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Original article
A cardiovascular disease policy model that predicts life expectancy taking into account socioeconomic deprivation
  1. J D Lewsey1,
  2. K D Lawson1,
  3. I Ford2,
  4. K A A Fox3,
  5. L D Ritchie4,
  6. H Tunstall-Pedoe5,
  7. G C M Watt6,
  8. M Woodward7,
  9. S Kent1,
  10. M Neilson1,
  11. A H Briggs1
  1. 1Health Economics and Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK
  2. 2Robertson Centre for Biostatistics, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK
  3. 3BHF Centre for Research Excellence, University of Edinburgh, Edinburgh, UK
  4. 4Centre of Academic Primary Care, University of Aberdeen, University of Aberdeen, Aberdeen, UK
  5. 5Institute of Cardiovascular Research, University of Dundee, Ninewells Hospital, Dundee, UK
  6. 6General Practice & Primary Care, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK
  7. 7The George Institute for Global Health, The University of Sydney, Sydney, New South Wales, Australia
  1. Correspondence to Dr Jim Lewsey, Health Economics and Health Technology Assessment (HEHTA), Institute of Health & Wellbeing, 1 Lilybank Gardens, University of Glasgow, Glasgow G12 8RZ, UK; jim.lewsey{at}


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.


This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:

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