Modeling the Economic Impact of Interventions for Older Populations with Multimorbidity: A Method of Linking Multiple Single-Disease Models

Ji-Hee Youn, Matt D. Stevenson, Praveen Thokala, Katherine Payne and Maria Goddard
Additional contact information
Ji-Hee Youn: Manchester Centre for Health Economics, Faculty of Biology, Medicine and Health, the University of Manchester, Oxford Road, Manchester, UK
Matt D. Stevenson: School of Health and Related Research, the University of Sheffield, Regent Court, Sheffield, UK
Praveen Thokala: School of Health and Related Research, the University of Sheffield, Regent Court, Sheffield, UK
Katherine Payne: Manchester Centre for Health Economics, Faculty of Biology, Medicine and Health, the University of Manchester, Oxford Road, Manchester, UK

Medical Decision Making, 2019, vol. 39, issue 7, 842-856

Abstract: Introduction. Individuals from older populations tend to have more than 1 health condition (multimorbidity). Current approaches to produce economic evidence for clinical guidelines using decision-analytic models typically use a single-disease approach, which may not appropriately reflect the competing risks within a population with multimorbidity. This study aims to demonstrate a proof-of-concept method of modeling multiple conditions in a single decision-analytic model to estimate the impact of multimorbidity on the cost-effectiveness of interventions. Methods. Multiple conditions were modeled within a single decision-analytic model by linking multiple single-disease models. Individual discrete event simulation models were developed to evaluate the cost-effectiveness of preventative interventions for a case study assuming a UK National Health Service perspective. The case study used 3 diseases (heart disease, Alzheimer’s disease, and osteoporosis) that were combined within a single linked model. The linked model, with and without correlations between diseases incorporated, simulated the general population aged 45 years and older to compare results in terms of lifetime costs and quality-adjusted life-years (QALYs). Results. The estimated incremental costs and QALYs for health care interventions differed when 3 diseases were modeled simultaneously (£840; 0.234 QALYs) compared with aggregated results from 3 single-disease models (£408; 0.280QALYs). With correlations between diseases additionally incorporated, both absolute and incremental costs and QALY estimates changed in different directions, suggesting that the inclusion of correlations can alter model results. Discussion. Linking multiple single-disease models provides a methodological option for decision analysts who undertake research on populations with multimorbidity. It also has potential for wider applications in informing decisions on commissioning of health care services and long-term priority setting across diseases and health care programs through providing potentially more accurate estimations of the relative cost-effectiveness of interventions.

Keywords: chronic diseases; comorbidity; cost-utility analysis; decision-analytic modeling; discrete event simulation; economic evaluation; health technology assessment; multimorbidity (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X19868987 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:39:y:2019:i:7:p:842-856

DOI: 10.1177/0272989X19868987

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().