Beyond the Antibiogram: The Significance and Advantages of WHONET-Driven AST Analysis in the Modern Food Microbiology Laboratory
L. C. Nnodim *
Department of Microbiology, Rivers State University, Port Harcourt, Nigeria.
E. B. Enaregha
Department of Biology, Isaac Jasper Boro College of Education, Sagbama, Nigeria.
F. O. Osakuade
Department of Microbiology, University of Port Harcourt, Port Harcourt, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Food microbiology laboratories generate large volumes of antimicrobial susceptibility testing data, yet the analytical conventions they apply were largely inherited from clinical bacteriology. The cumulative antibiogram, designed to inform empirical therapy for hospital patients, remains the dominant summary artefact in many food-sector laboratories, although its assumptions about patients, specimens and treatment decisions align poorly with commodity chains, regulatory monitoring and microbiological rather than clinical definitions of resistance. WHONET, free software distributed since 1989 for the management and analysis of microbiological laboratory data, has been taken up in more than one hundred countries, but its documented application to food isolates remains sparse relative to human clinical use. This critical narrative review evaluates what WHONET-driven analysis can and cannot contribute to food microbiology laboratories, and distinguishes claims supported by evidence from those resting on plausibility alone. Literature was identified through structured searching of publicly accessible scholarly indexes and citation registries, supplemented by backward and forward citation tracking and by examination of authoritative institutional sources, then appraised for design adequacy, transparency and topical relevance rather than pooled quantitatively. Four propositions are examined: that retention of quantitative test measurements permits interpretation against epidemiological cut-off values and retrospective reinterpretation as guidance changes; that structured extraction from laboratory information systems reduces transcription burden and supports international reporting; that resistance-profile analysis offers a phenotypic signal useful for detecting unusual clusters; and that internal consistency checks improve data quality. The evidence supporting the first and second propositions is reasonably firm and is corroborated by the design of European harmonised monitoring; evidence for the third derives almost entirely from clinical and public health settings and has not been demonstrated for food matrices; evidence for the fourth is largely descriptive. Reported food-sector applications are few, geographically uneven and frequently opaque about isolate selection, guideline version and denominators. Software adoption is treated in much of the literature as an outcome rather than as a means to analytical improvement, and this conflation obscures whether analytical quality has actually improved.
Keywords: Antimicrobial resistance surveillance, epidemiological cut-off value, food safety monitoring, laboratory data management, minimum inhibitory concentration, One Health, resistance phenotype