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cchsflow: an open science approach to transform and combine population health surveys
SETTING: The Canadian Community Health Survey (CCHS) is one of the world’s largest ongoing cross-sectional population health surveys, with over 130,000 respondents every two years or over 1.1 million respondents since its inception in 2001. While the survey remains relatively consistent over the yea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989714/ https://www.ncbi.nlm.nih.gov/pubmed/33761108 http://dx.doi.org/10.17269/s41997-020-00470-8 |
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author | Yusuf, Warsame Vyuha, Rostyslav Bennett, Carol Sequeira, Yulric Maskerine, Courtney Manuel, Douglas G. |
author_facet | Yusuf, Warsame Vyuha, Rostyslav Bennett, Carol Sequeira, Yulric Maskerine, Courtney Manuel, Douglas G. |
author_sort | Yusuf, Warsame |
collection | PubMed |
description | SETTING: The Canadian Community Health Survey (CCHS) is one of the world’s largest ongoing cross-sectional population health surveys, with over 130,000 respondents every two years or over 1.1 million respondents since its inception in 2001. While the survey remains relatively consistent over the years, there are differences between cycles that pose a challenge to analyze the survey over time. INTERVENTION: A program package called cchsflow was developed to transform and harmonize CCHS variables to consistent formats across multiple survey cycles. An open science approach was used to maintain transparency, reproducibility and collaboration. OUTCOMES: The cchsflow R package uses CCHS survey data between 2001 and 2014. Worksheets were created that identify variables, their names in previous cycles, their category structure, and their final variable names. These worksheets were then used to recode variables in each CCHS cycle into consistently named and labelled variables. Following, survey cycles can be combined. The package was then added as a GitHub repository to encourage collaboration with other researchers. IMPLICATION: The cchsflow package has been added to the Comprehensive R Archive Network (CRAN) and contains support for over 160 CCHS variables, generating a combined data set of over 1 million respondents. By implementing open science practices, cchsflow aims to minimize the amount of time needed to clean and prepare data for the many CCHS users across Canada. |
format | Online Article Text |
id | pubmed-7989714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-79897142021-03-25 cchsflow: an open science approach to transform and combine population health surveys Yusuf, Warsame Vyuha, Rostyslav Bennett, Carol Sequeira, Yulric Maskerine, Courtney Manuel, Douglas G. Can J Public Health Innovations in Policy and Practice SETTING: The Canadian Community Health Survey (CCHS) is one of the world’s largest ongoing cross-sectional population health surveys, with over 130,000 respondents every two years or over 1.1 million respondents since its inception in 2001. While the survey remains relatively consistent over the years, there are differences between cycles that pose a challenge to analyze the survey over time. INTERVENTION: A program package called cchsflow was developed to transform and harmonize CCHS variables to consistent formats across multiple survey cycles. An open science approach was used to maintain transparency, reproducibility and collaboration. OUTCOMES: The cchsflow R package uses CCHS survey data between 2001 and 2014. Worksheets were created that identify variables, their names in previous cycles, their category structure, and their final variable names. These worksheets were then used to recode variables in each CCHS cycle into consistently named and labelled variables. Following, survey cycles can be combined. The package was then added as a GitHub repository to encourage collaboration with other researchers. IMPLICATION: The cchsflow package has been added to the Comprehensive R Archive Network (CRAN) and contains support for over 160 CCHS variables, generating a combined data set of over 1 million respondents. By implementing open science practices, cchsflow aims to minimize the amount of time needed to clean and prepare data for the many CCHS users across Canada. Springer International Publishing 2021-03-24 /pmc/articles/PMC7989714/ /pubmed/33761108 http://dx.doi.org/10.17269/s41997-020-00470-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Innovations in Policy and Practice Yusuf, Warsame Vyuha, Rostyslav Bennett, Carol Sequeira, Yulric Maskerine, Courtney Manuel, Douglas G. cchsflow: an open science approach to transform and combine population health surveys |
title | cchsflow: an open science approach to transform and combine population health surveys |
title_full | cchsflow: an open science approach to transform and combine population health surveys |
title_fullStr | cchsflow: an open science approach to transform and combine population health surveys |
title_full_unstemmed | cchsflow: an open science approach to transform and combine population health surveys |
title_short | cchsflow: an open science approach to transform and combine population health surveys |
title_sort | cchsflow: an open science approach to transform and combine population health surveys |
topic | Innovations in Policy and Practice |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989714/ https://www.ncbi.nlm.nih.gov/pubmed/33761108 http://dx.doi.org/10.17269/s41997-020-00470-8 |
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