ArticlePopulation health metrics2021
Is the lack of smartphone data skewing wealth indices in low-income settings?
Article in Population health metrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic comparison of household income, consumption, and assets to measure health inequalities in low- and middle-income countries.Scientific reports · 2024Pooled it
- A cluster-randomized trial of labelled cash transfers for uptake of care for chronic conditions among middle-aged and older adults in Burkina Faso.Nature human behaviour · 2026Trial
- Exploring the Intricacies of Social Mobility Trajectories and Perceived Stress in Post-Reform China.Stress and health : journal of the International Society for the Investigation of Stress · 2025Article
- Serotonergic receptor binding in the brainstem in the Sudden Infant Death Syndrome in a high-risk population.PloS one · 2025Article
- A nationwide mobile phone survey for tobacco use in Tanzania: Sample quality and representativeness compared to a household survey.Preventive medicine reports · 2024Article
- Exploring risk factors of drive for muscularity and muscle dysmorphia in male adolescents from a resource-limited setting in Burkina Faso.Scientific reports · 2023Article
- Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review.PLOS digital health · 2023Article
- Developing digital tools for health surveys in low- and middle-income countries: Comparing findings of two mobile phone surveys with a nationally representative in-person survey in Bangladesh.PLOS global public health · 2023Article
- Telemedicine for Surgical Site Infection Diagnosis in Rural Rwanda: Concordance and Accuracy of Image Reviews.World journal of surgery · 2022Article
- Assessing the prevalence of young children living in households prepared for COVID-19 in 56 low- and middle-income countries.Global health research and policy · 2022Article
- Value of statistical life year in extreme poverty: a randomized experiment of measurement methods in rural Burkina Faso.Population health metrics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundSmartphones 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.
methodsWe 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.
resultsHouseholds 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.
conclusionsThe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.