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 an improved rationale 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. Assuming no fitness costs, 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.

Wild-Type Gyrase A Genotype of Neisseria gonorrhoeae Predicts In Vitro Susceptibility to Ciprofloxacin: A Systematic Review of the Literature and Meta-Analysis

“…Targeted therapy with ciprofloxacin might reduce the ongoing selection pressure caused by the widespread empiric use of extended-spectrum cephalosporins and have implications for the enhanced control of ceftriaxone resistant Neisseria gonorrhoeae [8]. A recent study suggested that treatment might be a major driver of ceftriaxone resistance among Neisseria gonorrhoeae [18]. …”

How Relevant Is Sexual Transmission of Zika Virus?

“…A prerequisite for a pathogen to spread as a sexually transmitted infection (STI) is that R 0 for sexual transmission, equivalent to the product of the infectious duration and the sexual transmission rate, is greater than one. If the infectious duration of an STI is short (for example, a few months, as is the case with gonorrhoea), transmissibility during a sexual partnership typically needs to be around 50% or more [13]. Of the few cases that have been studied, the longest duration of Zika virus RNA detection in semen is 188 days after the onset of symptoms [14], which is within the range for gonorrhoea.…”

Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity

“…where y i is the proportion of individuals in sexual activity class i who are infected, and x i is the proportion of all individuals who belong to sexual activity class i. The first and last term of the equation describe how individuals can change their sexual activity class at rate µ and be redistributed to either the same or another sexual activity class proportional to the size of sexual activity classes [24,26]. The middle terms describe the process of transmission and the clearance of infection at rate γ.…”

“…Since the transmission model is primarily used for illustrative purposes, we did not include changes in sexual behaviour, between Natsal-2 and Natsal-3, which were small [19]. We set µ = 1 per year as described previously [24,26], and set = 0, i.e., we assumed random proportional mixing between different sexual activity classes [27]. We then chose a particular combination of the infectious duration (1/γ) and the per partnership transmission probability (β) and ran the model into endemic equilibrium by numerically integrating the ODEs in the R software environment for statistical computing [28] using the function ode from the package deSolve We calculated the prevalence and Gini coefficient as described above.…”