Evidence map›Paper›PMID 27335653›Full record

ArticleOpen heart

A cardiovascular disease policy model: part 2-preparing for economic evaluation and to assess health inequalities.

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Open heart. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Review
  6. Article
  7. 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.

K D LawsonHealth Economics and Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK; Centre for Health Research, School of Medicine, Western Sydney University, Sydney, New South Wales, Australia.
J D LewseyHealth 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 FoxBHF Centre for Research Excellence, University of Edinburgh , Edinburgh , UK.
L D RitchieCentre of Academic Primary Care, University of Aberdeen , Aberdeen , UK.
H Tunstall-PedoeInstitute of Cardiovascular Research, Ninewells Hospital, University of Dundee , 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, University of Sydney, Sydney, New South Wales, Australia; Oxford Martin School, University of Oxford, Oxford, UK.
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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis is the second of the two papers introducing a cardiovascular disease (CVD) policy model. The first paper described the structure and statistical underpinning of the state-transition model, demonstrating how life expectancy estimates are generated for individuals defined by ASSIGN risk factors. This second paper describes how the model is prepared to undertake economic evaluation.

designTo generate quality-adjusted life expectancy (QALE), the Scottish Health Survey was used to estimate background morbidity (health utilities) and the impact of CVD events (utility decrements). The SF-6D algorithm generated utilities and decrements were modelled using ordinary least squares (OLS). To generate lifetime hospital costs, the Scottish Heart Health Extended Cohort (SHHEC) was linked to the Scottish morbidity and death records (SMR) to cost each continuous inpatient stay (CIS). OLS and restricted cubic splines estimated annual costs before and after each of the first four events. A Kaplan-Meier sample average (KMSA) estimator was then used to weight expected health-related quality of life and costs by the probability of survival.

resultsThe policy model predicts the change in QALE and lifetime hospital costs as a result of an intervention(s) modifying risk factors. Cost-effectiveness analysis and a full uncertainty analysis can be undertaken, including probabilistic sensitivity analysis. Notably, the impacts according to socioeconomic deprivation status can be made.

conclusionsThe policy model can conduct cost-effectiveness analysis and decision analysis to inform approaches to primary prevention, including individually targeted and population interventions, and to assess impacts on health inequalities.

Indexed as

CORONARY ARTERY DISEASEQUALITY OF CARE AND OUTCOMES

Identifiers

PMID27335653
PMCPMC4908904

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.