Estimating the causal effects of chronic disease combinations on 30-day hospital readmissions based on observational Medicaid data. (30th November 2017)

• Record Type: Journal Article
• Authors: Casucci, Sabrina; Lin, Li; Hewner, Sharon; Nikolaev, Alexander
• Is Part Of: Journal of the American Medical Informatics Association. Volume 25:Number 6(2018)
• Page Start: 670
• Page End: 678
• Publication Date: 2017-11-30
• Subjects: observational causal inference -- hospital readmissions -- matching -- chronic disease -- Medicaid; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocx141
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Demonstrate how observational causal inference methods can generate insights into the impact of chronic disease combinations on patients' 30-day hospital readmissions. Materials and Methods: Causal effect estimation was used to quantify the impact of each risk factor scenario (ie, chronic disease combination) associated with chronic kidney disease and heart failure (HF) for adult Medicaid beneficiaries with initial hospitalizations in 2 New York State counties. The experimental protocol: (1) created matched risk factor and comparator groups, (2) assessed covariate balance in the matched groups, and (3) estimated causal effects and their statistical significance. Causality lattices summarized the impact of chronic disease comorbidities on readmissions. Results: Chronic disease combinations were ordered with respect to their causal impact on readmissions. Of disease combinations associated with HF, the combination of HF, coronary artery disease, and tobacco abuse (in that order) had the highest causal effect on readmission rate (+22.3%); of disease combinations associated with chronic kidney disease, the combination of chronic kidney disease, coronary artery disease, and diabetes had the highest effect (+9.5%). Discussion: Multi-hypothesis causal analysis reveals the effects of chronic disease comorbidities on health outcomes. Understanding these effects will guide the development of health care programs that address unique care needs of different patient subpopulations. Additionally, these insights bring new attention to individuals at high risk for readmission based on chronic disease comorbidities, allowing for more personalized attention and prioritization of care. Conclusion: Multi-hypothesis causal analysis, a new methodological tool, generates meaningful insights from health care claims data, guiding the design of care and intervention programs.