TabMenu

Poverty and InequalitySexual and Reproductive HealthFamily, Maternal & Child HealthComputational Population & Health SciencesMethodology

Health Surveys Using Mobile Phones in Developing Countries: Automated Active Strata Monitoring and Other Statistical Considerations for Improving Precision and Reducing Biases

TitleHealth Surveys Using Mobile Phones in Developing Countries: Automated Active Strata Monitoring and Other Statistical Considerations for Improving Precision and Reducing Biases
Publication TypeJournal Article
Year of Publication2017
AuthorsLabrique, A, Blynn, E, Ahmed, S, Gibson, D, Pariyo, G, Hyder, AA
JournalJ Med Internet Res
Volume19
Paginatione121
Date PublishedMay 05
ISBN Number1438-8871
Accession Number28476726
Keywordsmobile health, mobile phone, research methodology, Sampling Studies, Surveys and Questionnaires
Abstract

In low- and middle-income countries (LMICs), historically, household surveys have been carried out by face-to-face interviews to collect survey data related to risk factors for noncommunicable diseases. The proliferation of mobile phone ownership and the access it provides in these countries offers a new opportunity to remotely conduct surveys with increased efficiency and reduced cost. However, the near-ubiquitous ownership of phones, high population mobility, and low cost require a re-examination of statistical recommendations for mobile phone surveys (MPS), especially when surveys are automated. As with landline surveys, random digit dialing remains the most appropriate approach to develop an ideal survey-sampling frame. Once the survey is complete, poststratification weights are generally applied to reduce estimate bias and to adjust for selectivity due to mobile ownership. Since weights increase design effects and reduce sampling efficiency, we introduce the concept of automated active strata monitoring to improve representativeness of the sample distribution to that of the source population. Although some statistical challenges remain, MPS represent a promising emerging means for population-level data collection in LMICs.