Background
Good surveillance data quality is vital for accurate planning and to application of timely and appropriate interventions. Data is a collection of items of information. It can be defined as the elements of measurements recorded during data collection. It is the collected data that creates information when it is further processed which will then improve the knowledge of end users. Knowledge is then assessed to improve the understanding of the researchers which provides wisdom. Wisdom is required to make evidence-based decisions for action [1]. Animal health surveillance provides essential information to allow action to be taken to protect animal health and welfare. This is also closely linked with the protection of human health. In addition, the detection of hazards in human populations may contribute to the detection of hazards in animal populations [2].
Data quality assessment (DQA) is the confirmation of the accuracy, completeness, consistency, and timeliness of data. Importance of DQA needs for the following purposes such as to reduce errors and inconsistencies within the data collected, reported and used, improve the quality of data to meet reporting requirements, provide accurate, consistent, complete, and timely data to all stakeholders, decisions made based on validated data are more defensible, ability to compare data across institutions and organizations, as the data becomes more accurate, less time and effort is spent on the validation and verification process, ultimately saving time and money [3].
It is important to monitor data quality and thus ensure that the collected data are meaningful so they meet the objectives of local, national, and international surveillance systems. The quality of the initial data may determine the data quality at all stages of the reporting process [4]. If stakeholders understand the value of the data collected and the impact of surveillance on animal and human health, this should lead to improved design of surveillance activities, enhanced compliance with data collection, increased likelihood of investment, and (ultimately) an increased probability of achieving the overall goal of the surveillance [5]. A key feature of surveillance is the need for systematic (continuous or repeated) measurement, collection, collation, analysis, interpretation, and timely dissemination of data [6].
The DQA and routine DQA tools are grounded in the components of data quality, namely, that programs and projects need accurate, reliable, precise, complete, and timely data reports that managers can use to effectively direct available resources and to evaluate progress toward established goals. Based on these components of data quality, the DQA tool is comprised of two components: (1) assessment of data management and reporting systems; and (2) verification of reported data for key indicators at selected sites [7]. It is widely considered the most cost-effective way to early detection of outbreaks and to gather information on the disease situation for decision-making on control strategies [8].
However, consistent, monthly reporting of two documents could provide evidence for the quality status of surveillance data of the livestock sector at the Woreda level in Ethiopia. These are conducted in the Oromia regional state of Jimma and Borana zone in Kersa and Guchi Woreda, respectively [1,9], which helped us to identify key factors influencing surveillance data quality at the district level.
Therefore, the investigation was conducted to determine the animal health surveillance data quality of the Woreda and to generate efficient recommendations that will help us to apply basic principles to approve data quality.
General objective
To determine and describe the monthly data quality audit and related gaps as well as to generate recommendations.
Specific objectives
- To evaluate how data is collected and managed in the Woreda.
- To evaluate the surveillance data and information flow.
- To determine the quality of Data of Disease Outbreak and Vaccination Activity Reports (DOVARs) monthly.
- To evaluate the awareness of the stakeholders about data use.
- To evaluate the link of the laboratory with field investigation.
The Woreda Background Description
In Ethiopia’s east is where you will find the East Hararghe zone. In the Meta Woreda of the Oromia regional state, from June 2023 to July 2023, a monthly audit of the quality of the data was performed. Meta Woreda is situated 448 km to the east of the capital Addis Abeba and 65 km south of Dire Dawa. At an elevation of 2,167.84 m above sea level, the location is at 9.3975701° latitude and 41.5615967° longitude. In Meta Woreda, there are over 200,747 cattle, 38,322 sheep, 190,837 goats, 428 horses, 28,665 donkeys, 115 mules, 2,418 camels, 185,652 chickens, and 2,580 various kinds of bee colonies based on animal populations obtained from Meta Woreda Livestock and Fishery Development Office that was recorded in 2013 according to the Ethiopian calendar. The 2007 national census reported a total population for this district of 252,269, of whom 127,371 were men and 124,898 were women; 12,459 or 5.19% of its population are urban dwellers. There are three distinct agroecological zones, known as lowland, mid-land, and highland, which make up 28.2%, 43.6%, and 28.2% of the district’s overall area, respectively. Its temperature ranges between 16°C and 20°C. Different plant and animal species live in these agroclimatic zones. The Woreda features sixteen “D” type veterinary clinics/posts, one “B” type, and one “C” type. In the Woreda, there are three private veterinary clinics. Table 1 below lists the Woreda’s veterinary personnel.
The Meta Woreda maps with boundary-shared Woreda
Meta is one of the districts in the Oromia of Ethiopia. Part of the East Hararghe zone, Meta is bordered on the southwest by Deder, on the northwest by Goro Gutu, on the north by Somali Region, on the northeast by Kersa, and the southeast by Bedeno. Towns in Meta include Chelenko and Kulubi. There is one new Woreda secession from Meta that is called Goro Muti.
Table 1.
Veterinary workforce and infrastructure of Meta Woreda.
| Qualification | MSc | DVM | BVSc | AHA | VLT | AHT | Total | |
|---|---|---|---|---|---|---|---|---|
| Number | 2 | 5 | 6 | 42 | 2 | - | 57 | |
| Veterinary clinics types | Standard | |||||||
| B | C | D | ||||||
| Number | 1 | 1 | 16 | 18 | ||||
MSc, Master of Science; DVM, Doctor of Veterinary Medicine; BVSc, Bachelor of Veterinary Science; AHA, animal health assistants; VLT, veterinary laboratory technician; AHT, animal health technician (Fig. 1).
Figure 1.
Map of Meta Woreda with shared boundary areas.
Results
Description of the surveillance system of the Woreda
In our wereda, the surveillance system in the local area, animal health assistants (AHAs) collect the information from farmers during the farmers come into their sites to inform them about the raised animals’ problems or through the mobile phone call received from the local area kebele administrators, after which the animal health assistant summits to the coordinator of livestock offices via phone call, and finally, the coordinator of livestock informs the district agricultural offices head.
Data collection, source, submission, and storage
The local veterinarian clinics provided the data for the animal health surveillance program on paper based on the standardization of record formats and verbally via phone calls from AHA on animal health- related disease occurrences that they come across on their site to exchange information to increase response times and feedback. Consequently, the local area AHA recognized the disease’s spread, and delivered the conditions to the district livestock development office via phone calls about it. The Woreda to transfer the call to the zone livestock development office and regional veterinary laboratory for immediate response and action, and the district will fill out DOVARS forms. Before wasting time and money on this, the focal persons who have taken training and responsibility can be identified and the reports they submit can be examined for accuracy, completeness, and timeliness. In Meta Woreda, DOVARS documents were stored in the veterinary clinics in safe and secure locations. Following sending for the zone, the remaining components are stored as known or valuable documents in secure locations (Fig. 2).
Figure 2.
Animal health surveillance data flow in Meta Woreda.
Data analysis
Long ago, the Woreda ignored analyzing the information kept in the veterinary clinics according to animal-time-place databases. Graphs were used to analyze and illustrate the recorded data in Microsoft Excel. The replies received from the district’s focal persons for animal health surveillance were assessed using the criteria of data quality.
Targeted variables
Completeness, accuracy, and timeliness are the things considered as targeted variables in this monthly data quality audit. The definition of variables is defined as follows. Completeness: the percentage of blank or unknown data, not zero/missing. All data should be in a disaggregated form to permit further analysis. Accuracy: the percentage of data variables on the collection form without an error: examples—missing data, incorrect coding, transposed error, incorrect units, incorrect/inconsistent format. Timeliness: the percentage of reports from the sub locals, and facilities that were received on time.
Surveillance data benefit (usefulness)
The district coordinator of the livestock development office has been invited by the aforementioned concerned authority to meet with the Woreda AHA who do not know about the effectiveness of the surveillance data and have little experience in the field. This constraint prevents these experts from comprehending the value of surveillance data and prevents them from filling out formats that are comparable to national DOVARS format reports.
Awareness of data use by stakeholders
As seen, surveillance data is used to track the spread of diseases and to provide reports for the zone; the Woreda do not have any goals for gathering really obvious surveillance data. The Meta Woreda response focal individuals claim that data gatherers for surveillance are aware of the collected data; however, the Woreda has not performed an analysis as of yet.
Use of laboratory in field investigation/surveillance
Hirna Veterinary Regional Laboratory is the responsible institution for providing diagnostic support for the Woreda. Consequently, there was collects the samples for diagnostic purposes at the laboratory in June months around 400 samples from two peasant associations. Then, it used maximum; it takes 3 days to collect and deliver samples to the laboratory, and the time required to receive feedback from the laboratory takes 3 weeks. This shows that there are better relationships between the laboratory and the Woreda livestock development offices and field investigation.
Assessment of DOVARS
From the total 78 DOVAR formats which are expected to be sent every month in the past 6 years and 6 months until this month included, only 65 reports were found in the Woreda office. Reports sent in 2021, 2022, and 2023, were complete until this month included while 7, 9, 9, and 10 reports were encountered for 2017, 2018, 2019, and 2020, respectively. The available reports can be described in the following (Table 2).
These available reports were assessed against the key data quality indicators. There were no outbreak reports in the past 3 years and 6 months until this month included. There were only 7.7% (5/65) of five outbreaks (two lump skin diseases, one peste des petits ruminants, one New Castle disease, and one ovine pasteurellosis). The rest of the reports (92.3%) are zero reports. It is reported that through the usual informal way to the Woreda and the Woreda veterinarians conducted field investigation. Out of 65 reviewed reports, 3.1% (2/65) have missing data while 50.8% (33/65) have a problem with accuracy. On the other hand, 13.85% (9/65) of reports have a problem of timeliness (Fig. 3).
Table 2.
Available DOVAR formats between 2017 and 2023 in Meta Woreda.
| Years | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|---|---|---|
| DOVARs | 7 | 9 | 9 | 10 | 12 | 12 | 6 |
From the total missing data counted, 100% (2/2) is related to the phone number of the reporting person (Fig. 4).
From the total 50.08% accuracy problem found 9% (3/33), 9% (3/33), and 3% (1/33) of the reports have typing errors (writing the general information on page 1 in small letters which were expected to be written in capital letter, month and year in our country calendar which was expected to write in Gregorian calendar and the zone place included abbreviation rather than written full zone name, respectively) and 76% (25/33) of the reports have no official stamp on it. On the other hand, 3% (1/33) have designation errors [filled by wrong designation like veterinary laboratory technician (VLT)]. As regards, the timeliness 13.85% (9/65) of reports were received lately (Figs. 5–8).
In case of outbreak filling that occurred in January 2019, peste des petits ruminants there was a lack of Geo Reference of Peasant Association rather than name only (Fig. 9).
Discussion
The present findings of the animal health surveillance data quality audit through retrospectively accredited documents of DOVAR from 2017 to 2023 in this study revealed that there are limitations on the animal health surveillance data quality of Meta Woreda. Similarly, quality-based data rely on the data sources, data collection, data recording, management, and reporting systems deserved to output better data quality for decision-makers. There is two recently documented case study in our country Ethiopia especially the Oromia regional state at the Woreda level relating to animal health surveillance data quality audit in the Jimma zone and in Borena zone specifically in Kersa and Guchi Woreda, respectively, which may help us to compare with and discuss the results of the present study. Nevertheless, the focal persons who take responsibility for reporting the issues need to take emphasis during filling out the surveillance data in this area.
Figure 3.
Data quality audit findings from DOVAR of Meta Woreda.
Figure 4.
Evidence of missing data (the phone number of the reporting person).
Figure 5.
The region, zone, and Woreda are written in noncapitalized letters.
Figure 6.
Month and year written incorrectly calendar then corrected.
Figure 7.
Zone name abbreviated rather than written full zone name.
Figure 8.
Filled by VLT rather than Master of Science, Doctor of Veterinary Medicine, AHA, and animal health technician.
Figure 9.
Lack of geo-references of the outbreak occurred sites.
The outbreaks of 7.7% were recorded in Meta DOVARS assessed in the past 6 years and 6 months until June month of 2023 included this finding is in agreement with studies conducted in Karsa Woreda, Jimma zone, Oromia, Ethiopia [1] reported 6.7% in their study on animal health surveillance DQA. The outbreak is 7.7% lower than [9] who reported 77% in their study on animal health surveillance DQA in Guchi Woreda, Borena zone, Oromia, Ethiopia.
The rest 92.3% of the assessed reports were zero reports this finding is in agreement with studies conducted in Karsa Woreda, Jimma zone, Oromia, Ethiopia [1] who reported 93% in their study on animal health surveillance DQA. The obtained targeted data quality variable is missing data, inaccurate data and problem of timeliness in the assessed DOVARs of Meta Woreda livestock development office agree with [1] who reported similar variables in their study on animal health surveillance DQA in Karsa Woreda, Jimma zone, Oromia, Ethiopia. The majority of the data quality problems found were encountered on page 1 (general information) and page 3 (the details of the outbreak reporting person) of the DOVARS format. 50.8% of inaccurate data is recorded in the woreda, but higher than (42.8%) of inaccurate data reported in Guchi Woreda, Borena zone, Oromia, Ethiopia, were less than 66.6% and those reported in Karsa Woreda, Jimma zone, Oromia, Ethiopia, were lower. According to the current study, the Meta Woreda animal health monitoring data quality audit’s timeliness is 13.85%. This finding is less than that of [1], who reported a timeliness rate of 31.6% in their investigation on the quality of the animal health surveillance data collected in Karsa Woreda, Jimma zone, Oromia, Ethiopia. The Woreda local area AHA or the local area kebele administrators are responsible for gathering the monthly animal health surveillance data quality reports, which are then forwarded by the animal health assistant to the district agricultural offices by the coordinator of livestock development offices via phone call. The DOVARS documents are kept in a secure location, but there is no data analysis on the district’s animal-place-time relationships. The zone livestock development offices were then informed. As a result, the zone livestock development office shared a standard DOVARs format filled form with the Hirna veterinary regional laboratory. Collaboration is improved by the connections to the regional veterinary laboratory in Hirna. Similar to this, the regional veterinary laboratory in Hirna switched to an internet system and sent data to the Oromia Livestock Development Agency. Finally, the Federal Ministry of Agriculture received a submission from the Oromia Livestock Development Agency. The issues with reporting on animal health surveillance were caused by a lack of emphasis, carelessness in filling out the form, and waiting for requests for reports from the aforementioned responsible authority.
Conclusion
The surveillance system in Meta Woreda needs to improve animal health surveillance data quality, identify filled information accurately, and ensure proper data collection and analysis. The lack of proper training and proper data collection methods, as well as adequate awareness of data usefulness, contribute to poor data quality. The lack of clear objectives for data collection and inadequate training in local veterinary clinics further hinder collaboration and result in poor data on DOVARS reports.
Depending on the above conclusion, I recommend Meta Woreda livestock development office animal health team they should:
- Preserving the completed DOVARS for analysis in a permanent location.
- Regularly supervising staff at local area Veterinary clinics to obtain data of the highest quality.
- Encouraging local AHA to provide accurate data for advancements in surveillance systems sustainability.
- Regional veterinary laboratories gather, verify, and input disease reports into the system, carry out data analysis, and then are required to provide feedback for the zone and district.
- Have specific goals in mind when gathering data for animal health surveillance.
- Create an organized strategy for gathering surveillance data.
