Evidence map›Paper›PMID 21974836›Full record

ArticleCost effectiveness and resource allocation : C/E2011

Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention.

Talitha L Feenstra, Pieter M van Baal, Monique O Jacobs-van der Bruggen, Rudolf T Hoogenveen, Geert-Jan Kommer, Caroline A Baan

Open access · goldAbstract read
In one paragraph

Article in Cost effectiveness and resource allocation : C/E, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
4.2field-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

4 citing papers in PubMed, 8 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

Talitha L FeenstraCentre for Prevention and Health Services Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands. Talitha.Feenstra@rivm.nl.
Pieter M van Baal
Monique O Jacobs-van der Bruggen
Rudolf T Hoogenveen
Geert-Jan Kommer
Caroline A Baan
National Institute for Public Health and the Environment · NLEMGO Institute for Health and Care Research · NLErasmus University Rotterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes mellitus brings an increased risk for cardiovascular complications and patients profit from prevention. This prevention also suits the general population. The question arises what is a better strategy: target the general population or diabetes patients.

methodsA mathematical programming model was developed to calculate optimal allocations for the Dutch population of the following interventions: smoking cessation support, diet and exercise to reduce overweight, statins, and medication to reduce blood pressure. Outcomes were total lifetime health care costs and QALYs. Budget sizes were varied and the division of resources between the general population and diabetes patients was assessed.

resultsFull implementation of all interventions resulted in a gain of 560,000 QALY at a cost of €640 per capita, about €12,900 per QALY on average. The large majority of these QALY gains could be obtained at incremental costs below €20,000 per QALY. Low or high budgets (below €9 or above €100 per capita) were predominantly spent in the general population. Moderate budgets were mostly spent in diabetes patients.

conclusionsMajor health gains can be realized efficiently by offering prevention to both the general and the diabetic population. However, a priori setting a specific distribution of resources is suboptimal. Resource allocation models allow accounting for capacity constraints and program size in addition to efficiency.

Identifiers

PMID21974836
PMCPMC3200148
OpenAlexW2164751860

What OpenQuestion holds

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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.