Evidence map›Paper›PMID 33842867›Full record

ArticleEClinicalMedicine2021

Impact of screening on the prevalence and incidence of

Jason J Ong, Luanqi Ruan, Aaron G Lim, Catriona S Bradshaw, David Taylor-Robinson, Magnus Unemo, Patrick J Horner, Peter Vickerman, Lei Zhang

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
  7. Frontiers in microbiology · 2023
    Article
  8. Frontiers in cellular and infection microbiology · 2023
    Article
  9. Article
  10. Article
  11. Prevalence ofJournal of medical microbiology · 2021
    Article
  12. Antibiotic Resistance and Genotypes ofAntibiotics (Basel, Switzerland) · 2021
    Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 4 countries.

Jason J OngCentral Clinical School, Monash University, Victoria, Melbourne, Australia.
Luanqi RuanChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
Aaron G LimPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
Catriona S BradshawCentral Clinical School, Monash University, Victoria, Melbourne, Australia.
David Taylor-RobinsonFaculty of Medicine, Imperial College London, London, United Kingdom.
Magnus UnemoWHO Collaborating Centre for Gonorrhoea and other STIs, Örebro University, Örebro, Sweden.
Patrick J HornerPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
Peter VickermanPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
Lei ZhangCentral Clinical School, Monash University, Victoria, Melbourne, Australia.
University of Bristol · GBImperial College London · GBLondon School of Hygiene & Tropical Medicine · GBMelbourne Sexual Health Centre · AUMonash University · AUÖrebro University · SEXi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background

methodsA 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.

findingsThe 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.

interpretationBased 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).

Identifiers

PMID33842867
PMCPMC8020166
OpenAlexW3129747376

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.