Evidence map›Paper›PMID 25324535›Full record

ArticleHeart (British Cardiac Society)2015

A cardiovascular disease policy model that predicts life expectancy taking into account socioeconomic deprivation.

J D Lewsey, K D Lawson, I Ford, K A A Fox, L D Ritchie, H Tunstall-Pedoe, G C M Watt, M Woodward, S Kent, M Neilson and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Heart (British Cardiac Society), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 2 countries.

J D LewseyHealth Economics and Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
K D LawsonHealth Economics and Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
I FordRobertson Centre for Biostatistics, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
K A A FoxBHF Centre for Research Excellence, University of Edinburgh, Edinburgh, UK.
L D RitchieCentre of Academic Primary Care, University of Aberdeen, University of Aberdeen, Aberdeen, UK.
H Tunstall-PedoeInstitute of Cardiovascular Research, University of Dundee, Ninewells Hospital, Dundee, UK.
G C M WattGeneral Practice & Primary Care, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
M WoodwardThe George Institute for Global Health, The University of Sydney, Sydney, New South Wales, Australia.
S KentHealth Economics and Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
M NeilsonHealth Economics and Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
A H BriggsHealth Economics and Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK.
University of Glasgow · GBNinewells Hospital · GBUniversity of Aberdeen · GBUniversity of Edinburgh · GBUniversity of Sydney · AU

Funding

Chief Scientist Office CZH/4/557
6 · The paper itself

Abstract

objectivesA 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.

designA 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.

resultsOur 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.

conclusionsOur model can be used to estimate the impact of primary prevention interventions on life expectancy and can assess the impact of interventions on inequalities.

Indexed as

Life ExpectancyModels, CardiovascularCardiovascular DiseasesCost-Benefit AnalysisFemaleHumansMaleMiddle AgedMorbidityPrimary PreventionRisk FactorsSocioeconomic FactorsSurvival RateUnited KingdomCORONARY ARTERY DISEASEQUALITY OF CARE AND OUTCOMES

Identifiers

PMID25324535
PMCPMC4316925
OpenAlexW2116096158

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.