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Is the lack of smartphone data skewing wealth indices in low-income settings?

BACKGROUND: Smartphones have rapidly become an important marker of wealth in low- and middle-income countries, but international household surveys do not regularly gather data on smartphone ownership and these data are rarely used to calculate wealth indices. METHODS: We developed a cross-sectional...

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Autores principales: Poirier, Mathieu J. P., Bärnighausen, Till, Harling, Guy, Sié, Ali, Grépin, Karen A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852097/
https://www.ncbi.nlm.nih.gov/pubmed/33526039
http://dx.doi.org/10.1186/s12963-021-00246-3
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author Poirier, Mathieu J. P.
Bärnighausen, Till
Harling, Guy
Sié, Ali
Grépin, Karen A.
author_facet Poirier, Mathieu J. P.
Bärnighausen, Till
Harling, Guy
Sié, Ali
Grépin, Karen A.
author_sort Poirier, Mathieu J. P.
collection PubMed
description BACKGROUND: Smartphones have rapidly become an important marker of wealth in low- and middle-income countries, but international household surveys do not regularly gather data on smartphone ownership and these data are rarely used to calculate wealth indices. METHODS: We developed a cross-sectional survey module delivered to 3028 households in rural northwest Burkina Faso to measure the effects of this absence. Wealth indices were calculated using both principal components analysis (PCA) and polychoric PCA for a base model using only ownership of any cell phone, and a full model using data on smartphone ownership, the number of cell phones, and the purchase of mobile data. Four outcomes (household expenditure, education level, and prevalence of frailty and diabetes) were used to evaluate changes in the composition of wealth index quintiles using ordinary least squares and logistic regressions and Wald tests. RESULTS: Households that own smartphones have higher monthly expenditures and own a greater quantity and quality of household assets. Expenditure and education levels are significantly higher at the fifth (richest) socioeconomic status (SES) quintile of full model wealth indices as compared to base models. Similarly, diabetes prevalence is significantly higher at the fifth SES quintile using PCA wealth index full models, but this is not observed for frailty prevalence, which is more prevalent among lower SES households. These effects are not present when using polychoric PCA, suggesting that this method provides additional robustness to missing asset data to measure underlying latent SES by proxy. CONCLUSIONS: The lack of smartphone data can skew PCA-based wealth index performance in a low-income context for the top of the socioeconomic spectrum. While some PCA variants may be robust to the omission of smartphone ownership, eliciting smartphone ownership data in household surveys is likely to substantially improve the validity and utility of wealth estimates.
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spelling pubmed-78520972021-02-03 Is the lack of smartphone data skewing wealth indices in low-income settings? Poirier, Mathieu J. P. Bärnighausen, Till Harling, Guy Sié, Ali Grépin, Karen A. Popul Health Metr Research BACKGROUND: Smartphones have rapidly become an important marker of wealth in low- and middle-income countries, but international household surveys do not regularly gather data on smartphone ownership and these data are rarely used to calculate wealth indices. METHODS: We developed a cross-sectional survey module delivered to 3028 households in rural northwest Burkina Faso to measure the effects of this absence. Wealth indices were calculated using both principal components analysis (PCA) and polychoric PCA for a base model using only ownership of any cell phone, and a full model using data on smartphone ownership, the number of cell phones, and the purchase of mobile data. Four outcomes (household expenditure, education level, and prevalence of frailty and diabetes) were used to evaluate changes in the composition of wealth index quintiles using ordinary least squares and logistic regressions and Wald tests. RESULTS: Households that own smartphones have higher monthly expenditures and own a greater quantity and quality of household assets. Expenditure and education levels are significantly higher at the fifth (richest) socioeconomic status (SES) quintile of full model wealth indices as compared to base models. Similarly, diabetes prevalence is significantly higher at the fifth SES quintile using PCA wealth index full models, but this is not observed for frailty prevalence, which is more prevalent among lower SES households. These effects are not present when using polychoric PCA, suggesting that this method provides additional robustness to missing asset data to measure underlying latent SES by proxy. CONCLUSIONS: The lack of smartphone data can skew PCA-based wealth index performance in a low-income context for the top of the socioeconomic spectrum. While some PCA variants may be robust to the omission of smartphone ownership, eliciting smartphone ownership data in household surveys is likely to substantially improve the validity and utility of wealth estimates. BioMed Central 2021-02-01 /pmc/articles/PMC7852097/ /pubmed/33526039 http://dx.doi.org/10.1186/s12963-021-00246-3 Text en © The Author(s) 2021 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/. 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 in a credit line to the data.
spellingShingle Research
Poirier, Mathieu J. P.
Bärnighausen, Till
Harling, Guy
Sié, Ali
Grépin, Karen A.
Is the lack of smartphone data skewing wealth indices in low-income settings?
title Is the lack of smartphone data skewing wealth indices in low-income settings?
title_full Is the lack of smartphone data skewing wealth indices in low-income settings?
title_fullStr Is the lack of smartphone data skewing wealth indices in low-income settings?
title_full_unstemmed Is the lack of smartphone data skewing wealth indices in low-income settings?
title_short Is the lack of smartphone data skewing wealth indices in low-income settings?
title_sort is the lack of smartphone data skewing wealth indices in low-income settings?
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852097/
https://www.ncbi.nlm.nih.gov/pubmed/33526039
http://dx.doi.org/10.1186/s12963-021-00246-3
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