Animal Health Surveillance Data Quality Assessment: The Case Study in Karsa Woreda, Jimma Zone, Oromia, Ethiopia, 2021

Abstract

Good surveillance data quality is vital for accurate planning and to apply timely and appropriate interventions. Data quality refers to completeness, accuracy and timeliness of data gathered. Despite consistent monthly reporting and clinical case registration, so far there is no document which could provide evidence for the quality status of surveillance data of the livestock sector at woreda level in Ethiopia. In action taken in Liberia after the quality audit, there was remarkable improvement in the quality of the data produced. Retrospective case study was conducted in Karsa woreda of Jimma zone Oromia regional state. The objective of the study is to assess the animal health surveillance data of the woreda and to provide new recommendations. The DOVAR format of the woreda from 2015-2020 and clinic case book were reviewed for the completeness, accuracy and timeliness. The records of data quality indicators in each variable of source document was counted and entered into Microsoft excel sheet. It then line listed and displayed in proportion. The overall data quality and related issues of the woreda were assessed by using a structured interview questions. The study shows that the assessed DOVARs are 83.3% complete. In the assessed reports there are 6.6% missing data, 35% inaccurate data and 31.6% late reports. This study also indicates that 89% of the sampled registered cases in the case book have missing data. The problem of accuracy in the case book is found to be 27.5%. The surveillance data of the woreda have the problem of completeness, accuracy and timeliness. Data collectors didn’t received training on surveillance. The woreda retain the collected data but do not analyze it. The woreda do not have clearly stated objectives for collecting surveillance data. These gaps lead them to have poor data quality DOVARs and case book. Therefore the woreda should develop clear objectives about the data that is needed; develop a clear plan about the best way of obtaining the data; use standardized formats that can capture the data required; train people on how to collect accurate and reliable data; store and retain data.

Keywords: Accuracy, Completeness, Data quality, Surveillance Data, Timeliness

DOI: 10.7176/JMPB/72-01

Publication date: January 31^st 2022