Evidence map›Paper›PMID 25872945›Full record

ArticleTrials2015

Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers.

Hilary Watt, Matthew Harris, Jane Noyes, Rhiannon Whitaker, Zoe Hoare, Rhiannon Tudor Edwards, Andy Haines

Abstract read
In one paragraph

Article in Trials, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. 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

7 authors.

Hilary WattDepartment of Primary Care and Public Health, Imperial College London, Reynolds Building, St Dunstans Road, London, W6 8RP, , England, UK. h.watt@imperial.ac.uk.
Matthew HarrisDepartment of Primary Care and Public Health, Imperial College London, Reynolds Building, St Dunstans Road, London, W6 8RP, , England, UK. m.harris@imperial.ac.uk.
Jane NoyesSchool of Social Sciences, Bangor University, Bangor, Wales, UK. jane.noyes@bangor.ac.uk.
Rhiannon WhitakerWhitaker Research Ltd., London, UK. Rhiannon@whitres.co.uk.
Zoe HoareNWORTH Clinical Trials Unit, Bangor University, Bangor, Wales, UK. z.hoare@bangor.ac.uk.
Rhiannon Tudor EdwardsCentre for Health Economics and Medicine Evaluation, Bangor University, Bangor, Wales, UK. r.t.edwards@bangor.ac.uk.
Andy HainesLondon School of Hygiene and Tropical Medicine, London, England, UK. andy.haines@lshtm.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn health services research, composite scores to measure changes in health-seeking behaviour and uptake of services do not exist. We describe the rationale and analytical considerations for a composite primary outcome for primary care research. We simulate its use in a large hypothetical population and use it to calculate sample sizes. We apply it within the context of a proposed cluster randomised controlled trial (RCT) of a Community Health Worker (CHW) intervention.

methodsWe define the outcome as the proportion of the services (immunizations, screening tests, stop-smoking clinics) received by household members, of those that they were eligible to receive. First, we simulated a population household structure (by age and sex), based on household composition data from the 2011 England and Wales census. The ratio of eligible to received services was calculated for each simulated household based on published eligibility criteria and service uptake rates, and was used to calculate sample size scenarios for a cluster RCT of a CHW intervention. We assume varying intervention percentage effects and varying levels of clustering.

resultsAssuming no disease risk factor clustering at the household level, 11.7% of households in the hypothetical population of 20,000 households were eligible for no services, 26.4% for 1, 20.7% for 2, 15.3% for 3 and 25.8% for 4 or more. To demonstrate a small CHW intervention percentage effect (10% improvement in uptake of services out of those who would not otherwise have taken them up, and additionally assuming intra-class correlation of 0.01 between households served by different CHWs), around 4,000 households would be needed in each of the intervention and control arms. This equates to 40 CHWs (each servicing 100 households) needed in the intervention arm. If the CHWs were more effective (20%), then only 170 households would be needed in each of the intervention and control arms.

conclusionsThis is a useful first step towards a process-centred composite score of practical value in complex community-based interventions. Firstly, it is likely to result in increased statistical power compared with multiple outcomes. Second, it avoids over-emphasis of any single outcome from a complex intervention.

Indexed as

Delivery of Health CarePatient Acceptance of Health CareAdolescentAdultAgedAge FactorsChildChild, PreschoolCluster AnalysisCommunity Health ServicesCommunity Health WorkersComputer SimulationEnglandFamily CharacteristicsFemaleHealth Resources

Identifiers

PMID25872945
PMCPMC4417521

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.