Developing a model to predict individualised treatment for gonorrhoea: a modelling study. Issue 6 (25th June 2021)

• Record Type: Journal Article
• Authors: Findlater, Lucy; Mohammed, Hamish; Gobin, Maya; Fifer, Helen; Ross, Jonathan; Geffen Obregon, Oliver; Turner, Katy M E
• Is Part Of: BMJ open. Volume 11:Issue 6(2021)
• Publication Date: 2021-06-25
• Subjects: public health -- public health -- epidemiology -- epidemiology -- genitourinary medicine -- sexual medicine; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-042893
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objective: To develop a tool predicting individualised treatment for gonorrhoea, enabling treatment with previously recommended antibiotics, to reduce use of last-line treatment ceftriaxone. Design: A modelling study. Setting: England and Wales. Participants: Individuals accessing sentinel health services. Intervention: Developing an Excel model which uses participants' demographic, behavioural and clinical characteristics to predict susceptibility to legacy antibiotics. Model parameters were calculated using data for 2015–2017 from the Gonococcal Resistance to Antimicrobials Surveillance Programme. Main outcome measures: Estimated number of doses of ceftriaxone saved, and number of people delayed effective treatment, by model use in clinical practice. Model outputs are the predicted risk of resistance to ciprofloxacin, azithromycin, penicillin and cefixime, in groups of individuals with different combinations of characteristics (gender, sexual orientation, number of recent sexual partners, age, ethnicity), and a treatment recommendation. Results: Between 2015 and 2017, 8013 isolates were collected: 64% from men who have sex with men, 18% from heterosexual men and 18% from women. Across participant subgroups, stratified by all predictors, resistance prevalence was high for ciprofloxacin (range: 11%–51%) and penicillin (range: 6%–33%). Resistance prevalence for azithromycin and cefixime ranged from 0% to 13% and for ceftriaxone it was 0%. Simulating model use, 88% of individuals could be given cefixime and 10% azithromycin, saving 97% of ceftriaxone doses, with 1% of individuals delayed effective treatment. Conclusions: Using demographic and behavioural characteristics, we could not reliably identify a participant subset in which ciprofloxacin or penicillin would be effective. Cefixime resistance was almost universally low; however, substituting ceftriaxone for near-uniform treatment with cefixime risks re-emergence of resistance to cefixime and ceftriaxone. Several subgroups had low azithromycin resistance, but widespread azithromycin monotherapy risks resistance at population level. However, this dataset had limitations; further exploration of individual characteristics to predict resistance to a wider range of legacy antibiotics may still be appropriate.