Using an intersectionality-based approach to evaluate mental health services use among gay, bisexual and other men who have sex with men in Montreal, Toronto and Vancouver

AIMS

To cope with homonegativity-generated stress, gay, bisexual and other men who have sex with men (GBM) use more mental health services (MHS) compared with heterosexual men. Most previous research on MHS among GBM uses data from largely white HIV-negative samples. Using an intersectionality-based approach, we evaluated the concomitant impact of racialization and HIV stigma on MHS use among GBM, through the mediating role of perceived discrimination (PD).

METHODS

We used baseline data from 2371 GBM enrolled in the Engage cohort study, collected between 2017 and 2019, in Montreal, Toronto and Vancouver, using respondent-driven sampling. The exposure was GBM groups: Group 1 (n = 1376): white HIV-negative; Group 2 (n = 327): white living with HIV; Group 3 (n = 577): racialized as non-white HIV-negative; Group 4 (n = 91): racialized as non-white living with HIV. The mediator was interpersonal PD scores measured using the Everyday Discrimination Scale (5-item version). The outcome was MHS use (yes/no) in the prior 6 months. We fit a three-way decomposition of causal mediation effects utilizing the imputation method for natural effect models. We obtained odds ratios (ORs) for pure direct effect (PDE, unmediated effect), pure indirect effect (PIE, mediated effect), mediated interaction effect (MIE, effect due to interaction between the exposure and mediator) and total effect (TE, overall effect). Analyses controlled for age, chronic mental health condition, Canadian citizenship, being cisgender and city of enrolment.

RESULTS

Mean PD scores were highest for racialized HIV-negative GBM (10.3, SD: 5.0) and lowest for white HIV-negative GBM (8.4, SD: 3.9). MHS use was highest in white GBM living with HIV (GBMHIV) (40.4%) and lowest in racialized HIV-negative GBM (26.9%). Compared with white HIV-negative GBM, white GBMHIV had higher TE (OR: 1.71; 95% CI: 1.27, 2.29) and PDE (OR: 1.68; 95% CI: 1.27, 2.24), and racialized HIV-negative GBM had higher PIE (OR: 1.09; 95% CI: 1.02, 1.17). Effects for racialized GBMHIV did not significantly differ from those of white HIV-negative GBM. MIEs across all groups were comparable.

CONCLUSIONS

Higher MHS use was observed among white GBMHIV compared with white HIV-negative GBM. PD positively mediated MHS use only among racialized HIV-negative GBM. MHS may need to take into account the intersecting impact of homonegativity, racism and HIV stigma on the mental health of GBM.

Variable selection for individualised treatment rules with discrete outcomes

An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement. Thus, selecting variables to improve the treatment rule is crucial. We propose a doubly robust variable selection method for ITRs, and show that it compares favourably with competing approaches. We illustrate the proposed method on data from an adaptive, web-based stress management tool.

Gabapentinoids and Risk for Severe Exacerbation in Chronic Obstructive Pulmonary Disease : A Population-Based Cohort Study

BACKGROUND

North American and European health agencies recently warned of severe breathing problems associated with gabapentinoids, including in patients with chronic obstructive pulmonary disease (COPD), although supporting evidence is limited.

OBJECTIVE

To assess whether gabapentinoid use is associated with severe exacerbation in patients with COPD.

DESIGN

Time-conditional propensity score-matched, new-user cohort study.

SETTING

Health insurance databases from the Régie de l'assurance maladie du Québec in Canada.

PATIENTS

Within a base cohort of patients with COPD between 1994 and 2015, patients initiating gabapentinoid therapy with an indication (epilepsy, neuropathic pain, or other chronic pain) were matched 1:1 with nonusers on COPD duration, indication for gabapentinoids, age, sex, calendar year, and time-conditional propensity score.

MEASUREMENTS

The primary outcome was severe COPD exacerbation requiring hospitalization. Hazard ratios (HRs) associated with gabapentinoid use were estimated in subcohorts according to gabapentinoid indication and in the overall cohort.

RESULTS

The cohort included 356 gabapentinoid users with epilepsy, 9411 with neuropathic pain, and 3737 with other chronic pain, matched 1:1 to nonusers. Compared with nonuse, gabapentinoid use was associated with increased risk for severe COPD exacerbation across the indications of epilepsy (HR, 1.58 [95% CI, 1.08 to 2.30]), neuropathic pain (HR, 1.35 [CI, 1.24 to 1.48]), and other chronic pain (HR, 1.49 [CI, 1.27 to 1.73]) and overall (HR, 1.39 [CI, 1.29 to 1.50]).

LIMITATION

Residual confounding, including from lack of smoking information.

CONCLUSION

In patients with COPD, gabapentinoid use was associated with increased risk for severe exacerbation. This study supports the warnings from regulatory agencies and highlights the importance of considering this potential risk when prescribing gabapentin and pregabalin to patients with COPD.

PRIMARY FUNDING SOURCE

Canadian Institutes of Health Research and Canadian Lung Association.

Principal stratification for quantile causal effects under partial compliance

Within the principal stratification framework in causal inference, the majority of the literature has focused on binary compliance with an intervention and modelling means. Yet in some research areas, compliance is partial, and research questions-and hence analyses-are concerned with causal effects on (possibly high) quantiles rather than on shifts in average outcomes. Modelling partial compliance is challenging because it can suffer from lack of identifiability. We develop an approach to estimate quantile causal effects within a principal stratification framework, where principal strata are defined by the bivariate vector of (partial) compliance to the two levels of a binary intervention. We propose a conditional copula approach to impute the missing potential compliance and estimate the principal quantile treatment effect surface at high quantiles, allowing the copula association parameter to vary with the covariates. A bootstrap procedure is used to estimate the parameter to account for inflation due to imputation of missing compliance. Moreover, we describe precise assumptions on which the proposed approach is based, and investigate the finite sample behavior of our method by a simulation study. The proposed approach is used to study the 90th principal quantile treatment effect of executive stay-at-home orders on mitigating the risk of COVID-19 transmission in the United States.

Dynamic Treatment Regimes Using Bayesian Additive Regression Trees for Censored Outcomes

To achieve the goal of providing the best possible care to each individual under their care, physicians need to customize treatments for individuals with the same health state, especially when treating diseases that can progress further and require additional treatments, such as cancer. Making decisions at multiple stages as a disease progresses can be formalized as a dynamic treatment regime (DTR). Most of the existing optimization approaches for estimating dynamic treatment regimes including the popular method of Q-learning were developed in a frequentist context. Recently, a general Bayesian machine learning framework that facilitates using Bayesian regression modeling to optimize DTRs has been proposed. In this article, we adapt this approach to censored outcomes using Bayesian additive regression trees (BART) for each stage under the accelerated failure time modeling framework, along with simulation studies and a real data example that compare the proposed approach with Q-learning. We also develop an R wrapper function that utilizes a standard BART survival model to optimize DTRs for censored outcomes. The wrapper function can easily be extended to accommodate any type of Bayesian machine learning model.

On "Reflections on the concept of optimality of single decision point treatment regimes"

This is a discussion of "Reflections on the concept of optimality of single decision point treatment regimes" by Trung Dung Tran, Ariel Alonso Abad, Geert Verbeke, Geert Molenberghs, and Iven Van Mechelen. The authors propose a thoughtful consideration of optimization targets and the implications of such targets for the resulting optimal treatment rule. However, we contest the assertation that targets of optimization have been overlooked and suggest additional considerations that researchers must contemplate as part of a complete framework for learning about optimal treatment regimes.

Causal inference for oncology: past developments and current challenges

In this paper, we review some important early developments on causal inference in medical statistics and epidemiology that were inspired by questions in oncology. We examine two classical examples from the literature and point to a current area of ongoing methodological development, namely the estimation of optimal adaptive treatment strategies. While causal approaches to analysis have become more routine in oncology research, many exciting challenges and open problems remain, particularly in the context of censored outcomes.

Bayesian inference for optimal dynamic treatment regimes in practice

In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of improving health outcomes; therefore, they are most interested in identifying optimal DTRs. Recent work has developed Bayesian methods for identifying optimal DTRs in a family indexed by ψ via Bayesian dynamic marginal structural models (MSMs) (Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)); we review the proposed estimation procedure and illustrate its use via the new BayesDTR R package. Although methods in Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. (Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)) can estimate optimal DTRs well, they may lead to biased estimators when the model for the expected outcome if everyone in a population were to follow a given treatment strategy, known as a value function, is misspecified or when a grid search for the optimum is employed. We describe recent work that uses a Gaussian process ( G P ) prior on the value function as a means to robustly identify optimal DTRs (Rodriguez Duque D, Stephens DA, Moodie EEM. Estimation of optimal dynamic treatment regimes using Gaussian processes; 2022. Available from: https://doi.org/10.48550/arXiv.2105.12259). We demonstrate how a G P approach may be implemented with the BayesDTR package and contrast it with other value-search approaches to identifying optimal DTRs. We use data from an HIV therapeutic trial in order to illustrate a standard analysis with these methods, using both the original observed trial data and an additional simulated component to showcase a longitudinal (two-stage DTR) analysis.

A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies

Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort.

Benefits of the PRISM Shelter-Based Program for Attainment of Stable Housing and Functional Outcomes by People Experiencing Homelessness and Mental Illness: A Quantitative Analysis

OBJECTIVE

To explore the housing trajectory, personal recovery, functional level, and quality of life of clients at discharge and 1 year after completing Projet Réaffiliation Itinérance Santé Mentale (PRISM), a shelter-based mental health and rehabilitation program intended to provide individuals experiencing homelessness and severe mental illness with transition housing and to reconnect them with mental health and social services.

METHOD

Housing status, psychiatric follow-up trajectory, personal recovery (Canadian Personal Recovery Outcome Measure), functional level (Multnomah Community Ability Scale), and quality of life (Lehman Quality of Life Interview) were assessed at program entry, at program discharge and 1 year later.

RESULTS

Of the 50 clients who participated in the study from May 31, 2018, to December 31, 2019, 43 completed the program. Of these, 76.7% were discharged to housing modalities and 78% were engaged with psychiatric follow-up at the program's end. Housing stability, defined as residing at the same permanent address since discharge, was achieved for 62.5% of participants at 1-year follow-up. Functional level and quality of life scores improved significantly both at discharge and at 1-year follow-up from baseline.

CONCLUSIONS

Admission to PRISM helped clients secure long-term stable housing and appropriate psychiatric follow-up. Stable housing was maintained for most clients at 1-year follow-up, and they benefited from sustained functional and quality of life outcomes in long-term follow-up.

Bayesian sample size calculations for comparing two strategies in SMART studies

In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individualized approach. As a result, sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, sample size and design considerations have generally been carried out in frequentist settings. However, standard frequentist formulae require assumptions on interim response rates and variance components. Misspecifying these can lead to incorrect sample size calculations and correspondingly inadequate levels of power. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the 'two priors' approach. Additionally, compared to the standard frequentist formulae, this methodology allows us to rely on fewer assumptions, integrate pre-trial knowledge, and switch the focus from the standardized effect size to the MDD. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full-scale SMART of an internet-based adaptive stress management intervention on cardiovascular disease patients using data from its pilot study conducted in two Canadian provinces.

Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms

Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment. We demonstrate an application of this new approach in combination with a weighted and penalized estimating equation to this challenging binary outcome setting. We demonstrate the double robustness of the method and its effectiveness for variable selection. The work is motivated by and applied to an analysis of treatment for unipolar depression using a population of patients treated at Kaiser Permanente Washington.

Role of fatty liver in the epidemic of advanced chronic liver disease among people with HIV: protocol for the Canadian LIVEHIV multicentre prospective cohort

INTRODUCTION

Advanced chronic liver disease (ACLD) is a major cause of death for people with HIV (PWH). While viral hepatitis coinfections are largely responsible for this trend, metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging concern for PWH. We aimed to assess the contribution of MASLD to incident ACLD in PWH.

METHODS AND ANALYSIS

This multicentre prospective observational cohort study will enrol 968 consecutive HIV monoinfected patients from four Canadian sites, excluding subjects with alcohol abuse, liver disease other than MASLD, or ACLD at baseline. Participants will be followed annually for 4 years by clinical evaluation, questionnaires, laboratory testing and Fibroscan to measure liver stiffness measurement (LSM) and controlled attenuation parameter (CAP). The primary outcome will be incidence of ACLD, defined as LSM>10 kPa, by MASLD status, defined as CAP≥285 dB/m with at least one metabolic abnormality, and to develop a score to classify PWH according to their risk of ACLD. Secondary outcomes will include health-related quality of life (HRQoL) and healthcare resource usage. Kaplan-Meier survival method and Cox proportional hazards regression will calculate the incidence and predictors of ACLD, respectively. Propensity score methods and marginal structural models will account for time-varying exposures. We will split the cohort into a training set (to develop the risk score) and a validation set (for validation of the score). HRQoL scores and healthcare resource usage will be compared by MASLD status using generalised linear mixed effects model.

ETHICS AND DISSEMINATION

This protocol has been approved by the ethics committees of all participating institutions. Written informed consent will be obtained from all study participants. The results of this study will be shared through scientific publications and public presentations to advocate for the inclusion of PWH in clinical trials of MASLD-targeted therapies and case-finding of ACLD in PWH.

Semiparametric Bayesian inference for optimal dynamic treatment regimes via dynamic marginal structural models

Considerable statistical work done on dynamic treatment regimes (DTRs) is in the frequentist paradigm, but Bayesian methods may have much to offer in this setting as they allow for the appropriate representation and propagation of uncertainty, including at the individual level. In this work, we extend the use of recently developed Bayesian methods for Marginal Structural Models to arrive at inference of DTRs. We do this (i) by linking the observational world with a world in which all patients are randomized to a DTR, thereby allowing for causal inference and then (ii) by maximizing a posterior predictive utility, where the posterior distribution has been obtained from nonparametric prior assumptions on the observational world data-generating process. Our approach relies on Bayesian semiparametric inference, where inference about a finite-dimensional parameter is made all while working within an infinite-dimensional space of distributions. We further study Bayesian inference of DTRs in the double robust setting by using posterior predictive inference and the nonparametric Bayesian bootstrap. The proposed methods allow for uncertainty quantification at the individual level, thereby enabling personalized decision-making. We examine the performance of these methods via simulation and demonstrate their utility by exploring whether to adapt HIV therapy to a measure of patients' liver health, in order to minimize liver scarring.

Variable selection in regression-based estimation of dynamic treatment regimes

Dynamic treatment regimes (DTRs) consist of a sequence of decision rules, one per stage of intervention, that aim to recommend effective treatments for individual patients according to patient information history. DTRs can be estimated from models which include interactions between treatment and a (typically small) number of covariates which are often chosen a priori. However, with increasingly large and complex data being collected, it can be difficult to know which prognostic factors might be relevant in the treatment rule. Therefore, a more data-driven approach to select these covariates might improve the estimated decision rules and simplify models to make them easier to interpret. We propose a variable selection method for DTR estimation using penalized dynamic weighted least squares. Our method has the strong heredity property, that is, an interaction term can be included in the model only if the corresponding main terms have also been selected. We show our method has both the double robustness property and the oracle property theoretically; and the newly proposed method compares favorably with other variable selection approaches in numerical studies. We further illustrate the proposed method on data from the Sequenced Treatment Alternatives to Relieve Depression study.

Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes

Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence of decision rules that map individual patient characteristics to a recommended treatment. Observational studies are commonly used for estimating dynamic treatment regimes due to the potentially prohibitive costs of conducting sequential multiple assignment randomized trials. However, estimating a dynamic treatment regime from observational data can lead to bias in the estimated regime due to unmeasured confounding. Sensitivity analyses are useful for assessing how robust the conclusions of the study are to a potential unmeasured confounder. A Monte Carlo sensitivity analysis is a probabilistic approach that involves positing and sampling from distributions for the parameters governing the bias. We propose a method for performing a Monte Carlo sensitivity analysis of the bias due to unmeasured confounding in the estimation of dynamic treatment regimes. We demonstrate the performance of the proposed procedure with a simulation study and apply it to an observational study examining tailoring the use of antidepressant medication for reducing symptoms of depression using data from Kaiser Permanente Washington.

Oral Anticoagulants and the Risk of Dementia in Patients With Nonvalvular Atrial Fibrillation: A Population-Based Cohort Study

BACKGROUND AND OBJECTIVES

Nonvalvular atrial fibrillation (NVAF) is associated with an increased risk of dementia. Oral anticoagulants (OACs) are essential for stroke prevention in NVAF, and studies have shown a possible protective effect on dementia. However, findings have been inconsistent and hampered by methodological limitations. Thus, we assessed whether the use of OACs is associated with a decreased incidence of dementia in patients with NVAF. In addition, we explored the impact of the cumulative duration of OAC use on the incidence of dementia.

METHODS

Using the UK Clinical Practice Research Datalink, we formed a cohort of all patients aged 50 years or older with an incident diagnosis of NVAF between 1988 and 2017 and no prior OAC use, with a follow-up until 2019. Patients were considered unexposed until 6 months after their first OAC prescription for latency considerations and exposed thereafter until the end of follow-up. We used time-dependent Cox regression models to estimate hazard ratios (HRs), adjusted for 54 covariates, with 95% CIs for dementia associated with OAC use, compared with nonuse. We also assessed whether the risk varied with the cumulative duration of OAC use, compared with nonuse, by comparing prespecified exposure categories defined in a time-varying manner and by modeling the HR using a restricted cubic spline.

RESULTS

The cohort included 142,227 patients with NVAF, with 8,023 cases of dementia over 662,667 person-years of follow-up (incidence rate 12.1, 95% CI 11.9-12.4 per 1,000 person-years). OAC use was associated with a decreased risk of dementia (HR 0.88, 95% CI 0.84-0.92) compared with nonuse. A restricted cubic spline also indicated a decreased risk of dementia, reaching a low at approximately 1.5 years of cumulative OAC use and stabilizing thereafter. Moreover, OAC use decreased the risk in patients aged 75 years and older (HR 0.84, 95% CI 0.80-0.89), but not in younger patients (HR 0.99, 95% CI 0.90-1.10).

DISCUSSION

In patients with incident NVAF, OACs were associated with a decreased risk of dementia, particularly in elderly individuals. This warrants consideration when weighing the risks and benefits of anticoagulation in this population.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with NVAF, OAC use (vs nonuse) is associated with a decreased risk of dementia.

Double robust estimation of optimal partially adaptive treatment strategies: An application to breast cancer treatment using hormonal therapy

Precision medicine aims to tailor treatment decisions according to patients' characteristics. G-estimation and dynamic weighted ordinary least squares are double robust methods to identify optimal adaptive treatment strategies. It is underappreciated that they require modeling all existing treatment-confounder interactions to be consistent. Identifying optimal partially adaptive treatment strategies that tailor treatments according to only a few covariates, ignoring some interactions, may be preferable in practice. Building on G-estimation and dWOLS, we propose estimators of such partially adaptive strategies and demonstrate their double robustness. We investigate these estimators in a simulation study. Using data maintained by the Centre des Maladies du Sein, we estimate a partially adaptive treatment strategy for tailoring hormonal therapy use in breast cancer patients. R software implementing our estimators is provided.

HIV incidence and related risks among gay, bisexual, and other men who have sex with men in Montreal, Toronto, and Vancouver: Informing blood donor selection criteria in Canada

BACKGROUND

An individualized behavior-based selection approach has potential to allow for a more equitable blood donor eligibility process. We collected biological and behavioral data from urban gay, bisexual, and other men who have sex with men (GBM) to inform the use of this approach in Canada.

STUDY DESIGN AND METHODS

Engage is a closed prospective cohort of sexually active GBM, aged 16+ years, recruited via respondent-driven-sampling (RDS) in Montreal, Toronto, and Vancouver, Canada. Participants completed a questionnaire on behaviors (past 6 months) and tested for HIV and sexually transmitted and blood-borne infections at each visit. Rate ratios for HIV infection and predictive values for blood donation eligibility criteria were estimated by RDS-adjusted Poisson regression.

RESULTS

Data on 2008 (study visits 2017-02 to 2021-08) HIV-negative participants were used. The HIV incidence rate for the three cities was 0.4|100 person-years [95%CI:0.3, 0.6]. HIV seroconversion was associated with age <30 years: adjusted rate ratio (aRR) 9.1 [95%CI:3.2, 26.2], 6-10 and >10 anal sex partners versus 1-6 aRR: 5.3 [2.1,13.5] and 8.4 [3.4, 20.9], and use of crystal methamphetamine during sex: 4.2 [1.5, 11.6]. Applying the combined selection criteria: drug injection, ≥2 anal sex partners, and a new anal sex partner, detected all participants who seroconverted (100% sensitivity, 100% negative predictive value), and would defer 63% of study participants from donating.

CONCLUSION

Using three screening questions regarding drug injection and sexual behaviors in the past 6 months would correctly identify potential GBM donors at high risk of having recently contracted HIV. Doing so would reduce the proportion of deferred sexually active GBM by one-third.

Mental health services use and depressive symptom scores among gay and bisexual men in Canada

PURPOSE

To evaluate the association between mental health services (MHS) use and depressive symptom scores among gay and bisexual men (GBM) and compare with heterosexual men in Canada.

METHODS

We used data from the 2015-2016 cycles of the Canadian Community Health Survey. Depressive symptoms were assessed using the PHQ-9 questionnaire (prior two weeks). MHS consultations with any licensed mental health professional (prior year) were categorized as 0, 1, 2-11, ≥ 12. We fit linear regression models to quantify the associations between MHS use and PHQ-9 scores, with an interaction term for sexual identity (GBM and heterosexual men). Models were adjusted for socioeconomic and health-related indicators.

RESULTS

Among 21,383 men, 97.3% self-identified as heterosexual and 2.7% as GBM. Compared to heterosexual men, GBM used any MHS (21% vs. 10%, p < 0.05) and consulted ≥ 2 health professionals for their mental health (6% vs. 2%, p < 0.05) in the preceding year more frequently. Overall, mean PHQ-9 scores were higher among GBM compared to heterosexual men (3.9 vs. 2.3, p < 0.05). Relative to no consultations, higher MHS use (2-11, ≥ 12 consultations) was associated with higher PHQ-9 scores (1.4-4.9 points higher). Associations between MHS use and PHQ-9 scores did not differ statistically between GBM and heterosexual men.

CONCLUSION

Our findings were inconclusive in demonstrating a difference between heterosexual men and GBM for the association between MHS use and PHQ-9 scores. However, GBM consistently had higher average PHQ-9 scores for every category of consultations. Considering the higher use of MHS and higher burden of depressive symptoms among GBM, more research is needed.

Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket

Background

Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Beverages (SSB). Sales of price discounted food items are known to increase during the period of discounting. However, the presence and extent of the lagged effect of discounting, a sustained level of sales after discounting ends, is previously unaccounted for. We investigated the presence of the lagged effect of discounting on the sales of five SSB categories, which are soda, fruit juice, sport and energy drink, sugar-sweetened coffee and tea, and sugar-sweetened drinkable yogurt.

Methods

We fitted distributed lag models to weekly volume-standardized sales and percent discounting generated by a supermarket in Montreal, Canada between January 2008 and December 2013, inclusive (n = 311 weeks).

Results

While the sales of SSB increased during the period of discounting, there was no evidence of a prominent lagged effect of discounting in four of the five SSB; the exception was sports and energy drinks, where a posterior mean of 28,459 servings (95% credible interval: 2661 to 67,253) of excess sales can be attributed to the lagged effect in the target store during the 6 years study period.

Conclusion

Our results indicate that studies that do not account for the lagged effect of promotions may not fully capture the effect of price discounting for some food categories.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13928-w.

The epidemiological impact of the Canadian COVID Alert app

Objectives

We analyzed the effectiveness of the Canadian COVID Alert app on reducing COVID-19 infections and deaths due to the COVID-19 virus.

Methods

Two separate but complementary approaches were taken. First, we undertook a comparative study to assess how the adoption and usage of the COVID Alert app compared to those of similar apps deployed in other regions. Next, we used data from the COVID Alert server and a range of plausible parameter values to estimate the numbers of infections and deaths averted in Canada using a model that combines information on number of notifications, secondary attack rate, expected fraction of transmissions that could be prevented, quarantine effectiveness, and expected size of the full transmission chain in the absence of exposure notification.

Results

The comparative analysis revealed that the COVID Alert app had among the lowest adoption levels among apps that reported usage. Our model indicates that use of the COVID Alert app averted between 6284 and 10,894 infections across the six Canadian provinces where app usage was highest during the March–July 2021 period. This range is equivalent to 1.6–2.9% of the total recorded infections across Canada in that time. Using province-specific case fatality rates, 57–101 deaths were averted during the same period. The number of cases and deaths averted was greatest in Ontario, whereas the proportion of cases and deaths averted was greatest in Newfoundland and Labrador. App impact measures were reported so rarely and so inconsistently by other regions that the relative assessment of impact is inconclusive.

Conclusion

While the nationwide rates are low, provinces with widespread adoption of the app showed high ratios of averted cases and deaths (upper bound was greater than 60% of averted cases). This finding suggests that the COVID Alert app, when adopted at sufficient levels, can be an effective public health tool for combatting a pandemic such as COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.17269/s41997-022-00632-w.

Extended Bayesian endemic–epidemic models to incorporate mobility data into COVID‐19 forecasting

Forecasting the number of daily COVID‐19 cases is critical in the short‐term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID‐19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic–epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed‐lag model in order to investigate the association between mobility and the number of reported COVID‐19 cases; we additionally include a weekly first‐order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.

Impact of Hepatitis C Virus Cure on Depressive Symptoms in the Human Immunodeficiency Virus-Hepatitis C Virus Coinfected Population in Canada

BACKGROUND

Depression is common in people with human immunodeficiency virus (HIV) and hepatitis C virus (HCV), with biological and psychosocial mechanisms at play. Direct acting antivirals (DAA) result in high rates of sustained virologic response (SVR), with minimal side-effects. We assessed the impact of SVR on presence of depressive symptoms in the HIV-HCV coinfected population in Canada during the second-generation DAA era (2013-2020).

METHODS

We used data from the Canadian CoInfection Cohort (CCC), a multicenter prospective cohort of people with a HIV and HCV coinfection, and its associated sub-study on food security. Because depression screening was performed only in the sub-study, we predicted Center for Epidemiologic Studies Depression Scale-10 classes in the CCC using a random forest classifier and corrected for misclassification. We included participants who achieved SVR and fit a segmented modified Poisson model using an interrupted time series design, adjusting for time-varying confounders.

RESULTS

We included 470 participants; 58% had predicted depressive symptoms at baseline. The median follow-up was 2.4 years (interquartile range [IQR]: 1.0-4.5.) pre-SVR and 1.4 years (IQR: 0.6-2.5) post-SVR. The pre-SVR trend suggested depressive symptoms changed little over time, with no immediate level change at SVR. However, post-SVR trends showed a reduction of 5% per year (risk ratio: 0.95 (95% confidence interval [CI]: .94-.96)) in the prevalence of depressive symptoms.

CONCLUSIONS

In the DAA era, predicted depressive symptoms declined over time following SVR. These improvements reflect possible changes in biological pathways and/or better general health. If such improvements in depression symptoms are durable, this provides an additional reason for treatment and early cure of HCV.

Prenatal Exposure to Insecticides and Weight Trajectories Among South African Children in the VHEMBE Birth Cohort

BACKGROUND

Dichlorodiphenyltrichloroethane (DDT) or pyrethroid insecticides are sprayed inside dwellings for malaria vector control, resulting in high exposure to millions of people, including pregnant women. These chemicals disrupt endocrine function and may affect child growth. To our knowledge, few studies have investigated the potential impact of prenatal exposure to DDT or pyrethroids on growth trajectories.

METHODS

We investigated associations between gestational insecticide exposure and child growth trajectories in the Venda Health Examination of Mothers, Babies and their Environment, a birth cohort of 751 children born between 2012 and 2013 in South Africa. Based on child weight measured at follow-up and abstracted from medical records, we modeled weight trajectories from birth to 5 years using SuperImposition, Translation and Rotation, which estimated two child-specific parameters: size (average weight) and tempo (age at peak weight velocity). We estimated associations between peripartum maternal concentrations of serum DDT, dichlorodiphenyldichloroethylene, or urinary pyrethroid metabolites and SuperImposition, Translation and Rotation parameters using marginal structural models.

RESULTS

We observed that a 10-fold increase in maternal concentrations of the pyrethroid metabolite trans-3-(2,2,-dicholorvinyl)-2,2-dimethyl-cyclopropane carboxylic acid was associated with a 21g (95% confidence interval = -40, -1.6) smaller size among boys but found no association among girls (Pinteraction = 0.07). Estimates suggested that pyrethroids may be associated with earlier tempo but were imprecise. We observed no association with serum DDT or dichlorodiphenyldichloroethylene.

CONCLUSIONS

Inverse associations between pyrethroids and weight trajectory parameters among boys are consistent with hypothesized disruption of androgen pathways and with our previous research in this population, and support the endocrine-disrupting potential of pyrethroids in humans.

Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes

Estimating individualized treatment rules-particularly in the context of right-censored outcomes-is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.

Adaptive web-based stress management programs among adults with a cardiovascular disease: A pilot Sequential Multiple Assignment Randomized Trial (SMART)

OBJECTIVE

To assess the feasibility and acceptability of using a Sequential Multiple Assignment Randomized Trial (SMART) to optimize the delivery of a web-based, stress management intervention for patients with a cardiovascular disease (CVD).

METHODS

59 patients with a CVD and moderate stress were randomized to a self-directed web-based stress management program (n = 30) or the same intervention plus lay telephone coaching (n = 29). After 6 weeks, non-responders were re-randomized to continue with their initial intervention or switched to motivational interviewing (MI). Feasibility, acceptability, and clinical significance were assessed.

RESULTS

SMART procedures were feasible. Attrition rates were almost twice as high in the web-only group than the lay coach group. This might be because of the low satisfaction (47%) in the web-only group. On average, 1.7/5 modules were completed. Effect sizes for stress and quality of life generally exceeded 0.2 (clinical benchmark), except for the group that initially received lay coaching and then switched to MI.

CONCLUSIONS

Results suggest that a larger trial would be feasible. Issues pertaining to attrition and satisfaction for non-responders need to be addressed.

PRACTICE IMPLICATIONS

Findings contribute to the evidence on how best to develop and deliver e-Health interventions to maximize their efficacy while remaining cost-effective.

Ranking tailoring variables for constructing individualized treatment rules: an application to schizophrenia

As with many chronic conditions, matching patients with schizophrenia to the best treatment options is difficult. Selecting antipsychotic medication is especially challenging because many of the medications can have burdensome side effects. Adjusting or tailoring medications based on patients' characteristics could improve symptoms. However, it is often not known which patient characteristics are most helpful for informing treatment selection. In this paper, we address the challenge of identifying and ranking important variables for tailoring treatment decisions. We consider a value-search approach implemented through dynamic marginal structural models to estimate an optimal individualized treatment rule. We apply our methodology to the Clinical Antipsychotics Trial of Intervention and Effectiveness (CATIE) study for schizophrenia, to evaluate if some tailoring variables have greater potential than others for selecting treatments for patients with schizophrenia (Stroup et al., 2003).

Depressive symptoms are no longer a barrier to HCV treatment initiation in the HIV–HCV co-infected population in Canada

Background

Psychiatric illness was a major barrier for HCV treatment during the Interferon (IFN) treatment era due to neuropsychiatric side effects. While direct acting antivirals (DAA) are better tolerated, patient-level barriers persist. We aimed to assess the effect of depressive symptoms on time to HCV treatment initiation among HIV–HCV co-infected persons during the IFN (2003–2011) and second-generation DAA (2013–2020) eras.

Methods

We used data from the Canadian Co-infection Cohort, a multicentre prospective cohort, and its associated sub-study on Food Security (FS). We predicted Center for Epidemiologic Studies Depression Scale-10 (CES-D-10) classes for depressive symptoms indicative of a depression risk using a random forest classifier and corrected for misclassification using predictive value-based record-level correction. We used marginal structural Cox proportional hazards models with inverse weighting for competing risks (death) to assess the effect of depressive symptoms on treatment initiation among HCV RNA-positive participants.

Results

We included 590 and 1127 participants in the IFN and DAA eras. The treatment initiation rate increased from 9 (95% confidence interval (CI): 7–10) to 21 (95% CI: 19–22) per 100 person-years from the IFN to DAA era. Treatment initiation was lower among those with depressive symptoms compared to those without in the IFN era (hazard ratio: 0.81 (95% CI: 0.69–0.95)) and was higher in the DAA era (1.19 (95% CI: 1.10–1.27)).

Conclusion

Depressive symptoms no longer appear to be a barrier to HCV treatment initiation in the co-infected population in the DAA era. The higher rate of treatment initiation in individuals with depressive symptoms suggests those previously unable to tolerate IFN are now accessing treatment.

Preserving data privacy when using multi-site data to estimate individualized treatment rules

Precision medicine is a rapidly expanding area of health research wherein patient level information is used to inform treatment decisions. A statistical framework helps to formalize the individualization of treatment decisions that characterize personalized management plans. Numerous methods have been proposed to estimate individualized treatment rules that optimize expected patient outcomes, many of which have desirable properties such as robustness to model misspecification. However, while individual data are essential in this context, there may be concerns about data confidentiality, particularly in multi-center studies where data are shared externally. To address this issue, we compared two approaches to privacy preservation: (i) data pooling, which is a covariate microaggregation technique and (ii) distributed regression. These approaches were combined with the doubly robust yet user-friendly method of dynamic weighted ordinary least squares to estimate individualized treatment rules. In simulations, we extensively evaluated the performance of the methods in estimating the parameters of the decision rule under different assumptions. The results demonstrate that double robustness is not maintained in data pooling setting and that this can result in bias, whereas the distributed regression provides good performance. We illustrate the methods via an analysis of optimal Warfarin dosing using data from the International Warfarin Consortium.

Community-Based Prevalence Estimates of Chlamydia trachomatis and Neisseria gonorrhoeae Infections Among Gay, Bisexual, and Other Men Who Have Sex With Men in Montréal, Canada

BACKGROUND

Reported cases of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) infections are increasing among Canadian men. Estimates of community-based CT/NG prevalence are lacking among gay, bisexual, and other men who have sex with men (GBM).

METHODS

Respondent driven sampling was used to recruit GBM in Montréal, Canada between February 2017 and June 2018. Specimens provided from urogenital, rectal, and pharyngeal sites were analyzed using nucleic acid amplification test to detect CT/NG. Prevalence estimates of CT/NG, overall and by anatomical site were calculated. All estimates are respondent-driven sampling-adjusted.

RESULTS

Among 1177 GBM, the prevalence of rectal, urogenital, pharyngeal and overall were respectively 2.4%, 0.4%, 0.4%, and 2.8% for CT infections, and 3.1%, 0.4%, 3.5%, and 5.6% for NG infections. If testing had been limited to the urogenital site, 80% and 94% of CT and NG infections, respectively, would have been missed.

CONCLUSIONS

This community-based study among GBM shows that the CT prevalence was about half of that observed for NG. A large part of CT/NG infections involves only the extragenital sites, highlighting the need for systematic multisite screening regardless of symptoms. In the mist of the COVID-19 pandemic and the limited CT/NG screening capacity due to test kits shortage, it might be considered to prioritize rectal and pharyngeal CT/NG testing over urogenital testing in asymptomatic GBM.

The effects of self-management interventions on depressive symptoms in adults with chronic physical disease(s) experiencing depressive symptomatology: a systematic review and meta-analysis

BACKGROUND

Chronic diseases are the leading cause of death worldwide. It is estimated that 20% of adults with chronic physical diseases experience concomitant depression, increasing their risk of morbidity and mortality. Low intensity psychosocial interventions, such as self-management, are part of recommended treatment; however, no systematic review has evaluated the effects of depression self-management interventions for this population. The primary objective was to examine the effect of self-management interventions on reducing depressive symptomatology in adults with chronic disease(s) and co-occurring depressive symptoms. Secondary objectives were to evaluate the effect of these interventions on improving other psychosocial and physiological outcomes (e.g., anxiety, glycemic control) and to assess potential differential effect based on key participant and intervention characteristics (e.g., chronic disease, provider).

METHODS

Studies comparing depression self-management interventions to a control group were identified through a) systematic searches of databases to June 2018 [MEDLINE (1946 -), EMBASE (1996 -), PsycINFO (1967 -), CINAHL (1984 -)] and b) secondary 'snowball' search strategies. The methodological quality of included studies was critically reviewed. Screening of all titles, abstracts, and full texts for eligibility was assessed independently by two authors. Data were extracted by one author and verified by a second.

RESULTS

Fifteen studies were retained: 12 for meta-analysis and three for descriptive review. In total, these trials included 2064 participants and most commonly evaluated interventions for people with cancer (n = 7) or diabetes (n = 4). From baseline to < 6-months (T1), the pooled mean effect size was - 0.47 [95% CI -0.73, - 0.21] as compared to control groups for the primary outcome of depression and - 0.53 [95% CI -0.91, - 0.15] at ≥ 6-months (T2). Results were also significant for anxiety (T1) and glycemic control (T2). Self-management skills of decision-making and taking action were significant moderators of depression at T1.

CONCLUSION

Self-management interventions show promise in improving depression and anxiety in those with concomitant chronic physical disease. The findings may contribute to the development of future Self-management interventions and delivering evidence-based care to this population. Further high-quality RCTs are needed to identify sources of heterogeneity and investigate key intervention components.

Immune recovery after antiretroviral therapy initiation: a challenge for people living with HIV in Brazil

Immune recovery reflects health conditions. Our goal was to estimate the time it takes to achieve immune recovery and its associated factors, in people living with HIV (PLHIV), after antiretroviral therapy (ART) initiation. A historical cohort study was performed among PLHIV (> 18 years-old) in Minas Gerais State, Brazil, using data from healthcare databases. Patients initiating ART between 2009-2018, with T-CD4+ lymphocytes and viral load recorded before and after antiretroviral therapy were included. The outcome is achievement of immune recovery, defined as the first T-CD4+ > 500 cells/µL after ART initiation. Explanatory variables were age, gender, place of residence, year of ART initiation, baseline viral load and T-CD4+, viral load status, and adherence to ART at follow-up. Descriptive analysis, cumulative, and person-time incidences of immune recovery were estimated. Median-time to immune recovery was estimated using Kaplan-Meier method. Factors associated with immune recovery were assessed by Cox regression. Among 26,430 PLHIV, 8,014 (30%) were eligible. Most were male (67%), mean age 38.7 years, resided in non-central region, median-baseline T-CD4+ = 228 cells/µL (< 200 cells/µL = 43%) and viral load median-baseline = 4.7 log10 copies/mL (detectable viral load = 99%). Follow-up time = 15,872 person-years. Cumulative and incidence rate were 58% (95%CI: 57-58) (n = 4,678) and 29.47 cases/100 person-years, respectively. Median-time to immune recovery was of 22.8 months (95%CI: 21.9-24.0). Women living with HIV, younger than 38 years of age, with T-CD4+ baseline > 200 cells/µL, detectable viral load (baseline), antiretroviral therapy-adherence and undetectable viral load (follow-up) were independently associated with immune recovery. Time to immune recovery remains long and depends on early treatment and antiretroviral therapy-adherence.

Estimating the marginal effect of a continuous exposure on an ordinal outcome using data subject to covariate-driven treatment and visit processes

In the statistical literature, a number of methods have been proposed to ensure valid inference about marginal effects of variables on a longitudinal outcome in settings with irregular monitoring times. However, the potential biases due to covariate-driven monitoring times and confounding have rarely been considered simultaneously, and never in a setting with an ordinal outcome and a continuous exposure. In this work, we propose and demonstrate a methodology for causal inference in such a setting, relying on a proportional odds model to study the effect of the exposure on the outcome. Irregular observation times are considered via a proportional rate model, and a generalization of inverse probability of treatment weights is used to account for the continuous exposure. We motivate our methodology by the estimation of the marginal (causal) effect of the time spent on video or computer games on suicide attempts in the Add Health study, a longitudinal study in the United States. Although in the Add Health data, observation times are prespecified, our proposed approach is applicable even in more general settings such as when analyzing data from electronic health records where observations are highly irregular. In simulation studies, we let observation times vary across individuals and demonstrate that not accounting for biasing imbalances due to the monitoring and the exposure schemes can bias the estimate for the marginal odds ratio of exposure.

Can the Risk of Severe Depression-Related Outcomes Be Reduced by Tailoring the Antidepressant Therapy to Patient Characteristics?

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for patients with unipolar depression, yet there is little guidance on which SSRI provides the most benefit to a patient, based on personal characteristics. In this work, we explore whether an individualized treatment strategy can be used by health-care providers to adapt their prescription pattern to reduce the risk of a severe depression-related outcome (SDO) when choosing between citalopram and fluoxetine, 2 commonly prescribed SSRIs. Our population-based cohort study used data from the Clinical Practice Research Datalink, the Hospital Episode Statistics repository, and the Office for National Statistics database in the United Kingdom to create a cohort of individuals diagnosed with depression who were prescribed citalopram or fluoxetine between April 1998 and December 2017. Patients were followed from treatment initiation until occurrence of the SDO outcome, treatment discontinuation, or end of study. To find an optimal treatment strategy, we used dynamic weighted survival modeling, considering patient features such as age, sex, body mass index, previous psychiatric diagnoses, and medications. Our findings suggest that using patient characteristics to tailor the antidepressant drug therapy is associated with an increase of 4 days in the median time to SDO (95% confidence interval: 2, 10 days).

Racial disparities in recurrent preterm delivery risk: mediation analysis of prenatal care timing

OBJECTIVES

We estimated the degree to which the association between race and spontaneous recurrent preterm delivery is mediated by the timing of the first prenatal care visit.

METHODS

A retrospective population-based cohort study was conducted using the U.S. National Center for Health Statistics Natality Files. We identified 644,576 women with a prior PTB who delivered singleton live neonates between 2011 and 2017. A mediation analysis was conducted using log-binomial regression to evaluate the mediating effect of timing of first prenatal care visit.

RESULTS

During the seven-year period, 349,293 (54.2%) White non-Hispanic women, 131,296 (20.4%) Black non-Hispanic women, 132,367 (20.5%) Hispanic women, and 31,620 (4.9%) Other women had a prior preterm delivery. The risk of late prenatal care initiation was higher in Black non-Hispanic women, Hispanic women, and Other women (women of other racial/ethnic backgrounds) compared to White non-Hispanic women, and the risk of preterm delivery was higher in women with late prenatal care initiation. Between 8 and 15% of the association between race and spontaneous recurrent preterm delivery acted through the delayed timing of the first prenatal care visit.

CONCLUSIONS

Racial disparities in spontaneous recurrent preterm delivery rates can be partly, but not primarily, attributed to timing of first prenatal care visit.

Physical Function and Survival in Older Adults: A longitudinal study accounting for time-varying effects

PURPOSE OF THE STUDY

Variation in physical function in older adults over time raises several methodological challenges in the study of its association with survival, many of which have largely been overlooked in previous studies. The objective of this study is to examine the relationship between time-varying measures of physical function and survival in men and women aged 70 years and over, while accounting for the time-varying effects of health and lifestyle characteristics.

METHODS

1,846 women and 1,245 men in the Cardiovascular Health Study followed annually for up to 10 years beginning at age 70-74 years were included. We estimated the effect of gait speed and grip strength on survival over the subsequent year, using age as the timescale.

RESULTS

A 0.1m/s higher gait speed was associated with a 12% decrease in the likelihood of death in the subsequent year among women (HR 0.88, 95% CI 0.82-0.94). There was no statistically significant effect of gait speed on survival among men (HR 0.97, 95% CI 0.91 to 1.03), or of grip strength on survival among women (HR 0.97, 95% CI 0.95-1.00) or men (HR 0.99, 95% CI 0.97-1.01), over one year.

CONCLUSIONS

Upon using time-varying measures of physical function while accounting for time-varying effects of health and lifestyle characteristics, higher gait speed was associated with increased survival among the women in our study. We found no evidence of an association between gait speed and one-year survival in men, or between grip strength and one-year survival in women or men.

Use of HIV pre-exposure prophylaxis among urban Canadian gay, bisexual and other men who have sex with men: a cross-sectional analysis of the Engage cohort study

BACKGROUND

In Canada, gay, bisexual and other men who have sex with men (GBM) are disproportionately affected by HIV. Our objective was to describe access to HIV pre-exposure prophylaxis (PrEP) and identify factors associated with not using PrEP among self-reported HIV-negative or HIV-unknown GBM.

METHODS

This was a cross-sectional analysis of the Engage study cohort. Between 2017 and 2019, sexually active GBM aged 16 years or more in Montréal, Toronto and Vancouver were recruited via respondent-driven sampling (RDS). Participation included testing for HIV and sexually transmitted and blood-borne infections, and completion of a questionnaire. We examined PrEP access using a health care services model and fit RDS-adjusted logistic regressions to determine correlates of not using PrEP among those for whom PrEP was clinically recommended and who were aware of the intervention.

RESULTS

A total of 2449 GBM were recruited, of whom 2008 were HIV-negative or HIV-unknown; 1159 (511 in Montréal, 247 in Toronto and 401 in Vancouver) met clinical recommendations for PrEP. Of the 1159, 1100 were aware of PrEP (RDS-adjusted proportion: Montréal 84.6%, Toronto 94.2%, Vancouver 92.7%), 678 had felt the need for PrEP in the previous 6 months (RDS-adjusted proportion: Montréal 39.2%, Toronto 56.1%, Vancouver 49.0%), 406 had tried to access PrEP in the previous 6 months (RDS-adjusted proportion: Montréal 20.6%, Toronto 33.2%, Vancouver 29.6%) and 319 had used PrEP in the previous 6 months (RDS-adjusted proportion: Montréal 14.5%, Toronto 21.6%, Vancouver 21.8%). Not using PrEP was associated with several factors, including not feeling at high enough risk, viewing PrEP as not completely effective, not having a primary care provider and lacking medication insurance.

INTERPRETATION

Although half of GBM met clinical recommendations for PrEP, less than a quarter of them reported use. Despite high levels of awareness, a programmatic response that addresses PrEP-related perceptions and health care system barriers is needed to scale up PrEP access among GBM in Canada.

Price discounting as a hidden risk factor of energy drink consumption

OBJECTIVES

Global consumption of caffeinated energy drinks (CED) has been increasing dramatically despite increasing evidence of their adverse health effects. Temporary price discounting is a rarely investigated but potentially powerful food marketing tactic influencing purchasing of CED. Using grocery transaction records generated by food stores in Montreal, we investigated the association between price discounting and purchasing of CED across socio-economic status operationalized by education and income levels in store neighbourhood.

METHODS

The outcome, log-transformed weekly store-level sales of CED, was modelled as a function of store-level percent price discounting, store- and neighbourhood-level confounders, and an interaction term between discounting and each of tertile education and income in store neighbourhood. The model was separately fit to transactions from supermarkets, pharmacies, supercentres, and convenience stores.

RESULTS

There were 18,743, 12,437, 3965, and 49,533 weeks of CED sales from supermarkets, pharmacies, supercentres, and convenience stores, respectively. Percent price discounting was positively associated with log sales of CED for all store types, and the interaction between education and discounting was prominent in supercentres: -0.039 [95% confidence interval (CI): -0.051, -0.028] and -0.039 [95% CI: -0.057, -0.021], for middle- and high-education neighbourhoods relative to low-education neighbourhoods, respectively. Relative to low-income areas, the associations of discounting and log CED sales in supercentres for neighbourhoods with middle- and high-income tertile were 0.022 [95% CI: 0.010, 0.033] and 0.015 (95% CI: -0.001, 0.031), respectively.

CONCLUSION

Price discounting is an important driver of CED consumption and has a varying impact across community education and income.

High-Risk Sexual Behavior, Binge Drinking and Use of Stimulants are Key Experiences on the Pathway to High Perceived HIV Risk Among Men Who Have Sex with Men in Brazil

In Brazil, pre-exposure prophylaxis (PrEP) is currently available for gay, bisexual, and other men who have sex with men. As PrEP use depends on an individual's perceived risk, we explored pathways by which potentially modifiable behaviors lead to high perceived HIV risk. Using online surveys (N = 16,667), we conducted a path analysis on the basis of ordered sequences of multivariate logistic regressions. High perceived HIV risk was low (26.3%) compared to condomless receptive anal sex (41.4%). While younger age increased the odds of binge drinking and of condomless receptive anal sex, it was associated with decreased odds of high perceived HIV risk. In contrast, use of stimulants increased the odds of condomless receptive anal sex and of high perceived HIV risk. Our results suggest that binge drinking and use of stimulants are key points in different pathways to high-risk sexual behavior and may lead to different perceptions of HIV risk.