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REVIEW 3 major objections 6 minor 43 references

Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A model shows that routine STI screening in PrEP programs can lower true chlamydia prevalence while the number of positive tests rises.

desk verdict A carefully derived mechanistic explanation for a surveillance paradox in PrEP-STI data; the central claim holds as a ceteris-paribus result but needs a coupled-path check. read the letter →

arxiv 2505.24433 v1 pith:XDLHAMCZ submitted 2025-05-30 q-bio.PE physics.soc-ph

Laura Müller , Piklu Mallick , Antonio B. Marín-Carballo , Philipp Dönges , Robyn J. N. Kettlitz , Carolina J. Klett-Tammen , Mirjam Kretzschmar , Viola Priesemann
show 1 more author
Seba Contreras
This is my paper
classification q-bio.PEphysics.soc-ph MSC 92D30
keywords PrEPSTIscreeningtestingparadoxchlamydiacompartmentalmodelriskcompensationMSMsurveillanceartifact
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This modeling study asks whether the rising bacterial STI rates observed among men who have sex with men taking HIV PrEP are real increases or partly a surveillance artifact. It develops a compartmental model of HIV and chlamydia spread in which PrEP uptake changes condom use, risk-aware self-protection, and the frequency of asymptomatic screening. The central finding is that when PrEP-related screening is frequent (about four times per year), increasing PrEP uptake reduces true chlamydia prevalence; when screening is less frequent, the opposite occurs. In a wide parameter region the number of positive test results rises while true prevalence falls, which the authors call the testing paradox and which can reconcile conflicting observational reports.

What carries the argument

The key object is a susceptible--asymptomatic-infectious--symptomatic-infectious--treated compartmental model with demographic turnover and an external influx of infections. The central derived quantities are the equilibrium prevalence $N$ and observed cases $N_{\mathrm{obs}}$, and the testing paradox is characterized by the condition $\frac{\partial N}{\partial P}\cdot \frac{\partial N_{\mathrm{obs}}}{\partial P} < 0$. The argument is carried by the comparison of the PrEP-related screening rate $\lambda_P$ with the risk-related testing rate $\lambda_H(H)$; when $\lambda_P$ exceeds a risk-awareness-dependent threshold, increasing $P$ reduces $N$, and when it does not, the effect reverses. The closed-form equilibrium solution (a quadratic for $I_a^*$) lets the authors compute the paradox regions analytically, and the stratified extension shows the pattern persists with heterogeneous risk groups.

What would settle it

A longitudinal study of MSM on PrEP that measures both clinic-diagnosed chlamydia cases and a screening-independent estimate of true prevalence (e.g., home-based self-sampling) would settle the claim: the paradox predicts observed cases can rise while true prevalence falls in high-frequency screening cohorts; observing the opposite, or no divergence, would falsify it.

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Extended reading notes

Core claim

The paper's central claim is that the observed association between PrEP use and rising STI incidence does not necessarily reflect a true increase in transmission. In the model, asymptomatically infected individuals on PrEP are tested and treated at rate $\lambda_P$, while HIV-risk-aware non-PrEP users reduce transmission and test voluntarily according to their perceived risk. Increasing PrEP uptake $P$ affects the force of infection via reduced condom use and via the changing composition of testing rates. The equilibrium prevalence $N = I_a^* + I_s^*$ can be pushed below its pre-PrEP level if $\lambda_P$ is high enough, yet the daily number of positive tests $N_{\mathrm{obs}} = \lambda_s I_s^* + \lambda_a I_a^*$ can increase because more people are being tested. The authors prove the existence of a unique positive fixed point and map the region of parameter space where $\partial N/\partial P$ and $\partial N_{\mathrm{obs}}/\partial P$ have opposite signs, i.e., where observed cases rise while true prevalence falls.

Load-bearing premise

The model assumes that the fraction of people on PrEP and the population's HIV-risk awareness are independent inputs, so that rising PrEP uptake does not itself lower HIV prevalence and awareness; if that feedback is strong, the predicted thresholds and paradox regions could change.

Editorial extensions

If this is right

  • Observational studies that use diagnosed STI cases as the sole outcome measure cannot distinguish true transmission declines from testing artifacts, so PrEP program evaluations should include testing-frequency data.
  • Maintaining or increasing PrEP-related STI screening frequency (around four times per year) is predicted to reduce community chlamydia prevalence even when condom use falls.
  • If PrEP programs cut screening below a critical frequency, increased PrEP uptake is predicted to raise true chlamydia prevalence, making the intervention harmful for STI control.
  • The model offers a single mechanism that reconciles studies reporting higher STI incidence among PrEP users and studies reporting no increase or a decline.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same testing paradox should apply to gonorrhea, syphilis, and trichomoniasis, since only parameter values differ; comparing their observed and true trends across cohorts would test this.
  • In reality, widespread PrEP use also reduces HIV prevalence, which would lower risk awareness $H$; the paper treats $H$ as fixed, so the paradox region may shift when that feedback is included.
  • A testable prediction is that sentinel surveys of true prevalence (e.g., home-based self-sampling independent of clinics) would show a declining trend in high-screening PrEP cohorts even as clinic-based positive counts rise.
  • The results imply that 'testing as intervention' could be separated from PrEP itself: monitoring and treatment frequency, not the drug, are what drive the STI benefit.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents a deterministic compartmental model of chlamydia transmission among high-risk MSM, treating HIV PrEP uptake P and HIV-risk awareness H as constant inputs. The model incorporates three mechanisms: risk-adapted self-protective behavior, condom-use reduction after PrEP initiation, and asymptomatic STI screening driven by both risk awareness and PrEP requirements. The authors derive a closed-form endemic steady state (S1.1), compute true prevalence N and observed positive tests Nobs, and use the partial derivatives ∂N/∂P and ∂Nobs/∂P to identify parameter regions in which increasing PrEP uptake lowers true prevalence while observed positives rise ('testing paradox'). A stratified four-group extension and sensitivity analyses over odds ratios are reported as robustness checks. The central claim is that this mechanism can reconcile conflicting observational evidence on STI trends among PrEP users.

Significance. If the mechanism is correct and robust along realistic intervention paths, the paper would be an important contribution: it offers a tractable explanation for why observed STI surveillance data may not reflect true transmission dynamics, with direct implications for interpreting PrEP program data. Strengths include the closed-form fixed-point derivation in S1.1, the explicit sign-region analysis leading to Fig. 3, public code availability on GitHub, and the qualitative stability of the main results under the stratified model and odds-ratio perturbations. The principal weakness is the treatment of P and H as independent inputs, which is the key assumption on which the paradox regions in Fig. 3 are computed; because the real intervention couples P and H, the explanatory claim needs a coupled-path check.

major comments (3)
  1. [Discussion; Fig. 3] The central explanatory claim—that increasing PrEP uptake can produce rising observed positives while true chlamydia prevalence falls—is demonstrated only for the partial derivatives ∂N/∂P and ∂Nobs/∂P with H held fixed. The model explicitly assumes 'no functional or causal relationship between PrEP uptake and HIV prevalence' (Discussion), yet the real intervention raises P and, through reduced HIV incidence, would plausibly lower HIV prevalence and hence H. Along a coupled path H(P) with dH/dP < 0, the net responses are dN/dP = ∂N/∂P + (∂N/∂H)(dH/dP) and dNobs/dP = ∂Nobs/∂P + (∂Nobs/∂H)(dH/dP); the additional terms can flip the signs that define the red paradox regions in Fig. 3. I request a quantitative coupled-path analysis: specify at least one plausible H(P) curve (or derive H dynamically from an embedded HIV model), recompute dN/dP and dNobs/dP along that path, and report whether the paradox region persists, shrinks, or disappears. Without this, the abstract's statement that the authors found a plausible mechanism to reconcile conflicting observational evidence overstates what is proven; the paper should either provide such a path analysis or explicitly restrict the claim to the fixed-H, ceteris-paribus case.
  2. [S1.5–S1.6] The robustness analysis of the stratified model does not address the H–P coupling. It varies the odds ratios kor,l across risk groups while still treating P and H as independent constant inputs, so Figs. S4 and S5 confirm stability with respect to heterogeneity in risk perception but say nothing about a realistic H(P) trajectory. The same coupled-path check should be implemented in the stratified model, or the authors should give a clear argument why the fixed-H result carries over to coupled paths. As it stands, the extended model inherits the same load-bearing assumption.
  3. [S1.2 and Fig. S1] The minimum PrEP-related testing frequency λP,min is characterized as a function of H at fixed H. Since H itself would change along a PrEP rollout, the practical threshold λP,min(H(P)) is not directly usable for policy recommendations unless the coupled-path analysis described above is performed. Please clarify whether this threshold is intended only as a ceteris-paribus characterization or as a policy-relevant quantity.
minor comments (6)
  1. [Abstract and Discussion] The final sentence of the abstract and the Discussion mention 'personalized infection treatment to minimize putative pressure to generate antibiotic resistance' as a key determinant, but the model contains no antibiotic-resistance dynamics and no treatment-personalization mechanism. This claim should be removed or explicitly labeled as an external policy suggestion.
  2. [S1.3] There is a typo: 'Williamet al.' should read 'Williams et al.'
  3. [S1.5] The equilibrium stopping criterion '|x(t) − x(t−1)| ≤ ε' needs a numerical value for ε and the time step used in the simulations; without these, the equilibrium claim is not reproducible.
  4. [S1.1] Equation (24) appears as an empty numbered equation between Eq. (23) and the fixed-point solution; please remove the stray equation number or fill in the intended expression.
  5. [Table 2 and Figs. 2–3] β_STI^0 is listed as a set {0.008, 0.0112} day^−1, but the figure captions refer to 'low' and 'high' transmission rates; the text should state explicitly which value corresponds to 'low' and which to 'high' in each panel.
  6. [Fig. 2 caption] The caption describes panels g–l, while the main text refers to 'Fig. 2h,i'; please check that the panel citations are consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the testing paradox emerges from the model equations; observed cases and true prevalence are distinct outputs, and no fitted quantity is relabeled as a prediction.

full rationale

The paper is self-contained rather than circular. It defines observed cases as Nobs = λs Is + λa Ia (Eq. 11), which makes the direction 'more testing can produce more positive tests' partly structural, but the central paradoxical claim is that true prevalence N falls while Nobs rises. That fall in N is not assumed: it is derived from the compartmental equations at the endemic fixed point, where screening, treatment, immunity loss, contagion, and demographic turnover balance (Eqs. 1-9, 15-20). No parameter is fitted to reproduce the paradox; the red regions in Fig. 3 are computed from closed-form steady states over scanned parameter ranges (λP, H, β0), not from data. The self-citations [22,23] support generic risk-perception and behavioral-feedback assumptions, but they are not invoked as uniqueness theorems, fitted constraints, or external validation forcing the result, and the qualitative conclusion is reproduced in the numerically solved stratified model (S1.5). The Discussion explicitly acknowledges the simplifying assumption of no functional relationship between PrEP uptake and HIV prevalence ('We assume no functional or causal relationship between PrEP uptake and HIV prevalence'), which is a real-world caveat about coupling between P and H, not a circular step: the central derivation is an internally consistent ceteris-paribus analysis. Overall, no load-bearing step reduces to its own inputs by construction, so the circularity score is 0.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

The central claim rests on several hand-chosen parameters and domain assumptions. Hmax and Sigma are assumed without sensitivity analysis; the functional forms for mitigation and testing are chosen for tractability; and the independence of PrEP uptake and HIV risk awareness, plus full compliance, are explicit but load-bearing simplifications. No new entities are introduced. The extended stratified model and sensitivity analyses mitigate but do not eliminate these dependencies.

free parameters (3)
  • Hmax = 0.2
    Assumed in Table 2 as the characteristic reaction awareness. Controls how quickly mitigation saturates with risk awareness; no sensitivity analysis over Hmax is reported, so the paradox regions depend on this hand-chosen scale.
  • Sigma (external STI influx) = 0.01 yr^-1
    Assumed in Table 2. Prevents a disease-free equilibrium and sets a floor on prevalence; no empirical estimate or sensitivity analysis is given.
  • mmin and mmax = 0 and 1
    Assumed in Table 2 as minimum and maximum mitigation. Extreme bounds for condom-use mitigation; qualitative behavior likely persists, but quantitative thresholds depend on these values.
assumptions (7)
  • domain assumption Timescale separation: HIV prevalence and risk awareness H are constant during STI dynamics.
    Methods: 'we treat the fraction of the population that is on PrEP P and HIV prevalence, thus the population's infection risk awareness H, as constant inputs.' Makes closed-form analysis possible but removes HIV-PrEP feedback.
  • domain assumption No causal relationship between PrEP uptake and HIV prevalence.
    Discussion: 'We assume no functional or causal relationship between PrEP uptake and HIV prevalence.' This is the basis for treating P and H as independent in Figs. 2 and 3.
  • domain assumption Full compliance of PrEP users with screening and administration recommendations.
    Methods: 'In our framework, individuals on PrEP fully comply with the screening requirements and administration recommendations.' Partial compliance would effectively lower the screening rate lambda_P.
  • domain assumption PrEP users do not engage in additional mitigation or voluntary risk-related testing.
    Methods: 'they change their sexual behavior by not engaging in additional mitigation measures and not showing a marked response to HIV infection risk.' Drives the negative effect of PrEP on STI spread.
  • ad hoc to paper Exponential mitigation function and linear testing-rate composition.
    Eqs. 6-9. The functional forms m(H)=mmin+(mmax-mmin)(1-exp(-H/Hmax)) and lambda_a=lambda_H(1-P)+lambda_P P are chosen for tractability; the qualitative location of paradox regions depends on them.
  • domain assumption Demographic equilibrium with constant population size.
    Eqs. 12-14: S+Ia+Is+T=1 and Phi=mu. Standard for such models, but excludes demographic changes that could alter prevalence.
  • domain assumption Homogeneous mixing in the minimal model.
    The minimal model treats the high-risk MSM population as one well-mixed compartment; the extended model with four risk groups partially relaxes this, but the main figures use the homogeneous version.

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Cite this review

Pith. "Pith review of Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study." pith.science (2026). https://pith.science/paper/XDLHAMCZ

@misc{pith2026250524433,
  author       = {Pith},
  title        = {Pith review of: Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among MSM on HIV PrEP: A Modeling Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XDLHAMCZ}},
  note         = {Machine review of arXiv:2505.24433}
}
read the original abstract

HIV pre-exposure Prophylaxis (PrEP) has become essential for global HIV control, but its implementation coincides with rising bacterial STI rates among men who have sex with men (MSM). While risk-compensation behavioral changes like reduced condom use are frequently reported, we examine whether intensified asymptomatic screening in PrEP programs creates surveillance artifacts that could be misinterpreted. We developed a compartmental model to represent the simultaneous spread of HIV and chlamydia (as an example of a curable STI), integrating three mechanisms: 1) risk-mediated self-protective behavior, 2) condom use reduction post-PrEP initiation, and 3) PrEP-related asymptomatic STI screening. Increasing PrEP uptake may help to reduce chlamydia prevalence, only if the PrEP-related screening is frequent enough. Otherwise, the effect of PrEP can be disadvantageous, as the drop in self-protective actions caused by larger PrEP uptake cannot be compensated for. Additionally, the change in testing behavior may lead to situations where the trend in the number of positive tests is not a reliable sign of the actual dynamics. We found a plausible mechanism to reconcile conflicting observational evidence on the effect of PrEP on STI rates, showing that simultaneous changes in testing and spreading rates may generate conflicting signals, i.e., that observed trends increase while true prevalence decreases. Asymptomatic screening, together with personalized infection treatment to minimize putative pressure to generate antibiotic resistance, is one of the key determinants of the positive side effects of PrEP in reducing STI incidence.

Figures

Figures reproduced from arXiv: 2505.24433 by the authors.

Figure 1
Figure 1. Minimal susceptible-infectious-treated-susceptible model for chlamydia transmission among high-risk MSM, influenced by HIV risk perception and PrEP uptake. This model distinguishes between symptomatic and asymptomatic infections and incorporates three feedback mechanisms: i) increased risk awareness leads to higher condom use (blue, reducing transmission rates), ii) lower condom use among PrEP users (red, increasing… view at source ↗
Figure 2
Figure 2. Increasing PrEP use among high-infection-risk groups can mitigate the spread of chlamydia. a–f We analyze the expected prevalence of chlamydia infections under two scenarios of transmission rates (low and high; parameters in Tab. 2). The frequency of PrEP-related screening determines the effects of PrEP uptake, particularly at low levels of risk awareness in the population (H). For instance, when PrEP users are requ… view at source ↗
Figure 3
Figure 3. Mismatch in the dynamics of observed and real cases. a An increase in PrEP uptake will change the prevalence N and observed cases N obs. Both can either increase or decrease, resulting in four different combinations that can be assigned into three categories: (light blue) both N and N obs will increase as a consequence of increased PrEP uptake in the population, (dark blue) both N and N obs will decrease as a conseq… view at source ↗

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Works this paper leans on

43 extracted references · 43 canonical work pages

  1. [1]

    Emtricitabine-tenofovir concentrations and pre-exposure prophylaxis efficacy in men who have sex with men

    Anderson PL, Glidden DV, Liu A, Buchbinder S, Lama JR, Guanira JV, et al. Emtricitabine-tenofovir concentrations and pre-exposure prophylaxis efficacy in men who have sex with men. Science translational medicine. 2012;4(151):151ra125-5

    work page 2012

  2. [2]

    Pre-exposure prophylaxis to prevent the acquisition of HIV-1 infection (PROUD): effectiveness results from the pilot phase of a pragmatic open-label randomised trial

    McCormack S, Dunn DT, Desai M, Dolling DI, Gafos M, Gilson R, et al. Pre-exposure prophylaxis to prevent the acquisition of HIV-1 infection (PROUD): effectiveness results from the pilot phase of a pragmatic open-label randomised trial. The Lancet. 2016;387(10013):53-60. 12 Testing Paradox May Explain Increased Observed Prevalence of Bacterial STIs among M...

    work page 2016

  3. [3]

    Elimination prospects of the Dutch HIV epidemic among men who have sex with men in the era of preexposure prophylaxis

    Rozhnova G, Heijne J, Bezemer D, Van Sighem A, Presanis A, De Angelis D, et al. Elimination prospects of the Dutch HIV epidemic among men who have sex with men in the era of preexposure prophylaxis. Aids. 2018;32(17):2615-23

    work page 2018

  4. [4]

    Oral pre-exposure prophylaxis (PrEP) to prevent HIV: a systematic review and meta-analysis of clinical effectiveness, safety, adherence and risk compensation in all populations

    Murchu EO, Marshall L, Teljeur C, Harrington P, Hayes C, Moran P, et al. Oral pre-exposure prophylaxis (PrEP) to prevent HIV: a systematic review and meta-analysis of clinical effectiveness, safety, adherence and risk compensation in all populations. BMJ open. 2022;12(5):e048478

    work page 2022

  5. [5]

    Change in sexual risk behaviour after 6 months of pre-exposure prophylaxis use: results from the Amsterdam pre-exposure prophylaxis demonstration project

    Hoornenborg E, Coyer L, van Laarhoven A, Achterbergh R, de Vries H, Prins M, et al. Change in sexual risk behaviour after 6 months of pre-exposure prophylaxis use: results from the Amsterdam pre-exposure prophylaxis demonstration project. Aids. 2018;32(11):1527-32

    work page 2018

  6. [6]

    Holt M, Lea T, Mao L, Kolstee J, Zablotska I, Duck T, et al. Community-level changes in condom use and uptake of HIV pre-exposure prophylaxis by gay and bisexual men in Melbourne and Sydney, Australia: results of repeated behavioural surveillance in 2013–17. The lancet HIV. 2018;5(8):e448-56

    work page 2013

  7. [7]

    Gonorrhoea treatment failure caused by a Neisseria gonorrhoeae strain with combined ceftriaxone and high-level azithromycin resistance, England, February 2018

    Eyre DW, Sanderson ND, Lord E, Regisford-Reimmer N, Chau K, Barker L, et al. Gonorrhoea treatment failure caused by a Neisseria gonorrhoeae strain with combined ceftriaxone and high-level azithromycin resistance, England, February 2018. Eurosurveillance. 2018;23(27):1800323

    work page 2018

  8. [8]

    The role of sexually transmitted diseases in HIV transmission

    Galvin SR, Cohen MS. The role of sexually transmitted diseases in HIV transmission. Nature Reviews Microbiology. 2004;2(1):33-42

    work page 2004

Show all 43 references
  1. [9]

    Risk compensation and STI incidence in PrEP programmes

    Quaife M, MacGregor L, Ong JJ, Gafos M, Torres-Rueda S, Grant H, et al. Risk compensation and STI incidence in PrEP programmes. The lancet HIV. 2020;7(4):e222-3

    2020

  2. [10]

    Challenges and Solutions to STI Control in the Era of HIV and STI Prophylaxis

    Mogaka FO, Stewart J, Omollo V, Bukusi E. Challenges and Solutions to STI Control in the Era of HIV and STI Prophylaxis. Current HIV/AIDS Reports. 2023;20(5):312-9

    2023

  3. [11]

    Efficacy of risk-reduction counseling to prevent human immunodeficiency virus and sexually transmitted diseases: a randomized controlled trial

    Kamb ML, Fishbein M, Douglas Jr JM, Rhodes F, Rogers J, Bolan G, et al. Efficacy of risk-reduction counseling to prevent human immunodeficiency virus and sexually transmitted diseases: a randomized controlled trial. Jama. 1998;280(13):1161-7

    1998

  4. [12]

    HIV Voluntary Counseling and Testing (VCT-HIV) effectiveness for sexual risk-reduction among key populations: A systematic review and meta-analysis

    Costa AB, Viscardi LH, Feijo M, Fontanari AMV. HIV Voluntary Counseling and Testing (VCT-HIV) effectiveness for sexual risk-reduction among key populations: A systematic review and meta-analysis. EClinicalMedicine. 2022;52

    2022

  5. [13]

    Summary of German-Austrian HIV PrEP guideline

    Spinner C, Lang G, Boesecke C, Jessen H, Schewe K, consensus conference meeting 2018 GAP, et al. Summary of German-Austrian HIV PrEP guideline. HIV medicine. 2019;20(6):368-76

    2018

  6. [14]

    Can we screen less frequently for STI among PrEP users? Assessing the effect of biannual STI screening on timing of diagnosis and transmission risk in the AMPrEP Study

    Jongen VW, Zimmermann HM, Goedhart M, Bogaards JA, Davidovich U, Coyer L, et al. Can we screen less frequently for STI among PrEP users? Assessing the effect of biannual STI screening on timing of diagnosis and transmission risk in the AMPrEP Study. Sexually Transmitted Infect...

    2023

  7. [15]

    The challenges of containing SARS-CoV-2 via test-trace-and-isolate

    Contreras S, Dehning J, Loidolt M, Zierenberg J, Spitzner FP, Urrea-Quintero JH, et al. The challenges of containing SARS-CoV-2 via test-trace-and-isolate. Nature communications. 2021;12(1):1-13

    2021

  8. [16]

    Diverging trends in incidence of HIV versus other sexually transmitted infections in HIV-negative MSM in Amsterdam

    van Bilsen WP, Boyd A, van der Loeff MF, Davidovich U, Hogewoning A, van der Hoek L, et al. Diverging trends in incidence of HIV versus other sexually transmitted infections in HIV-negative MSM in Amsterdam. Aids. 2020;34(2):301-9

    2020

  9. [17]

    STI in times of PrEP: high prevalence of chlamydia, gonorrhea, and mycoplasma at different anatomic sites in men who have sex with men in Germany

    Jansen K, Steffen G, Potthoff A, Schuppe AK, Beer D, Jessen H, et al. STI in times of PrEP: high prevalence of chlamydia, gonorrhea, and mycoplasma at different anatomic sites in men who have sex with men in Germany. BMC infectious diseases. 2020;20:1-14

    2020

  10. [18]

    Association of HIV preexposure prophylaxis with incidence of sexually transmitted infections among individuals at high risk of HIV infection

    Traeger MW, Cornelisse VJ, Asselin J, Price B, Roth NJ, Willcox J, et al. Association of HIV preexposure prophylaxis with incidence of sexually transmitted infections among individuals at high risk of HIV infection. Jama. 2019;321(14):1380-90. 13 Testing Paradox May Explain In...

    2019

  11. [19]

    Werner RN, Gaskins M, Nast A, Dressler C. Incidence of sexually transmitted infections in men who have sex with men and who are at substantial risk of HIV infection–A meta-analysis of data from trials and observational studies of HIV pre-exposure prophylaxis. PloS one. 2018;13...

    2018

  12. [20]

    Determining the screening frequency for sexually transmitted infections for people who use HIV pre-exposure prophylaxis: a systematic review and meta-analysis

    Kim Cm, Zhao V, De Mello MB, Baggaley R, Johnson CC, Spielman E, et al. Determining the screening frequency for sexually transmitted infections for people who use HIV pre-exposure prophylaxis: a systematic review and meta-analysis. International Journal of Infectious Diseases....

    2023

  13. [21]

    Ong JJ, Baggaley RC, Wi TE, Tucker JD, Fu H, Smith MK, et al. Global epidemiologic characteristics of sexually transmitted infections among individuals using preexposure prophylaxis for the prevention of HIV infection: a systematic review and meta-analysis. JAMA network open. ...

    2019

  14. [22]

    Societal self-regulation induces complex infection dynamics and chaos

    Wagner J, Bauer S, Contreras S, Fleddermann L, Parlitz U, Priesemann V. Societal self-regulation induces complex infection dynamics and chaos. Physical Review Research. 2025;7(1):013308

    2025

  15. [23]

    Interplay between risk perception, behavior, and COVID-19 spread

    Dönges P, Wagner J, Contreras S, Iftekhar EN, Bauer S, Mohr SB, et al. Interplay between risk perception, behavior, and COVID-19 spread. Frontiers in Physics. 2022;10:842180

    2022

  16. [24]

    Statutory health insurance-covered pre-exposure prophylaxis in Germany: changing trends in nationwide tenofovir disoproxil/emtricitabine prescriptions during the COVID-19 pandemic

    Prins H, Dörre A, Schmidt D. Statutory health insurance-covered pre-exposure prophylaxis in Germany: changing trends in nationwide tenofovir disoproxil/emtricitabine prescriptions during the COVID-19 pandemic. Frontiers in Pharmacology. 2023;14:1241310

    2023

  17. [25]

    Evaluating the impact and cost-effectiveness of chlamydia management strategies in Hong Kong: A modeling study

    Montes-Olivas S, Ozten Y, Homer M, Turner K, Fairley CK, Hocking JS, et al. Evaluating the impact and cost-effectiveness of chlamydia management strategies in Hong Kong: A modeling study. Frontiers in Public Health. 2022;10:932096

    2022

  18. [26]

    Effect of screening young men for Chlamydia trachomatis on the rates among women: a network modelling study for high-prevalence communities

    Qu Z, Azizi A, Schmidt N, Craig-Kuhn MC, Stoecker C, Mac Hyman J, et al. Effect of screening young men for Chlamydia trachomatis on the rates among women: a network modelling study for high-prevalence communities. BMJ open. 2021;11(1):e040789

    2021

  19. [27]

    Chlamydia trachomatis infection in asymptomatic men

    LaMontagne DS, Fine DN, Marrazzo JM. Chlamydia trachomatis infection in asymptomatic men. American journal of preventive medicine. 2003;24(1):36-42

    2003

  20. [28]

    What proportion of episodes of gonorrhoea and chlamydia becomes symptomatic? International journal of STD & AIDS

    Korenromp EL, Sudaryo MK, de Vlas SJ, Gray RH, Sewankambo NK, Serwadda D, et al. What proportion of episodes of gonorrhoea and chlamydia becomes symptomatic? International journal of STD & AIDS. 2002;13(2):91-101

    2002

  21. [29]

    Consolidated guidelines on HIV prevention, testing, treatment, service delivery and monitoring: recommendations for a public health approach

    Organization WH, et al. Consolidated guidelines on HIV prevention, testing, treatment, service delivery and monitoring: recommendations for a public health approach. World Health Organization; 2021

    2021

  22. [30]

    PrEP continuation, HIV and STI testing rates, and delivery of preventive care in a clinic-based cohort

    Hevey MA, Walsh JL, Petroll AE. PrEP continuation, HIV and STI testing rates, and delivery of preventive care in a clinic-based cohort. AIDS Education and Prevention. 2018;30(5):393-405

    2018

  23. [31]

    Actual sexual risk and perceived risk of HIV acquisition among HIV-negative men who have sex with men in Toronto, Canada

    Kesler MA, Kaul R, Liu J, Loutfy M, Gesink D, Myers T, et al. Actual sexual risk and perceived risk of HIV acquisition among HIV-negative men who have sex with men in Toronto, Canada. BMC public health. 2016;16:1-9

    2016

  24. [32]

    Frequent screening for asymptomatic chlamydia and gonorrhoea infections in men who have sex with men: time to re-evaluate? The Lancet Infectious Diseases

    Williams E, Williamson DA, Hocking JS. Frequent screening for asymptomatic chlamydia and gonorrhoea infections in men who have sex with men: time to re-evaluate? The Lancet Infectious Diseases. 2023;23(12):e558-66

    2023

  25. [33]

    Streeck H, Jansen K, Crowell TA, Esber A, Jessen HK, Cordes C, et al. HIV pre-exposure prophy- laxis was associated with no impact on sexually transmitted infection prevalence in a high-prevalence population of predominantly men who have sex with men, Germany, 2018 to 2019. Eu...

    2018

  26. [34]

    Low incidence of HIV infection and decreasing incidence of sexually transmitted infections among PrEP users in 2020 in Germany

    Schmidt D, Kollan C, Bartmeyer B, Bremer V, Schikowski T, Friebe M, et al. Low incidence of HIV infection and decreasing incidence of sexually transmitted infections among PrEP users in 2020 in Germany. Infection. 2023;51(3):665-78. 14 Testing Paradox May Explain Increased Obs...

    2020

  27. [35]

    Incidence of gonorrhea and chlamydia following human immunodeficiency virus preexposure prophylaxis among men who have sex with men: a modeling study

    Jenness SM, Weiss KM, Goodreau SM, Gift T, Chesson H, Hoover KW, et al. Incidence of gonorrhea and chlamydia following human immunodeficiency virus preexposure prophylaxis among men who have sex with men: a modeling study. Clinical Infectious Diseases. 2017;65(5):712-8

    2017

  28. [36]

    Is screening for chlamydia and gonorrhea in men who have sex with men associated with reduction of the prevalence of these infections? A systematic review of observational studies

    Tsoumanis A, Hens N, Kenyon CR. Is screening for chlamydia and gonorrhea in men who have sex with men associated with reduction of the prevalence of these infections? A systematic review of observational studies. Sexually transmitted diseases. 2018;45(9):615-22

    2018

  29. [37]

    Screening for STIs is one of the main drivers of macrolide consumption in PrEP users

    Vanbaelen T, Van Dijck C, De Baetselier I, Florence E, Reyniers T, Vuylsteke B, et al. Screening for STIs is one of the main drivers of macrolide consumption in PrEP users. International journal of STD & AIDS. 2021;32(12):1183-4

    2021

  30. [38]

    Vanbaelen T, Tsoumanis A, Florence E, Van Dijck C, Sauvage AS, Herssens N, et al. Effect of screening for Neisseria gonorrhoeae and Chlamydia trachomatis on incidence of these infections in men who have sex with men and transgender women taking HIV pre-exposure prophylaxis (th...

    2024

  31. [39]

    2016 European guideline on the management of non-gonococcal urethritis

    Horner PJ, Blee K, Falk L, van der Meijden W, Moi H. 2016 European guideline on the management of non-gonococcal urethritis. International journal of STD & AIDS. 2016;27(11):928-37

    2016

  32. [40]

    Understanding Nash epidemics

    Schnyder SK, Molina JJ, Yamamoto R, Turner MS. Understanding Nash epidemics. Proceedings of the National Academy of Sciences. 2025;122(9):e2409362122

    2025

  33. [41]

    Kettlitz R, Harries M, Ortmann J, Krause G, Aigner A, Lange B. Association of known SARS-CoV-2 serostatus and adherence to personal protection measures and the impact of personal protective measures on seropositivity in a population-based cross-sectional study (MuSPAD) in Germ...

    2023

  34. [42]

    Effect of screening and treatment for gonorrhea and chlamydia on HIV incidence among men who have sex with men in the United States: a modeling analysis

    Jones J, Le Guillou A, Gift TL, Chesson H, Bernstein KT, Delaney KP, et al. Effect of screening and treatment for gonorrhea and chlamydia on HIV incidence among men who have sex with men in the United States: a modeling analysis. Sexually transmitted diseases. 2022;49(10):669-76

    2022

  35. [43]

    Impact of heterogeneity in sexual behavior on effectiveness in reducing HIV transmission with test-and-treat strategy

    Rozhnova G, van der Loeff MFS, Heijne JC, Kretzschmar ME. Impact of heterogeneity in sexual behavior on effectiveness in reducing HIV transmission with test-and-treat strategy. PLoS computational biology. 2016;12(8):e1005012. 15 Testing Paradox May Explain Increased Observed P...

    2016

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.