S2520 The Hidden Cost of Inequity: Education, Language, and Insurance Status Shape Cirrhosis Outcomes

Introduction: Cirrhosis, characterized by high mortality and frequent hospitalizations, necessitates complex medication regimens to manage liver disease and associated complications. Non-adherence to these treatments, often due to side effects or changes in routine, leads to preventable readmissions. This study utilizes the Healthcare Cost and Utilization Project (HCUP) National Database to examine the factors influencing hospital readmissions. We aim to identify key predictors of readmission and evaluate the impact of socio-economic factors on patient outcomes. Methods: In our study, we conducted a retrospective analysis using the HCUP National Database, covering 2019 to 2024. We identified the cirrhotic patient population using specific ICD codes. Data collected included information on comorbidities, insurance status, educational level, and hospital admissions. we utilized area under the receiver operating characteristic curve (AUROC) and relationship mapping techniques to investigate the risk and predictive accuracy of interacting variables within the logistic regression model. Results: Using HCUP data from 2019 to 2024 (n = 2,858,576), we identified 78,546 cirrhotic patients and found that low education (P = 0.012), inadequate insurance (P = 0.023), and English as a second language (P = 0.011) significantly increased 30-day readmission rates. Language barriers (HR = 0.346, P = 0.043) and low educational status (HR = 0.756, P = 0.025) were also associated with delayed recovery. AUROC values for education (0.645, P < 0.01) and insurance (0.554, P < 0.01) supported their predictive value. These findings highlight the need for targeted interventions addressing socio-economic and linguistic barriers in cirrhotic patient care. Conclusion: The findings from this comprehensive analysis of the HCUP National Database highlight significant socio-economic factors, such as low education level, inadequate insurance, and language barriers, that contribute to higher readmission rates among cirrhotic patients. The statistical significance of these factors suggests that tailored interventions focusing on education and language support could mitigate these risks. This study underscores the necessity for healthcare systems to integrate socio-economic considerations into the management plans for patients with cirrhosis to reduce preventable hospitalizations and improve overall patient outcomes.

Publication Details

Journal
The American Journal of Gastroenterology
Published
2025-10-01
DOI
https://doi.org/10.14309/01.ajg.0001137540.29284.1c
Primary Topic
Liver Disease and Transplantation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

S2520 The Hidden Cost of Inequity: Education, Language, and Insurance Status Shape Cirrhosis Outcomes

The American Journal of Gastroenterology
Liver Disease and Transplantation
article

S2520 The Hidden Cost of Inequity: Education, Language, and Insurance Status Shape Cirrhosis Outcomes

article en

Abstract

Introduction: Cirrhosis, characterized by high mortality and frequent hospitalizations, necessitates complex medication regimens to manage liver disease and associated complications. Non-adherence to these treatments, often due to side effects or changes in routine, leads to preventable readmissions. This study utilizes the Healthcare Cost and Utilization Project (HCUP) National Database to examine the factors influencing hospital readmissions. We aim to identify key predictors of readmission and evaluate the impact of socio-economic factors on patient outcomes. Methods: In our study, we conducted a retrospective analysis using the HCUP National Database, covering 2019 to 2024. We identified the cirrhotic patient population using specific ICD codes. Data collected included information on comorbidities, insurance status, educational level, and hospital admissions. we utilized area under the receiver operating characteristic curve (AUROC) and relationship mapping techniques to investigate the risk and predictive accuracy of interacting variables within the logistic regression model. Results: Using HCUP data from 2019 to 2024 (n = 2,858,576), we identified 78,546 cirrhotic patients and found that low education (P = 0.012), inadequate insurance (P = 0.023), and English as a second language (P = 0.011) significantly increased 30-day readmission rates. Language barriers (HR = 0.346, P = 0.043) and low educational status (HR = 0.756, P = 0.025) were also associated with delayed recovery. AUROC values for education (0.645, P < 0.01) and insurance (0.554, P < 0.01) supported their predictive value. These findings highlight the need for targeted interventions addressing socio-economic and linguistic barriers in cirrhotic patient care. Conclusion: The findings from this comprehensive analysis of the HCUP National Database highlight significant socio-economic factors, such as low education level, inadequate insurance, and language barriers, that contribute to higher readmission rates among cirrhotic patients. The statistical significance of these factors suggests that tailored interventions focusing on education and language support could mitigate these risks. This study underscores the necessity for healthcare systems to integrate socio-economic considerations into the management plans for patients with cirrhosis to reduce preventable hospitalizations and improve overall patient outcomes.

The American Journal of GastroenterologyVol. 120(10S2)
Quality Education
Openalex Percentile: Top 33%
Liver Disease and Transplantation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.