Evidence map›Paper›PMID 28283037›Full record

ArticleBMC health services research2017

Tuberculosis cure rates and the ETR.Net: investigating the quality of reporting treatment outcomes from primary healthcare facilities in Mpumalanga province, South Africa.

A W Dreyer, D Mbambo, M Machaba, C E M Oliphant, M M Claassens

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Article in BMC health services research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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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

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

5 authors.

A W DreyerDepartment of Medical Microbiology, University of Pretoria and the National Health Laboratory Services (NHLS), Pretoria, South Africa. aw.dreyer@gmail.com.
D MbamboDepartment of Health, Mpumalanga Province, Mpumalanga, South Africa.
M MachabaDepartment of Health, Mpumalanga Province, Mpumalanga, South Africa.
C E M OliphantDesmond Tutu TB Centre, Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa.
M M ClaassensDesmond Tutu TB Centre, Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis control programs rely on accurate collection of routine surveillance data to inform program decisions including resource allocation and specific interventions. The electronic TB register (ETR.Net) is dependent on accurate data transcription from both paperbased clinical records and registers at the facilities to report treatment outcome data. The study describes the quality of reporting of TB treatment outcomes from facilities in the Ehlanzeni District, Mpumalanga Province.

methodsA descriptive crossectional study of primary healthcare facilities in the district for the period 1 January - 31 December 2010 was performed. New smear positive TB cure rate data was obtained from the ETR.Net followed by verification of paperbased clinical records, both TB folders and the TB register, of 20% of all new smear positive cases across the district for correct reporting to the ETR.Net. Facilities were grouped according to high (>70%) and low cure rates (≤ 70%) as well as high (> 20%) and low (≤ 20%) error proportions in reporting. Kappa statistic was used to determine agreement between paperbased record, TB register and ETR.Net.

resultsOf the100 facilities (951 patient clinical records), 51(51%) had high cure rates and high error proportions, 14(14%) had a high cure rate and low error proportion whereas 30(30%) had low cure rates and high error proportions and five (5%) had a low cure rate with low error proportion. Fair agreement was observed (Kappa = 0.33) overall and between registers. Of the 473 patient clinical records which indicated cured, 383(81%) was correctly captured onto the ETR.Net, whereas 51(10.8%) was incorrectly captured and 39(8.2%) was not captured at all. Over reporting of treatment success of 12% occurred on the ETR.Net.

conclusionsThe high error proportion in reporting onto the ETR.Net could result in a false sense of improvement in the TB control programme in the Ehlanzeni district.

Indexed as

Cross-Sectional StudiesData AccuracyHumansPrimary Health CareSouth AfricaTreatment OutcomeTuberculosisETR.NetQuality of dataTreatment outcome

Identifiers

PMID28283037
PMCPMC5345238

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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.