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Impact of screening on the prevalence and incidence of Mycoplasma genitalium and its macrolide resistance in men who have sex with men living in Australia: A mathematical model
Central Clinical School, Monash University, Victoria, Melbourne, Australia; Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China; Research Base of Key Laboratory of Surveillance and Early Warning on Infectious Disease, Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai, China.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
Central Clinical School, Monash University, Victoria, Melbourne, Australia.
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2021 (English)In: eClinicalMedicine, E-ISSN 2589-5370, Vol. 33, article id 100779Article in journal (Refereed) Published
Abstract [en]

Background: (MG) causes a sexually transmitted infection (STI) with a rising rate of antimicrobial resistance. Currently, guidelines do not recommend screening asymptomatic men who have sex with men (MSM). We developed a mathematical model of MG transmission to examine the impact of various screening strategies on the incidence and prevalence of MG among MSM attending a sexual health clinic.

Methods: A compartmental mathematical model of MG transmission among MSM was constructed and calibrated using data from the Melbourne Sexual Health center, where resistance-guided therapy provides high treatment effectiveness (92-95%). The model stratified men by symptom status, sexual risk behaviours and whether or not they had MG with macrolide resistance. We simulated the impact on endemic steady-state MG prevalence and incidence of the following screening scenarios, namely screening: 1) no MSM; 2) only symptomatic MSM (the current recommendation); 3) all symptomatic and high-risk asymptomatic MSM; and 4) all MSM. Our base case analysis assumed a treatment effectiveness of 92-95% using resistance-guided therapy. We also examined the impact of treatment effectiveness (i.e. the proportion of detected MG that were cured) and screening coverage (i.e. testing rate) on MG prevalence.

Findings: The model predicts that the overall endemic MG prevalence is 9.1% (95% CI: 7.9-10.0) in the current situation where screening is only offered to symptomatic MSM (base-case). This would increase to 11·4% (95% confidence intervals (CI): 10.2-13.7) if no MSM are offered screening, but would decrease to 7.3% (95% CI: 5.7-8.4) if all symptomatic and high-risk asymptomatic MSM were offered screening and 6.4% (95% CI: 4.7-7·7) if all MSM were offered screening. Increasing coverage of MSM screening strategies shows a similar effect on decreasing endemic MG incidence. When evaluating the simultaneous impact of treatment effectiveness and screening coverage, we found that offering screening to more MSM may reduce the overall prevalence but leads to a higher proportion of macrolide-resistant MG, particularly when using treatment regimens with lower effectiveness.

Interpretation: Based on the available treatment options, offering screening for MG to other MSM (beyond the currently recommended group of symptomatic MSM) could slightly reduce the prevalence and incidence of MG. However, further increasing screening coverage must be weighed against the impact of lower treatment effectiveness (i.e. when not using resistance-guided therapy), increasing the selection of macrolide resistance, and other negative consequences related to AMR and management (e.g. unnecessary psychological morbidity from infections that do not need treatment).

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 33, article id 100779
National Category
Infectious Medicine
Identifiers
URN: urn:nbn:se:oru:diva-91005DOI: 10.1016/j.eclinm.2021.100779ISI: 000645885100028PubMedID: 33842867Scopus ID: 2-s2.0-85103078731OAI: oai:DiVA.org:oru-91005DiVA, id: diva2:1544117
Note

Funding Agencies:

National Health and Medical Research Council of Australia GNT1104781

NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol  

National Natural Science Foundation of China (NSFC) 81950410639

Outstanding Young Scholars Support Program 3111500001

Xi'an Jiaotong University Basic Research and Profession Grant xtr022019003 xzy032020032

Epidemiology modeling and risk assessment 20200344

Xi'an Jiaotong University Young Scholar Support Grant YX6J004

Available from: 2021-04-14 Created: 2021-04-14 Last updated: 2023-07-13Bibliographically approved

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