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Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia

BACKGROUND: Global action to reduce obesity prevalence requires digital transformation of the public health sector to enable precision public health (PPH). Useable data for PPH of obesity is yet to be identified, collated and appraised and there is currently no accepted approach to creating this sin...

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Autores principales: Canfell, Oliver J., Davidson, Kamila, Sullivan, Clair, Eakin, Elizabeth, Burton-Jones, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953390/
https://www.ncbi.nlm.nih.gov/pubmed/35331189
http://dx.doi.org/10.1186/s12889-022-12939-x
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author Canfell, Oliver J.
Davidson, Kamila
Sullivan, Clair
Eakin, Elizabeth
Burton-Jones, Andrew
author_facet Canfell, Oliver J.
Davidson, Kamila
Sullivan, Clair
Eakin, Elizabeth
Burton-Jones, Andrew
author_sort Canfell, Oliver J.
collection PubMed
description BACKGROUND: Global action to reduce obesity prevalence requires digital transformation of the public health sector to enable precision public health (PPH). Useable data for PPH of obesity is yet to be identified, collated and appraised and there is currently no accepted approach to creating this single source of truth. This scoping review aims to address this globally generic problem by using the State of Queensland (Australia) (population > 5 million) as a use case to determine (1) availability of primary data sources usable for PPH for obesity (2) quality of identified sources (3) general implications for public health policymakers. METHODS: The Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Unique search strategies were implemented for ‘designed’ (e.g. surveys) and ‘organic’ (e.g. electronic health records) data sources. Only primary sources of data (with stratification to Queensland) with evidence-based determinants of obesity were included. Primary data source type, availability, sample size, frequency of collection and coverage of determinants of obesity were extracted and curated into an evidence map. Data source quality was qualitatively assessed. RESULTS: We identified 38 primary sources of preventive data for obesity: 33 designed and 5 organic. Most designed sources were survey (n 20) or administrative (n 10) sources and publicly available but generally were not contemporaneous (> 2 years old) and had small sample sizes (10-100 k) relative to organic sources (> 1 M). Organic sources were identified as the electronic medical record (ieMR), wearables, environmental (Google Maps, Crime Map) and billing/claims. Data on social, biomedical and behavioural determinants of obesity typically co-occurred across sources. Environmental and commercial data was sparse and interpreted as low quality. One organic source (ieMR) was highly contemporaneous (routinely updated), had a large sample size (5 M) and represented all determinants of obesity but is not currently used for public health decision-making in Queensland. CONCLUSIONS: This review provides a (1) comprehensive data map for PPH for obesity in Queensland and (2) globally translatable framework to identify, collate and appraise primary data sources to advance PPH for obesity and other noncommunicable diseases. Significant challenges must be addressed to achieve PPH, including: using designed and organic data harmoniously, digital infrastructure for high-quality organic data, and the ethical and social implications of using consumer-centred health data to improve public health. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-12939-x.
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spelling pubmed-89533902022-03-26 Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia Canfell, Oliver J. Davidson, Kamila Sullivan, Clair Eakin, Elizabeth Burton-Jones, Andrew BMC Public Health Research BACKGROUND: Global action to reduce obesity prevalence requires digital transformation of the public health sector to enable precision public health (PPH). Useable data for PPH of obesity is yet to be identified, collated and appraised and there is currently no accepted approach to creating this single source of truth. This scoping review aims to address this globally generic problem by using the State of Queensland (Australia) (population > 5 million) as a use case to determine (1) availability of primary data sources usable for PPH for obesity (2) quality of identified sources (3) general implications for public health policymakers. METHODS: The Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Unique search strategies were implemented for ‘designed’ (e.g. surveys) and ‘organic’ (e.g. electronic health records) data sources. Only primary sources of data (with stratification to Queensland) with evidence-based determinants of obesity were included. Primary data source type, availability, sample size, frequency of collection and coverage of determinants of obesity were extracted and curated into an evidence map. Data source quality was qualitatively assessed. RESULTS: We identified 38 primary sources of preventive data for obesity: 33 designed and 5 organic. Most designed sources were survey (n 20) or administrative (n 10) sources and publicly available but generally were not contemporaneous (> 2 years old) and had small sample sizes (10-100 k) relative to organic sources (> 1 M). Organic sources were identified as the electronic medical record (ieMR), wearables, environmental (Google Maps, Crime Map) and billing/claims. Data on social, biomedical and behavioural determinants of obesity typically co-occurred across sources. Environmental and commercial data was sparse and interpreted as low quality. One organic source (ieMR) was highly contemporaneous (routinely updated), had a large sample size (5 M) and represented all determinants of obesity but is not currently used for public health decision-making in Queensland. CONCLUSIONS: This review provides a (1) comprehensive data map for PPH for obesity in Queensland and (2) globally translatable framework to identify, collate and appraise primary data sources to advance PPH for obesity and other noncommunicable diseases. Significant challenges must be addressed to achieve PPH, including: using designed and organic data harmoniously, digital infrastructure for high-quality organic data, and the ethical and social implications of using consumer-centred health data to improve public health. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-12939-x. BioMed Central 2022-03-24 /pmc/articles/PMC8953390/ /pubmed/35331189 http://dx.doi.org/10.1186/s12889-022-12939-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Canfell, Oliver J.
Davidson, Kamila
Sullivan, Clair
Eakin, Elizabeth
Burton-Jones, Andrew
Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia
title Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia
title_full Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia
title_fullStr Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia
title_full_unstemmed Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia
title_short Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia
title_sort data sources for precision public health of obesity: a scoping review, evidence map and use case in queensland, australia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953390/
https://www.ncbi.nlm.nih.gov/pubmed/35331189
http://dx.doi.org/10.1186/s12889-022-12939-x
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