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Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone
BACKGROUND: Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412785/ https://www.ncbi.nlm.nih.gov/pubmed/32767983 http://dx.doi.org/10.1186/s12913-020-05591-x |
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author | Njuguna, Charles Vandi, Mohamed Mugagga, Malimbo Kanu, Joseph Liyosi, Evans Chimbaru, Alexander Gachari, Wilson Caulker, Victor Sesay, Stephen Githuku, Jane Yoti, Zabulon Yahaya, Ali Ahmed Talisuna, Ambrose Fall, Ibrahima Socé |
author_facet | Njuguna, Charles Vandi, Mohamed Mugagga, Malimbo Kanu, Joseph Liyosi, Evans Chimbaru, Alexander Gachari, Wilson Caulker, Victor Sesay, Stephen Githuku, Jane Yoti, Zabulon Yahaya, Ali Ahmed Talisuna, Ambrose Fall, Ibrahima Socé |
author_sort | Njuguna, Charles |
collection | PubMed |
description | BACKGROUND: Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor accuracy of IDSR data. METHODS: Starting 2016, data quality assessments (DQA) were conducted in randomly selected health facilities. Structured electronic checklist was used to interview district health management teams (DHMT) and health facility staff. We used malaria data, to assess data accuracy, as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of confirmed malaria cases recorded in health facility registers to the number of malaria cases in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF < 95% were considered over reporting while VF > 105 was underreporting. Differences in the proportion of accurate reports at baseline and subsequent assessments were compared using Z-test for two proportions. RESULTS: Between 2016 and 2018, four DQA were conducted in 444 health facilities where 1729 IDSR reports were reviewed. Registers and IDSR technical guidelines were available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from over- reporting of 4.7% (VF 95.3%) in 2016 to under-reporting of 0.2% (VF 100.2%) in 2018. Compared to 2016, proportion of accurate IDSR reports increased by 14.8% (95% CI 7.2, 22.3%) in May 2017 and 19.5% (95% CI 12.5–26.5%) by 2018. Over reporting was more common in private clinics and not- for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities and missing source documents in 47 (10.6%) health facilities. CONCLUSION: This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data. |
format | Online Article Text |
id | pubmed-7412785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74127852020-08-10 Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone Njuguna, Charles Vandi, Mohamed Mugagga, Malimbo Kanu, Joseph Liyosi, Evans Chimbaru, Alexander Gachari, Wilson Caulker, Victor Sesay, Stephen Githuku, Jane Yoti, Zabulon Yahaya, Ali Ahmed Talisuna, Ambrose Fall, Ibrahima Socé BMC Health Serv Res Research Article BACKGROUND: Public health agencies require valid, timely and complete health information for early detection of outbreaks. Towards the end of the Ebola Virus Disease (EVD) outbreak in 2015, the Ministry of Health and Sanitation (MoHS), Sierra Leone revitalized the Integrated Disease Surveillance and Response System (IDSR). Data quality assessments were conducted to monitor accuracy of IDSR data. METHODS: Starting 2016, data quality assessments (DQA) were conducted in randomly selected health facilities. Structured electronic checklist was used to interview district health management teams (DHMT) and health facility staff. We used malaria data, to assess data accuracy, as malaria was endemic in Sierra Leone. Verification factors (VF) calculated as the ratio of confirmed malaria cases recorded in health facility registers to the number of malaria cases in the national health information database, were used to assess data accuracy. Allowing a 5% margin of error, VF < 95% were considered over reporting while VF > 105 was underreporting. Differences in the proportion of accurate reports at baseline and subsequent assessments were compared using Z-test for two proportions. RESULTS: Between 2016 and 2018, four DQA were conducted in 444 health facilities where 1729 IDSR reports were reviewed. Registers and IDSR technical guidelines were available in health facilities and health care workers were conversant with reporting requirements. Overall data accuracy improved from over- reporting of 4.7% (VF 95.3%) in 2016 to under-reporting of 0.2% (VF 100.2%) in 2018. Compared to 2016, proportion of accurate IDSR reports increased by 14.8% (95% CI 7.2, 22.3%) in May 2017 and 19.5% (95% CI 12.5–26.5%) by 2018. Over reporting was more common in private clinics and not- for profit facilities while under-reporting was more common in lower level government health facilities. Leading reasons for data discrepancies included counting errors in 358 (80.6%) health facilities and missing source documents in 47 (10.6%) health facilities. CONCLUSION: This is the first attempt to institutionalize routine monitoring of IDSR data quality in Sierra Leone. Regular data quality assessments may have contributed to improved data accuracy over time. Data compilation errors accounted for most discrepancies and should be minimized to improve accuracy of IDSR data. BioMed Central 2020-08-07 /pmc/articles/PMC7412785/ /pubmed/32767983 http://dx.doi.org/10.1186/s12913-020-05591-x 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 Njuguna, Charles Vandi, Mohamed Mugagga, Malimbo Kanu, Joseph Liyosi, Evans Chimbaru, Alexander Gachari, Wilson Caulker, Victor Sesay, Stephen Githuku, Jane Yoti, Zabulon Yahaya, Ali Ahmed Talisuna, Ambrose Fall, Ibrahima Socé Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone |
title | Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone |
title_full | Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone |
title_fullStr | Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone |
title_full_unstemmed | Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone |
title_short | Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone |
title_sort | institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in sierra leone |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412785/ https://www.ncbi.nlm.nih.gov/pubmed/32767983 http://dx.doi.org/10.1186/s12913-020-05591-x |
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