Evidence map›Paper›PMID 30741642›Full record

Trial reportJMIR mHealth and uHealth2019

Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation.

Atinkut Alamirrew Zeleke, Abebaw Gebeyehu Worku, Adina Demissie, Fabian Otto-Sobotka, Marc Wilken, Myriam Lipprandt, Binyam Tilahun, Rainer Röhrig

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Scoping review of health information technology usability methods leveraged in Africa.Journal of the American Medical Informatics Association : JAMIA · 2023
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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

8 authors.

Atinkut Alamirrew ZelekeDivision of Medical Informatics, Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.ORCID 0000-0001-7838-9050
Abebaw Gebeyehu WorkuDepartment of Reproductive Health, Institute of Public Health, University of Gondar, Gondar, Ethiopia.ORCID 0000-0003-0231-9018
Adina DemissieDepartment of Health Informatics, Institute of Public Health, University of Gondar, Gondar, Ethiopia.ORCID 0000-0002-8177-4598
Fabian Otto-SobotkaDivision of Epidemiology and Biometry, Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.ORCID 0000-0002-9874-1311
Marc WilkenDivision of Medical Informatics, Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.ORCID 0000-0002-4759-6935
Myriam LipprandtDivision of Medical Informatics, Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.ORCID 0000-0001-9371-0551
Binyam TilahunDepartment of Health Informatics, Institute of Public Health, University of Gondar, Gondar, Ethiopia.ORCID 0000-0002-1813-167X
Rainer RöhrigDivision of Medical Informatics, Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.ORCID 0000-0002-0032-5118

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeriodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes.

objectiveThis study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia.

methodsA randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit-based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles.

resultsFrom the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics.

conclusionsEDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site.

Indexed as

AdultCross-Over StudiesData AccuracyData CollectionEthiopiaFemaleHealth SurveysHumansMaleProspective StudiesSurveys and QuestionnairesTechnology Assessment, Biomedicaldata collectiondata qualityEthiopiamaternal healthmHealthpublic healthsurveillancesurveytablet computer

Identifiers

PMID30741642
PMCPMC6388101

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.