Abstract

Data quality audits help ensure that the data collected through animal health surveillance systems is accurate and reliable. This is essential for making informed decisions about disease control and prevention strategies. It’s also important to monitor, determine and describe the monthly data quality audit and related gaps as well as to generate recommendations. The monthly data quality audit was conducted in the Oromia regional state in Meta woreda from June 2023 to July 2023, Ethiopia. From the total 78 DOVAR format which are expected to be sent every month in the past six years and six month until these month included, only 65 reports were found in the woreda office. These available reports were assessed against the key data quality indicators. There were only 7.7 % (5/65) five outbreaks (Two Lump Skin Disease, One Peste des Petits Ruminants, one New Castle Disease and one Ovine Pasteurellosis. The rest of the reports (92.3%) are zero reports. It was reported that through the usual informal way to the woreda and the woreda veterinarians conducted field investigation. Out of sixty five reviewed reports 3.1% (2/65) have missing data while 50.8% (33/65) have a problem of accuracy. On the other hand 13.85% (9/65) of reports have a problem of timeliness. The surveillance data of the woreda have the problem of completeness, accuracy and timeliness. The woreda doesn't make an effort to identify missing data, errors and the timeliness of the reports. The lack of clear objectives for data collection and inadequate training in local veterinary clinics further hinders collaboration and results in poor data on DOVARS reports.

Key words: Data Quality, Data Recording, Meta, Reporting Systems, Surveillance Systems.