Evidence map›Paper›PMID 40051538›Full record

ArticleBMJ public health2025

Assessing trends in non-coverage bias in mobile phone surveys for estimating insecticide-treated net coverage: a cross-sectional analysis in Tanzania, 2007-2017.

Matt Worges, Ruth A Ashton, Janna Wisniewski, Paul Hutchinson, Hannah Koenker, Tory Taylor, Hannah Metcalfe, Ester Elisaria, Mponeja P Gitanya, Charles Dismas Mwalimu and 2 more

Abstract read
In one paragraph

Article in BMJ public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Matt WorgesTropical Health, New Orleans, Louisiana, USA.ORCID 0000-0002-7332-9275
Ruth A AshtonTulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.
Janna WisniewskiTulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.
Paul HutchinsonTulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.
Hannah KoenkerTropical Health LLP, Baltimore, Maryland, USA.
Tory TaylorThe University of North Carolina, Carolina Population Center, Chapel Hill, North Carolina, USA.
Hannah MetcalfeViamo, Dar es Salaam, Tanzania, United Republic of.
Ester ElisariaDepartment of Impact Evaluation, Ifakara Health Institute, Ifakara, Dar es Salaam, Tanzania, United Republic of.
Mponeja P GitanyaNational Malaria Control Program, Ministry of Health, Dodoma, Tanzania, United Republic of.
Charles Dismas MwalimuNational Malaria Control Program, Ministry of Health, Dodoma, Tanzania, United Republic of.
Frank ChackyNational Malaria Control Program, Ministry of Health, Dodoma, Tanzania, United Republic of.
Joshua O YukichTulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Monitoring insecticide-treated net (ITN) coverage and use generally relies on household surveys which occur on a relatively infrequent basis. Because indicators of coverage are used to forecast the need for ITNs and aid in planning ITN distribution campaigns, higher frequency monitoring could be helpful to guide programme strategies. The use of mobile phone-based survey (MPS) strategies in low-income and middle-income countries has emerged as a rapid and comparatively inexpensive complement to large-scale population-based household surveys, considering the dramatic growth trend of mobile phone ownership. Methods: The potential for non-coverage bias in the calculation of ITN coverage estimates from MPSs was assessed through the use of five consecutive Tanzania-specific Demographic and Health Surveys (DHS). Primary comparisons were made between all households included in the data sets (the reference standard) and mobile phone-owning households (the comparator). Deviations in ITN coverage estimates between the reference standard and mobile phone-owning households were used as a proxy for assessing potential non-coverage bias, with estimates calculated using a bootstrap method. Results: By the 2017 DHS, regional measures of non-coverage bias for ITN coverage indicators rarely exceeded a ±3 percentage point difference when comparing mobile phone-owning households to the overall sample. However, larger differences were observed when comparing mobile phone-owning households to non-mobile phone-owning households, particularly in periods without recent mass ITN distributions. Conclusion: Results suggest that MPSs can reliably estimate ITN coverage at the population level when both ITN coverage and mobile phone ownership are high. However, as ITN coverage declines, the gap between phone-owning and non-phone-owning households widens, indicating potential non-coverage bias and underscoring the need for caution in interpreting MPS data under such conditions.

Indexed as

Cross-Sectional StudiesPopulation SurveillancePublic Healthtrends

Identifiers

PMID40051538
PMCPMC11883883

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.