Evidence map›Paper›PMID 38018430›Full record

ArticleGlobal health action2023

"We don't trust all data coming from all facilities": factors influencing the quality of care network data quality in Ethiopia.

Asebe Amenu Tufa, Geremew Gonfa, Anene Tesfa, Theodros Getachew, Desalegn Bekele, Ftalew Dagnaw, Nehla Djellouli, Tim Colbourn, Tanya Marchant, Seblewengel Lemma

Open access · goldAbstract read
In one paragraph

Article in Global health action, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 10% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Article
  5. 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

10 authors at 3 institutions in 2 countries.

Asebe Amenu TufaEthiopian Public Health Institute, HSRHRD, Addis Ababa, Ethiopia.
Geremew GonfaEthiopian Public Health Institute, HSRHRD, Addis Ababa, Ethiopia.
Anene TesfaEthiopian Public Health Institute, HSRHRD, Addis Ababa, Ethiopia.
Theodros GetachewEthiopian Public Health Institute, HSRHRD, Addis Ababa, Ethiopia.
Desalegn BekeleEthiopian Ministry of Health, Quality and Clinical Service Directorate, Addis Ababa, Ethiopia.
Ftalew DagnawEthiopian Ministry of Health, Quality and Clinical Service Directorate, Addis Ababa, Ethiopia.
Nehla DjellouliInstitute for Global Health, University College London, London, UK.
Tim ColbournInstitute for Global Health, University College London, London, UK.
Tanya MarchantDepartment of Disease Control, London School of Hygiene & Tropical Medicine, London, UK.
Seblewengel LemmaDepartment of Disease Control, London School of Hygiene & Tropical Medicine, London, UK.
Ethiopian Public Health Institute · ETGovernment of Ethiopia · ETUniversity College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGood quality data are a key to quality health care. In 2017, WHO has launched the Quality of Care Network (QCN) to reduce maternal, newborn and stillbirth mortality via learning and sharing networks. Guided by the principle of equity and dignity, the network members agreed to implement the programme in 2017-2021.

objectiveThis paper seeks to explore how QCN has contributed to improving data quality and to identify factors influencing quality of data in Ethiopia.

methodsWe conducted a qualitative study in selected QCN facilities in Ethiopia using key informant interview and observation methods. We interviewed 40 people at national, sub-national and facility levels. Non-participant observations were carried out in four purposively selected health facilities; we accessed monthly reports from 41 QCN learning facilities. A codebook was prepared following a deductive and inductive analytical approach, coded using Nvivo 12 and thematically analysed.

resultsThere was a general perception that QCN had improved health data documentation and use in the learning facilities, achieved through coaching, learning and building from pre-existing initiatives. QCN also enhanced the data elements available by introducing a broader set of quality indicators. However, the perception of poor data quality persisted. Factors negatively affecting data quality included a lack of integration of QCN data within routine health system activities, the perception that QCN was a pilot, plus a lack of inclusive engagement at different levels. Both individual and system capabilities needed to be strengthened.

conclusionThere is evidence of QCN's contribution to improving data awareness. But a lack of inclusive engagement of actors, alignment and limited skill for data collection and analysis continued to affect data quality and use. In the absence of new resources, integration of new data activities within existing routine health information systems emerged as the most important potential action for positive change.

Indexed as

Data AccuracyTrustEthiopiaHealth FacilitiesHumansInfant, NewbornQuality of Health CareSurveys and Questionnairesdata learningdata qualitydata reliabilitydata sharingQuality of care network

Identifiers

PMID38018430
PMCPMC10795578
OpenAlexW4389143628

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.