| Peer-Reviewed

Assessment on Animal Health Surveillance Data Quality: The Case Study in Guchi Woreda, Borena Zone, Ethiopia 2022

Received: 9 September 2022     Accepted: 24 October 2022     Published: 30 October 2022
Views:       Downloads:
Abstract

High-quality surveillance data provide valid and useful evidence for decision-making and rapid response. Data is pieces of information; it can be defined as the elements of measurements recorded during data collection. Data quality is a measure of data condition based on factors such as accuracy, completeness, reliability, and whether it’s up-to-date. There is no enough research in Ethiopia that describes the quality of animal health surveillance data reports. Therefore, the objective of the study is to analysis the animal health surveillance data of the woreda and to comment on identified problem. Retrospective case study was conducted in Guchi woreda of Borena zone, Oromia regional state. The district 2021 DOVAR report format was examined for timeliness, correctness, and completeness. To ascertain the reporting rates and quality issues, Microsoft Excel was employed. Using previously created structured interview questions, the woreda's overall data quality and associated problems were evaluated. Based on this study's evaluation of the DOVAR report, 77% of outbreaks were reported in the district last year; the remaining 22.2% of reports were zero reports. Nine reports were examined, and 66.6 % were inaccurate, while 44.4% had a timeliness issue. On the other hand, there is a problem with completeness in 77.7% of the reports. The surveillance data of the woreda have the problem of accuracy, completeness and timeliness. The woreda's goals for gathering surveillance data are well known. However, due to the high data quality issues in their DOVARs, the woreda should establish clear objectives for the data that is required, create a plan for the best way to collect the data, use standardized formats to capture the necessary data, train staff on how to collect accurate and reliable data, and store and retain data.

Published in Animal and Veterinary Sciences (Volume 10, Issue 5)
DOI 10.11648/j.avs.20221005.16
Page(s) 161-169
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Data Quality, Surveillance Data, Accuracy, Completeness, Timeliness

References
[1] Alonso V, Santos JV, Pinto M, et al. Health records as the basis of clinical coding: is the quality adequate? A qualitative study of medical coders' perceptions. Health Inf Manag 2020; 49: 28-37.
[2] Arscott-Mills S, Holder Y, Gordon G, System JIS. Comparative evaluation of different modes of a national accident and emergency department-based injury surveillance system: Jamaican experience. Inj Control Saf Promot. 2002 Dec; 9 (4): 235-9.
[3] Balcha, C., & Dvm, J. (2022). Animal Health Surveillance Data Quality Assessment: The Case Study in Karsa Woreda, Jimma Zone, Oromia, Ethiopia, 2021. Journal of Medicine, Physiology and Biophysics, 72, 1–13. https://doi.org/10.7176/jmpb/72-01
[4] Baldissera S, Campostrini S, Binkin N, Minardi V, Minelli G, Ferrante G, et al. Features and initial assessment of the Italian behavioral risk factor surveillance system (PASSI), 2007-2008. Prev Chronic Dis. 2011 Jan; 8 (1): A24.
[5] Boston (2022), Data Management Body of Knowledge https://www.ataccama.com/dist/images/icons/icon-link.svg
[6] Fleckenstein, M.; Fellows, L. (2018). "Chapter 11: Data Quality". Modern Data Strategy. Springer. pp. 101–120. ISBN 9783319689920. Archived from the original on 31 July 2020. Retrieved 18 April 2020.
[7] German RR, Lee LM, Horan JM, et al. Updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines Working group. MMWR Recomm Rep 2001; 50: 1-35.
[8] Guchi agricultural office (2022).
[9] Jack V, (2019). “DEFINITION data quality”
[10] Lippeveld T, Sauerborn R, Bodart C. 2000. Design and Implementation of Health Information Systems. Geneva: World Health Organization.
[11] Santos, C. C.-, Neves, A. L., Correia, R., Santos, P., Monteiro-, M., Freitas, A., Henriques, T. S., Rodrigues, P. P., Vaz, I. R.-, Pereira, A. C.-, Pereira, A. M., & Fonseca, J. A. (2021). 19 surveillance data quality issues : a national consecutive case series. 1–10. https://doi.org/10.1136/bmjopen-2020-047623
[12] UNAIDS (2008). Organizing Framework for a Functional National HIV Monitoring and Evaluation System. Geneva: UNAIDS.
Cite This Article
  • APA Style

    Gerade Abduljami. (2022). Assessment on Animal Health Surveillance Data Quality: The Case Study in Guchi Woreda, Borena Zone, Ethiopia 2022. Animal and Veterinary Sciences, 10(5), 161-169. https://doi.org/10.11648/j.avs.20221005.16

    Copy | Download

    ACS Style

    Gerade Abduljami. Assessment on Animal Health Surveillance Data Quality: The Case Study in Guchi Woreda, Borena Zone, Ethiopia 2022. Anim. Vet. Sci. 2022, 10(5), 161-169. doi: 10.11648/j.avs.20221005.16

    Copy | Download

    AMA Style

    Gerade Abduljami. Assessment on Animal Health Surveillance Data Quality: The Case Study in Guchi Woreda, Borena Zone, Ethiopia 2022. Anim Vet Sci. 2022;10(5):161-169. doi: 10.11648/j.avs.20221005.16

    Copy | Download

  • @article{10.11648/j.avs.20221005.16,
      author = {Gerade Abduljami},
      title = {Assessment on Animal Health Surveillance Data Quality: The Case Study in Guchi Woreda, Borena Zone, Ethiopia 2022},
      journal = {Animal and Veterinary Sciences},
      volume = {10},
      number = {5},
      pages = {161-169},
      doi = {10.11648/j.avs.20221005.16},
      url = {https://doi.org/10.11648/j.avs.20221005.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.avs.20221005.16},
      abstract = {High-quality surveillance data provide valid and useful evidence for decision-making and rapid response. Data is pieces of information; it can be defined as the elements of measurements recorded during data collection. Data quality is a measure of data condition based on factors such as accuracy, completeness, reliability, and whether it’s up-to-date. There is no enough research in Ethiopia that describes the quality of animal health surveillance data reports. Therefore, the objective of the study is to analysis the animal health surveillance data of the woreda and to comment on identified problem. Retrospective case study was conducted in Guchi woreda of Borena zone, Oromia regional state. The district 2021 DOVAR report format was examined for timeliness, correctness, and completeness. To ascertain the reporting rates and quality issues, Microsoft Excel was employed. Using previously created structured interview questions, the woreda's overall data quality and associated problems were evaluated. Based on this study's evaluation of the DOVAR report, 77% of outbreaks were reported in the district last year; the remaining 22.2% of reports were zero reports. Nine reports were examined, and 66.6 % were inaccurate, while 44.4% had a timeliness issue. On the other hand, there is a problem with completeness in 77.7% of the reports. The surveillance data of the woreda have the problem of accuracy, completeness and timeliness. The woreda's goals for gathering surveillance data are well known. However, due to the high data quality issues in their DOVARs, the woreda should establish clear objectives for the data that is required, create a plan for the best way to collect the data, use standardized formats to capture the necessary data, train staff on how to collect accurate and reliable data, and store and retain data.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Assessment on Animal Health Surveillance Data Quality: The Case Study in Guchi Woreda, Borena Zone, Ethiopia 2022
    AU  - Gerade Abduljami
    Y1  - 2022/10/30
    PY  - 2022
    N1  - https://doi.org/10.11648/j.avs.20221005.16
    DO  - 10.11648/j.avs.20221005.16
    T2  - Animal and Veterinary Sciences
    JF  - Animal and Veterinary Sciences
    JO  - Animal and Veterinary Sciences
    SP  - 161
    EP  - 169
    PB  - Science Publishing Group
    SN  - 2328-5850
    UR  - https://doi.org/10.11648/j.avs.20221005.16
    AB  - High-quality surveillance data provide valid and useful evidence for decision-making and rapid response. Data is pieces of information; it can be defined as the elements of measurements recorded during data collection. Data quality is a measure of data condition based on factors such as accuracy, completeness, reliability, and whether it’s up-to-date. There is no enough research in Ethiopia that describes the quality of animal health surveillance data reports. Therefore, the objective of the study is to analysis the animal health surveillance data of the woreda and to comment on identified problem. Retrospective case study was conducted in Guchi woreda of Borena zone, Oromia regional state. The district 2021 DOVAR report format was examined for timeliness, correctness, and completeness. To ascertain the reporting rates and quality issues, Microsoft Excel was employed. Using previously created structured interview questions, the woreda's overall data quality and associated problems were evaluated. Based on this study's evaluation of the DOVAR report, 77% of outbreaks were reported in the district last year; the remaining 22.2% of reports were zero reports. Nine reports were examined, and 66.6 % were inaccurate, while 44.4% had a timeliness issue. On the other hand, there is a problem with completeness in 77.7% of the reports. The surveillance data of the woreda have the problem of accuracy, completeness and timeliness. The woreda's goals for gathering surveillance data are well known. However, due to the high data quality issues in their DOVARs, the woreda should establish clear objectives for the data that is required, create a plan for the best way to collect the data, use standardized formats to capture the necessary data, train staff on how to collect accurate and reliable data, and store and retain data.
    VL  - 10
    IS  - 5
    ER  - 

    Copy | Download

Author Information
  • Yabello Regional Veterinary Laboratory Center, Epidemiology Department, Yabello, Ethiopia

  • Sections