Mathieu J P Poirier1,2, Till Bärnighausen3,4,5,6, Guy Harling4,5,6,7,8, Ali Sié9, Karen A Grépin10. 1. School of Global Health, Faculty of Health, York University, 4700 Keele Street, Dahdaleh Building 5022C, Toronto, Ontario, M3J 1P3, Canada. matp33@yorku.ca. 2. Global Strategy Lab, York University, 4700 Keele Street, Dahdaleh Building 5022C, Toronto, Ontario, M3J 1P3, Canada. matp33@yorku.ca. 3. Heidelberg Institute of Global Health (HIGH), Medical Faculty and University Hospital, Heidelberg University, Heidelberg, Germany. 4. Africa Health Research Institute (AHRI), Somkhele, KwaZulu-Natal, South Africa. 5. MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 6. Center for Population and Development Studies, Harvard University, Cambridge, MA, USA. 7. Institute for Global Health, University College London, London, UK. 8. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, USA. 9. Centre de Recherche en Santé de Nouna, Institut National de Santé Publique, Nouna, Burkina Faso. 10. School of Public Health, University of Hong Kong, Hong Kong, China.
Abstract
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.
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.
Entities:
Keywords:
Burkina Faso; Development; Education; Health inequality; Household expenditures; Principal components analysis; Smartphones; Socioeconomic status; Wealth index
Authors: Maria Lisa Odland; Collin Payne; Miles D Witham; Mark J Siedner; Till Bärnighausen; Mamadou Bountogo; Boubacar Coulibaly; Pascal Geldsetzer; Guy Harling; Jennifer Manne-Goehler; Lucienne Ouermi; Ali Sie; Justine I Davies Journal: BMJ Glob Health Date: 2020-03-29
Authors: Miles D Witham; Justine I Davies; Till Bärnighausen; Mamadou Bountogo; Jennifer Manne-Goehler; Collin F Payne; Lucienne Ouermi; Ali Sie; Mark J Siedner; Guy Harling Journal: Wellcome Open Res Date: 2019-09-11
Authors: Stefan T Trautmann; Yilong Xu; Christian König-Kersting; Bryan N Patenaude; Guy Harling; Ali Sié; Till Bärnighausen Journal: Popul Health Metr Date: 2021-11-17