Evidence map›Paper›PMID 40562539›Full record

ArticleBMJ global health2025

A multi-country comparison between mobile phone surveys and face-to-face household surveys to estimate the prevalence of non-communicable diseases behavioural risk factors in low- and middle-income settings.

Julián A Fernández-Niño, Saifuddin Ahmed, Gulam Muhammed Al Kibria, Stacy Davlin, Rachael Phadnis, Melanie Cowan, Romina Costa Beltrán, Juan Carlos Zevallos Lopez, Juan Vásconez, Jones K Masiye and 13 more

Abstract readComparative Study
In one paragraph

Article in BMJ global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

23 authors.

Julián A Fernández-NiñoDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Saifuddin AhmedPopulation, Family And Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Gulam Muhammed Al KibriaDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Stacy DavlinCDC Foundation Inc, Atlanta, Georgia, USA.
Rachael PhadnisCDC Foundation Inc, Atlanta, Georgia, USA.ORCID http://orcid.org/0000-0002-0150-7749
Melanie CowanWorld Health Organization, Geneva, Switzerland.
Romina Costa BeltránMinistry of Public Health of Ecuador, Quito, Ecuador.
Juan Carlos Zevallos LopezMinistry of Public Health of Ecuador, Quito, Ecuador.
Juan VásconezMinistry of Public Health of Ecuador, Quito, Ecuador.
Jones K MasiyeGovernment of Malawi Ministry of Health, Lilongwe, Malawi.
Hicham El BerriGovernment of the Kingdom of Morocco Ministry of Public Health, Rabat, Morocco.
Samir MounachGovernment of the Kingdom of Morocco Ministry of Public Health, Rabat, Morocco.
Mangla GomareBrihanmumbai Municipal Corporation, Mumbai, Maharashtra, India.
Daksha ShahBrihanmumbai Municipal Corporation, Mumbai, Maharashtra, India.
Gulnar KhanBrihanmumbai Municipal Corporation, Mumbai, Maharashtra, India.
Niraj DaveNielsen India, Mumbai, Maharashtra, India.
Champika WickramasingheGovernment of Sri Lanka Ministry of Health Sri Lanka, Colombo, Sri Lanka.
Udara PereraGovernment of Sri Lanka Ministry of Health Sri Lanka, Colombo, Sri Lanka.
Kennedy LishimpiZambia Ministry of Health, Lusaka, Zambia.
Wilbroad MutaleZambia Ministry of Health, Lusaka, Zambia.
Namasiku SiyumbwaZambia Ministry of Health, Lusaka, Zambia.ORCID http://orcid.org/0009-0007-1387-8882
Leanne RileyWorld Health Organization, Geneva, Switzerland.
Dustin G GibsonCenter for Global Digital Health Innovation, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA dgibso28@jhu.edu.ORCID http://orcid.org/0000-0002-9073-3376

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundAlthough mobile phone surveys (MPS) are routinely used to collect health information in high-income countries, concerns remain about the impact of bias on population-level estimates in low-income settings and validation studies are lacking. This study aims to compare non-communicable diseases (NCDs) risk factor estimates obtained from MPS and nationally representative face-to-face household surveys in six low- and middle-income settings.

methodsThe MPS contained core questions from the standard STEPwise approach to NCD risk factor surveillance questionnaire. MPS sampling frames were generated by random digit dialling, while data collection was done by interactive voice response and SMS. At the same time, a nationally representative household survey (WHO STEPS) was conducted using multi-stage sampling. Participants aged 18 and older were included. Absolute differences and prevalence ratios, with 95% CIs, were analysed. The distribution of the differences between estimates by sex, age and education was also explored.

resultsMPS and STEPS surveys were conducted in Ecuador, Malawi, Morocco, Zambia, Mumbai (India) and Sri Lanka between 2017 and 2022. Overall, MPS estimates of NCDs were most similar to STEPS estimates in Ecuador and Sri Lanka, and most dissimilar in Mumbai and Malawi. Broadly, smoking tobacco, fruit and vegetable consumption, and current drinking questions performed similarly across settings, whereas questions on smokeless tobacco, salt intake and hypertension yielded dissimilar results.

conclusionsMPS estimates were most similar to household estimates in settings with high levels of mobile phone ownership. MPS have the potential to serve as a valuable tool to monitor and address NCD risk factors, in addition to traditional face-to-face household surveys. However, producing nationally representative MPS estimates requires careful adjustments to sampling strategies, addressing coverage biases and overcoming technological limitations. Currently, face-to-face household surveys reach a more representative sample of the population, including those in remote and lower educational demographics.

Indexed as

Cell PhoneDeveloping CountriesHealth SurveysNoncommunicable DiseasesAdolescentAdultAgedFemaleHumansMaleMiddle AgedPrevalenceRisk FactorsSurveys and QuestionnairesYoung AdultCross-sectional surveyEpidemiologyGlobal Health

Identifiers

PMID40562539
PMCPMC12198783

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LicenceCC BY
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Registered trials

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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.