A cardiovascular disease policy model that predicts life expectancy taking into account socioeconomic deprivation
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., and Briggs, A.H. (2015) A cardiovascular disease policy model that predicts life expectancy taking into account socioeconomic deprivation. Heart, 101 (3). pp. 201-208.
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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.
| Item ID: | 36974 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 1468-201X |
| Additional Information: | 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: http://creativecommons.org/licenses/by-nc/4.0/ |
| Funders: | Chief Scientist Office, Scotland |
| Projects and Grants: | Chief Scientist Office, Scotland CZH/4/557 |
| Date Deposited: | 28 Jan 2015 04:54 |
| FoR Codes: | 11 MEDICAL AND HEALTH SCIENCES > 1102 Cardiovascular Medicine and Haematology > 110201 Cardiology (incl Cardiovascular Diseases) @ 50% 14 ECONOMICS > 1402 Applied Economics > 140208 Health Economics @ 50% |
| SEO Codes: | 92 HEALTH > 9202 Health and Support Services > 920206 Health Policy Economic Outcomes @ 34% 92 HEALTH > 9202 Health and Support Services > 920207 Health Policy Evaluation @ 33% 92 HEALTH > 9202 Health and Support Services > 920208 Health Inequalities @ 33% |
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