Surveillance strategies using routine microbiology for antimicrobial resistance in low- and middle-income countries. (October 2021)

• Record Type: Journal Article
• Authors: Lim, Cherry; Ashley, Elizabeth A.; Hamers, Raph L.; Turner, Paul; Kesteman, Thomas; Akech, Samuel; Corso, Alejandra; Mayxay, Mayfong; Okeke, Iruka N.; Limmathurotsakul, Direk; van Doorn, H. Rogier
• Is Part Of: Clinical microbiology and infection. Volume 27:Number 10(2021)
• Page Start: 1391
• Page End: 1399
• Publication Date: 2021-10
• Subjects: Antimicrobial resistance -- Drug-resistant infections -- Low- and middle-income countries -- Routine microbiology -- Surveillance; Medical microbiology -- Periodicals; Diagnostic microbiology -- Periodicals; Communicable diseases -- Periodicals; Infection -- Periodicals; 616.01
• DOI: 10.1016/j.cmi.2021.05.037
• Languages: English
• ISSNs: 1198-743X

Abstract

Abstract: Background: Routine microbiology results are a valuable source of antimicrobial resistance (AMR) surveillance data in low- and middle-income countries (LMICs) as well as in high-income countries. Different approaches and strategies are used to generate AMR surveillance data. Objectives: We aimed to review strategies for AMR surveillance using routine microbiology results in LMICs and to highlight areas that need support to generate high-quality AMR data. Sources: We searched PubMed for papers that used routine microbiology to describe the epidemiology of AMR and drug-resistant infections in LMICs. We also included papers that, from our perspective, were critical in highlighting the biases and challenges or employed specific strategies to overcome these in reporting AMR surveillance in LMICs. Content: Topics covered included strategies of identifying AMR cases (including case-finding based on isolates from routine diagnostic specimens and case-based surveillance of clinical syndromes), of collecting data (including cohort, point-prevalence survey, and case–control), of sampling AMR cases (including lot quality assurance surveys), and of processing and analysing data for AMR surveillance in LMICs. Implications: The various AMR surveillance strategies warrant a thorough understanding of their limitations and potential biases to ensure maximum utilization and interpretation of local routine microbiology data across time and space. For instance, surveillance using case-finding based on results from clinical diagnostic specimens is relatively easy to implement and sustain in LMIC settings, but the estimates of incidence and proportion of AMR is at risk of biases due to underuse of microbiology. Case-based surveillance of clinical syndromes generates informative statistics that can be translated to clinical practices but needs financial and technical support as well as locally tailored trainings to sustain. Innovative AMR surveillance strategies that can easily be implemented and sustained with minimal costs will be useful for improving AMR data availability and quality in LMICs.