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Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers
BACKGROUND: In 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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4417521/ https://www.ncbi.nlm.nih.gov/pubmed/25872945 http://dx.doi.org/10.1186/s13063-015-0625-1 |
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author | Watt, Hilary Harris, Matthew Noyes, Jane Whitaker, Rhiannon Hoare, Zoe Edwards, Rhiannon Tudor Haines, Andy |
author_facet | Watt, Hilary Harris, Matthew Noyes, Jane Whitaker, Rhiannon Hoare, Zoe Edwards, Rhiannon Tudor Haines, Andy |
author_sort | Watt, Hilary |
collection | PubMed |
description | BACKGROUND: In 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. METHODS: We 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. RESULTS: Assuming 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. CONCLUSIONS: This 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. |
format | Online Article Text |
id | pubmed-4417521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44175212015-05-04 Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers Watt, Hilary Harris, Matthew Noyes, Jane Whitaker, Rhiannon Hoare, Zoe Edwards, Rhiannon Tudor Haines, Andy Trials Research BACKGROUND: In 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. METHODS: We 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. RESULTS: Assuming 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. CONCLUSIONS: This 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. BioMed Central 2015-03-21 /pmc/articles/PMC4417521/ /pubmed/25872945 http://dx.doi.org/10.1186/s13063-015-0625-1 Text en © Watt et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Watt, Hilary Harris, Matthew Noyes, Jane Whitaker, Rhiannon Hoare, Zoe Edwards, Rhiannon Tudor Haines, Andy Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers |
title | Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers |
title_full | Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers |
title_fullStr | Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers |
title_full_unstemmed | Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers |
title_short | Development of a composite outcome score for a complex intervention - measuring the impact of Community Health Workers |
title_sort | development of a composite outcome score for a complex intervention - measuring the impact of community health workers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4417521/ https://www.ncbi.nlm.nih.gov/pubmed/25872945 http://dx.doi.org/10.1186/s13063-015-0625-1 |
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