Evidence map›Paper›PMID 29391038›Full record

ArticlePopulation health metrics2018

Collecting verbal autopsies: improving and streamlining data collection processes using electronic tablets.

Abraham D Flaxman, Andrea Stewart, Jonathan C Joseph, Nurul Alam, Sayed Saidul Alam, Hafizur Chowdhury, Meghan D Mooney, Rasika Rampatige, Hazel Remolador, Diozele Sanvictores and 7 more

Abstract readComparative Study
In one paragraph

Article in Population health metrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

17 authors.

Abraham D FlaxmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. abie@uw.edu.ORCID 0000-0001-6033-4713
Andrea StewartInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Jonathan C JosephInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Nurul AlamInternational Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Sayed Saidul AlamInternational Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Hafizur ChowdhurySchool of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Meghan D MooneyInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Rasika RampatigeSchool of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Hazel RemoladorResearch Institute for Tropical Medicine, Muntinlupa City, Philippines.
Diozele SanvictoresResearch Institute for Tropical Medicine, Muntinlupa City, Philippines.
Peter T SerinaInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Peter Kim StreatfieldInternational Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Veronica TalloResearch Institute for Tropical Medicine, Muntinlupa City, Philippines.
Christopher J L MurrayInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Bernardo HernandezInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Alan D LopezSchool of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.
Ian Douglas RileySchool of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.

Funding

National Health and Medical Research Council (AU) 631494
6 · The paper itself

Abstract

backgroundThere is increasing interest in using verbal autopsy to produce nationally representative population-level estimates of causes of death. However, the burden of processing a large quantity of surveys collected with paper and pencil has been a barrier to scaling up verbal autopsy surveillance. Direct electronic data capture has been used in other large-scale surveys and can be used in verbal autopsy as well, to reduce time and cost of going from collected data to actionable information.

methodsWe collected verbal autopsy interviews using paper and pencil and using electronic tablets at two sites, and measured the cost and time required to process the surveys for analysis. From these cost and time data, we extrapolated costs associated with conducting large-scale surveillance with verbal autopsy.

resultsWe found that the median time between data collection and data entry for surveys collected on paper and pencil was approximately 3 months. For surveys collected on electronic tablets, this was less than 2 days. For small-scale surveys, we found that the upfront costs of purchasing electronic tablets was the primary cost and resulted in a higher total cost. For large-scale surveys, the costs associated with data entry exceeded the cost of the tablets, so electronic data capture provides both a quicker and cheaper method of data collection.

conclusionsAs countries increase verbal autopsy surveillance, it is important to consider the best way to design sustainable systems for data collection. Electronic data capture has the potential to greatly reduce the time and costs associated with data collection. For long-term, large-scale surveillance required by national vital statistical systems, electronic data capture reduces costs and allows data to be available sooner.

Indexed as

Cause of DeathComputersCost-Benefit AnalysisDeathAutopsyBangladeshCosts and Cost AnalysisData CollectionElectronicsHumansPhilippinesPopulation SurveillanceSurveys and Questionnaires

Identifiers

PMID29391038
PMCPMC5793369

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.