Antibiotic-resistant Neisseria gonorrhoeae spread faster with more treatment, not more sexual partners

Abstract

The sexually transmitted bacterium Neisseria gonorrhoeae has developed resistance to all antibiotic classes that have been used for treatment and strains resistant to multiple antibiotic classes have evolved. In many countries, there is only one antibiotic remaining for empirical N. gonorrhoeae treatment and antibiotic management to counteract resistance spread is urgently needed. Understanding dynamics and drivers of resistance spread can provide rationales for antibiotic management. In our study, we first used antibiotic resistance surveillance data to estimate the rates at which antibiotic-resistant N. gonorrhoeae spread in two host populations, heterosexual men (HetM) and men who have sex with men (MSM). We found higher rates of spread for MSM (0.86 to 2.38 y−1, mean doubling time: 6 months) compared to HetM (0.24 to 0.86 y−1, mean doubling time: 16 months). We then developed a dynamic transmission model to reproduce the observed dynamics of N. gonorrhoeae transmission in populations of heterosexual men and women (HMW) and MSM. We parameterized the model using sexual behavior data and calibrated it to N. gonorrhoeae prevalence and incidence data. In the model, antibiotic-resistant N. gonorrhoeae spread with a median rate of 0.88 y−1 in HMW and 3.12 y−1 in MSM. These rates correspond to median doubling times of 9 (HMW) and 3 (MSM) months. The model shows the difference in the host population’s treatment rate rather than the difference in the number of sexual partners explains the differential spread of resistance. As higher treatment rates result in faster spread of antibiotic resistance, treatment recommendations for N. gonorrhoeae should carefully balance prevention of infection and avoidance of resistance spread.

A dynamic power-law sexual network model of gonorrhoea outbreaks

“…While it is important to take heterogeneity into account, the traditional 239 formulation of a core group [5,20] might approximate the true transmission dynamics of gonorrhoea 240 better than using a static power-law network. Our findings add support to previous modelling work that 241 suggested that having more sexual partners does not greatly impact the rate at which antibiotic resistant 242 gonorrhoea can spread [8]. 243 We used our realistic dynamic power-law network model to investigate the impact of a range of 244 interventions against gonorrhoea, including increased condom use, more frequent screening and 245 immunisation.…”

“…At a time when 23 resistance to first line therapies is increasingly observed [3], it is more important than ever to understand 24 the transmission dynamics of the infection, and how interventions might be used to reduce the burden 25 on antibiotic treatment [4]. 26 It has been well documented that heterogeneity in sexual activity levels has an impact on disease 27 transmission, with individuals who have many partners bearing much of the burden of disease [5][6][7][8]. 28 However, the risk of acquiring and passing on a sexually transmitted infection (STI) depends not only 29 on an individual's sexual risk profile, but also on their position in the wider sexual network [9,10].…”

Antibiotic resistance detection is essential for gonorrhoea point-of-care testing: A mathematical modelling study

“…However, reduced time to treatment and increased follow up with POC tests might increase the rate of gonorrhoea treatment. Since higher treatment rates can lead to faster spread of resistance [14,15], POC tests might increase resistance levels. We extended a previously developed mathematical model of gonorrhoea transmission [15] to compare the effects of current conventional tests, culture and NAAT, with POC tests that reduce time to treatment and loss to follow up.…”

“…Since higher treatment rates can lead to faster spread of resistance [14,15], POC tests might increase resistance levels. We extended a previously developed mathematical model of gonorrhoea transmission [15] to compare the effects of current conventional tests, culture and NAAT, with POC tests that reduce time to treatment and loss to follow up. We investigated the potential impact of POC tests on resistance and on the number of gonorrhoea cases for a population at high risk of infection [16], men who have sex with men (MSM), and a population at lower risk of infection, heterosexual men and women (HMW).…”

Recent advances in the development and use of molecular tests to predict antimicrobial resistance inNeisseria gonorrhoeae

“…First, the promotion of increased use of screening tests without AMR testing to reduce the prevalence of gonorrhea could, paradoxically, increase the spread of resistance to at least some antimicrobials such as CIP. Notably, a recently developed mathematical model suggests that an increase in the treatment rate of gonorrhea can be the main driver of spread of CIP resistance, likely because the increase in empirical treatment imposes selection pressure for the emergence of AMR strains [79][80][81]. Second, POC tests that detect both NG and determinants of AMR could control the spread of AMR gonorrhea by allowing personalized specific antimicrobial treatment, provided that AMR surveillance continues also with culturebased phenotypic methods to detect new AMR determinants [81].…”