Implementation of a standardised and quality-assured enhanced gonococcal antimicrobial surveillance programme in accordance with WHO protocols in Kampala, Uganda
Francis Kakooza, Patrick Musinguzi, Meklit Workneh, Richard Walwema, Peter Kyambadde, Emmanuel Mande, Christopher Lubega, Jhamira Martha Nakasi, Reuben Kiggundu, Matthew M. Hamill, Bernard S. Bagaya, Mohammed Lamorde, Magnus Unemo, Yukari C. Manabe
Abstract
OBJECTIVES: (NG) is a major global health threat necessitating response and control measures. NG antimicrobial resistance (AMR) surveillance data from sub-Saharan countries is exceedingly limited. This paper aims to describe the establishment, design and implementation of a standardised and quality-assured gonococcal surveillance programme and to describe the susceptibility patterns of the cultured gonococcal isolates in Kampala, Uganda. METHODS: From March 2018 to September 2019, using the WHO Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP) protocol, consecutive males with urethral discharge syndrome were recruited from 10 surveillance sites in Kampala City, Uganda, in collaboration with the Ministry of Health. Males completed a questionnaire and provided a urethral swab specimen. Culture, identification and antimicrobial susceptibility testing (Etest) were performed. RESULTS: Of the 1013 males recruited, 73.1% (740/1013) had a positive Gram stain and 51.1% (n=518) were culture-positive for NG. Using Etest (458 isolates), the resistance to ciprofloxacin was 99.6%. Most isolates were susceptible to azithromycin, cefoxitin and gentamicin, that is, 99.8%, 98.5% and 92.4%, respectively, and all isolates were susceptible to ceftriaxone and cefixime. CONCLUSIONS: We established a standardised, quality-assured WHO EGASP. Using Etest, 458 isolates were characterised, with associated epidemiological surveillance data, in 1.5 years, which by far exceed the minimum 100 isolates per year and country requested in the WHO Global GASP, to detect AMR levels with confidence. These isolates with the epidemiological data can be used to develop population level interventions.