PT  - JOURNAL ARTICLE
AU  - Lewsey, J D
AU  - Lawson, K D
AU  - Ford, I
AU  - Fox, K A A
AU  - Ritchie, L D
AU  - Tunstall-Pedoe, H
AU  - Watt, G C M
AU  - Woodward, M
AU  - Kent, S
AU  - Neilson, M
AU  - Briggs, A H
TI  - A cardiovascular disease policy model that predicts life expectancy taking into account socioeconomic deprivation
AID  - 10.1136/heartjnl-2014-305637
DP  - 2015 Feb 01
TA  - Heart
PG  - 201--208
VI  - 101
IP  - 3
4099  - http://heart.bmj.com/content/101/3/201.short
4100  - http://heart.bmj.com/content/101/3/201.full
SO  - Heart2015 Feb 01; 101
AB  - 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.