Lifetime alcohol consumption patterns and young-onset breast cancer by subtype among Non-Hispanic Black and White women in the Young Women's Health History Study.

PURPOSE: The role of alcohol in young-onset breast cancer (YOBC) is unclear. We examined associations between lifetime alcohol consumption and YOBC in the Young Women's Health History Study, a population-based case-control study of breast cancer among Non-Hispanic Black and White women < 50 years of age. METHODS: Breast cancer cases (n = 1,812) were diagnosed in the Metropolitan Detroit and Los Angeles County SEER registry areas, 2010-2015. Controls (n = 1,381) were identified through area-based sampling and were frequency-matched to cases by age, site, and race. Alcohol consumption and covariates were collected from in-person interviews. Weighted multivariable logistic regression was conducted to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for associations between alcohol consumption and YOBC overall and by subtype (Luminal A, Luminal B, HER2, or triple negative). RESULTS: Lifetime alcohol consumption was not associated with YOBC overall or with subtypes (all ptrend ≥ 0.13). Similarly, alcohol consumption in adolescence, young and middle adulthood was not associated with YOBC (all ptrend ≥ 0.09). An inverse association with triple-negative YOBC, however, was observed for younger age at alcohol use initiation (< 18 years vs. no consumption), aOR (95% CI) = 0.62 (0.42, 0.93). No evidence of statistical interaction by race or household poverty was observed. CONCLUSIONS: Our findings suggest alcohol consumption has a different association with YOBC than postmenopausal breast cancer-lifetime consumption was not linked to increased risk and younger age at alcohol use initiation was associated with a decreased risk of triple-negative YOBC. Future studies on alcohol consumption in YOBC subtypes are warranted.

Estimating contact network properties by integrating multiple data sources associated with infectious diseases.

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Theory, methods, and operational results of the Young Women's Health History Study: a study of young-onset breast cancer incidence in Black and White women.

PURPOSE: The etiology of young-onset breast cancer (BC) is poorly understood, despite its greater likelihood of being hormone receptor-negative with a worse prognosis and persistent racial and socioeconomic inequities. We conducted a population-based case-control study of BC among young Black and White women and here discuss the theory that informed our study, exposures collected, study methods, and operational results. METHODS: Cases were non-Hispanic Black (NHB) and White (NHW) women age 20-49 years with invasive BC in metropolitan Detroit and Los Angeles County SEER registries 2010-2015. Controls were identified through area-based sampling from the U.S. census and frequency matched to cases on study site, race, and age. An eco-social theory of health informed life-course exposures collected from in-person interviews, including socioeconomic, reproductive, and energy balance factors. Measured anthropometry, blood (or saliva), and among cases SEER tumor characteristics and tumor tissue (from a subset of cases) were also collected. RESULTS: Of 5,309 identified potentially eligible cases, 2,720 sampled participants were screened and 1,812 completed interviews (682 NHB, 1140 NHW; response rate (RR): 60%). Of 24,612 sampled control households 18,612 were rostered, 2,716 participants were sampled and screened, and 1,381 completed interviews (665 NHB, 716 NHW; RR: 53%). Ninety-nine% of participants completed the main interview, 82% provided blood or saliva (75% blood only), and SEER tumor characteristics (including ER, PR and HER2 status) were obtained from 96% of cases. CONCLUSIONS: Results from the successfully established YWHHS should expand our understanding of young-onset BC etiology overall and by tumor type and identify sources of racial and socioeconomic inequities in BC.

Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.

Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd.

A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.

When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large-scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open-source software, integrated into the treatSens package for the R statistical programming language. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Linkage of viral sequences among HIV-infected village residents in Botswana: estimation of linkage rates in the presence of missing data.

Linkage analysis is useful in investigating disease transmission dynamics and the effect of interventions on them, but estimates of probabilities of linkage between infected people from observed data can be biased downward when missingness is informative. We investigate variation in the rates at which subjects' viral genotypes link across groups defined by viral load (low/high) and antiretroviral treatment (ART) status using blood samples from household surveys in the Northeast sector of Mochudi, Botswana. The probability of obtaining a sequence from a sample varies with viral load; samples with low viral load are harder to amplify. Pairwise genetic distances were estimated from aligned nucleotide sequences of HIV-1C env gp120. It is first shown that the probability that randomly selected sequences are linked can be estimated consistently from observed data. This is then used to develop estimates of the probability that a sequence from one group links to at least one sequence from another group under the assumption of independence across pairs. Furthermore, a resampling approach is developed that accounts for the presence of correlation across pairs, with diagnostics for assessing the reliability of the method. Sequences were obtained for 65% of subjects with high viral load (HVL, n = 117), 54% of subjects with low viral load but not on ART (LVL, n = 180), and 45% of subjects on ART (ART, n = 126). The probability of linkage between two individuals is highest if both have HVL, and lowest if one has LVL and the other has LVL or is on ART. Linkage across groups is high for HVL and lower for LVL and ART. Adjustment for missing data increases the group-wise linkage rates by 40-100%, and changes the relative rates between groups. Bias in inferences regarding HIV viral linkage that arise from differential ability to genotype samples can be reduced by appropriate methods for accommodating missing data.

Evidence of secular trends during the COVID-19 pandemic in a stepped wedge cluster randomized trial examining sexual and reproductive health outcomes among Indigenous youth.

BACKGROUND: Nen UnkUmbi/EdaHiYedo ("We Are Here Now," or NE) is an intervention to prevent STIs, HIV, HCV, and teen pregnancy among Assiniboine and Sioux youth of the Fort Peck Reservation in the state of Montana in the USA. A cluster-randomized stepped-wedge design (SWD) trial is used to evaluate NE, where clusters are schools. The purpose of this study is to evaluate whether there is evidence of a secular trend associated with the COVID-19 pandemic. METHODS: The original study design is a cluster-randomized stepped-wedge design (SWD), in which five schools that youth from Fort Peck attend are the clusters to be randomized into the intervention one at a time, with all schools eventually being randomized to the intervention across three steps. N/E is a 5-year study involving 456 15- to 18-year-old youth. For this study, we use a mixed quantitative and qualitative methods approach to understand how the COVID-19 pandemic may have been associated with the study's primary outcome variables. Data were drawn from the first cluster exposed to the intervention and one control cluster that did not yet receive the intervention during the period in which COVID-19 mitigation efforts were being implemented. A pre-post COVID questionnaire was added to core measures administered, and semistructured qualitative interviews were conducted with youths regarding their perceptions of how the pandemic altered their sexual behaviors. RESULTS: One hundred eighteen youth responded to the questionnaire and 31 youth participated in semistructured qualitative interviews. Youth reporting having sex with less people due to COVID-19 reported more sex acts (incident rate ratio (IRR)=3.6, 95% CI 1.6-8.1) in comparison to those who did not report having sex with less people, and youth who reported having sex with the same amount of people due to COVID-19 reported less sex acts (IRR=0.31, 95% CI 0.14-0.7) in comparison to those who did not report having sex with the same amount of people. Youth reporting having sex less times due to COVID-19 experienced a greater number of sex acts in comparison to those who did not report having sex less times (IRR=2.7, 1.2-6.4). Results suggest that more sexually active individuals reported perceiving having sex with less people and less frequent engagement in sex during the pandemic. It is possible that the COVID-19 pandemic period was associated with a truncation in the distribution of sexual activity that would bias an estimate of the intervention's effect. CONCLUSION: Findings suggest evidence of a secular trend. This trend must be accounted for at trial end, and sensitivity analyses are recommended. Documenting and reporting on these findings encourages transparent reporting during the implementation of a SWD trial during a global pandemic, and informs endline analyses. TRIAL REGISTRATION: This trial is registered with the Clinical trials registry of the US National Library of Medicine at the National Institutes of Health (NIH). It was registered on October 1, 2018. The study presented in this manuscript is funded by NIH National Institute on Minority Health and Health Disparities (NIMHD), Award # R01MD012761-01, Elizabeth Rink (Principal Investigator). The study's ClinicalTrials.gov number is NCT03694418.

Calibration of an Adaptive Genetic Algorithm for Modeling Opinion Diffusion.

Genetic algorithms mimic the process of natural selection in order to solve optimization problems with minimal assumptions and perform well when the objective function has local optima on the search space. These algorithms treat potential solutions to the optimization problem as chromosomes, consisting of genes which undergo biologically-inspired operators to identify a better solution. Hyperparameters or control parameters determine the way these operators are implemented. We created a genetic algorithm in order to fit a DeGroot opinion diffusion model using limited data, making use of selection, blending, crossover, mutation, and survival operators. We adapted the algorithm from a genetic algorithm for design of mixture experiments, but the new algorithm required substantial changes due to model assumptions and the large parameter space relative to the design space. In addition to introducing new hyperparameters, these changes mean the hyperparameter values suggested for the original algorithm cannot be expected to result in optimal performance. To make the algorithm for modeling opinion diffusion more accessible to researchers, we conduct a simulation study investigating hyperparameter values. We find the algorithm is robust to the values selected for most hyperparameters and provide suggestions for initial, if not default, values and recommendations for adjustments based on algorithm output.

Trait emotional intelligence in American pilots.

There is a dearth of trait emotional intelligence (trait EI) research within an aviation context. Using the Trait Emotional Intelligence Questionnaire (TEIQue), the present study investigated potential trait EI differences between pilots and general population controls in the United States. The forty-four pilots who volunteered to participate were primarily male (93%) and between 24 and 67 years with a wide range of flight experience (150-5000 + hrs.) They were matched with controls based on age, gender, and ethnicity. Comparisons on global trait EI and the four trait EI factors revealed significant differences, with pilots scoring consistently lower than their matched counterparts in global trait EI, Well-being, Emotionality, and Sociability, but not Self-control. Overall, the findings indicated that pilots felt less connected to their emotional world than controls. Though limited by sample size and participant diversity, the results provide a basis for future studies into the trait EI profile of pilots, which had not been previously investigated.

Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senegal.

This study estimates the overall effect of two influenza vaccination programs consecutively administered in a cluster-randomized trial in western Senegal over the course of two influenza seasons from 2009-2011. We apply cutting-edge methodology combining social contact data with infection data to reduce bias in estimation arising from contamination between clusters. Our time-varying estimates reveal a reduction in seasonal influenza from the intervention and a nonsignificant increase in H1N1 pandemic influenza. We estimate an additive change in overall cumulative incidence (which was 6.13% in the control arm) of -0.68 percentage points during Year 1 of the study (95% CI: -2.53, 1.18). When H1N1 pandemic infections were excluded from analysis, the estimated change was -1.45 percentage points and was significant (95% CI, -2.81, -0.08). Because cross-cluster contamination was low (0-3% of contacts for most villages), an estimator assuming no contamination was only slightly attenuated (-0.65 percentage points). These findings are encouraging for studies carefully designed to minimize spillover. Further work is needed to estimate contamination - and its effect on estimation - in a variety of settings.

Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection.

The incidence of new infections is a key measure of the status of the HIV epidemic, but accurate measurement of incidence is often constrained by limited data. Karon et al. (Statist. Med. 2008; 27:4617­4633) developed a model to estimate the incidence of HIV infection from surveillance data with biologic testing for recent infection for newly diagnosed cases. This method has been implemented by public health departments across the United States and is behind the new national incidence estimates, which are about 40 per cent higher than previous estimates. We show that the delta method approximation given for the variance of the estimator is incomplete, leading to an inflated variance estimate. This contributes to the generation of overly conservative confidence intervals, potentially obscuring important differences between populations. We demonstrate via simulation that an innovative model-based bootstrap method using the specified model for the infection and surveillance process improves confidence interval coverage and adjusts for the bias in the point estimate. Confidence interval coverage is about 94­97 per cent after correction, compared with 96­99 per cent before. The simulated bias in the estimate of incidence ranges from −6.3 to +14.6 per cent under the original model but is consistently under 1 per cent after correction by the model-based bootstrap. In an application to data from King County, Washington in 2007 we observe correction of 7.2 per cent relative bias in the incidence estimate and a 66 per cent reduction in the width of the 95 per cent confidence interval using this method. We provide open-source software to implement the method that can also be extended for alternate models.

Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions.

Leveraging social influence is an increasingly common strategy to change population behavior or acceptance of public health policies and interventions; however, assessing the effectiveness of these social network interventions and projecting their performance at scale requires modeling of the opinion diffusion process. We previously developed a genetic algorithm to fit the DeGroot opinion diffusion model in settings with small social networks and limited follow-up of opinion change. Here, we present an assessment of the algorithm performance under the less-than-ideal conditions likely to arise in practical applications. We perform a simulation study to assess the performance of the algorithm in the presence of ordinal (rather than continuous) opinion measurements, network sampling, and model misspecification. We found that the method handles alternate models well, performance depends on the precision of the ordinal scale, and sampling the full network is not necessary to use this method. We also apply insights from the simulation study to investigate notable features of opinion diffusion models for a social network intervention to increase uptake of pre-exposure prophylaxis (PrEP) among Black men who have sex with men (BMSM).

Using a novel genetic algorithm to assess peer influence on willingness to use pre-exposure prophylaxis in networks of Black men who have sex with men.

The DeGroot model for opinion diffusion over social networks dates back to the 1970s and models the mechanism by which information or disinformation spreads through a network, changing the opinions of the agents. Extensive research exists about the behavior of the DeGroot model and its variations over theoretical social networks; however, research on how to estimate parameters of this model using data collected from an observed network diffusion process is much more limited. Existing algorithms require large data sets that are often infeasible to obtain in public health or social science applications. In order to expand the use of opinion diffusion models to these and other applications, we developed a novel genetic algorithm capable of recovering the parameters of a DeGroot opinion diffusion process using small data sets, including those with missing data and more model parameters than observed time steps. We demonstrate the efficacy of the algorithm on simulated data and data from a social network intervention leveraging peer influence to increase willingness to take pre-exposure prophylaxis in an effort to decrease transmission of human immunodeficiency virus among Black men who have sex with men.

No Longer an Island: A Social Network Intervention Engaging Black Men Through CBPR.

The aim of this study was to assess outcomes from a multilevel social network intervention to promote the health of Black men. Through a community-academic collaboration and using a participatory research approach, we implemented the intervention over 4 years in a 110-block area of an urban neighborhood. The project aimed to implement a neighborhood peer outreach and leadership network to strengthen social support of Black men and increase community and family engagement. Intervention activities included three 12-month intergenerational peer support groups (N = 46), a door-to-door outreach campaign (N = 186), media and communication efforts, and a community partner network. Primary outcomes for the peer support groups were measured using a pretest/posttest cohort design and included social support, perceived stress, social capital, and global self-esteem. Primary outcomes for the door-to-door outreach campaign were measured using a repeated cross-sectional design and included a sense of community, neighborhood social interaction, perceived neighborhood control, and self-rated health status. Significant findings from the peer support groups included an increase in social support overall (p = .027), driven by improvements in guidance, reliable alliance, and reassurance of worth; and an improvement in perceived stress (p = .047). Significant findings from the door-to-door outreach campaign included increases in neighborhood social interaction (p < .0001) and perceived neighborhood control (p = .036). This project provides evidence that a participatory approach to planning and delivering a health promotion intervention aimed at creating positive social spaces and enhancing social connections can result in significant outcomes and successful engagement of Black men.

Youth Chef Academy: Pilot Results From a Plant-Based Culinary and Nutrition Literacy Program for Sixth and Seventh Graders.

BACKGROUND: National data confirm that youth are not eating recommended amounts of fruits and vegetables (F/V), legumes, and whole grains (WGs). Establishing plant-based eating patterns early in life may positively impact long-term health through tracking of adolescent eating patterns into adulthood and through potential associations between adolescent dietary intake and adult disease risk. The study aim was to examine the effectiveness of Youth Chef Academy (YCA), a classroom-based experiential culinary and nutrition literacy intervention for sixth and seventh graders (11- to 13-year-olds) designed to impact healthy eating. METHODS: Study used a nonequivalent control group design with 8 schools selected for similarity in: free/reduced-price lunch, race/ethnicity, and student mobility rate (N = 248). Primary outcomes were times per day of F/V, vegetable, and WG consumption. Students completed a survey to assess primary outcomes and other measures at baseline and post-intervention. RESULTS: Significant increases in times per day of F/V (p = .022) and vegetable only (p = .015) consumption in the intervention group compared to the control group. Increases in WG consumption showed trended toward significance (p = .071). Student engagement and nutrition knowledge showed significant intervention effects. CONCLUSIONS: YCA positively impacts behavioral and knowledge variables related to healthy eating and increases students' engagement in their classrooms.

Estimation of the overall treatment effect in the presence of interference in cluster-randomized trials of infectious disease prevention.

An issue that remains challenging in the field of causal inference is how to relax the assumption of no interference between units. Interference occurs when the treatment of one unit can affect the outcome of another, a situation which is likely to arise with outcomes that may depend on social interactions, such as occurrence of infectious disease. Existing methods to accommodate interference largely depend upon an assumption of "partial interference" - interference only within identifiable groups but not among them. There remains a considerable need for development of methods that allow further relaxation of the no-interference assumption. This paper focuses on an estimand that is the difference in the outcome that one would observe if the treatment were provided to all clusters compared to that outcome if treatment were provided to none - referred as the overall treatment effect. In trials of infectious disease prevention, the randomized treatment effect estimate will be attenuated relative to this overall treatment effect if a fraction of the exposures in the treatment clusters come from individuals who are outside these clusters. This source of interference - contacts sufficient for transmission that are with treated clusters - is potentially measurable. In this manuscript, we leverage epidemic models to infer the way in which a given level of interference affects the incidence of infection in clusters. This leads naturally to an estimator of the overall treatment effect that is easily implemented using existing software.

An approximation method for improving dynamic network model fitting.

There has been a great deal of interest recently in the modeling and simulation of dynamic networks, i.e., networks that change over time. One promising model is the separable temporal exponential-family random graph model (ERGM) of Krivitsky and Handcock, which treats the formation and dissolution of ties in parallel at each time step as independent ERGMs. However, the computational cost of fitting these models can be substantial, particularly for large, sparse networks. Fitting cross-sectional models for observations of a network at a single point in time, while still a non-negligible computational burden, is much easier. This paper examines model fitting when the available data consist of independent measures of cross-sectional network structure and the duration of relationships under the assumption of stationarity. We introduce a simple approximation to the dynamic parameters for sparse networks with relationships of moderate or long duration and show that the approximation method works best in precisely those cases where parameter estimation is most likely to fail-networks with very little change at each time step. We consider a variety of cases: Bernoulli formation and dissolution of ties, independent-tie formation and Bernoulli dissolution, independent-tie formation and dissolution, and dependent-tie formation models.

Targeting pre-exposure prophylaxis among men who have sex with men in the United States and Peru: partnership types, contact rates, and sexual role.

BACKGROUND: We aim to identify optimal strategies for deploying pre-exposure prophylaxis among men who have sex with men (MSM) in the United States and Peru to maximize population-level effectiveness in an efficient manner. We use epidemic models to simulate the impact of targeting strategies. Most studies have focused on targeting either the general population or high-risk MSM. Alternative strategies, including serodiscordant couples, may better balance effectiveness and efficiency. METHODS: We use dynamic stochastic sexual network models based on exponential-family random graph modeling, parameterized from behavioral surveys of MSM in the United States and Peru. These models represent main partnerships and casual contacts separately, permitting modeling of interventions targeting men whose risk derives from combinations of relational types. We also model varying rates of uptake and adherence to pre-exposure prophylaxis (PrEP). We assess sensitivity of results to risk compensation through increases in condomless casual contacts and condomless sex in main partnerships. RESULTS: Targeting all men who are not exclusively insertive has the largest impact on HIV incidence, but targeting only those with high levels of casual activity yields comparable results using fewer person-years on PrEP. The effect is robust to risk compensation in the United States, but less so in Peru. Targeting serodiscordant main partnerships does not significantly impact incidence, but requires fewer person-years on PrEP per infection averted than other strategies. CONCLUSIONS: PrEP could be effective in reducing new infections at the population level in both settings. Serodiscordant partnerships are an attractive component of a targeting program, but targeting should include other high-risk men.

Can male circumcision have an impact on the HIV epidemic in men who have sex with men?

BACKGROUND: Three trials have demonstrated the prophylactic effect of male circumcision (MC) for HIV acquisition among heterosexuals, and MC interventions are underway throughout sub-Saharan Africa. Similar efforts for men who have sex with men (MSM) are stymied by the potential for circumcised MSM to acquire HIV easily through receptive sex and transmit easily through insertive sex. Existing work suggests that MC for MSM should reach its maximum potential in settings where sexual role segregation is historically high and relatively stable across the lifecourse; HIV incidence among MSM is high; reported willingness for prophylactic circumcision is high; and pre-existing circumcision rates are low. We aim to identify the likely public health impact that MC interventions among MSM would have in one setting that fulfills these conditions-Peru-as a theoretical upper bound for their effectiveness among MSM generally. METHODS AND FINDINGS: We use a dynamic, stochastic sexual network model based in exponential-family random graph modeling and parameterized from multiple behavioral surveys of Peruvian MSM. We consider three enrollment criteria (insertive during 100%, >80% or >60% of UAI) and two levels of uptake (25% and 50% of eligible men); we explore sexual role proportions from two studies and different frequencies of switching among role categories. Each scenario is simulated 10 times. We estimate that efficiency could reach one case averted per 6 circumcisions. However, the population-level impact of an optimistic MSM-MC intervention in this setting would likely be at most ∼5-10% incidence and prevalence reductions over 25 years. CONCLUSIONS: Roll-out of MC for MSM in Peru would not result in a substantial reduction in new HIV infections, despite characteristics in this population that could maximize such effects. Additional studies are needed to confirm these results for other MSM populations, and providers may consider the individual health benefits of offering MC to their MSM patients.