A cardiovascular disease policy model that predicts life expectancy taking into account socioeconomic deprivation. Issue 3 (16th October 2014)

• Record Type: Journal Article
• Authors: Lewsey, J D; Lawson, K D; Ford, I; Fox, K A A; Ritchie, L D; Tunstall-Pedoe, H; Watt, G C M; Woodward, M; Kent, S; Neilson, M; Briggs, A H
• Is Part Of: Heart. Volume 101:Issue 3(2015)
• Page Start: 201
• Page End: 208
• Publication Date: 2014-10-16
• Subjects: CORONARY ARTERY DISEASE -- QUALITY OF CARE AND OUTCOMES; Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/heartjnl-2014-305637
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : Objectives: A policy model is a model that can evaluate the effectiveness and cost-effectiveness of interventions and inform policy decisions. In this study, we introduce a cardiovascular disease (CVD) policy model which can be used to model remaining life expectancy including a measure of socioeconomic deprivation as an independent risk factor for CVD. Design: A state transition model was developed using the Scottish Heart Health Extended Cohort (SHHEC) linked to Scottish morbidity and death records. Individuals start in a CVD-free state and can transit to three CVD event states plus a non-CVD death state. Individuals who have a non-fatal first event are then followed up until death. Taking a competing risk approach, the cause-specific hazards of a first event are modelled using parametric survival analysis. Survival following a first non-fatal event is also modelled parametrically. We assessed discrimination, validation and calibration of our model. Results: Our model achieved a good level of discrimination in each component (c-statistics for men (women)—non-fatal coronary heart disease (CHD): 0.70 (0.74), non-fatal cerebrovascular disease (CBVD): 0.73 (0.76), fatal CVD: 0.77 (0.80), fatal non-CVD: 0.74 (0.72), survival after non-fatal CHD: 0.68 (0.67) and survival after non-fatal CBVD: 0.65 (0.66)). In general, our model predictions were comparable with observed event rates for a Scottish randomised statin trial population which has an overlapping follow-up period with SHHEC. After applying a calibration factor, our predictions of life expectancy closely match those published in recent national life tables. Conclusions: Our model can be used to estimate the impact of primary prevention interventions on life expectancy and can assess the impact of interventions on inequalities.