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Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission

The expansion in the scope, scale, and sources of data on the wider social determinants of health (SDH) in the last decades could bridge gaps in information available for decision-making. However, challenges remain in making data widely available, accessible, and useful towards improving population...

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Autores principales: Torres, Irene, Thapa, Bishnu, Robbins, Grace, Koya, Shaffi Fazaludeen, Abdalla, Salma M, Arah, Onyebuchi A., Weeks, William B, Zhang, Luxia, Asma, Samira, Morales, Jeanette Vega, Galea, Sandro, Larson, Heidi J., Rhee, Kyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409472/
https://www.ncbi.nlm.nih.gov/pubmed/34472014
http://dx.doi.org/10.1007/s11524-021-00558-7
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author Torres, Irene
Thapa, Bishnu
Robbins, Grace
Koya, Shaffi Fazaludeen
Abdalla, Salma M
Arah, Onyebuchi A.
Weeks, William B
Zhang, Luxia
Asma, Samira
Morales, Jeanette Vega
Galea, Sandro
Larson, Heidi J.
Rhee, Kyu
author_facet Torres, Irene
Thapa, Bishnu
Robbins, Grace
Koya, Shaffi Fazaludeen
Abdalla, Salma M
Arah, Onyebuchi A.
Weeks, William B
Zhang, Luxia
Asma, Samira
Morales, Jeanette Vega
Galea, Sandro
Larson, Heidi J.
Rhee, Kyu
author_sort Torres, Irene
collection PubMed
description The expansion in the scope, scale, and sources of data on the wider social determinants of health (SDH) in the last decades could bridge gaps in information available for decision-making. However, challenges remain in making data widely available, accessible, and useful towards improving population health. While traditional, government-supported data sources and comparable data are most often used to characterize social determinants, there are still capacity and management constraints on data availability and use. Conversely, privately held data may not be shared. This study reviews and discusses the nature, sources, and uses of data on SDH, with illustrations from two middle-income countries: Kenya and the Philippines. The review highlights opportunities presented by new data sources, including the use of big data technologies, to capture data on social determinants that can be useful to inform population health. We conducted a search between October 2010 and September 2020 for grey and scientific publications on social determinants using a search strategy in PubMed and a manual snowball search. We assessed data sources and the data environment in both Kenya and the Philippines. We found limited evidence of the use of new sources of data to study the wider SDH, as most of the studies available used traditional sources. There was also no evidence of qualitative big data being used. Kenya has more publications using new data sources, except on the labor determinant, than the Philippines. The Philippines has a more consistent distribution of the use of new data sources across the HEALTHY determinants than Kenya, where there is greater variation of the number of publications across determinants. The results suggest that both countries use limited SDH data from new data sources. This limited use could be due to a number of factors including the absence of standardized indicators of SDH, inadequate trust and acceptability of data collection methods, and limited infrastructure to pool, analyze, and translate data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11524-021-00558-7.
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spelling pubmed-84094722021-09-02 Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission Torres, Irene Thapa, Bishnu Robbins, Grace Koya, Shaffi Fazaludeen Abdalla, Salma M Arah, Onyebuchi A. Weeks, William B Zhang, Luxia Asma, Samira Morales, Jeanette Vega Galea, Sandro Larson, Heidi J. Rhee, Kyu J Urban Health Article The expansion in the scope, scale, and sources of data on the wider social determinants of health (SDH) in the last decades could bridge gaps in information available for decision-making. However, challenges remain in making data widely available, accessible, and useful towards improving population health. While traditional, government-supported data sources and comparable data are most often used to characterize social determinants, there are still capacity and management constraints on data availability and use. Conversely, privately held data may not be shared. This study reviews and discusses the nature, sources, and uses of data on SDH, with illustrations from two middle-income countries: Kenya and the Philippines. The review highlights opportunities presented by new data sources, including the use of big data technologies, to capture data on social determinants that can be useful to inform population health. We conducted a search between October 2010 and September 2020 for grey and scientific publications on social determinants using a search strategy in PubMed and a manual snowball search. We assessed data sources and the data environment in both Kenya and the Philippines. We found limited evidence of the use of new sources of data to study the wider SDH, as most of the studies available used traditional sources. There was also no evidence of qualitative big data being used. Kenya has more publications using new data sources, except on the labor determinant, than the Philippines. The Philippines has a more consistent distribution of the use of new data sources across the HEALTHY determinants than Kenya, where there is greater variation of the number of publications across determinants. The results suggest that both countries use limited SDH data from new data sources. This limited use could be due to a number of factors including the absence of standardized indicators of SDH, inadequate trust and acceptability of data collection methods, and limited infrastructure to pool, analyze, and translate data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11524-021-00558-7. Springer US 2021-09-01 2021-08 /pmc/articles/PMC8409472/ /pubmed/34472014 http://dx.doi.org/10.1007/s11524-021-00558-7 Text en © The New York Academy of Medicine 2021
spellingShingle Article
Torres, Irene
Thapa, Bishnu
Robbins, Grace
Koya, Shaffi Fazaludeen
Abdalla, Salma M
Arah, Onyebuchi A.
Weeks, William B
Zhang, Luxia
Asma, Samira
Morales, Jeanette Vega
Galea, Sandro
Larson, Heidi J.
Rhee, Kyu
Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission
title Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission
title_full Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission
title_fullStr Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission
title_full_unstemmed Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission
title_short Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3-D Commission
title_sort data sources for understanding the social determinants of health: examples from two middle-income countries: the 3-d commission
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409472/
https://www.ncbi.nlm.nih.gov/pubmed/34472014
http://dx.doi.org/10.1007/s11524-021-00558-7
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