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Antibiotic-Resistant Neisseria gonorrhoeae Spread Faster with More Treatment, Not More Sexual Partners

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  • Stephanie M Fingerhuth
  • Sebastian Bonhoeffer
  • Nicola Low
  • Christian L Althaus

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.Author Summary: More and more infectious disease treatments fail because the causative pathogens are resistant to the drugs used for treatment. For the treatment of Neisseria gonorrhoeae, a sexually transmitted bacterium, drug resistance is a particularly big problem: there is only a single antibiotic left that is recommended for treatment. We aimed to understand how antibiotic-resistant N. gonorrhoeae spread in a sexually active host population and how the spread of resistance can be slowed. From antibiotic resistance surveillance data, we first estimated the rate at which antibiotic-resistant N. gonorrhoeae spread. Second, we reproduced the observed dynamics in a mathematical model describing the transmission between hosts. We found that antibiotic-resistant N. gonorrhoeae spread faster in host populations of men who have sex with men than in host populations of heterosexuals. We could attribute the faster spread of resistant pathogens to higher treatment rates. This finding implies that promoting screening to control antibiotic-resistant N. gonorrhoeae could in fact accelerate their spread.

Suggested Citation

  • Stephanie M Fingerhuth & Sebastian Bonhoeffer & Nicola Low & Christian L Althaus, 2016. "Antibiotic-Resistant Neisseria gonorrhoeae Spread Faster with More Treatment, Not More Sexual Partners," PLOS Pathogens, Public Library of Science, vol. 12(5), pages 1-15, May.
    • Handle: RePEc:plo:ppat00:1005611
    DOI: 10.1371/journal.ppat.1005611
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    References listed on IDEAS

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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
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    Cited by:

    1. Lilith K Whittles & Peter J White & Xavier Didelot, 2019. "A dynamic power-law sexual network model of gonorrhoea outbreaks," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-20, March.
    2. Jonas I Liechti & Gabriel E Leventhal & Sebastian Bonhoeffer, 2017. "Host population structure impedes reversion to drug sensitivity after discontinuation of treatment," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-19, August.
    3. Barlow, Euan & Morton, Alec & Megiddo, Itamar & Colson, Abigail, 2022. "Optimal subscription models to pay for antibiotics," Social Science & Medicine, Elsevier, vol. 298(C).

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