Characteristics of the Sexual Networks of Men Who Have Sex With Men in Montréal, Toronto, and Vancouver: Insights from Canada's 2022 Mpox Outbreak.

BACKGROUND: The 2022-2023 global mpox outbreak disproportionately affected gay, bisexual, and other men who have sex with men (GBM). We investigated differences in GBM's sexual partner distributions across Canada's 3 largest cities and over time, and how they shaped transmission. METHODS: The Engage Cohort Study (2017-2023) recruited GBM via respondent-driven sampling in Montréal, Toronto, and Vancouver (n = 2449). We compared reported sexual partner distributions across cities and periods: before COVID-19 (2017-2019), pandemic (2020-2021), and after lifting of restrictions (2021-2023). We used Bayesian regression and poststratification to model partner distributions. We estimated mpox's basic reproduction number (R0) using a risk-stratified compartmental model. RESULTS: Pre-COVID-19 pandemic distributions were comparable: fitted average partners (past 6 months) were 10.4 (95% credible interval: 9.4-11.5) in Montréal, 13.1 (11.3-15.1) in Toronto, and 10.7 (9.5-12.1) in Vancouver. Sexual activity decreased during the pandemic and increased after lifting of restrictions, but remained below prepandemic levels. Based on reported cases, we estimated R0 of 2.4 to 2.7 and similar cumulative incidences (0.7%-0.9%) across cities. CONCLUSIONS: Similar sexual partner distributions may explain comparable R0 and cumulative incidence across cities. With potential for further recovery in sexual activity, mpox vaccination and surveillance strategies should be maintained.

Examining the Influence of Imbalanced Social Contact Matrices in Epidemic Models.

Transmissible infections such as those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread according to who contacts whom. Therefore, many epidemic models incorporate contact patterns through contact matrices. Contact matrices can be generated from social contact survey data. However, the resulting matrices are often imbalanced, such that the total number of contacts reported by group A with group B do not match those reported by group B with group A. We examined the theoretical influence of imbalanced contact matrices on the estimated basic reproduction number (R0). We then explored how imbalanced matrices may bias model-based epidemic projections using an illustrative simulation model of SARS-CoV-2 with 2 age groups (<15 and ≥15 years). Models with imbalanced matrices underestimated the initial spread of SARS-CoV-2, had later time to peak incidence, and had smaller peak incidence. Imbalanced matrices also influenced cumulative infections observed per age group, as well as the estimated impact of an age-specific vaccination strategy. Stratified transmission models that do not consider contact balancing may generate biased projections of epidemic trajectory and the impact of targeted public health interventions. Therefore, modeling studies should implement and report methods used to balance contact matrices for stratified transmission models.

Challenges and Opportunities in Big Data Science to Address Health Inequities and Focus the HIV Response.

PURPOSE OF REVIEW: Big Data Science can be used to pragmatically guide the allocation of resources within the context of national HIV programs and inform priorities for intervention. In this review, we discuss the importance of grounding Big Data Science in the principles of equity and social justice to optimize the efficiency and effectiveness of the global HIV response. RECENT FINDINGS: Social, ethical, and legal considerations of Big Data Science have been identified in the context of HIV research. However, efforts to mitigate these challenges have been limited. Consequences include disciplinary silos within the field of HIV, a lack of meaningful engagement and ownership with and by communities, and potential misinterpretation or misappropriation of analyses that could further exacerbate health inequities. Big Data Science can support the HIV response by helping to identify gaps in previously undiscovered or understudied pathways to HIV acquisition and onward transmission, including the consequences for health outcomes and associated comorbidities. However, in the absence of a guiding framework for equity, alongside meaningful collaboration with communities through balanced partnerships, a reliance on big data could continue to reinforce inequities within and across marginalized populations.

Testing Whether Higher Contact Among the Vaccinated Can Be a Mechanism for Observed Negative Vaccine Effectiveness.

Evidence from early observational studies suggested negative vaccine effectiveness (${V}_{Eff}$) for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant. Since true ${V}_{Eff}$ is unlikely to be negative, we explored how differences in contact among vaccinated persons (e.g., potentially from the implementation of vaccine mandates) could lead to observed negative ${V}_{Eff}$. Using a susceptible-exposed-infectious-recovered (SEIR) transmission model, we examined how vaccinated-contact heterogeneity, defined as an increase in the contact rate only between vaccinated individuals, interacted with 2 mechanisms of vaccine efficacy: vaccine efficacy against susceptibility ($V{E}_S$) and vaccine efficacy against infectiousness ($V{E}_I$), to produce underestimated and in some cases, negative measurements of ${V}_{Eff}$. We found that vaccinated-contact heterogeneity led to negative estimates when $V{E}_I$, and especially $V{E}_S$, were low. Moreover, we determined that when contact heterogeneity was very high, ${V}_{Eff}$ could still be underestimated given relatively high vaccine efficacies (0.7), although its effect on ${V}_{Eff}$ was strongly reduced. We also found that this contact heterogeneity mechanism generated a signature temporal pattern: The largest underestimates and negative measurements of ${V}_{Eff}$ occurred during epidemic growth. Overall, our research illustrates how vaccinated-contact heterogeneity could have feasibly produced negative measurements during the Omicron period and highlights its general ability to bias observational studies of ${V}_{Eff}$.

Maximizing the impact of limited vaccine supply under different early epidemic conditions: a 2-city modelling analysis of monkeypox virus transmission among men who have sex with men.

BACKGROUND: The current global monkeypox virus (MPXV) outbreak has disproportionately affected gay, bisexual and other men who have sex with men (GBMSM). Given that many jurisdictions have been faced with limited supplies of MPXV vaccine, we aimed to explore optimal vaccine allocation between 2 linked GBMSM transmission networks over a short-term time horizon, across several epidemic conditions. METHODS: We constructed a deterministic compartmental MPXV transmission model. We parameterized the model to reflect 2 representative, partially connected GBMSM sexual networks ( cities), using 2022 data from Ontario. We simulated a roll-out of 5000 vaccine doses over 30 days that started 45 days after epidemic seeding with 10 imported cases. Within this model, we varied the relative city (network) sizes, epidemic potentials (R 0), between-city mixing and distribution of seed cases between cities. For each combination of varied factors, we identified the allocation of doses between cities that maximized infections averted by day 90. RESULTS: Under our modelling assumptions, we found that a limited MPXV vaccine supply could generally avert more early infections when prioritized to networks that were larger, had more initial infections or had greater R 0. Greater between-city mixing decreased the influence of initial seed cases and increased the influence of city R 0 on optimal allocation. Under mixed conditions (e.g., fewer seed cases but greater R 0), optimal allocation required doses shared between cities. INTERPRETATION: In the context of the current global MPXV outbreak, we showed that prioritization of a limited supply of vaccines based on network-level factors can help maximize infections averted during an emerging epidemic. Such prioritization should be grounded in an understanding of context-specific risk drivers and should acknowledge potential connectedness of multiple transmission networks.

Population-Level Sexual Mixing According to HIV Status and Preexposure Prophylaxis Use Among Men Who Have Sex With Men in Montreal, Canada: Implications for HIV Prevention.

Using cross-sectional survey data (Engage, 2017-2018) from 1,137 men who have sex with men, ≥16 years old, in Montreal, we compared observed human immunodeficiency virus (HIV) seroconcordance in previous-6-months' sexual partnerships with what would have been observed by chance if zero individuals serosorted. Of 5 recent partnerships where both individuals were HIV-negative, we compared observed concordance in preexposure prophylaxis (PrEP) use with the counterfactual if zero individuals selected partners based on PrEP use. We estimated the concordance by chance using a balancing-partnerships approach assuming proportionate mixing. HIV-positive respondents had a higher proportion of HIV-positive partners (66.4%, 95% confidence interval (CI): 64.0, 68.6) than by chance (23.9%, 95% CI: 23.1, 24.7). HIV-negative respondents (both on and not on PrEP) had higher proportions of HIV-negative partners (82.9% (95% CI: 81.1, 84.7) and 90.7% (95% CI: 89.6, 91.7), respectively) compared with by chance (76.1%, 95% CI: 75.3, 76.9); however, those on PrEP had a higher proportion of HIV-positive partners than those not on PrEP (17.1% (95% CI: 15.3, 18.9) vs. 9.3% (95% CI: 8.3, 10.4). Those on PrEP also had a higher proportion of partners on PrEP among their HIV-negative partners (50.6%, 95% CI: 42.5, 58.8) than by chance (28.5%, 95% CI: 27.5, 29.4). The relationship between PrEP and sexual-mixing patterns demonstrated by less population-level serosorting among those on PrEP and PrEP-matching warrants consideration during PrEP roll-out.

Enhancing your practice: debriefing in interventional radiology.

LEARNING OBJECTIVES: Review the history of debriefing and provide an Interventional Radiologist (IR) specific framework for leading an effective debrief. BACKGROUND: A debrief is often regarded as a meeting with persons who were involved in a stressful, traumatic and/or emotionally challenging situation to review processes, communicate concerns or gather feedback. The goals of these sessions can be for learning/quality improvement (QI) or psychological/emotional support, or a mix of both. Debriefing after tough situations has become a standard tool of many medical specialties, such as surgery, critical care and emergency medicine, with specialty specific literature available. However, there is a paucity of Interventional Radiology specific literature available for debriefing techniques. CLINICAL FINDINGS/PROCEDURE DETAILS: We will review the history and types of debriefing and why a debrief could be considered. We will provide a framework for leading a successful debrief in Interventional Radiology. CONCLUSION: Debriefing can be a useful tool for learning and QI as well as psychological or emotional support after a challenging or tough situation. Debriefing can address multiple variables and can stylistically be tailored to suit specific needs. IRs have an opportunity to take a leadership role in debriefing, providing comfort and quality improvement through communication and support.

Quantifying the impact of cascade inequalities: a modelling study on the prevention impacts of antiretroviral therapy scale-up in Eswatini.

Background: Inequalities in the antiretroviral therapy (ART) cascade across subpopulations remain an ongoing challenge in the global HIV response. Eswatini achieved the UNAIDS 95-95-95 targets by 2020, with differentiated programs to minimize inequalities across subpopulations, including for female sex workers (FSW) and their clients. We sought to estimate additional HIV infections expected in Eswatini if cascade scale-up had not been equal, and under which epidemic conditions these inequalities could have the largest influence. Methods: Drawing on population-level and FSW-specific surveys in Eswatini, we developed a compartmental model of heterosexual HIV transmission which included eight subpopulations and four sexual partnership types. We calibrated the model to stratified HIV prevalence, incidence, and ART cascade data. Taking observed cascade scale-up in Eswatini as the base-case-reaching 95-95-95 in the overall population by 2020-we defined four counterfactual scenarios in which the population overall reached 80-80-90 by 2020, but where FSW, clients, both, or neither were disproportionately left behind, reaching only 60-40-80. We quantified relative additional cumulative HIV infections by 2030 in counterfactual vs base-case scenarios. We further estimated linear effects of viral suppression gap among FSW and clients on additional infections by 2030, plus effect modification by FSW/client population sizes, rates of turnover, and HIV prevalence ratios. Results: Compared with the base-case scenario, leaving behind neither FSW nor their clients led to the fewest additional infections by 2030: median (95% credible interval) 14.9 (10.4, 18.4)% vs 26.3 (19.7, 33.0)% if both were left behind-a 73 (40, 149)% increase. The effect of lower cascade on additional infections was larger for clients vs FSW, and both effects increased with population size and relative HIV incidence. Conclusions: Inequalities in the ART cascade across subpopulations can undermine the anticipated prevention impacts of cascade scale-up. As Eswatini has shown, addressing inequalities in the ART cascade, particularly those that intersect with high transmission risk, could maximize incidence reductions from cascade scale-up.

Adjusting for hidden biases in sexual behaviour data: a mechanistic approach.

Background: Two required inputs to mathematical models of sexually transmitted infections are the average duration in epidemiological risk states (e.g., selling sex) and the average rates of sexual partnership change. These variables are often only available as aggregate estimates from published cross-sectional studies, and may be subject to distributional, sampling, censoring, and measurement biases. Methods: We explore adjustments for these biases using aggregate estimates of duration in sex work and numbers of reported sexual partners from a published 2011 survey of female sex worker in Eswatini. We develop adjustments from first principles, and construct Bayesian hierarchical models to reflect our mechanistic assumptions about the bias-generating processes. Results: We show that different mechanisms of bias for duration in sex work may "cancel out" by acting in opposite directions, but that failure to consider some mechanisms could over- or underestimate duration in sex work by factors approaching 2. We also show that conventional interpretations of sexual partner numbers are biased due to implicit assumptions about partnership duration, but that unbiased estimators of partnership change rate can be defined that explicitly incorporate a given partnership duration. We highlight how the unbiased estimator is most important when the survey recall period and partnership duration are similar in length. Conclusions: While we explore these bias adjustments using a particular dataset, and in the context of deriving inputs for mathematical modelling, we expect that our approach and insights would be applicable to other datasets and motivations for quantifying sexual behaviour data.

Corrigendum to "Estimating effective reproduction number using generation time versus serial interval, with application to covid-19 in the Greater Toronto Area, Canada" [Infectious Disease Modelling 5 (2020) 889-896].

[This corrects the article DOI: 10.1016/j.idm.2020.10.009.].