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Assessing thresholds of resistance prevalence at which empiric treatment of gonorrhea should change among men who have sex with men in the US: A cost-effectiveness analysis [1]

['Xuecheng Yin', 'Department Of Management Science', 'Information Systems', 'Spears School Of Business', 'Oklahoma State University', 'Tulsa', 'Oklahoma', 'United States Of America', 'Department Of Health Policy', 'Management']

Date: 2024-07

Abstract Background Since common diagnostic tests for gonorrhea do not provide information about susceptibility to antibiotics, treatment of gonorrhea remains empiric. Antibiotics used for empiric therapy are usually changed once resistance prevalence exceeds a certain threshold (e.g., 5%). A low switch threshold is intended to increase the probability that an infection is successfully treated with the first-line antibiotic, but it could also increase the pace at which recommendations are switched to newer antibiotics. Little is known about the impact of changing the switch threshold on the incidence of gonorrhea, the rate of treatment failure, and the overall cost and quality-adjusted life-years (QALYs) associated with gonorrhea. Methods and findings We developed a transmission model of gonococcal infection with multiple resistant strains to project gonorrhea-associated costs and loss in QALYs under different switch thresholds among men who have sex with men (MSM) in the United States. We accounted for the costs and disutilities associated with symptoms, diagnosis, treatment, and sequelae, and combined costs and QALYs in a measure of net health benefit (NHB). Our results suggest that under a scenario where 3 antibiotics are available over the next 50 years (2 suitable for the first-line therapy of gonorrhea and 1 suitable only for the retreatment of resistant infections), changing the switch threshold between 1% and 10% does not meaningfully impact the annual number of gonorrhea cases, total costs, or total QALY losses associated with gonorrhea. However, if a new antibiotic is to become available in the future, choosing a lower switch threshold could improve the population NHB. If in addition, drug-susceptibility testing (DST) is available to inform retreatment regimens after unsuccessful first-line therapy, setting the switch threshold at 1% to 2% is expected to maximize the population NHB. A limitation of our study is that our analysis only focuses on the MSM population and does not consider the influence of interventions such as vaccine and common use of rapid drugs susceptibility tests to inform first-line therapy. Conclusions Changing the switch threshold for first-line antibiotics may not substantially change the health and financial outcomes associated with gonorrhea. However, the switch threshold could be reduced when newer antibiotics are expected to become available soon or when in addition to future novel antibiotics, DST is also available to inform retreatment regimens.

Author summary Why was this study done? Antibiotics used for the empiric therapy of gonorrhea are usually changed once the prevalence of resistance to the antibiotic exceeds a certain threshold, currently set at 5%.

A low switch threshold is often selected to ensure that the first-line antibiotic remains effective for most patients with gonorrhea.

However, little is known about the impact of changing the switch threshold on the incidence of gonorrhea, the rate of treatment failure, and the overall cost and quality-adjusted life-years (QALYs) associated with gonorrhea. What did the researchers do and find? We developed a mathematical model of gonococcal infection among a population of men who have sex with men (MSM) in the United States to project the burden of gonorrhea and the overall associated cost and QALYs under various switch thresholds and scenarios for the future availability of antibiotics and drug-susceptibility testing (DST).

We found that changing the switch threshold between 1% and 10% does not meaningfully impact the annual number of gonorrhea cases, and total cost and total QALY loss associated with gonorrhea.

However, if a new antibiotic is expected to become available in the future choosing a lower threshold could improve the population net health benefit (NHB). What do these findings mean? Changing the switch threshold may not substantially impact the health and financial outcomes associated with gonorrhea. However, the switch threshold could be reduced when newer antibiotics are expected to become available soon or when DSTs is available to inform retreatment regiments.

Our study was limited to MSM in the US and future studies should evaluate the generalizability of our findings to other populations.

Citation: Yin X, Li Y, Rönn MM, Li S, Yuan Y, Gift TL, et al. (2024) Assessing thresholds of resistance prevalence at which empiric treatment of gonorrhea should change among men who have sex with men in the US: A cost-effectiveness analysis. PLoS Med 21(7): e1004424. https://doi.org/10.1371/journal.pmed.1004424 Received: February 16, 2024; Accepted: June 5, 2024; Published: July 8, 2024 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability: All relevant data are within the manuscript and its Supporting Information files. Funding: This work was supported by CDC through the National Center for HIV, Viral Hepatitis, STD, and TB Prevention (Economic Modeling for HIV/AIDS, Viral Hepatitis, STD, and TB agreement 5NU38PS004651 awarded to J.A.S., a co-author) The CDC's involvement was primarily via TLG, who is employed by the CDC and a co-author on this manuscript. Manuscripts with any CDC-employed co-author must go through CDC clearance. Through this process, the manuscript was reviewed by a CDC scientist. The reviewer provided feedback, which we addressed, but they cannot be a co-author on the manuscript as they do not fulfil the criteria for authorship. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the CDC. R01AI153351 from the National Institute of Allergy and Infectious Diseases (https://www.niaid.nih.gov/) to R.Y. R01AI132606 from the National Institute of Allergy and Infectious Diseases (https://www.niaid.nih.gov/) to Y.H.G. The National Institute of Allergy and Infectious Diseases had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: T.L.G is employed by CDC; other authors declare no competing interests. Abbreviations: AMR, antimicrobial-resistant; DGI, disseminated gonococcal infection; Doxy-PEP, doxycycline post-exposure prophylaxis; DST, drug-susceptibility testing; GISP, Gonococcal Isolate Surveillance Project; MSM, men who have sex with men; NHB, net health benefit; PHC, Personal Health Care; QALY, quality-adjusted life-years; WTP, willingness-to-pay

Introduction Gonorrhea, caused by the pathogen Neisseria gonorrhoeae, is one of the most common notifiable diseases with 677,769 reported cases in 2020 in the United States [1] and an estimated 87 million incident cases worldwide in 2016 [2,3]. N. gonorrhoeae has developed resistance to each of the first-line antibiotics recommended to treat it. Due to the high prevalence of gonorrhea among certain groups (e.g., men who have sex with men (MSM)) and increasing antibiotic resistance, the US Centers for Disease Control and Prevention (CDC) named antimicrobial-resistant (AMR) gonorrhea one of the 3 most urgent antimicrobial resistance threats in the United States [4]. Gonorrhea is most commonly diagnosed by a nucleic acid amplification test, which does not routinely provide results on antibiotic susceptibility. Therefore, the treatment of gonorrhea remains empiric and based on standardized treatment guidelines [5]. These guidelines are determined based on the estimated prevalence of resistance reported by national surveillance systems such as the Gonococcal Isolate Surveillance Project (GISP) in the US [6]. Currently, the guidelines recommend ceftriaxone for the first-line treatment of uncomplicated gonorrhea [5]. The percentage of GISP isolates that exhibited resistance to ceftriaxone has fluctuated around 0.2% between 2016 and 2020 [7]. To ensure the effectiveness of empiric treatment, guidelines recommend using antibiotics with a prevalence of resistance below 5% and to switch to a new antibiotic for first-line therapy when the prevalence of resistance exceeds this threshold [8,9]. However, the evidence to support this threshold is not clear, and the impact of changing this switch threshold on the gonorrhea-related outcomes (e.g., number of gonorrhea cases or treatment failure rate) has not been studied before. Increasing the threshold delays switching to a new antibiotic, which decreases the probability that standardized first-line therapy is effective for a gonococcal infection. This could result in many individuals with AMR gonorrhea receiving ineffective therapy, experiencing a lower quality of life while symptomatic, and contributing to the spread of AMR gonorrhea. Decreasing the threshold could improve the effectiveness of empiric therapy, but also would lead to earlier and more extensive use of second-line regimens, which could shorten their life span and lead to the emergence of additional resistance [9]. To evaluate the health and cost consequences of different switch thresholds, we developed a simulation model of gonococcal transmission among MSM in the US. We used this model to project the cost and quality-adjusted life-years (QALYs) loss due to gonorrhea among MSM over 50 years under different switch thresholds. We conducted cost-effectiveness analyses to identify the optimal switch threshold under different scenarios representing the availability and cost of novel antibiotics and drug-susceptibility testing (DST). We conducted our cost-effectiveness analyses from a healthcare sector perspective, which accounts for medical costs incurred by healthcare payers or patients including the costs of diagnosis, treatment, and sequela associated with gonococcal infection.

Discussion The antibiotics included in the guidelines for the empiric treatment of gonorrhea are recommended to change when the resistance prevalence exceeds 5% [8,9]. Using a transmission model of gonococcal infection among the MSM population in the US, we projected how changing this switch threshold would impact the overall cost and loss in QALYs associated with gonorrhea. Under the scenario where 3 antibiotics are available over the next 50 years (2 suitable for the first-line therapy of gonorrhea and 1 suitable only for the retreatment of resistant infections), changing the switch threshold between 1% and 10% does not meaningfully impact the annual number of gonorrhea cases, total costs or total QALY losses associated with gonorrhea. Our analysis, however, suggests that the switch threshold that maximizes the population NHB depends on the availability of future antibiotics, the cost of future antibiotics, and access to DSTs to inform the retreatment antibiotic. When a new antibiotic is expected to become available in the future, a lower threshold would lead to a higher population NHB, compared to using the 5% threshold. Reducing the switch threshold increases the probability that standardized regimen matches the susceptibility profile of a gonococcal infection, which improves treatment outcomes and reduces the secondary transmission of infection. However, it also shortens the lifespan of first-line antibiotics as it leads to switching to newer antibiotics at a faster pace, which could result in emergence and spread of additional resistant gonococcal strains. The introduction of a new antibiotic minimizes these adverse consequences. Our analysis also indicates that if DSTs are available to identify retreatment antibiotics when first-line therapy is not successful, selecting a lower switch threshold would result in higher population NHB (Fig 4D). DSTs allow customizing the treatment regimen according to the antibiotic susceptibility of an individual infection. As a substantial portion of infections may be susceptible to older antibiotics (e.g., to Drug A when Drug B is the recommended first-line therapy), DSTs allow to prescribe older antibiotics, which alleviates the selective pressure for the development of resistance to newer antibiotics [13,23,24]. Our analysis also suggests that the gain in NHB from optimizing the switch threshold is relatively small (Fig 5A and 5B, panels A and B of Fig E in S1 Appendix, panels A and B of Fig H in S1 Appendix, and panels A and B of Fig K in S1 Appendix). This is because under the scenarios of antibiotic and DST availability considered here, changing the switch threshold does not have a meaningful impact on outcomes such as rate of gonorrhea cases or treatment failure, that determine the overall cost and QALYs loss associated with gonorrhea (Fig 3A and 3B). In contrast, an introduction of a new antibiotic that is suitable for first-line therapy of gonorrhea over the next 30 years is expected to substantially improve these outcomes and the population NHB (Fig 5C and 5D, Figs E, H, and K in S1 Appendix). Our study has several limitations. First, our model describes the spread of N. gonorrhoeae only among MSM. Compared to heterosexual men and women, the prevalence of gonorrhea and AMR gonorrhea is particularly high among MSM [12,25]. Therefore, the benefits of optimizing the switch threshold or the gain from regular introduction of new antibiotics might be lower for populations with lower burden of gonorrhea and AMR gonorrhea. Second, as suggested by our results, the impact of a switch threshold on the overall cost and burden of gonorrhea depends on control measures that would become available in the future. We considered scenarios related to the availability of new antibiotics and DSTs for retreatment of cases. We, however, noted that other future scenarios could also be considered including scenarios related to the availability of vaccine or common use of rapid DSTs to inform first-line therapy. In addition, the increased use of doxycycline post-exposure prophylaxis (Doxy-PEP) could impact the burden of gonorrhea and AMR gonorrhea [26]. Studies suggest that the potential impact of Doxy-PEP on gonorrhea prevalence is likely short term and depends on the level of doxycycline resistance in the population [27]. Moreover, use of doxycycline may lead to an increase in resistance overall, not just to doxycycline, but to other drugs, because of linked resistance [28]. As limited data are available on the uptake of these innovations and their impact on sexual behavior and the burden of gonorrhea and AMR gonorrhea, we did not consider them in our modeling analysis. Third, we assumed that treatment guidelines are updated when the resistance prevalence reaches a prespecified threshold. In practice, policymakers may not necessarily wait until the selected threshold is reached to revise the treatment guidelines. For example, the decision to abandon the use of the oral extended spectrum cephalosporin cefixime as first-line therapy was made before the resistance to this drug reaches the 5% resistance and it was based on the observed upward trajectory of resistance [29]. Fourth, since our simulation was a compartmental model, it did not include certain complexities related to the spread and the control of sexually transmitted diseases such as the treatment of sex partners when a partner is diagnosed with gonorrhea. Finally, while we assumed that only 1 antibiotic is prescribed for first-line therapy, dual therapy has also been occasionally recommended. For example, between 2010 and 2020, dual therapy with ceftriaxone and azithromycin were recommended for the first-line treatment of gonorrhea in the US [5,30]. Fifth, antibiotics for gonorrhea treatment might also be used for other infections and their use might have impact on other infections and microbiome. We did not account for these indirect impacts in estimating the cost and QALYs associated with different scenarios considered in our analysis. The main strength of our study is the use of a calibrated simulation model of gonococcal infection to project the long-term population-level cost and health outcomes under different health strategies and future scenarios. This allows us to investigate how changing the resistance switch threshold and the cost and the availability of novel antibiotics are expected to impact the health and financial burden of gonorrhea and AMR gonorrhea in the long term. In conclusion, changing the resistance threshold that determines first-line therapy is not expected to substantially impact the overall cost and QALYs associated with gonorrhea. Nevertheless, the optimal switch threshold depends on the availability of future antibiotics and DSTs for informing regimen for the retreatment of infections that do not respond to first-line therapy. Our findings highlight the importance of regular, dependable development of new antibiotics, and in such scenarios where the future pipeline of antibiotics is reasonably certain, lowering the switch threshold would result in higher population NHB. As our analysis relied on a simulation model of AMR gonorrhea among the US MSM population, additional research is required to evaluate the generalizability of our findings to settings outside the US and to non-MSM population. Future studies could also investigate the impact of choosing the switch thresholds based on the population-level risk of AMR gonococcal infection, which could vary over geographic regions and/or population groups [31,32]. This allows for making newer antibiotics available to populations at higher risk of AMR gonorrhea while curbing its use to minimize the risk for the emergence of new resistant strains.

Supporting information S1 Checklist. CHEERS 2022 Checklist. https://doi.org/10.1371/journal.pmed.1004424.s001 (PDF) S1 Appendix. Additional model details and results of sensitivity analyses. Table A. Model notation. Table B. Transitions between model compartments when a new drug (Drug C) becomes available in addition to Drugs A and B. Table C. Prior and Posterior distribution of parameters. Fig A. The prevalence of infection and the spread of resistance in different sexual activity group for trajectories displayed in Fig 2. Fig B. Posterior distribution of model parameters listed in Table C in S1 Appendix. Fig C. Probability tree for infected individuals without or fail the treatment. Fig D. The impact of changing the switch threshold on the total discounted cost and QALY loss over 50 years of simulation (panels A and B), and identifying the optimal switch threshold (panels C and D), when the cost of Drug B is twice the cost Drug A. Fig E. The impact of optimizing the switch threshold (panels A and B) and the scenarios for the availability of new antibiotics in the future (panels C and D) on the population net health benefit (NHB) when the cost of Drug B is twice the cost of Drug A. Fig F. The impact of changing the switch threshold on the average annual rate of gonorrhea cases (panel A), average annual rate of treatment failure (panel B), the average annual rate of drug-susceptibility testing (DST) (panel C), and the proportion of cases treated with Drug A, Drug B, and Drug M (panels D–F), when the probability that resistance develops during treatment is increased to 10[−5,−3]. Fig G. The impact of changing the switch threshold on the total discounted cost and QALY loss over 50 years of simulation (panels A and B), and identifying the optimal switch threshold for each scenario (panels C and D) when the probability that resistance develops during treatment is increased to 10[−5,−3]. Fig H. The impact of optimizing the switch threshold (panels A and B) and the scenarios for the availability of new antibiotics in the future (panels C and D) on the population net health benefit (NHB) when the probability that resistance develops during treatment is increased to 10[−5,−3]. Fig I. The impact of changing the switch threshold on the average annual rate of gonorrhea cases (panel A), average annual rate of treatment failure (panel B), the average annual rate of drug-susceptibility testing (DST) (panel C), and the proportion of cases treated with Drug A, Drug B, and Drug M (panels D–F) when the relative transmissibility of resistant strains is [0.6, 1]. Fig J. The impact of changing the switch threshold on the total discounted cost and QALY loss over 50 years of simulation (panels A and B), and identifying the optimal switch threshold for each scenario (panels C and D) when the relative transmissibility of resistant strains is [0.6, 1]. Fig K. The impact of optimizing the switch threshold (panels A and B) and the scenarios for the availability of new antibiotics in the future (panels C and D) on the population net health benefit (NHB) when the relative transmissibility of resistant strains is [0.6, 1]. https://doi.org/10.1371/journal.pmed.1004424.s002 (PDF)

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