readmissions Reduction Program. Awareness of the critical elements of the care delivery environment, including hospital infrastructure and patient clinical profiles that predispose toward readmission, is essential to proactively decrease readmissions.

Methods

The Healthcare Cost and Utilization Project-State Inpatient Database, American Hospital Association Annual Health Survey Database, and Healthcare Information Management Systems Society data sets were merged to create a single data set of patient- and hospital-level data from 8 states. Isolated coronary artery bypass grafting procedures were queried for all-cause 30-day readmission, and backwards stepwise logistic regression was performed. Readmission rate was then used to categorize hospitals into quartiles, and analysis focused on the hospitals with the lowest (Q1) and highest (Q4) readmission rates. Univariate analysis was performed comparing Q1 and Q4 hospitals.

Results

A total of 150,215 patients underwent isolated coronary artery bypass grafting with 23,244 (15.5%) readmitted patients among 903 hospitals. Model area under the curve was 0.709 (95% confidence interval, 0.702–0.716), with the top 3 readmission determinants related to discharge disposition. Compared to Q1, Q4 patients more often were female, were > 70 years of age, and had Medicare as a primary payor (P < .001). Low readmission rate hospitals were characterized by higher costs; not-for-profit status; having Joint Commission accreditation; and higher total admissions, operative volume, hospital/ICU beds, full-time physicians, nurses, and ancillary personnel (P < .001).

Conclusion

Readmission after coronary artery bypass grafting is strongly influenced by discharge disposition. However, hospital factors such as scale, personnel, and ownership structure are significant contributors to readmission. Focus beyond patient factors to include the entire continuum of care is required to enhance outcomes, of which readmission is one surrogate measure.

Socio-demographic and -economic factors associated with 30-day readmission for conditions targeted by the hospital readmissions reduction program: a population-based study

Background

Early hospital readmissions remain common in patients with conditions targeted by the CMS Hospital Readmission Reduction Program (HRRP). There is still no consensus on whether readmission measures should be adjusted based on social factors, and there are few population studies within the U.S. examining how social characteristics influence readmissions for HRRP-targeted conditions. The objective of this study was to determine if specific socio-demographic and -economic factors are associated with 30-day readmissions in HRRP-targeted conditions: acute exacerbation of chronic obstructive pulmonary disease, pneumonia, acute myocardial infarction, and heart failure.

Methods

The Nationwide Readmissions Database was used to identify patients admitted with HRRP-targeted conditions between January 1, 2010 and September 30, 2015. Stroke was included as a control condition because it is not included in the HRRP. Multivariate models were used to assess the relationship between three social and economic characteristics (gender, urban/rural hospital designation, and estimated median household income within the patient’s zip code) and 30-day readmission rates using a hierarchical two-level logistic model. Age-adjusted models were used to assess relationship differences between Medicare vs. non-Medicare populations.

Results

There were 19,253,997 weighted index hospital admissions for all diagnoses and 3,613,488 30-day readmissions between 2010 and 2015. Patients in the lowest income quartile (≤$37,999) had an increased odds of 30-day readmission across all conditions (P < 0.0001). Female gender and rural hospital designation were associated with a decreased odds of 30-day readmission for most targeted conditions (P < 0.05). Similar findings were also seen in patients ≥65 years old.

Conclusions

Socio-demographic and -economic factors are associated with 30-day readmission rates and should be incorporated into tools or interventions to improve discharge planning and mitigate against readmission.

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

OBJECTIVE

To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions.

METHODS

Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams.

RESULTS

Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment.

CONCLUSION

Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

Are Improvements Still Needed to the Modified Hospital Readmissions Reduction Program: a Health and Retirement Study (2000-2014)?

BACKGROUND

To address concerns that the Hospital Readmissions Reduction Program (HRRP) unfairly penalized safety net hospitals treating patients with high social and functional risks, Medicare recently modified HRRP to compare hospitals with similar proportions of high-risk, dual-eligible patients ("peer group hospitals"). Whether the change fully accounts for patients' social and functional risks is unknown.

OBJECTIVE

Examine risk-standardized readmission rates (RSRRs) and hospital penalties after adding patient-level social and functional and community-level risk factors.

DESIGN

Using 2000-2014 Medicare hospital discharge, Health and Retirement Study, and community-level data, latent factors for patient social and functional factors and community factors were identified. We estimated RSRRs for peer groups and by safety net status using four hierarchical logistic regression models: "base" (HRRP model); "patient" (base plus patient factors); "community" (base plus community factors); and "full" (all factors). The proportion of hospitals penalized was calculated by safety net status.

PATIENTS

20,255 fee-for-service Medicare beneficiaries (65+) with eligible index hospitalizations MAIN MEASURES: RSRRs KEY RESULTS: Half of safety net hospitals are in peer group 5. Compared with other hospitals, peer group 5 hospitals (most dual-eligibles) treated sicker, more functionally limited patients from socially disadvantaged groups. RSRRs decreased by 0.7% for peer groups 2 and 4 and 1.3% for peer group 5 under the patient and full (versus base) models. Measured performance improved after adjusting for patient risk factors for hospitals in peer group 4 and 5 hospitals, but worsened for those in peer groups 1, 2, and 3. Under the patient (versus base) model, fewer safety net hospitals (48.7% versus 51.3%) but more non-safety net hospitals (50.0% versus 49.1%) were penalized.

CONCLUSIONS

Patient-level risk adjustment decreased RSRRs for hospitals serving more at-risk patients and proportion of safety net hospitals penalized, while modestly increasing RSRRs and proportion of non-safety net hospitals penalized. Results suggest HRRP modifications may not fully account for hospital variation in patient-level risk.

Community factors and hospital wide readmission rates: Does context matter?

Background

The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain.

Objective

To 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 design

We 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.

Subjects

Medicare 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.

Methods

We 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.

Results

Among 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%).

Conclusions

Readmissions 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.

Clarifying differences in viewpoints between multiple healthcare professionals during discharge planning assessments when discharging patients from a long-term care hospital to home

Comprehensive discharge planning provided by interprofessional collaboration is critical for discharging patients from hospitals to home. For effective interprofessional discharge planning, the evaluation that clarifies the differences in assessment viewpoints between various healthcare professionals is needed. This study aimed to clarify the assessment viewpoints of multiple healthcare professional groups when discharging patients from a long-term care hospital (LTCH) to home. We reviewed 102 medical records from an LTCH in Japan, extracted descriptions of discharge planning assessments written by 3 doctors, 13 nurses, 3 physical therapists, 13 care workers, and 2 social workers, linked these to the International Classification of Functioning, Disability and Health, and conducted the statistical analysis. Doctors and nurses significantly focused on "Body Functions". Physical therapists and care workers significantly focused on "Activities and Participation". Social workers significantly focused on "Environmental Factors". We also identified the factors less or missing from assessments in the clinical field of the LTCH. Our findings could be contributed as a base of knowledge to foster a better understanding of different healthcare professionals' assessment viewpoints. The further development of comprehensive discharge planning assessment tools, service programs, and research on discharge planning methods that could contribute to effective interprofessional discharge planning is needed.

Impact of Area Deprivation Index on the Performance of Claims-Based Risk-Adjustment Models in Predicting Health Care Costs and Utilization

Traditionally, risk-adjustment models do not address the characteristics of minority populations, such as race or socioeconomic status. This study aimed to evaluate the added value of place-based social determinants on risk-adjustment models in explaining health care costs and utilization. Statewide commercial claims from the Maryland Medical Care Database were used, including 1,150,984 Maryland residents aged 18 to 63 with ≥6 months enrollment in 2013 and 2014. Area Deprivation Index (ADI) was assigned to individuals through zip code. The authors examined the addition of ADI to predictive models of concurrent and prospective costs and utilization; linear regression was adopted for costs and logistic regression for utilization markers. Performance measures included R² for costs (total, pharmacy, and medical costs) and the area under the curve (AUC) for utilization (being top 5% top users, having any hospitalization, having any emergency room [ER] visit, having any avoidable ER visit, and having any readmission). All performance measures were derived from the bootstrapping analysis with 200 iterations. Study subjects were ∼48% male with a mean age of ∼41 years. Adding ADI to the demographics or claims-based models generally did not improve performance except in predicting the probability of having any ER or any avoidable ER visit; for example, AUC of avoidable ER visits increased significantly from .610 to .613 when using ADI rank deciles in claims-based models. Future research should focus on patients with a higher need for social services, assess more granular place-based determinants (eg, Census block group), and evaluate the added value of individual social variables.

Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death

OBJECTIVES

Early hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission or death and target interventions. However, existing models usually do not incorporate social determinants of health (SDH) information, although this information is of great importance to address health disparities related to social risk factors. The objective of this study is to examine the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission.

METHODS

We extracted electronic health record data for 19,941 hospital admissions between January 2015 and November 2017 at an academic medical center in New York City. We applied the Simplified HOSPITAL score model to predict potentially avoidable 30-day readmission or death and examined if incorporating individual- and community-level SDH could improve the prediction using cross-validation. We calculated the C-statistic for discrimination, Brier score for accuracy, and Hosmer-Lemeshow test for calibration for each model using logistic regression. Analysis was conducted for all patients and three subgroups that may be disproportionately affected by social risk factors, namely Medicaid patients, patients who are 65 or older, and obese patients.

RESULTS

The Simplified HOSPITAL score model achieved similar performance in our sample compared to previous studies. Adding SDH did not improve the prediction among all patients. However, adding individual- and community-level SDH at the US census tract level significantly improved the prediction for all three subgroups. Specifically, C-statistics improved from 0.70 to 0.73 for Medicaid patients, from 0.66 to 0.68 for patients 65 or older, and from 0.70 to 0.73 for obese patients.

CONCLUSIONS

Patients from certain subgroups may be more likely to be affected by social risk factors. Incorporating SDH into predictive models may be helpful to identify these patients and reduce health disparities associated with vulnerable social conditions.

Evaluation of Clinical Pharmacist Services in a Transitions of Care Program Provided to Patients at Highest Risk for Readmission

BACKGROUND

An ambulatory transition of care program, including a pharmacist-provided comprehensive medication review (CMR), was implemented.

OBJECTIVES

The objectives were to: (1) compare 30-day hospital readmission rates between those who received the pharmacist CMR versus eligible patients not scheduled, (2) describe identified problems and recommendations, and (3) quantify recommendation acceptance rates.

METHODS

A retrospective cohort study was conducted between March and October 2016. Inclusion criteria were: LACE score of ≥13, established Michigan Medicine primary care, and discharged from specific inpatient services to home. The primary outcome was 30-day hospital readmission rates. Pharmacist-identified problems, recommendations, and recommendation acceptance rates were examined. χ² analysis and descriptive statistics were used.

RESULTS

355 discharges met inclusion criteria and pharmacists provided CMRs for 159 patients. The average age was 60 years (standard deviation [SD]: 14.3), the majority were female (54%), and white/Caucasian (69%). There was no significant difference in 30-day readmission rates in patients who received a CMR (p = .96). A mean of 3.1 problems were identified per visit (SD: 1.8, range: 1-10). 509 recommendations were provided and approximately 50% were provider accepted.

CONCLUSIONS

Reduced readmission rates were not observed; however, pharmacists identified many areas for intervention in highest risk patients during the transition from hospital to home.

Home Oxygen Use and 1-Year Readmission among Infants Born Preterm with Bronchopulmonary Dysplasia Discharged from Children's Hospital Neonatal Intensive Care Units

OBJECTIVE

To determine associations between home oxygen use and 1-year readmissions for preterm infants with bronchopulmonary dysplasia (BPD) discharged from regional neonatal intensive care units.

STUDY DESIGN

We performed a secondary analysis of the Children's Hospitals Neonatal Database, with readmission data via the Pediatric Hospital Information System and demographics using ZIP-code-linked census data. We included infants born <32 weeks of gestation with BPD, excluding those with anomalies and tracheostomies. Our primary outcome was readmission by 1 year corrected age; secondary outcomes included readmission duration, mortality, and readmission diagnosis-related group codes. A staged multivariable logistic regression was adjusted for center, clinical, and social risk factors; at each stage we included variables associated at P < .1 in bivariable analysis with home oxygen use or readmission.

RESULTS

Home oxygen was used in 1906 of 3574 infants (53%) in 22 neonatal intensive care units. Readmission occurred in 34%. Earlier gestational age, male sex, gastrostomy tube, surgical necrotizing enterocolitis, lower median income, nonprivate insurance, and shorter hospital-to-home distance were associated with readmission. Home oxygen was not associated with odds of readmission (OR, 1.2; 95% CI, 0.98-1.56), readmission duration, or mortality. Readmissions for infants with home oxygen were more often coded as BPD (16% vs 4%); readmissions for infants on room air were more often gastrointestinal (29% vs 22%; P < .001). Clinical risk factors explained 72% of center variance in readmission.

CONCLUSIONS

Home oxygen use is not associated with readmission for infants with BPD in regional neonatal intensive care units. Center variation in home oxygen use does not impact readmission risk. Nonrespiratory problems are important contributors to readmission risk for infants with BPD.

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission

Background

Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge.

Methods

Using a cohort of surgical patients at a tertiary care academic medical center, surgical, demographic, lab, medication, care team, and current procedural terminology data were extracted from the electronic health record. The primary outcome was whether there existed a future hospital readmission originating from the emergency department within 30 days of surgery. Secondarily, the time interval from surgery to the prediction was analyzed at 0, 12, 24, 36, 48, and 60 h. Different machine learning models for predicting the primary outcome were evaluated with respect to the area under the receiver-operator characteristic curve metric using different permutations of the available features.

Results

Surgical hospital admissions (N = 34,532) from April 2013 to December 2016 were included in the analysis. Surgical and demographic features led to moderate discrimination for prediction after discharge (area under the curve: 0.74 to 0.76), whereas medication, consulting team, and current procedural terminology features did not improve the discrimination. Lab features improved discrimination, with gradient-boosted trees attaining the best performance (area under the curve: 0.866, SD 0.006). This performance was sustained during temporal validation with 2017 to 2018 data (area under the curve: 0.85 to 0.88). Lastly, the discrimination of the predictions calculated 36 h after surgery (area under the curve: 0.88 to 0.89) nearly matched those from time of discharge.

Conclusions

A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data.

Editor’s Perspective

What We Already Know about This Topic

What This Article Tells Us That Is New

Assessment of Hospital Readmissions From the Emergency Department After Implementation of Medicare's Hospital Readmissions Reduction Program

IMPORTANCE

The Medicare Hospital Readmissions Reduction Program (HRRP) is associated with reduced readmission rates, but it is unknown how this decrease occurred.

OBJECTIVE

To examine whether the HRRP was associated with changes in the probability of readmission at emergency department (ED) visits after hospital discharge (ED revisits) overall and depending on whether admission is typically indicated for the patient's condition at the ED revisit.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used hospital and ED discharge data from California, Florida, and New York from January 1, 2010, to December 31, 2014. A difference-in-differences analysis examined change in readmission probability at ED revisits for recently discharged patients; ED revisits with clinical presentations for which admission is typically indicated vs those for which admission is more variable (ie, discretionary) were examined separately. Inclusion criteria were Medicare patients 65 years and older who revisited an ED within 30 days of inpatient discharge. Data were analyzed from December 18, 2018, to September 11, 2019.

EXPOSURES

Before and after HRRP implementation among patients initially hospitalized for targeted vs nontargeted conditions.

MAIN OUTCOMES AND MEASURES

Thirty-day unplanned hospital readmissions at the ED revisit.

RESULTS

A total of 9 914 068 index hospitalizations were identified in California, Florida, and New York from 2010 to 2014. Of 2 052 096 discharges in 2010, 1 168 126 (56.9%) discharges were women and 566 957 discharges (27.6%) were among patients older than 85 years. Among 1 421 407 patients with an unplanned readmission within 30 days of discharge, 1 266 107 patients (89.1%) were admitted through the ED. A total of 1 906 498 ED revisits were identified. After adjusting for patient demographic and clinical characteristics from the index hospitalization, HRRP implementation was associated with fewer readmissions from the ED, with a difference-in-difference estimate of -0.9 (95% CI, -1.4 to -0.4) percentage points (P < .001), or a 1.4% relative decrease from the 65.8% pre-HRRP readmission rates. Implementation of the HRRP was associated with fewer readmissions at the ED revisit involving clinical presentations for which admission is typically indicated (difference-in-differences estimate, -1.1 [95% CI, -1.6 to -0.6] percentage points; P < .001), or a 1.2% relative decrease from the 93.6% pre-HRRP rate. These results appear to be associated with patients presenting at the ED revisit with congestive heart failure (difference-in-difference estimate, -1.2 [95% CI, -2.0 to -0.4] percentage points; P = .003).

CONCLUSIONS AND RELEVANCE

These findings suggest that implementation of the HRRP was associated with a lower likelihood of readmission for recently discharged patients presenting to the ED, specifically for congestive heart failure. This highlights the critical role of the ED in readmission reduction under the HRRP and suggests that patient outcomes after HRRP implementation should be further studied.

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

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.

The association of hospital teaching intensity with 30-day postdischarge heart failure readmission and mortality rates

OBJECTIVE

To investigate risk-adjusted, 30-day postdischarge heart failure mortality and readmission rates stratified by hospital teaching intensity.

DATA SOURCES AND STUDY SETTING

A total of 709 221 Medicare fee-for-service beneficiaries discharged from 3135 US hospitals between 1/1/2013 and 11/30/2014 with a principal diagnosis of heart failure.

STUDY DESIGN

Hospitals were classified as Council of Teaching Hospitals and Health Systems (COTH) major teaching hospitals, non-COTH teaching hospitals, and nonteaching hospitals. Hospital teaching status was linked with MedPAR patient data and FY2016 Hospital Readmission Reduction Program penalties. Index hospitalization survival probabilities were estimated with hierarchical logistic regression and used to stratify index hospitalization survivors into severity deciles. Decile-specific models were estimated for 30-day postdischarge readmission and mortality. Thirty-day postdischarge outcomes were estimated by teaching intensity and penalty categories.

PRINCIPAL FINDINGS

Averaged across deciles, adjusted 30-day COTH hospital readmission rates were, on a relative scale ([COTH minus nonteaching] ÷ nonteaching), 1.63 percent higher (95% CI: 0.89 percent, 2.25 percent) than at nonteaching hospitals, but their average adjusted 30-day postdischarge mortality rates were 11.55 percent lower (95% CI: -13.78 percent, -9.37 percent). Penalized COTH hospitals had the highest readmission rates of all categories (23.99 percent [95% CI: 23.50 percent, 24.49 percent]) but the lowest 30-day postdischarge mortality (8.30 percent [95% CI: 7.99 percent, 8.57 percent] vs 9.84 percent [95% CI: 9.69 percent, 9.99 percent] for nonpenalized, nonteaching hospitals).

CONCLUSIONS

Heart failure readmission penalties disproportionately impact major teaching hospitals and inadequately credit their better postdischarge survival.

Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices

Background

Readmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients.

Objectives

We compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model.

Methods

We analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time.

Results

In analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses.

Conclusions

Both comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Prevalence of Screening for Food Insecurity, Housing Instability, Utility Needs, Transportation Needs, and Interpersonal Violence by US Physician Practices and Hospitals

Importance

Social needs, including food, housing, utilities, transportation, and experience with interpersonal violence, are linked to health outcomes. Identifying patients with unmet social needs is a necessary first step to addressing these needs, yet little is known about the prevalence of screening.

Objective

To characterize screening for social needs by physician practices and hospitals.

Design, Setting, and Participants

Cross-sectional survey analyses of responses by physician practices and hospitals to the 2017-2018 National Survey of Healthcare Organizations and Systems. Responses were collected from survey participants from June 16, 2017, to August 17, 2018.

Exposures

Organizational characteristics, including participation in delivery and payment reform.

Main Outcomes and Measures

Self-report of screening patients for food insecurity, housing instability, utility needs, transportation needs, and experience with interpersonal violence.

Results

Among 4976 physician practices, 2333 responded, a response rate of 46.9%. Among hospitals, 757 of 1628 (46.5%) responded. After eliminating responses because of ineligibility, 2190 physician practices and 739 hospitals remained. Screening for all 5 social needs was reported by 24.4% (95% CI, 20.0%-28.7%) of hospitals and 15.6% (95% CI, 13.4%-17.9%) of practices, whereas 33.3% (95% CI, 30.5%-36.2%) of practices and 8.0% (95% CI, 5.8%-11.0%) of hospitals reported no screening. Screening for interpersonal violence was most common (practices: 56.4%; 95% CI, 53.3%-2 59.4%; hospitals: 75.0%; 95% CI, 70.1%-79.3%), and screening for utility needs was least common (practices: 23.1%; 95% CI, 20.6%-26.0%; hospitals: 35.5%; 95% CI, 30.0%-41.0%) among both hospitals and practices. Among practices, federally qualified health centers (yes: 29.7%; 95% CI, 21.5%-37.8% vs no: 9.4%; 95% CI, 7.2%-11.6%; P < .001), bundled payment participants (yes: 21.4%; 95% CI, 17.1%-25.8% vs no: 10.7%; 95% CI, 7.9%-13.4%; P < .001), primary care improvement models (yes: 19.6%; 95% CI, 16.5%-22.6% vs no: 9.6%; 95% CI, 6.0%-13.1%; P < .001), and Medicaid accountable care organizations (yes: 21.8%; 95% CI, 17.4%-26.2% vs no: 11.2%; 95% CI, 8.6%-13.7%; P < .001) had higher rates of screening for all needs. Practices in Medicaid expansion states (yes: 17.7%; 95% CI, 14.8%-20.7% vs no: 11.4%; 95% CI, 8.1%-14.6%; P = .007) and those with more Medicaid revenue (highest tertile: 17.1%; 95% CI, 11.4%-22.7% vs lowest tertile: 9.0%; 95% CI, 6.1%-11.8%; P = .02) were more likely to screen. Academic medical centers were more likely than other hospitals to screen (49.5%; 95% CI, 34.6%-64.4% vs 23.0%; 95% CI, 18.5%-27.5%; P < .001).

Conclusions and Relevance

This study's findings suggest that few US physician practices and hospitals screen patients for all 5 key social needs associated with health outcomes. Practices that serve disadvantaged patients report higher screening rates. The role of physicians and hospitals in meeting patients' social needs is likely to increase as more take on accountability for cost under payment reform. Physicians and hospitals may need additional resources to screen for or address patients' social needs.

Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance

BACKGROUND

Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown.

METHODS AND RESULTS

We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models.

CONCLUSIONS

Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions.

Integrating social and behavioral determinants of health into patient care and population health at Veterans Health Administration: a conceptual framework and an assessment of available individual and population level data sources and evidence-based measurements

The premise of this project was that social and behavioral determinants of health (SBDH) affect the use of healthcare services and outcomes for patients in an integrated healthcare system such as the Veterans Health Administration (VHA), and thus individual patient level socio-behavioral factors in addition to the neighborhood characteristics and geographically linked factors could add information beyond medical factors mostly considered in clinical decision making, patient care, and population health. To help VHA better address SBDH risk factors for the veterans it cares for within its primary care clinics, we proposed a conceptual and analytic framework, a set of evidence-based measures, and their data source. The framework and recommended SBDH metrics can provide a road map for other primary care-centric healthcare organizations wishing to use health analytic tools to better understand how SBDH affect health outcomes.

Strategies to Reduce Hospital Readmission Rates in a Non-Medicaid-Expansion State

On October 1, 2012, as part of the Affordable Care Act, the Centers for Medicare and Medicaid Services began to reduce payments to hospitals with excessive rehospitalization rates through the Hospital Readmissions Reduction Program. These financial penalties have intensified hospital leaders' efforts to implement strategies to reduce readmission rates. The purpose of this multiple case study was to explore organizational strategies that leaders use to reduce readmission rates in hospitals located in a non-Medicaid-expansion state. The data collection included semistructured interviews with 15 hospital leaders located in five metropolitan and rural hospitals in southwest Missouri. Consistent with prior research, the use of predictive analytics stratified by patient population was acknowledged as a key strategy to help reduce avoidable rehospitalization. Study findings suggest that leveraging data from the electronic health records to identify at-risk patients provides comprehensive health information to reduce readmissions. Hospital leaders also revealed the need to understand and address the health needs of their local population, including social determinants such as lack of access to transportation as well as food and housing.

The role of sociodemographics in the occurrence of orthopaedic trauma

INTRODUCTION

We sought to determine the effects of sociodemographic factors on the occurrence of orthopaedic injuries in an adult population presenting to a level 1 trauma center.

MATERIALS AND METHODS

We conducted a retrospective chart review of patients who received orthopaedic trauma care at a level 1 academic trauma center.

RESULTS

20,919 orthopaedic trauma injury cases were treated at an academic level 1 trauma center between 01 January 1993 and 27 August 2017. Following application of inclusion/exclusion criteria, a total of 14,654 patients were retrieved for analysis. Out of 14,654 patients, 4602 (31.4%) belonged to low socioeconomic status (SES), 4961 (32.0%) to middle SES and 5361 (36.6%) to high SES. Following adjustment for age, sex, race, insurance status and injury severity score (ISS), patients belonging to middle SES vs. low SES (OR 0.77 [95% CI 0.63-0.94]; p = 0.009) or high SES vs. low SES (OR 0.77 [95% CI 0.62-0.95]; p = 0.016) had lower odds of receiving a penetrating injury as compared to a blunt injury.

CONCLUSION

The results from this study indicate that a link exists between sociodemographic factors and the occurrence of orthopaedic injuries presenting to a level 1 trauma center. The most common cause of injury varied within age groups, by sex, and within the different socioeconomic groups.

The Impact of Social Determinants of Health on Hospitalization in the Veterans Health Administration

INTRODUCTION

This study aims to assess the effect of individual and geographic-level social determinants of health on risk of hospitalization in the Veterans Health Administration primary care clinics known as the Patient Aligned Care Team.

METHODS

For a population of Veterans enrolled in the primary care clinics, the study team extracted patient-level characteristics and healthcare utilization records from 2015 Veterans Health Administration electronic health record data. They also collected census data on social determinants of health factors for all U.S. census tracts. They used generalized estimating equation modeling and a spatial-based GIS analysis to assess the role of key social determinants of health on hospitalization. Data analysis was completed in 2018.

RESULTS

A total of 6.63% of the Veterans Health Administration population was hospitalized during 2015. Most of the hospitalized patients were male (93.40%) and white (68.80%); the mean age was 64.5 years. In the generalized estimating equation model, white Veterans had a 15% decreased odds of hospitalization compared with non-white Veterans. After controlling for patient-level characteristics, Veterans residing in census tracts with the higher neighborhood SES index experienced decreased odds of hospitalization. A spatial-based analysis presented variations in the hospitalization rate across the Veterans Health Administration primary care clinics and identified the clinic sites with an elevated risk of hospitalization (hotspots) compared with other clinics across the country.

CONCLUSIONS

By linking patient and population-level data at a geographic level, social determinants of health assessments can help with designing population health interventions and identifying features leading to potentially unnecessary hospitalization in selected geographic areas that appear to be outliers.

Association of Stratification by Dual Enrollment Status With Financial Penalties in the Hospital Readmissions Reduction Program

IMPORTANCE

Beginning in fiscal year 2019, Medicare's Hospital Readmissions Reduction Program (HRRP) stratifies hospitals into 5 peer groups based on the proportion of each hospital's patient population that is dually enrolled in Medicare and Medicaid. The effect of this policy change is largely unknown.

OBJECTIVE

To identify hospital and state characteristics associated with changes in HRRP-related performance and penalties after stratification.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional analysis was performed of all 3049 hospitals participating in the HRRP in fiscal years 2018 and 2019, using publicly available data on hospital penalties, merged with information on hospital characteristics and state Medicaid eligibility cutoffs.

EXPOSURES

The HRRP, under the 2018 traditional method and the 2019 stratification method.

MAIN OUTCOMES AND MEASURES

Performance on readmissions, as measured by the excess readmissions ratio, and penalties under the HRRP both in relative percentage change and in absolute dollars.

RESULTS

The study sample included 3049 hospitals. The mean proportion of dually enrolled beneficiaries ranged from 9.5% in the lowest quintile to 44.7% in the highest quintile. At the hospital level, changes in penalties ranged from an increase of $225 000 to a decrease of more than $436 000 after stratification. In total, hospitals in the lowest quintile of dual enrollment saw an increase of $12 330 157 in penalties, while those in the highest quintile of dual enrollment saw a decrease of $22 445 644. Teaching hospitals (odds ratio [OR], 2.13; 95% CI, 1.76-2.57; P < .001) and large hospitals (OR, 1.51; 95% CI, 1.22-1.86; P < .001) had higher odds of receiving a reduced penalty. Not-for-profit hospitals (OR, 0.64; 95% CI, 0.52-0.80; P < .001) were less likely to have a penalty reduction than for-profit hospitals, and hospitals in the Midwest (OR, 0.44; 95% CI, 0.34-0.57; P < .001) and South (OR, 0.42; 95% CI, 0.30-0.57; P < .001) were less likely to do so than hospitals in the Northeast. Hospitals with patients from the most disadvantaged neighborhoods (OR, 2.62; 95% CI, 2.03-3.38; P < .001) and those with the highest proportion of beneficiaries with disabilities (OR, 3.12; 95% CI, 2.50-3.90; P < .001) were markedly more likely to see a reduction in penalties, as were hospitals in states with the highest Medicaid eligibility cutoffs (OR, 1.79; 95% CI, 1.50-2.14; P < .001).

CONCLUSIONS AND RELEVANCE

Stratification of the hospitals under the HRRP was associated with a significant shift in penalties for excess readmissions. Policymakers should monitor the association of this change with readmission rates as well as hospital financial performance as the policy is fully implemented.

Development and validation of risk-adjustment models for elective, single-level posterior lumbar spinal fusions

Background

There is a paucity of literature examining the development and subsequent validation of risk-adjustment models that inform the trade-off between adequate risk-adjustment and data collection burden. We aimed to evaluate patient risk stratification by surgeons with the development and validation of risk-adjustment models for elective, single-level, posterior lumbar spinal fusions (PLSFs).

Methods

Patients undergoing PLSF from 2011-2014 were identified in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). The derivation cohort included patients from 2011-2013, while the validation cohort included patients from 2014. Outcomes of interest were severe adverse events (SAEs) and unplanned readmission. Bivariate analysis of risk factors followed by a stepwise logistic regression model was used. Limited risk-adjustment models were created and analyzed by sequentially adding variables until the full model was reached.

Results

A total of 7,192 and 4,182 patients were included in our derivation and validation cohorts, respectively. Full model performance was similar for the derivation and validation cohorts in both 30-day SAEs (C-statistic =0.66 vs. 0.69) and 30-day unplanned readmission (C-statistic =0.62 vs. 0.65). All models demonstrated good calibration and fit (P≥0.58). Intraoperative variables, laboratory values, and comorbid conditions explained >75% of the variation in 30-day SAEs; ASA class, laboratory values, and comorbid conditions accounted for >80% of model risk prediction for 30-day unplanned readmission. Four variables for the 30-day SAE models (age, gender, ASA ≥3, operative time) and 3 variables for the 30-day unplanned readmission models (age, ASA ≥3, operative time) were sufficient to achieve a C-statistic within four percentage points of the full model.

Conclusions

Risk-adjustment models for PLSF demonstrated acceptable calibration and discrimination using variables commonly found in health records and demonstrated only a limited set of variables were required to achieve an appropriate level of risk prediction.

Integrating Social and Behavioral Determinants of Health into Population Health Analytics: A Conceptual Framework and Suggested Road Map

There is growing recognition that social and behavioral risk factors impact population health outcomes. Interventions that target these risk factors can improve health outcomes. This study presents a review of existing literature and proposes a conceptual framework for the integration of social and behavioral data into population health analytics platforms. The authors describe several use cases for these platforms at the patient, health system, and community levels, and align these use cases with the different types of prevention identified by the Centers for Disease Control and Prevention. They then detail the potential benefits of these use cases for different health system stakeholders and explore currently available and potential future sources of social and behavioral domains data. Also noted are several potential roadblocks for these analytic platforms, including limited data interoperability, expense of data acquisition, and a lack of standardized technical terminology for socio-behavioral factors.

Impact of oral anticoagulants on 30-day readmission: a study from a single academic centre

Researchers are extensively searching for modifiable risk factors including high-risk medications such as anticoagulation to avoid rehospitalisation. The influence of oral anticoagulant therapy on hospital readmission is not known. We investigated the impact of warfarin and direct oral anticoagulants (DOACs) on all cause 30-day hospital readmission retrospectively in an academic centre. We study the eligible cohort of 1781 discharges over 2-year period. Data on age, gender, diagnoses, 30-day hospital readmission, discharge medications and variables in the HOSPITAL score (Haemoglobin level at discharge, Oncology at discharge, Sodium level at discharge, Procedure during hospitalisation, Index admission, number of hospital Admissions, Length of stay) and LACE index (Length of stay, Acute/emergent admission, Charlson comorbidity index score, Emergency department visits in previous 6 months), which have higher predictability for readmission were extracted and matched for analysis. Warfarin was the most common anticoagulant prescribed at discharge (273 patients) with a readmission rate of 20% (p<0.01). DOACs were used by 94 patients at discharge with a readmission rate of 4% (p=0.219). Multivariate logistic regression showed an increased risk of readmission with warfarin therapy (OR 1.36, p=0.045). Logistic regression did not show DOACs to be a risk factor for hospital readmission. Our data suggests that warfarin therapy is a risk factor for all-cause 30-day hospital readmission. DOAC therapy is not found to be associated with a higher risk of hospital readmission. Warfarin anticoagulation may be an important target for interventions to reduce hospital readmissions.

Potentially Preventable Hospital and Emergency Department Events: Lessons from a Large Innovation Project

INTRODUCTION

There are few proven strategies to reduce the frequency of potentially preventable hospitalizations and Emergency Department (ED) visits. To facilitate strategy development, we documented these events among complex patients and the factors that contribute to them in a large care-improvement initiative.

METHODS

Observational study with retrospective audits and selective interviews by the patients' care managers among 12 diverse medical groups in California, Minnesota, Pennsylvania, and Washington that participated in an initiative to implement collaborative care for patients with both depression and either uncontrolled diabetes, uncontrolled hypertension, or both. We reviewed information about 373 adult patients with the required conditions who belonged to these medical groups and had experienced 389 hospitalizations or ED visits during the 12-month study period from March 30, 2014, through March 29, 2015. The main outcome measures were potentially preventable hospitalizations or ED visit events.

RESULTS

Of the studied events, 28% were considered to be potentially preventable (39% of ED visits and 14% of hospitalizations) and 4.6% of patients had 40% of events. Only type of insurance coverage; patient lack of resources, caretakers, or understanding of care; and inability to access clinic care were more frequent in those with potentially preventable events. Neither disease control nor ambulatory care-sensitive conditions were associated with potentially preventable events.

CONCLUSION

Among these complex patients, patient characteristics, disease control, and the presence of ambulatory care-sensitive conditions were not associated with likelihood of ED visits or hospital admissions, including those considered to be potentially preventable. The current focus on using ambulatory care-sensitive conditions as a proxy for potentially preventable events needs further evaluation.

Impact of Patient-Provider Attachment on Hospital Readmissions Among People Living With HIV: A Population-Based Study

BACKGROUND

Hospital readmission 30 days after discharge is associated with adverse health outcomes, and people living with HIV (PLWH) experience elevated rates of hospital readmission. Although continuity of care with a health care provider is associated with lower rates of 30-day readmission among the general population, little is known about this relationship among PLWH. The objective of this study is to examine whether engaging with the same provider, defined as patient-provider attachment, is associated with 30-day readmission for this population.

SETTING

Data were derived from the Seek and Treat for Optimal Prevention of HIV in British Columbia cohort.

METHODS

Using generalized estimating equation with a logit link function, we examined the association between patient-provider attachment and 30-day hospital readmission. We determined whether readmission was due to all cause or to a similar cause as the index admission.

RESULTS

Seven thousand thirteen PLWH were hospitalized during the study period. Nine hundred twenty-one (13.1%) were readmitted to hospital for all cause and 564 (8.0%) for the similar cause as the index admission. Patient-provider attachment was negatively associated with 30-day readmission for all causes (adjusted odds ratio = 0.85, confidence interval = 0.83 to 0.86). A second multivariable model indicated that patient-provider attachment was also negatively associated with 30-day readmission for a similar cause (adjusted odds ratio = 0.86, confidence interval = 0.84 to 0.88).

CONCLUSIONS

Our results indicate that a higher proportion of patient-provider attachment was negatively associated with 30-day hospital readmission among PLWH. Our study findings support the adoption of interventions that seek to build patient-provider relationships to optimize outcomes for PLWH and enhance health care sustainability.

Incidence, Cost, and Risk Factors for Readmission After Coronary Artery Bypass Grafting

BACKGROUND

Readmissions adversely affect hospital reimbursement and quality measures. We aimed to evaluate the incidence, cost, and risk factors for readmission following coronary artery bypass grafting (CABG).

METHODS

We queried the National Readmissions Database and isolated patients who underwent CABG from 2013 to 2014. We determined the top reasons for readmission and compared demographics, comorbidities, in-hospital outcomes, and costs between readmitted and nonreadmitted patients. Generalized linear regression was performed to identify independent predictors for readmission.

RESULTS

We identified 288,059 patients who underwent isolated CABG in the United States between 2013 and 2014. A total of 12.2% were readmitted within 30 days of discharge. Postoperative infection, heart failure, and arrhythmia were the most common reasons for readmission. The median time to readmit was 11 days, with a length of stay (LOS) of 6 days and a cost of $13,499 ± $201. Independent preoperative predictors for readmission were Medicaid status (odds ratio [OR], 1.33), female sex (OR, 1.32), chronic renal failure (OR, 1.26), greater than 4 Elixhauser comorbidities (OR, 1.20), chronic pulmonary disease (OR, 1.15), and nonelective operation (OR, 1.10) (all p < 0.05). In-hospital predictors included LOS greater than 10 days (OR, 1.52), acute kidney injury (OR, 1.30), atrial fibrillation (OR, 1.20), pneumonia (OR, 1.13), and discharge to skilled nursing facility (OR, 1.43) (all p < 0.05).

CONCLUSIONS

Thirty-day readmissions after CABG are frequent and related to preoperative comorbidities and complex postoperative course. Medicaid status, prolonged LOS, and disposition to a skilled nursing facility are strong predictors for 30-day readmission following CABG. Readmission reduction efforts should consider improvements for patients in these cohorts.

Augmenting community-level social determinants of health data with individual-level survey data

Social determinants of health (SDH) such as education and socioeconomic status are strongly associated with health and health outcomes. Incorporating SDH variables into clinical data sets could therefore improve the accuracy of predictive analytics, but individual-level SDH are rarely available and must be inferred from community-level data. We propose a method for doing so leveraging the joint probability distribution of the basic demographics available from the patient's clinical record and known community-level SDH. We demonstrate the method using two data sets, the New York City (NYC) subset of the US census data and the NYC Health and Nutrition Estimation Survey (NYCHANES) and provide sample results for 2 census tracts in NYC. The advantage of this approach is that it does not simplistically assume that all residents within a census tract share the same average/median socioeconomic status, but instead recognizes and leverages the strong known associations between demographics and SDH within localities. Results could explain some of the discrepancies appearing in the SDH-big data literature. Future studies are needed for using the augmented SHD to improve clinically relevant use cases, such as predictive analytics.

Impact of Event Severity on Hospital Rankings Based on Heart Failure Readmission Rates

The Medicare Readmissions Reduction Program penalizes hospitals with higher than expected readmission rates after discharge for congestive heart failure (CHF). This exploratory study analyzed whether categorizing readmissions by event severity might have implications for the program. The authors used the 5% MedPAR (Medicare Provider and Analysis Review) data for 2008 to 2014 and ranked 1820 hospitals based on all readmissions, readmissions for CHF, short-stay CHF readmissions, and readmissions for severe CHF with evidence of cardiogenic shock. Ranking hospitals based on severe CHF readmissions changes their relative rank order significantly compared to counting all readmissions. If confirmed in the full Medicare data, the finding could inform the design of the Readmission Reduction Program.

Managing the Social Determinants of Health: Part I: Fundamental Knowledge for Professional Case Management

OBJECTIVES

PRIMARY PRACTICE SETTING(S):: Applicable to health and behavioral health settings, wherever case management is practiced.

FINDING/CONCLUSION

The SDH pose major challenges to the health care workforce in terms of effective resource provision, health and behavioral health treatment planning plus adherence, and overall coordination of care. Obstacles and variances to needed interventions easily lead to less than optimal outcomes for case managers and their health care organizations. Possessing sound knowledge and clear understanding of each SDH, the historical perspectives, main theories, and integral dynamics, as well as creative resource solutions, all support a higher level of intentional and effective professional case management practice.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

Those persons and communities impacted most by the SDH comprise every case management practice setting. These clients can be among the most vulnerable and disenfranchised members of society, which can easily engender biases on the part of the interprofessional workforce. They are also among the costliest to care for with 50% of costs for only 5% of the population. Critical attention to knowledge about managing the SDH leverages and informs case management practice, evolves more effective programming, and enhances operational outcomes across practice settings.

High hospital readmission rates for patients aged ≥65 years associated with low socioeconomic status in a Swedish region: a cross-sectional study in primary care

OBJECTIVE

There is a presumption that hospital readmission rates amongst persons aged ≥65 years are mainly dependent on the quality of care. In this study, our primary aim was to explore the association between 30-day hospital readmission for patients aged ≥65 years and socioeconomic characteristics of the studied population. A secondary aim was to explore the association between self-reported lack of strategies for working with older patients at primary health care centres and early readmission.

DESIGN

A cross-sectional ecological study and an online questionnaire sent to the heads of the primary health care centres. We performed correlation and regression analyses.

SETTING AND SUBJECTS

Register data of 283,063 patients in 29 primary health care centres in the Region Örebro County (Sweden) in 2014.

MAIN OUTCOME MEASURE

Thirty-day hospital readmission rates for patients aged ≥65 years. Covariates were socioeconomic characteristics among patients registered at the primary health care centre and eldercare workload.

RESULTS

Early hospital readmission was found to be associated with low socioeconomic status of the studied population: proportion foreign-born (r = 0.74; p < 0.001), proportion unemployed (r = 0.73; p < 0.001), Care Need Index (r = 0.74; p < 0.001), sick leave rate (r = 0.51; p < 0.01) and average income (r = -0.40; p = 0.03). The proportion of unemployed alone could explain up to 71.4% of the variability in hospital readmission (p < 0.001). Primary health care centres reporting lack of strategies to prevent readmissions in older patients did not have higher hospital readmission rates than those reporting they had such strategies.

CONCLUSION

Primary health care centres localized in neighbourhoods with low socioeconomic status had higher rates of hospital readmission for patients aged ≥65. Interventions aimed at reducing hospital readmissions for older patients should also consider socioeconomic disparities. Key Points In Sweden, hospital readmission within 30 days among patients aged ≥65 has been used as a measure of quality of primary care for the elderly. However, in our study, elderly 30-day readmission was associated with low neighbourhood socioeconomic status. A simple survey in one Swedish region showed that the primary health care centres that lacked active strategies for working with aged patients did not have higher hospital readmission rates than those that reported having strategies. Interventions aimed at reducing elderly hospital readmissions should therefore also consider the socioeconomic disparities in the elderly.

Identifying risk factors for 30-day readmission events among American Indian patients with diabetes in the Four Corners region of the southwest from 2009 to 2016

Objective

The objective of this study was to identify risk factors for 30-day readmission events for American Indian patients with diabetes in the southwest.

Research design and methods

Data from patients with diabetes admitted to Gallup Indian Medical Center between 2009 and 2016 were analyzed using logistic regression analyses.

Results

Of 2,660 patients, 394 (14.8%) patients had at least one readmission within 30 days of discharge. Older age (OR (95% CI) = 1.26, (1.17, 1.36)), longer length of stay (OR (95% CI) = 1.01, (1.0001, 1.0342)), and a history of substance use disorder (OR (95% CI) = 1.80, (1.25, 2.60)) were risk factors for 30-day readmission. An American Indian language preference was protective against readmission.

Conclusions

Readmission events are complex and may reflect broad and interwoven disparities in community systems. Future research should work to support community-defined interventions to address both in hospital and external factors that impact risk factors for readmission.

Mental Health Conditions and Unplanned Hospital Readmissions in Children

OBJECTIVE

Mental health conditions (MHCs) are prevalent among hospitalized children and could influence the success of hospital discharge. We assessed the relationship between MHCs and 30-day readmissions.

METHODS

This retrospective, cross-sectional study of the 2013 Nationwide Readmissions Database included 512,997 hospitalizations of patients ages 3 to 21 years for the 10 medical and 10 procedure conditions with the highest number of 30-day readmissions. MHCs were identified by using the International Classification of Diseases, 9th Revision-Clinical Modification codes. We derived logistic regression models to measure the associations between MHC and 30-day, all-cause, unplanned readmissions, adjusting for demographic, clinical, and hospital characteristics.

RESULTS

An MHC was present in 17.5% of medical and 13.1% of procedure index hospitalizations. Readmission rates were 17.0% and 6.2% for medical and procedure hospitalizations, respectively. In the multivariable analysis, compared with hospitalizations with no MHC, hospitalizations with MHCs had higher odds of readmission for medical admissions (adjusted odds ratio [AOR], 1.23; 95% confidence interval [CI], 1.19-1.26] and procedure admissions (AOR, 1.24; 95% CI, 1.15-1.33). Three types of MHCs were associated with higher odds of readmission for both medical and procedure hospitalizations: depression (medical AOR, 1.57; 95% CI, 1.49-1.66; procedure AOR, 1.39; 95% CI, 1.17-1.65), substance abuse (medical AOR, 1.24; 95% CI, 1.18-1.30; procedure AOR, 1.26; 95% CI, 1.11-1.43), and multiple MHCs (medical AOR, 1.43; 95% CI, 1.37-1.50; procedure AOR, 1.26; 95% CI, 1.11-1.44).

CONCLUSIONS

MHCs are associated with a higher likelihood of hospital readmission in children admitted for medical conditions and procedures. Understanding the influence of MHCs on readmissions could guide strategic planning to reduce unplanned readmissions for children with cooccurring physical and mental health conditions.