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

A state-level analysis of macro-level factors associated with hospital readmissions

Investigation of the factors that contribute to hospital readmissions has focused largely on individual level factors. We extend the knowledge base by exploring macrolevel factors that may contribute to readmissions. We point to environmental, behavioral, and socioeconomic factors that are emerging as correlates to readmissions. Data were taken from publicly available reports provided by multiple agencies. Partial Least Squares-Structural Equation Modeling was used to test the association between economic stability and environmental factors on opioid use which was in turn tested for a direct association with hospital readmissions. We also tested whether hospital access as measured by the proportion of people per hospital moderates the relationship between opioid use and hospital readmissions. We found significant associations between Negative Economic Factors and Opioid Use, between Environmental Factors and Opioid Use, and between Opioid Use and Hospital Readmissions. We found that Hospital Access positively moderates the relationship between Opioid Use and Readmissions. A priori assumptions about factors that influence hospital readmissions must extend beyond just individualistic factors and must incorporate a holistic approach that also considers the impact of macrolevel environmental factors.

The Association of Frailty and Neighborhood Disadvantage with Emergency Department Visits and Hospitalizations in Older Adults

BACKGROUND

Risk stratification and population management strategies are critical for providing effective and equitable care for the growing population of older adults in the USA. Both frailty and neighborhood disadvantage are constructs that independently identify populations with higher healthcare utilization and risk of adverse outcomes.

OBJECTIVE

To examine the joint association of these factors on acute healthcare utilization using two pragmatic measures based on structured data available in the electronic health record (EHR).

DESIGN

In this retrospective observational study, we used EHR data to identify patients aged ≥ 65 years at Atrium Health Wake Forest Baptist on January 1, 2019, who were attributed to affiliated Accountable Care Organizations. Frailty was categorized through an EHR-derived electronic Frailty Index (eFI), while neighborhood disadvantage was quantified through linkage to the area deprivation index (ADI). We used a recurrent time-to-event model within a Cox proportional hazards framework to examine the joint association of eFI and ADI categories with healthcare utilization comprising emergency visits, observation stays, and inpatient hospitalizations over one year of follow-up.

KEY RESULTS

We identified a cohort of 47,566 older adults (median age = 73, 60% female, 12% Black). There was an interaction between frailty and area disadvantage (P = 0.023). Each factor was associated with utilization across categories of the other. The magnitude of frailty's association was larger than living in a disadvantaged area. The highest-risk group comprised frail adults living in areas of high disadvantage (HR 3.23, 95% CI 2.99-3.49; P < 0.001). We observed additive effects between frailty and living in areas of mid- (RERI 0.29; 95% CI 0.13-0.45; P < 0.001) and high (RERI 0.62, 95% CI 0.41-0.83; P < 0.001) neighborhood disadvantage.

CONCLUSIONS

Considering both frailty and neighborhood disadvantage may assist healthcare organizations in effectively risk-stratifying vulnerable older adults and informing population management strategies. These constructs can be readily assessed at-scale using routinely collected structured EHR data.

Measuring Equity in Readmission as a Distinct Assessment of Hospital Performance

Importance

Equity is an essential domain of health care quality. The Centers for Medicare & Medicaid Services (CMS) developed 2 Disparity Methods that together assess equity in clinical outcomes.

Objectives

To define a measure of equitable readmissions; identify hospitals with equitable readmissions by insurance (dual eligible vs non-dual eligible) or patient race (Black vs White); and compare hospitals with and without equitable readmissions by hospital characteristics and performance on accountability measures (quality, cost, and value).

Design, Setting, and Participants

Cross-sectional study of US hospitals eligible for the CMS Hospital-Wide Readmission measure using Medicare data from July 2018 through June 2019.

Main Outcomes and Measures

We created a definition of equitable readmissions using CMS Disparity Methods, which evaluate hospitals on 2 methods: outcomes for populations at risk for disparities (across-hospital method); and disparities in care within hospitals' patient populations (within-a-single-hospital method).

Exposures

Hospital patient demographics; hospital characteristics; and 3 measures of hospital performance-quality, cost, and value (quality relative to cost).

Results

Of 4638 hospitals, 74% served a sufficient number of dual-eligible patients, and 42% served a sufficient number of Black patients to apply CMS Disparity Methods by insurance and race. Of eligible hospitals, 17% had equitable readmission rates by insurance and 30% by race. Hospitals with equitable readmissions by insurance or race cared for a lower percentage of Black patients (insurance, 1.9% [IQR, 0.2%-8.8%] vs 3.3% [IQR, 0.7%-10.8%], P < .01; race, 7.6% [IQR, 3.2%-16.6%] vs 9.3% [IQR, 4.0%-19.0%], P = .01), and differed from nonequitable hospitals in multiple domains (teaching status, geography, size; P < .01). In examining equity by insurance, hospitals with low costs were more likely to have equitable readmissions (odds ratio, 1.57 [95% CI, 1.38-1.77), and there was no relationship between quality and value, and equity. In examining equity by race, hospitals with high overall quality were more likely to have equitable readmissions (odds ratio, 1.14 [95% CI, 1.03-1.26]), and there was no relationship between cost and value, and equity.

Conclusion and Relevance

A minority of hospitals achieved equitable readmissions. Notably, hospitals with equitable readmissions were characteristically different from those without. For example, hospitals with equitable readmissions served fewer Black patients, reinforcing the role of structural racism in hospital-level inequities. Implementation of an equitable readmission measure must consider unequal distribution of at-risk patients among hospitals.

Telehealth-based transitional care management programme to improve access to care

BACKGROUND

The transition from hospital to home is a vulnerable time for patients and families that can be improved through care coordination and structured discharge planning.

LOCAL PROBLEM

Our organisation aimed to develop and expand a programme that could improve 30-day readmission rates on overall and disease-specific populations by assessing the impact of a telehealth outreach by a registered nurse (RN) after discharge from an acute care setting on 30-day hospital readmission.

METHODS

This is a prospective observational design conducted from May 2021 to December 2022 with an urban, non-academic, acute care hospital in Westchester County, New York. Outcomes for patients discharged home following inpatient hospitalisation were analysed within this study. We analysed overall and disease-specific populations (congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and pneumonia (PNA)) as compared with a 40-month prestudy cohort.

INTERVENTIONS

Patients were identified in a non-random fashion meeting criterion of being discharged home after an inpatient admission. Participants received a telephonic outreach by an RN within 72 hours of discharge. Contacted patients were asked questions addressing discharge instructions, medication access, follow-up appointments and social needs. Patients were offered services and resources based on their individual needs in response to the survey.

RESULTS

68.2% of the 24 808 patients were contacted to assess and offer services. Median readmission rates for these patients were 1.2% less than the prestudy cohort (11.0% to 9.8%). Decreases were also noted for disease-specific conditions (CHF (14.3% to 9.1%), COPD (20.0% to 13.4%) and PNA (14.9% to 14.0%)). Among those in the study period, those that were contacted between 24 and 48 hours after discharge were 1.2 times less likely to be readmitted than if unable to be contacted (254/3742 (6.8%) vs 647/7866 (8.2%); p=0.005).

CONCLUSIONS

Using a multifaceted telehealth approach to improve patient engagement and access reduced 30-day hospital readmission for patients discharged from the acute care setting.

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.

Measuring health disparities using a continuous social risk factor

OBJECTIVE

To propose and evaluate a novel approach for measuring hospital-level disparities according to the effect of a continuous, polysocial risk factor on those outcomes.

STUDY SETTING

Our cohort consisted of Medicare Fee-for-Service (FFS) patients 65 years and older admitted to acute care hospitals for one of six common conditions or procedures. Medicare administrative claims data for six hospital readmission measures including hospitalizations from July 2015 to June 2018 were used.

STUDY DESIGN

We adapted existing methodologies that were developed to report hospital-level disparities using dichotomous social risk factors (SRFs). The existing methods report disparities within and across hospitals; we developed and tested modified approaches for both methods using the Agency for Healthcare Research and Quality Socioeconomic Status Index. We applied the adapted methodologies to six 30-day hospital readmission measures included in the Centers for Medicare & Medicaid Services Hospital Readmissions Reduction Program measures. We compared the within- and across-hospital results for each to those obtained from using the original methods and dichotomizing the AHRQ SES Index into "low" and "high" scores.

DATA COLLECTION

We used Medicare FFS administrative claims data linked to U.S. Census data.

PRINCIPAL FINDINGS

For all six readmission measures we find that, when compared with the existing methods, the methods for continuous SRFs provide disparity results for more facilities though across a narrower range of values. Measures of disparity based on this approach are moderately to highly correlated with those based on a dichotomous version of the same risk factor, while reflecting a fuller spectrum of risk. This approach represents an opportunity for detection of provider-level results that more closely align with underlying social risk.

CONCLUSION

We have demonstrated the feasibility and utility of estimating hospital disparities of care using a continuous, polysocial risk factor. This approach expands the potential for reporting hospital-level disparities while better accounting for the multifactorial nature of social risk on hospital outcomes.

Readmissions to hospital following a decision to eat and drink with acknowledged risk

People with a dysphagia may eat and drink with acknowledged risks (EDAR). The FORWARD care bundle (Feeding via the Oral Route With Acknowledged Risk of Deterioration) is used at our hospital to support patients who are EDAR. This two-year retrospective study of patients supported by FORWARD aimed to determine incidence of EDAR-related readmissions and effects of discharge location and documented preferred place of care in advance care plans. Of 316 patients supported by FORWARD, 200 were discharged alive. 63% (n=126) were not readmitted within six months. Of 74 patients readmitted, 49% had an EDAR-related readmission. Significantly fewer patients wishing to remain at home had EDAR-related readmissions (7%, n=4) than those without a documented preferred place of care (23%, n=30, p<0.01), suggesting advance care plans are effective. Significantly more (23%, n=29) patients discharged to private homes had EDAR-related readmissions than those in nursing/care homes (10%, n=6, p<0.05), which could suggest residential care provides more support.

Addressing social risk factors in the inpatient setting: Initial findings from a screening and referral pilot at an urban safety-net academic medical center in Virginia, USA

Social Determinants of Health (SDOH) impact health outcomes; thus, a pilot to screen for important SDOH domains (food, housing, and transportation) and address social needs in hospitalized patients was implemented in an urban safety-net academic medical center. This study describes the pilot implementation and examines patient characteristics associated with SDOH-related needs. An internal medicine unit was designated as a pilot site. Outreach workers approached eligible patients (n = 1,135) to complete the SDOH screening survey at time of admission with 54% (n = 615) completing the survey between May 2019 and July 2020. Data from patient screening survey and electronic health records were linked to allow for examination of associations between SDOH needs for food, housing, and transportation and various demographic and clinical characteristics of patients in multivariate logistic regression models. Of 615 screened patients, 45% screened positive for any need. Of 275 patients with needs, 33% reported needs in 2, and 34% - in 3 domains. Medicaid beneficiaries were more likely than patients with private health insurance to screen positive for 2 and 3 needs; Black patients were more likely than White patients to screen positive for 1 and 3 needs; Patients with no designated primary care physician status screened positive for 1 need; Patients with a history of substance use disorder screened positive for all 3 needs. SDOH screening assisted in addressing social risk factors of inpatients, informed their discharge plans and linkage to community resources. SDOH screening demonstrated significant correlations of positive screens with race/ethnicity, insurance type, and certain clinical characteristics.

Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction

Objective

Early and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance.

Methods

This is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance.

Results

Our study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days. Readmitted patients had more challenges with individual-level SDOH and were more likely to reside in communities with poor SDOH conditions. Adding a combination of individual and community-level SDOH improved LACE performance from AUC = 0.698 (95% CI [0.695–0.7]; ref) to AUC = 0.708 (95% CI [0.705–0.71]; P < .001). The increase in AUC was highest in black patients (+1.6), patients aged 65 years or older (+1.4), and male patients (+1.4).

Discussion

We demonstrated the value of SDOH in improving the LACE index. Further, the additional predictive value of SDOH on readmission risk varies by subpopulations. Vulnerable populations like black patients and the elderly are likely to benefit more from the inclusion of SDOH in readmission prediction.

Conclusion

These findings provide potential SDOH factors that health systems and policymakers can target to reduce overall readmissions.