Evidence map›Paper›PMID 33334323›Full record

ArticleBMC medical informatics and decision making2020

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania.

Susan F Rumisha, Emanuel P Lyimo, Irene R Mremi, Patrick K Tungu, Victor S Mwingira, Doris Mbata, Sia E Malekia, Catherine Joachim, Leonard E G Mboera

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing 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

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

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2 more citing papers are in PubMed but not listed here.

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

9 authors.

Susan F RumishaNational Institute for Medical Research, Headquarters, Dar es Salaam, Tanzania.
Emanuel P LyimoNational Institute for Medical Research, Headquarters, Dar es Salaam, Tanzania.
Irene R MremiNational Institute for Medical Research, Headquarters, Dar es Salaam, Tanzania.
Patrick K TunguNational Institute for Medical Research, Amani Research Centre, Muheza, Tanzania.
Victor S MwingiraNational Institute for Medical Research, Amani Research Centre, Muheza, Tanzania.
Doris MbataNational Institute for Medical Research, Headquarters, Dar es Salaam, Tanzania.
Sia E MalekiaNational Institute for Medical Research, Headquarters, Dar es Salaam, Tanzania.
Catherine JoachimMinistry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania.
Leonard E G MboeraSACIDS Foundation for One Health, Sokoine University of Agriculture, Morogoro, Tanzania. lmboera@gmail.com.ORCID 0000-0001-5746-3776

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffective planning for disease prevention and control requires accurate, adequately-analysed, interpreted and communicated data. In recent years, efforts have been put in strengthening health management information systems (HMIS) in Sub-Saharan Africa to improve data accessibility to decision-makers. This study assessed the quality of routine HMIS data at primary healthcare facility (HF) and district levels in Tanzania.

methodsThis cross-sectional study involved reviews of documents, information systems and databases, and collection of primary data from facility-level registers, tally sheets and monthly summary reports. Thirty-four indicators from Outpatient, Inpatient, Antenatal care, Family Planning, Post-natal care, Labour and Delivery, and Provider-Initiated Testing and Counselling service areas were assessed. Indicator records were tracked and compared across the process of data collection, compilation and submission to the district office. Copies of monthly report forms submitted by facilities to the district were also reviewed. The availability and utilization of HMIS tools were assessed, while completeness and data accuracy levels were quantified for each phase of the reporting system.

resultsA total of 115 HFs (including hospitals, health centres, dispensaries) in 11 districts were involved. Registers (availability rate = 91.1%; interquartile range (IQR) 66.7-100%) and report forms (86.9%; IQR 62.2-100%) were the most utilized tools. There was a limited use of tally-sheets (77.8%; IQR 35.6-100%). Tools availability at the dispensary was 91.1%, health centre 82.2% and hospital 77.8%, and was low in urban districts. The availability rate at the district level was 65% (IQR 48-75%). Wrongly filled or empty cells in registers and poor adherence to the coding procedures were observed. Reports were highly over-represented in comparison to registers' records, with large differences observed at the HF phase of the reporting system. The OPD and IPD areas indicated the highest levels of mismatch between data source and district office. Indicators with large number of clients, multiple variables, disease categorization, or those linked with dispensing medicine performed poorly.

conclusionThere are high variations in the tool utilisation and data accuracy at facility and district levels. The routine HMIS is weak and data at district level inaccurately reflects what is available at the source. These results highlight the need to design tailored and inter-service strategies for improving data quality.

Indexed as

Data AccuracyManagement Information SystemsCross-Sectional StudiesData CollectionFemaleHumansMalePrimary Health CareTanzaniaAccuracyCompletenessDataDistrictFacilityHealth informationIndicatorsQualityTanzania

Identifiers

PMID33334323
PMCPMC7745510

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Registered trials

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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.