Dynamic Variation in Sexual Contact Rates in a Cohort of HIV-Negative Gay Men

Substance use and variation in sexual partnership rates among young MSM and young transgender women: Disaggregating between and within-person associations

BACKGROUND

Substance use has been extensively linked to sexual behavior and HIV/STI risk among men who have sex with men (MSM) and transgender women (TW). However, the impact of specific substances and on specific partnership types is not well characterized. The current study seeks to estimate the association between specific substances and partnership rates while carefully disaggregating between and within-person associations to characterize the nature of these associations and inform prevention interventions.

METHODS

Using data from a longitudinal cohort (n = 1159) of young MSM (YMSM) and young TW (YTW), we utilized a series of hybrid mixed effect models to estimate the associations between substance use (i.e., heavy episodic drinking [HED], marijuana, cocaine, ecstasy, methamphetamine, poppers, prescription stimulant, prescription painkiller, and prescription depressants) and partnerships (i.e., one-time, casual, and main).

RESULTS

Results from multivariable models indicated people using substances had higher one-time (HED, poppers) and casual (HED, methamphetamine, poppers) partnership rates. In addition, participants reported higher rates of one-time (HED, ecstasy, methamphetamine, poppers) and casual partners (HED, marijuana, cocaine, methamphetamines, poppers) during periods of substance use.

CONCLUSION

These findings confirm that the highest rates of sexual activity occur among YMSM-YTW using substances during periods of substance use. Yet, these findings should caution researchers against simplistic generalizations as these associations differ across substance and partnership types. Efforts to promote the health of MSM-YTW who use substances should carefully consider this complexity as interventions accounting for the unique cultural context of substance use in these populations are most likely to be successful.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Temporal Variation in One-Time Partnership Rates Among Young Men Who Have Sex With Men and Transgender Women

BACKGROUND

Volatility in sexual contact rates has been recognized as an important factor influencing HIV transmission dynamics. One-time partnerships may be particularly important given the potential to quickly accumulate large number of contacts. Yet, empirical data documenting individual variation in contact rates remain rare. This study provides much needed data on temporal variation in one-time partners to better understand behavioral dynamics and improve the accuracy of transmission models.

METHODS

Data for this study were obtained from a longitudinal cohort study of young men who have sex with men and transgender women in Chicago. Participants provided sexual network data every 6 months for 2 years. A series of random effects models examined variation in one-time partnership rates and disaggregated within and between associations of exposure variables. Exposure variables included prior number of one-time partners, number of casual partners, and having a main partner.

RESULTS

Results indicated substantial between-person and within-person variation in one-time partners. Casual partnerships were positively associated and main partnerships negatively associated with one-time partnership rates. There remained a small positive association between prior one-time partnerships and the current number of one-time partnerships.

CONCLUSIONS

Despite the preponderance of a low number of one-time partners, substantial variation in one-time partnership rates exists among young men who have sex with men and transgender women. Accordingly, focusing on high contact rate individuals alone may be insufficient to identify periods of highest risk. Future studies should use these estimates to more accurately model how volatility impacts HIV transmission and better understand how this variation influences intervention effectiveness.

Panel Data Analysis via Mechanistic Models

Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising due to the combination of model complexity and dataset size. Supplementary materials for this article are available online.

Impact of sexual trajectories of men who have sex with men on the reduction in HIV transmission by pre-exposure prophylaxis

Changes in sexual risk behavior over the life course in men who have sex with men (MSM) can influence population-level intervention efficacy. Our objective was to investigate the impact of incorporating sexual trajectories describing long-term changes in risk levels on the reduction in HIV prevalence by pre-exposure prophylaxis (PrEP) among MSM. Based on the Amsterdam Cohort Study data, we developed two models of HIV transmission in a population stratified by sexual behavior. In the first model, individuals were stratified into low, medium and high risk levels and did not change their risk levels. The second model had the same stratification but incorporated additionally three types of sexual behavior trajectories. The models assumed universal antiretroviral treatment of HIV⁺ MSM, and PrEP use by high risk HIV^- MSM. We computed the relative reduction in HIV prevalence in both models for annual PrEP uptakes of 10% to 80% at different time points after PrEP introduction. We then investigated the impact of sexual trajectories on the effectiveness of PrEP intervention. The impact of sexual trajectories on the overall prevalence and prevalence in individuals at low, medium and high risk levels varied with PrEP uptake and time after PrEP introduction. Compared to the model without sexual trajectories, the model with trajectories predicted a higher impact of PrEP on the overall prevalence, and on the prevalence among the medium and high risk individuals. In low risk individuals, there was more reduction in prevalence during the first 15 years of PrEP intervention if sexual trajectories were not incorporated in the model. After that point, at low risk level there was more reduction in the model with trajectories. In conclusion, our study predicts that sexual trajectories increase the estimated impact of PrEP on reducing HIV prevalence when compared to a population where risk levels do not change.

Numerical optimal control for HIV prevention with dynamic budget allocation

This article is about numerical control of HIV propagation. The contribution of the article is threefold: first, a novel model of HIV propagation is proposed; second, the methods from numerical optimal control are successfully applied to the developed model to compute optimal control profiles; finally, the computed results are applied to the real problem yielding important and practically relevant results.

Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention

Predicting the population-level effects of an infectious disease intervention that incorporate multiple modes of intervention is complicated by the joint non-linear dynamics of both infection transmission and the intervention itself. In this paper, we consider the sensitivity of Dynamic Optimal Control Profiles (DOCPs) for the optimal joint investment in both a contagiousness and susceptibility-based control of HIV to bio-behavioral, economic, and programmatic assumptions. The DOCP is calculated using recently developed numerical algorithms that allow controls to be represented by a set of piecewise constant functions that maintain a constant yearly budget. Our transmission model assumes multiple stages of HIV infection corresponding to acute and chronic infection and both within- and between-individual behavioral heterogeneity. We parameterize a baseline scenario from a longitudinal study of sexual behavior in MSM and consider sensitivity of the DOCPs to deviations from that baseline scenario. In the baseline scenario, the primary determinant of the dominant control were programmatic factors, regardless of budget. In sensitivity analyses, the qualitative aspects of the optimal control policy were often robust to significant deviation in assumptions regarding transmission dynamics. In addition, we found several conditions in which long-term joint investment in both interventions was optimal. Our results suggest that modeling in the service of decision support for intervention design can improve population-level effects of a limited set of economic resources. We found that economic and programmatic factors were as important as the inherent transmission dynamics in determining population-level intervention effects. Given our finding that the DOCPs were robust to alternative biological and behavioral assumptions it may be possible to identify DOCPs even when the data are not sufficient to identify a transmission model.

Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series

Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa.

A second-order iterated smoothing algorithm

Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximation for the first and second derivatives of the log likelihood via simulation-based sequential Monte Carlo smoothing and proved that the approximation has some attractive theoretical properties. We investigated an iterated smoothing algorithm carrying out likelihood maximization using these derivative approximations. Further, we developed a new iterated smoothing algorithm, using a modification of these derivative estimates, for which we establish both theoretical results and effective practical performance. On benchmark computational challenges, this method beat the first-order iterated filtering algorithm. The method's performance was comparable to a recently developed iterated filtering algorithm based on an iterated Bayes map. Our iterated smoothing algorithm and its theoretical justification provide new directions for future developments in simulation-based inference for latent variable models such as partially observed Markov process models.

Large cluster outbreaks sustain the HIV epidemic among MSM in Quebec

OBJECTIVE

HIV-1 epidemics among MSM remain unchecked despite advances in treatment and prevention paradigms. This study combined viral phylogenetic and behavioural risk data to better understand underlying factors governing the temporal growth of the HIV epidemic among MSM in Quebec (2002-2015).

METHODS

Phylogenetic analysis of pol sequences was used to deduce HIV-1 transmission dynamics (cluster size, size distribution and growth rate) in first genotypes of treatment-naïve MSM (2002-2015, n = 3901). Low sequence diversity of first genotypes (0-0.44% mixed base calls) was used as an indication of early-stage infection. Behavioural risk data were obtained from the Montreal rapid testing site and primary HIV-1-infection cohorts.

RESULTS

Phylogenetic analyses uncovered high proportion of clustering of new MSM infections. Overall, 27, 45, 48, 53 and 57% of first genotypes within one (singleton, n = 1359), 2-4 (n = 692), 5-9 (n = 367), 10-19 (n = 405) and 20+ (n = 1277) cluster size groups were early infections (<0.44% diversity). Thirty viruses within large 20+ clusters disproportionately fuelled the epidemic, representing 13, 25 and 42% of infections, first genotyped in 2004-2007 (n = 1314), 2008-2011 (n = 1356) and 2012-2015 (n = 1033), respectively. Of note, 35, 21 and 14% of MSM belonging to 20+, 2-19 and one (singleton) cluster groups were under 30 years of age, respectively. Half of persons seen at the rapid testing site (2009-2011, n = 1781) were untested in the prior year. Poor testing propensity was associated with fewer reported partnerships.

CONCLUSION

Addressing the heterogeneity in transmission dynamics among HIV-1-infected MSM populations may help guide testing, treatment and prevention strategies.

Frequency and determinants of consistent STI/HIV testing among men who have sex with men testing at STI outpatient clinics in the Netherlands: a longitudinal study

Objectives

Men who have sex with men (MSM) are at highest risk for STIs and HIV infections in the Netherlands. However, official guidelines on STI testing among MSM are lacking. They are advised to test for STIs at least every six months, but their testing behaviour is not well known. This study aimed to get insight into the proportion and determinants of consistent 6-monthly STI testing among MSM testing at STI outpatient clinics in the Netherlands.

Methods

This study included longitudinal surveillance data of STI consultations among MSM from all 26 STI outpatient clinics in the Netherlands between 1 June 2014 and 31 December 2015. Multinomial logistic regression analysis was used to identify determinants of consistent 6-monthly testing compared with single testing and inconsistent testing. Determinants of time between consultations among men with multiple consultations were analysed using a Cox Prentice-Williams-Peterson gap-time model.

Results

A total of 34 605 STI consultations of 18 634 MSM were included. 8966 (48.1%) men had more than one consultation, and 3516 (18.9%) men tested consistently 6-monthly. Indicators of high sexual risk behaviour, including having a history of STI, being HIV positive and having more than 10 sex partners, were positively associated with both being a consistent tester and returning to the STI clinic sooner. Men who were notified by a partner or who reported STI symptoms were also more likely to return to the STI clinic sooner, but were less likely to be consistent testers, identifying a group of event-driven testers.

Conclusions

The proportion of consistent 6-monthly testers among MSM visiting Dutch STI outpatient clinics was low. Testing behaviour was associated with sexual risk behaviour, but exact motives to test consistently remain unclear. Evidence-based testing guidelines are needed to achieve optimal reductions in STI transmission in the future.

Impact of Heterogeneity in Sexual Behavior on Effectiveness in Reducing HIV Transmission with Test-and-Treat Strategy

The WHO’s early-release guideline for antiretroviral treatment (ART) of HIV infection based on a recent trial conducted in 34 countries recommends starting treatment immediately upon an HIV diagnosis. Therefore, the test-and-treat strategy may become more widely used in an effort to scale up HIV treatment and curb further transmission. Here we examine behavioural determinants of HIV transmission and how heterogeneity in sexual behaviour influences the outcomes of this strategy. Using a deterministic model, we perform a systematic investigation into the effects of various mixing patterns in a population of men who have sex with men (MSM), stratified by partner change rates, on the elimination threshold and endemic HIV prevalence. We find that both the level of overdispersion in the distribution of the number of sexual partners and mixing between population subgroups have a large influence on endemic prevalence before introduction of ART and on possible long term effectiveness of ART. Increasing heterogeneity in risk behavior may lead to lower endemic prevalence levels, but requires higher coverage levels of ART for elimination. Elimination is only feasible for populations with a rather low degree of assortativeness of mixing and requires treatment coverage of almost 80% if rates of testing and treatment uptake by all population subgroups are equal. In this case, for fully assortative mixing and 80% coverage endemic prevalence is reduced by 57%. In the presence of heterogeneity in ART uptake, elimination is easier to achieve when the subpopulation with highest risk behavior is tested and treated more often than the rest of the population, and vice versa when it is less. The developed framework can be used to extract information on behavioral heterogeneity from existing data which is otherwise hard to determine from population surveys.

HIV is endemic in populations of MSM in Western countries. As ART reduces transmission risk, increased testing and treatment rates are expected to lower HIV incidence. However, concerns are that in MSM populations changing risk behavior may counteract the impact of ART on transmission. Using a mathematical model, we investigated how heterogeneity in sexual behavior influences the possible effects of a test-and-treat strategy on HIV prevalence and in particular the prospects of eliminating HIV from these populations. We demonstrated that behavioral heterogeneity plays an important role in determining the impact of ART on reducing HIV transmission. Knowledge of behavioral heterogeneity is key in setting intervention goals in populations of MSM.

Strong influence of behavioral dynamics on the ability of testing and treating HIV to stop transmission

Choosing between strategies to control HIV transmission with antivirals requires understanding both the dynamics affecting those strategies' effectiveness and what causes those dynamics. Alternating episodes of high and low contact rates (episodic risk) interact with increased transmission probabilities during early infection to strongly influence HIV transmission dynamics. To elucidate the mechanics of this interaction and how these alter the effectiveness of universal test and treat (UT8T) strategies, we formulated a model of UT8T effects. Analysis of this model shows how and why changing the dynamics of episodic risk changes the fraction of early transmissions (FET) and the basic reproduction number (R₀) and consequently causes UT8T to vary from easily eliminating transmission to having little effect. As the length of risk episodes varies from days to lifetimes, FET first increases, then falls. Endemic prevalence varies similarly. R₀, in contrast, increases monotonically and is the major determinant of UT8T effects. At some levels of episodic risk, FET can be high, but eradication is easy because R₀ is low. At others FET is lower, but a high R₀ makes eradication impossible and control ineffective. Thus changes in individual risk over time must be measured and analyzed to plan effective control strategies with antivirals.