Observational studyMalaria journal2025
The limitations of mobile phone data for measuring movement patterns of populations at risk of malaria.
Observational study in Malaria journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
backgroundAs global mobile phone adoption increases, mobile phone data has been increasingly used to measure movement patterns of populations at risk of malaria. However, the representativeness of mobile phone data for populations at risk of malaria has not been assessed. This study aimed to assess this representativeness using prospectively collected data on mobile phone ownership and use from malaria patients in Lao PDR.
methodsA prospective observational study was conducted from 2017 to 2021. 6320 patients with confirmed malaria in 107 health facilities in the five southernmost provinces of Lao PDR were surveyed regarding their demographics, mobile phone ownership and use. Data on the demographics of mobile phone owners and users in the general population of Lao PDR were obtained from the 2017 Lao Social Indicator Survey II, which was a nationally representative survey sample. Descriptive analysis was performed, and logistic regression with weights on aggregate data was used to compare the demographic distribution of mobile phone ownership and use in malaria patients with that in the general population.
resultsMost patients with malaria (76%) did not own or use a mobile phone. From 2017 to 2021, mobile phone usage in the general population consistently ranged between 53 and 67%, whereas among malaria patients, usage remained significantly lower, fluctuating between 20 and 28%. At the district level, log malaria incidence rate (API) was weakly negatively correlated with the proportion of mobile owners (R
conclusionMobility data from anonymized and aggregated call data records (CDR) from the general population may not sufficiently represent the population at risk of malaria to accurately model disease transmission. Yet mobile phone data is commonly used to model malaria transmission in endemic countries. Before doing so, it is critical to quantify mobile usage among the population at risk of malaria. Where this is low, either movement estimates derived from mobile phone data need to be adjusted to increase model accuracy, or another method should be used to measure the mobility of populations with malaria.
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