See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research article
Leonard Mboera
National Institute for Medical Research
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5746-3776
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Effective 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.
Methods: This 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.
Results: A 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.
Conclusion: There 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.
Keywords
health information, data, quality, indicators, completeness, accuracy, facility, district, Tanzania
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View the published article at BMC Medical Informatics and Decision Making.
Version 4
Posted 17 Dec, 2020
No community comments so far
Editorial decision: Accept
On 07 Dec, 2020
Editor assigned
On 06 Dec, 2020
Submission checks complete
On 06 Dec, 2020
Editor invited
On 06 Dec, 2020
Version (no preprint)
Posted 02 Dec, 2020
Submission checks complete
On 02 Dec, 2020
Editorial decision: Minor revision
On 30 Nov, 2020
This version was not preprinted
No comments provided
Reviewers invited
Invitations sent on 16 Nov, 2020
Editor assigned
On 15 Nov, 2020
Submission checks complete
On 15 Nov, 2020
Editor invited
On 15 Nov, 2020
No comments provided
Editorial decision: Major revision
On 26 Oct, 2020
Reviewer #4 agreed
On 19 Sep, 2020
Review #4 received
Received 19 Sep, 2020
Reviewer #3 agreed
On 17 Sep, 2020
Review #3 received
Received 17 Sep, 2020
Reviewers invited
Invitations sent on 16 Sep, 2020
Reviewer #1 agreed
On 16 Sep, 2020
Reviewer #2 agreed
On 16 Sep, 2020
Review #1 received
Received 16 Sep, 2020
Review #2 received
Received 16 Sep, 2020
Editor assigned
On 15 Sep, 2020
Submission checks complete
On 14 Sep, 2020
Editor invited
On 14 Sep, 2020
No comments provided
Editorial decision: Major revision
On 13 Aug, 2020
Review #2 received
Received 10 Aug, 2020
Review #4 received
Received 10 Aug, 2020
Review #3 received
Received 07 Aug, 2020
Review #5 received
Received 04 Aug, 2020
Reviewer #5 agreed
On 24 Jul, 2020
Reviewer #3 agreed
On 21 Jul, 2020
Reviewer #4 agreed
On 21 Jul, 2020
Editor assigned
On 20 Jul, 2020
Reviewers invited
Invitations sent on 20 Jul, 2020
Reviewer #1 agreed
On 20 Jul, 2020
Reviewer #2 agreed
On 20 Jul, 2020
Review #1 received
Received 20 Jul, 2020
Submission checks complete
On 10 Jul, 2020
Editor invited
On 08 Jul, 2020
First submitted
On 28 Jun, 2020
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See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Leonard Mboera
National Institute for Medical Research
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5746-3776
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
Version 4
Posted 17 Dec, 2020
No community comments so far
Editorial decision: Accept
On 07 Dec, 2020
Editor assigned
On 06 Dec, 2020
Submission checks complete
On 06 Dec, 2020
Editor invited
On 06 Dec, 2020
Version (no preprint)
Posted 02 Dec, 2020
Submission checks complete
On 02 Dec, 2020
Editorial decision: Minor revision
On 30 Nov, 2020
This version was not preprinted
No comments provided
Reviewers invited
Invitations sent on 16 Nov, 2020
Editor assigned
On 15 Nov, 2020
Submission checks complete
On 15 Nov, 2020
Editor invited
On 15 Nov, 2020
No comments provided
Editorial decision: Major revision
On 26 Oct, 2020
Reviewer #4 agreed
On 19 Sep, 2020
Review #4 received
Received 19 Sep, 2020
Reviewer #3 agreed
On 17 Sep, 2020
Review #3 received
Received 17 Sep, 2020
Reviewers invited
Invitations sent on 16 Sep, 2020
Reviewer #1 agreed
On 16 Sep, 2020
Reviewer #2 agreed
On 16 Sep, 2020
Review #1 received
Received 16 Sep, 2020
Review #2 received
Received 16 Sep, 2020
Editor assigned
On 15 Sep, 2020
Submission checks complete
On 14 Sep, 2020
Editor invited
On 14 Sep, 2020
No comments provided
Editorial decision: Major revision
On 13 Aug, 2020
Review #2 received
Received 10 Aug, 2020
Review #4 received
Received 10 Aug, 2020
Review #3 received
Received 07 Aug, 2020
Review #5 received
Received 04 Aug, 2020
Reviewer #5 agreed
On 24 Jul, 2020
Reviewer #3 agreed
On 21 Jul, 2020
Reviewer #4 agreed
On 21 Jul, 2020
Editor assigned
On 20 Jul, 2020
Reviewers invited
Invitations sent on 20 Jul, 2020
Reviewer #1 agreed
On 20 Jul, 2020
Reviewer #2 agreed
On 20 Jul, 2020
Review #1 received
Received 20 Jul, 2020
Submission checks complete
On 10 Jul, 2020
Editor invited
On 08 Jul, 2020
First submitted
On 28 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Informatics and Decision Making.
Background: Effective 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.
Methods: This 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.
Results: A 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.
Conclusion: There 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.
Figure 1
Figure 1
Figure 1
Figure 1
Figure 1
Figure 1
Figure 1
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
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