Evidence map›Paper›PMID 26682549›Full record

ReviewThe European journal of health economics : HEPAC : health economics in prevention and care2016

Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature.

David Epstein, Leticia García-Mochón, Stephen Kaptoge, Simon G Thompson

Open access · hybridFull text readReview
In one paragraph

Review in The European journal of health economics : HEPAC : health economics in prevention and care, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
3.7field-weighted citation impact, top 5% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.

  1. Pooled it
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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

4 authors at 3 institutions in 2 countries.

David EpsteinDepartment of Applied Economics, University of Granada, Campus de la Cartuja, 18071, Granada, Spain. davidepstein@ugr.es.
Leticia García-MochónEscuela Andaluza de Salud Pública, Granada, Spain.
Stephen KaptogeDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Simon G ThompsonDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
University of Cambridge · GBAndalusian School of Public Health · ESUniversidad de Granada · ES

Funding

British Heart Foundation RG/08/014/24067Medical Research Council G0800270Medical Research Council MR/L003120/1
6 · The paper itself

Abstract

backgroundStrategies for screening and intervening to reduce the risk of cardiovascular disease (CVD) in primary care settings need to be assessed in terms of both their costs and long-term health effects. We undertook a literature review to investigate the methodologies used.

methodsIn a framework of developing a new health-economic model for evaluating different screening strategies for primary prevention of CVD in Europe (EPIC-CVD project), we identified seven key modeling issues and reviewed papers published between 2000 and 2013 to assess how they were addressed.

resultsWe found 13 relevant health-economic modeling studies of screening to prevent CVD in primary care. The models varied in their degree of complexity, with between two and 33 health states. Programmes that screen the whole population by a fixed cut-off (e.g., predicted 10-year CVD risk >20 %) identify predominantly elderly people, who may not be those most likely to benefit from long-term treatment. Uncertainty and model validation were generally poorly addressed. Few studies considered the disutility of taking drugs in otherwise healthy individuals or the budget impact of the programme.

conclusionsModel validation, incorporation of parameter uncertainty, and sensitivity analyses for assumptions made are all important components of model building and reporting, and deserve more attention. Complex models may not necessarily give more accurate predictions. Availability of a large enough source dataset to reliably estimate all relevant input parameters is crucial for achieving credible results. Decision criteria should consider budget impact and the medicalization of the population as well as cost-effectiveness thresholds.

Indexed as

Models, EconomicAdultAgedAged, 80 and overAlgorithmsCardiovascular DiseasesCost-Benefit AnalysisFemaleHealth PromotionHumansHydroxymethylglutaryl-CoA Reductase InhibitorsMaleMiddle AgedPrimary PreventionQuality-Adjusted Life YearsReproducibility of ResultsHydroxymethylglutaryl-CoA Reductase InhibitorsCardiovascular diseaseCost-effectiveness analysisLiterature reviewPrimary preventionScreeningStatins

Identifiers

PMID26682549
PMCPMC5047941
OpenAlexW2208699478

What OpenQuestion holds

Textfull text, public
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.