Lower household income is associated with an increased risk of hospital readmission in patients with decompensated cirrhosis

Pragmatic strategies to address health disparities along the continuum of care in chronic liver disease

Racial, ethnic, and socioeconomic disparities exist in the prevalence and natural history of chronic liver disease, access to care, and clinical outcomes. Solutions to improve health equity range widely, from digital health tools to policy changes. The current review outlines the disparities along the chronic liver disease health care continuum from screening and diagnosis to the management of cirrhosis and considerations of pre-liver and post-liver transplantation. Using a health equity research and implementation science framework, we offer pragmatic strategies to address barriers to implementing high-quality equitable care for patients with chronic liver disease.

A novel, nurse-led 'one stop' clinic for patients with liver cirrhosis results in fewer liver-related unplanned readmissions and improved survival

OBJECTIVE

Delivering effective secondary preventive and integrated care has the potential to break the revolving-door phenomenon of frequent readmissions in patients with advanced chronic liver disease. To address this, we launched the Care Coordination of Liver Disease (CCoLD) pilot, a novel nurse-led cirrhosis clinic in Western Sydney.

METHODS AND ANALYSIS

Following an index presentation to Blacktown or Mount Druitt hospitals (BMDH), patients (n = 89, matched by age, sex, and MELD-NA) were consecutively either followed up by the CCoLD clinical nurse consultant (intervention cohort) or received standard care (control cohort). Controlled evaluation of the impact of the nurse-led clinic was carried out for a 3-month period including readmission rates, survival, and cost effectiveness.

RESULTS

The inaugural nurse-led clinic led to improvement in patient-level outcomes including a reduction in unplanned liver-related readmissions (2.08% for intervention cohort vs 12.2% for control cohort, p < 0.01), and mortality at 30 days (0% for intervention cohort vs 7.3% for control cohort, p = 0.03). Similar trends were observed at 90 days from index discharge. No deaths were observed in the intervention cohort as compared to the control cohort at 90 days (0% versus 7.3%, p = 0.03), while unplanned liver-related readmissions were 10.41% for the intervention cohort vs 19.5% for the control cohort (p = 0.115). Moreover, time to readmission was significantly longer in the intervention cohort, resulting in an overall cost-effective intervention.

CONCLUSION

These findings highlight the significant impact of optimised care-coordination. A nurse-led clinic can deliver patient-centred, goal-directed, and cost-effective secondary prevention and care. A multicentre randomised trial for wider evaluation of these findings is warranted.

Surgical Treatment at an Academic Medical Center is Associated with Statistically Insignificant Lung Cancer Survival Outcome Differences Related to ZIP Code

BACKGROUND

Low socioeconomic status is a well-characterized adverse prognostic factor in large lung cancer databases. However, such characterizations may be confounded as patients of lower socioeconomic status are more often treated at low-volume, non-academic centers. We evaluated whether socioeconomic status, as defined by ZIP code median income, was associated with differences in lung cancer resection outcomes within a high-volume academic medical center.

METHODS

Consecutive patients undergoing resection for non-small cell lung cancer were identified from a prospectively maintained database (2011-18). Patients were assigned an income value based on the median income of their ZIP code as determined by census-based geographic data. We stratified the population into income quintiles representative of SES and compared demographics (chi-square), surgical outcomes, and survival (Kaplan-Meier).

RESULTS

We identified 1,693 patients, representing 516 ZIP codes. Income quintiles were Q1: $24,421-53,151; Q2:$53,152-73,982; Q3:$73,983-99,063; Q4:$99,064-123,842; and Q5:$123,843-250,001. Compared to Q5 patients, Q1 patients were younger (median 69 vs. 73, p < 0.001), more likely male (44 vs. 36%, p = 0.035), and more likely Asian, Black, or self-identified as other than white, Asian, or Black. (67 vs. 11%, p =  < 0.001). We found minor differences in surgical outcomes and no significant difference in 5-year survival between Q1 and Q5 patients (5-year: 86 vs. 85%, p = 0.886).

CONCLUSIONS

Surgical care patterns at a high-volume academic medical center are similar among patients from varying ZIP codes. Surgical treatment at such a center is associated with no survival differences based upon socioeconomic status as determined by ZIP code. Centralization of lung cancer surgical care to high-volume centers may reduce socioeconomic outcome disparities.

Miller N, L. Pecina J, D. Decker M, I. Wi C, Garrison GM. Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure

Background

While socioeconomic status has been linked to hospital readmissions for several conditions, reliable measures of individual socioeconomic status are often not available. HOUSES, a new measure of individual socioeconomic status based upon objective public data about one's housing unit, is inversely associated with overall hospitalization rate but it has not been studied with respect to readmissions.

Purpose

To determine if patients in the lowest HOUSES quartile are more likely to be readmitted within 30 days (short-term) and 180 days (long-term).

Methods

A retrospective cohort study of 11 993 patients having 21 633 admissions was conducted using generalized linear mixed-effects models.

Results

HOUSES quartile did not show any significant association with early readmission. However, when compared to the lowest HOUSES quartile, the second quartile (OR = 0.90, 95%CI 0.83-0.98) and the third quartile (OR = 0.91, 95%CI 0.83-0.99) were associated with lower odds of late readmission while the highest quartile (OR = 0.91, 95%CI 0.82-1.01) was not statistically different.

Conclusion

HOUSES was associated with late readmission, but not early readmission. This may be because early readmissions are influenced by medical conditions and hospital care while late readmissions are influenced by ambulatory care and home-based factors. Since HOUSES relies on public county assessor data, it is generally available and may be used to focus interventions on those at highest risk for late readmission.