Cost-Effectiveness of a Government Policy to Incentivise Chronic Disease Management following Stroke: A Modelling Study

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Abstract

Introduction: Little is known about the cost-effectiveness of government policies that support primary care physicians to provide comprehensive chronic disease management (CDM). This paper aimed to estimate the potential cost-effectiveness of CDM policies over a lifetime for longtime survivors of stroke. 

Methods: A Markov model, using three health states (stable, hospitalised, dead), was developed to simulate the costs and benefits of CDM policies over 30 years (with 1-year cycles). Transition probabilities and costs from a health system perspective were obtained from the linkage of data between the Australian Stroke Clinical Registry (cohort n = 12,368, 42% female, median age 70 years, 45% had CDM claims) and government-held hospital, Medicare, and pharmaceutical claims datasets. Quality-adjusted life years (QALYs) were obtained from a comparable cohort (n = 512, 34% female, median age 69.6 years, 52% had CDM claims) linked with Medicare claims and death data. A 3% discount rate was applied to costs in Australian dollars (AUD, 2016) and QALYs beyond 12 months. Probabilistic sensitivity analyses were used to understand uncertainty. Results: Per-person average total lifetime costs were AUD 142,939 and 8.97 QALYs for those with a claim, and AUD 103,889 and 8.98 QALYs for those without a claim. This indicates that these CDM policies were costlier without improving QALYs. The probability of cost effectiveness of CDM policies was 26.1%, at a willingness to-pay threshold of AUD 50,000/QALY. 

Conclusion: CDM policies, designed to encourage comprehensive care, are unlikely to be cost-effective for stroke compared to care without CDM. Further research to understand how to deliver such care cost-effectively is needed.

Original languageEnglish
Pages (from-to)208-217
Number of pages10
JournalNeuroepidemiology
Volume58
Issue number3
DOIs
Publication statusPublished - Jun 2024

Keywords

  • Chronic disease management ? Stroke ? Cost-effectiveness ? Economic evaluation ? Markov modelling

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