PROFILE
Professor Nicholas Tete Kwaku Dzifa Dayie is a Professor of Medical Microbiology at the University of Ghana Medical School whose career has focused on advancing antimicrobial resistance (AMR) research, strengthening laboratory systems, and developing surveillance frameworks that support evidence-based healthcare and public health policy. Since joining the University of Ghana in 2006, he has contributed extensively to research on bacterial pathogens, antimicrobial stewardship, vaccine effectiveness, and AMR genomics. His work has informed Ghana's national treatment guidelines and surveillance strategies, while collaborations with international partners have helped secure over US$12 million in research and capacity-building grants to strengthen infectious disease research and laboratory systems.
Beyond his research, Professor Dayie has played a leading role in academic leadership, national policy development, and capacity building. He has spearheaded national proficiency testing programmes, developed integrated AMR surveillance strategies, contributed to global AMR initiatives, and authored more than 80 peer-reviewed publications. Within the University of Ghana, he has mentored numerous undergraduate and postgraduate students while serving in several academic leadership positions. His inaugural lecture reflects the culmination of nearly two decades of work, highlighting the critical role of scientifically robust laboratory evidence in guiding clinical decisions, informing public health action, and strengthening health systems.
ABSTRACT
Antimicrobial Resistance (AMR) refers to the ability of microorganisms to withstand the effects of antimicrobial agents, whether through intrinsic biological properties or through acquired genetic mechanisms, resulting in reduced or complete loss of therapeutic effectiveness. In doing so, microbes ‘speak’, not in words, but through their resistance patterns, revealing biological adaptation that laboratories must accurately detect and interpret. Thus, AMR is not merely a biological phenomenon; it is also a test of the reliability of the systems constructed to measure it.
In 2019, bacterial AMR was directly responsible for approximately 1.27 million deaths worldwide and a contributing factor to a further five million more deaths. Projections estimate that by 2050, AMR could account for up to 10 million deaths annually, surpassing current global mortality from cancer and disproportionately affecting Africa and Asia. Beyond mortality, AMR poses profound economic consequences, with projected global costs reaching USD 100 trillion and an estimated reduction of global GDP by 2–3 percent. These figures elevate AMR from a clinical challenge to a global health security and development imperative. These global trends are increasingly reflected in clinical practice, where resistant pathogens such as extended-spectrum β-lactamase–producing Escherichia coli compromise the effectiveness of commonly used antibiotics such as ceftriaxone, while the emergence of carbapenem-resistant Klebsiella pneumoniae threatens the effectiveness of last-line therapies.
Behind these global estimates lie laboratory reports that shape clinical decisions, surveillance systems that inform therapeutic guidelines and aggregated datasets that anchor public health policy. The integrity of AMR surveillance is, therefore, not a matter of secondary importance; it is central to patient safety, institutional credibility and global health security. At the most immediate level, the accuracy of AMR detection is first a matter of patient care. In clinical bacteriology, misclassification of susceptibility to a specific antibiotic is not a statistical anomaly; it is a potential therapeutic error that may lead to ineffective treatment and adverse clinical outcomes. Thus, correct detection and reporting of resistance mechanisms are cornerstones of safe clinical microbiology practice before they become components of surveillance architecture.
Bacteria and their resistance mechanisms exist independently of how accurately we measure them. A bacterium either harbours resistance determinants or it does not. Laboratories do not create this biological reality; they attempt to identify it accurately. Yet between bacterial biology and national or global surveillance data lies a structured chain of processes such as specimen collection, organism identification, antimicrobial susceptibility testing, interpretive standardisation, reporting, aggregation and governance within which distortion may occur.
When antimicrobial resistance data appear inconsistent across laboratories, institutions or time, a critical scientific question emerges: are we observing genuine biological variation or structural and methodological weaknesses within the systems designed to measure it? Variability in resistance patterns may represent true biological processes such as responses to antibiotic selection pressure, clonal expansion of resistant strains, horizontal gene transfer, or ecological shifts in pathogen populations. However, similar patterns may also arise from methodological factors such as deviations in standard operating procedures, poor quality reagents, inconsistent quality control practices, outdated interpretive breakpoints, weaknesses in laboratory quality management systems, fragmented data aggregation processes, or variability in the expression of resistance determinants under differing external test conditions. If we cannot distinguish true population-level shifts in resistance from methodological variability or test condition-dependent expression differences, the evidence guiding clinical care and national policy becomes uncertain.
The Mapping Antimicrobial Resistance and Antimicrobial Use Partnership project reported that Ghana’s laboratory network comprises approximately 4,841 laboratories, of which only 93 have bacteriology testing capacity. From this subset, national AMR estimates have been derived from 16 laboratories meeting defined analytical criteria. This distribution reflects structural capacity constraints rather than institutional deficiency. When surveillance coverage is limited, the confidence that can be assigned to national resistance estimates is inherently shaped by the robustness of the surveillance architecture. Further insight was gained through the implementation of Ghana’s national AMR Proficiency Testing scheme supported by the Fleming Fund Scheme. Eighteen laboratories across the One Health spectrum participated, comprising 13 from the human health sector, 4 from the animal health sector, and 1 from the environmental sector. In the first proficiency testing cycle, four of six major human health laboratories tested did not achieve the predefined 80 percent performance threshold. Performance variability was also observed across other participating sites. These findings were communicated confidentially to participating institutions and informed structured corrective action plans. The results underscore that inconsistency in reported resistance patterns may reflect evolving validation systems rather than purely biological change. In this lecture, therefore, “falsehood” does not denote deliberate deception; it refers to distortion; instances in which laboratory results fail to accurately represent microbial biology because validation structures are incomplete or insufficiently institutionalized.
Truth in antimicrobial resistance surveillance refers to evidence that accurately and reproducibly reflects the biological characteristics of pathogens within defined populations. Such evidence depends on harmonized standard operating procedures, rigorous quality assurance, validated proficiency testing, current interpretive standards, structured data aggregation, and effective governance. Without these systems, it becomes difficult to determine whether changes in resistance patterns reflect genuine epidemiological shifts or weaknesses in measurement, thereby limiting the strength of epidemiological inference.
This lecture argues that safeguarding scientific integrity in antimicrobial resistance surveillance requires sustained investment in robust surveillance architecture as a national and global responsibility. As externally funded initiatives transition toward national ownership, surveillance systems must be embedded within durable governance frameworks rather than dependent on donor cycles. Accurate AMR detection is first a clinical safety obligation; surveillance is its population-level extension. Both are scientific duties and matters of public trust. When microbes speak, our systems must hear clearly.