Conference Abstract | Volume 9, Abstract 016 (ConfProc7) | Published: 20 Apr 2026
Luke Atiewin Atiah1,&, George Akowuah2, Samuel Dapaa2, Samuel Sackey2, Mavis Osafo2, Belinda Osei-Akoto2, Magdalene Odikro2, Joseph Asamoah Frimpong2, Martha Arkaa Kotey2
1Veterinary Services Department, Tempane District, 2Ministry of Food and Agriculture, Tempane District, 3Ghana Field Epidemiology and Laboratory Training Programme (GFELTP), UG SPH, Legon Accra
&Corresponding author: Luke Atiewin Atiah, Veterinary Services Department, Tempane District, Email: atiewinluke@gmail.com
Received: 29 Aug 2025, Accepted: 28 Oct 2025, Published: 20 Apr 2026
Domain: Infectious Disease Epidemiology
Keywords: Data Quality, Audit, disease surveillance, Veterinary services, Tempane District
©Luke Atiewin Atiah et al Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Luke Atiewin Atiah et al. Data quality audit report of the Veterinary Services Department, Tempane District, March- December, 2024. Journal of Interventional Epidemiology and Public Health. 2026;9(ConfProc7):016. https://doi.org/10.37432/JIEPH-CONFPRO7-0016
Data quality refers to the degree in which data is fit and can be used confidently for a particular purpose. It is an important aspect of data management and data analysis, poor data quality can lead to inaccurate decision making. Accordingly, Veterinary Services in the Tempane district seeks to create an animal health care system, which provides quality health for animals.
The data audit was conducted at Tempane district in the Upper East Region of Ghana. A descriptive cross-sectional design was used for the data audit. Questionnaires, monthly reports and field notebooks were reviewed. The data was analyzed using SWOT analysis and the results of the three operational areas were further presented on tables.
A total of eight staffs were engaged; 87.5% (7/8) were males and 12.5% (1/8) female with an average working experience of three years. The operational areas had the list of reportable diseases which was not pasted. The monthly reporting forms were available. No case definitions pasted and no sample collection tools.
Furthermore, inadequate infrastructure, insufficient supply of vaccines, low number of staff in the district and inadequate logistics were threats. These contributed to inconsistent and incomplete data records, leading to completeness of 90% and 95% timeliness.
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