Retrospective Analysis of Veterinary Pathology Laboratory Submissions to Characterise Diagnostic Patterns in South...

Veterinary pathology laboratories play an important role in animal health, food security and disease surveillance by generating large volumes of diagnostic data. However routinely collected laboratory data is often underutilised as a source of epidemiological information. This study evaluated the potential of routinely collected veterinary pathology laboratory data to identify temporal, species-related and submitting-client location patterns in diagnostic submission in South Africa. A retrospective analysis of 3786 post-mortem cases submitted to the Onderstepoort Pathology Laboratory (OPL) between January 2018 and November 2023 was conducted using a business intelligence tool. Cases were classified as Infectious, Non-infectious, No diagnosis or Complex, and analysed according to year, season, species and submitting-client location. Non-infectious cases constituted the largest proportion of the diagnostic caseload (1803/3786; 48%), followed by Infectious cases (1532/3786; 40%). Non-infectious cases were particularly represented among canine and exotic and game submissions, whereas Infectious cases constituted a larger proportion of submissions from several production-animal groups. Seasonal variation was observed in the diagnostic caseload, with differences between pathological categories across seasons; however, these patterns were descriptive and did not establish seasonal disease risk. Gauteng accounted for 76% of submissions, followed by Northwest (8%), Limpopo (6%) and Mpumalanga (4%), reflecting the geographic distribution of submitting-client locations to a laboratory located in Gauteng rather than population-level disease occurrence. Retrospective veterinary pathology laboratory data can provide useful information on diagnostic workload and identify temporal, species and submitting-client location patterns that may contribute to veterinary disease surveillance. The findings also highlight the importance of standardised data capture, appropriate interpretation of referral-based datasets and integration of laboratory information with other surveillance streams. The 2023 caseload represents January to November only and is therefore not directly comparable with full-year totals.