A cardiovascular disease policy model: part 2 – preparing for economic evaluation and to assess health inequalities (original) (raw)

Lawson, Kenny, Briggs, Andrew ORCID: https://orcid.org/0000-0002-0777-1997, Lewsey, James ORCID: https://orcid.org/0000-0002-3811-8165, Ford, Ian ORCID: https://orcid.org/0000-0001-5927-1823, Watt, Graham, Tunstall-Pedoe, Hugh, Woodward, Mark, Ritchie, Lewis, Kent, Seamus and Neilson, Matthew(2016) A cardiovascular disease policy model: part 2 – preparing for economic evaluation and to assess health inequalities.Open Heart, 3(1), e000140. (doi: 10.1136/openhrt-2014-000140) (PMID:27335653) (PMCID:PMC4908904)

Abstract

Objectives: This is the second of the two papers introducing a cardiovascular disease (CVD) policy model. The first paper described the structure and statistical underpinning of the state-transition model, demonstrating how life expectancy estimates are generated for individuals defined by ASSIGN risk factors. This second paper describes how the model is prepared to undertake economic evaluation. Design: To generate quality-adjusted life expectancy (QALE), the Scottish Health Survey was used to estimate background morbidity (health utilities) and the impact of CVD events (utility decrements). The SF-6D algorithm generated utilities and decrements were modelled using ordinary least squares (OLS). To generate lifetime hospital costs, the Scottish Heart Health Extended Cohort (SHHEC) was linked to the Scottish morbidity and death records (SMR) to cost each continuous inpatient stay (CIS). OLS and restricted cubic splines estimated annual costs before and after each of the first four events. A Kaplan-Meier sample average (KMSA) estimator was then used to weight expected health-related quality of life and costs by the probability of survival. Results: The policy model predicts the change in QALE and lifetime hospital costs as a result of an intervention(s) modifying risk factors. Cost-effectiveness analysis and a full uncertainty analysis can be undertaken, including probabilistic sensitivity analysis. Notably, the impacts according to socioeconomic deprivation status can be made. Conclusions: The policy model can conduct cost-effectiveness analysis and decision analysis to inform approaches to primary prevention, including individually targeted and population interventions, and to assess impacts on health inequalities.

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