Cargando…
Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania
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 dec...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745510/ https://www.ncbi.nlm.nih.gov/pubmed/33334323 http://dx.doi.org/10.1186/s12911-020-01366-w |
_version_ | 1783624620716326912 |
---|---|
author | Rumisha, Susan F. Lyimo, Emanuel P. Mremi, Irene R. Tungu, Patrick K. Mwingira, Victor S. Mbata, Doris Malekia, Sia E. Joachim, Catherine Mboera, Leonard E. G. |
author_facet | Rumisha, Susan F. Lyimo, Emanuel P. Mremi, Irene R. Tungu, Patrick K. Mwingira, Victor S. Mbata, Doris Malekia, Sia E. Joachim, Catherine Mboera, Leonard E. G. |
author_sort | Rumisha, Susan F. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7745510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77455102020-12-18 Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania Rumisha, Susan F. Lyimo, Emanuel P. Mremi, Irene R. Tungu, Patrick K. Mwingira, Victor S. Mbata, Doris Malekia, Sia E. Joachim, Catherine Mboera, Leonard E. G. BMC Med Inform Decis Mak Research Article 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. BioMed Central 2020-12-17 /pmc/articles/PMC7745510/ /pubmed/33334323 http://dx.doi.org/10.1186/s12911-020-01366-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Rumisha, Susan F. Lyimo, Emanuel P. Mremi, Irene R. Tungu, Patrick K. Mwingira, Victor S. Mbata, Doris Malekia, Sia E. Joachim, Catherine Mboera, Leonard E. G. Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania |
title | Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania |
title_full | Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania |
title_fullStr | Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania |
title_full_unstemmed | Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania |
title_short | Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania |
title_sort | data quality of the routine health management information system at the primary healthcare facility and district levels in tanzania |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745510/ https://www.ncbi.nlm.nih.gov/pubmed/33334323 http://dx.doi.org/10.1186/s12911-020-01366-w |
work_keys_str_mv | AT rumishasusanf dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT lyimoemanuelp dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT mremiirener dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT tungupatrickk dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT mwingiravictors dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT mbatadoris dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT malekiasiae dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT joachimcatherine dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania AT mboeraleonardeg dataqualityoftheroutinehealthmanagementinformationsystemattheprimaryhealthcarefacilityanddistrictlevelsintanzania |