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The COVID-19 pandemic has highlighted the importance of non-pharmacological interventions (NPIs) for controlling epidemics of emerging infectious diseases. Despite their importance, NPIs have been monitored mainly through the manual efforts of volunteers. This approach hinders measurement of the NPI effectiveness and development of evidence to guide their use to control the global pandemic. We present EpiTopics, a machine learning approach to support automation of NPI prediction and monitoring at both the document level and country level by mining the vast amount of unlabeled news reports on COVID-19. EpiTopics uses a 3-stage, transfer-learning algorithm to classify documents according to NPI categories, relying on topic modeling to support result interpretation. We identified 25 interpretable topics under 4 distinct and coherent COVID-related themes. Importantly, the use of these topics resulted in significant improvements over alternative automated methods in predicting the NPIs in labeled documents and in predicting country-level NPIs for 42 countries., The bigger picture Accurate, scalable detection of the timing of changes to public health interventions for COVID-19 is an important step toward automating evaluation of the effectiveness of interventions. We show that it is possible to train an interpretable deep-learning model called EpiTopics on media news data to predict (1) the interventions mentioned in individual news articles and (2) the temporal change of intervention status at the country level. We addressed a main challenge of label scarcity among the news reports. Using EpiTopics, we modeled the latent semantics from 1.2 million unlabeled news reports on COVID-19 over 42 countries recorded from November 1, 2019 to July 31, 2020, identifying 25 interpretable topics under 4 COVID-related themes. Using the learned topic model, we inferred topic mixture membership for each labeled article, which allowed us to learn an accurate connection between the topics and the public health interventions at both the document level and country level., We developed a machine learning model called EpiTopics toward automatic detection of the changes in the status of non-pharmacological interventions (NPI) for COVID-19 from news reports. EpiTopics learns country-dependent topics from large numbers of COVID-19 news reports that do not have NPI labels. Subsequently, EpiTopics learns accurate connections between these topics and changes in NPI status from a set of labeled news reports, which enables accurate detection of temporal NPI status for each country referred to in the news reports.

Urinary tract infections caused by the bacterium Escherichia coli are among the most frequently encountered infections and are a common reason for antimicrobial prescriptions. Resistance to fluoroquinolone antimicrobial agents, particularly ciprofloxacin, has increased in recent decades. It is intuitive that variation in fluoroquinolone resistance is driven by changes in antimicrobial use, but careful study of this association requires the use of time-series methods. Between April 2010 and December 2014, we studied seasonal variation in resistance to ciprofloxacin, trimethoprim-sulfamethoxazole, and ampicillin in community-acquired urinary E. coli isolates in Montreal, Quebec, Canada. Using dynamic linear models, we investigated whether seasonal variation in resistance could be explained by seasonal variation in community antimicrobial use. We found a positive association between total fluoroquinolone use lagged by 1 and 2 months and the proportion of isolates resistant to ciprofloxacin. Our results suggest that resistance to ciprofloxacin is responsive to short-term variation in antimicrobial use. Thus, antimicrobial stewardship campaigns to reduce fluoroquinolone use, particularly in the winter when use is highest, are likely to be a valuable tool in the struggle against antimicrobial resistance.

BACKGROUND Super learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversely affect the performance of the super learner. METHODS In this case study, we used data from a Canadian electronic prescribing system to predict when primary care physicians prescribed antidepressants for indications other than depression. The analysis included 73,576 antidepressant prescriptions and 373 candidate predictors. We derived two super learners: one using tuned hyperparameter values for each machine learning algorithm identified through an iterative grid search procedure and the other using the default values. We compared the performance of the tuned super learner to that of the super learner using default values ("untuned") and a carefully constructed logistic regression model from a previous analysis. RESULTS The tuned super learner had a scaled Brier score (R) of 0.322 (95% [confidence interval] CI = 0.267, 0.362). In comparison, the untuned super learner had a scaled Brier score of 0.309 (95% CI = 0.256, 0.353), corresponding to an efficiency loss of 4% (relative efficiency 0.96; 95% CI = 0.93, 0.99). The previously-derived logistic regression model had a scaled Brier score of 0.307 (95% CI = 0.245, 0.360), corresponding to an efficiency loss of 5% relative to the tuned super learner (relative efficiency 0.95; 95% CI = 0.88, 1.01). CONCLUSIONS In this case study, hyperparameter tuning produced a super learner that performed slightly better than an untuned super learner. Tuning the hyperparameters of individual algorithms in a super learner may help optimize performance.

The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public’s health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and fields within AI and illustrate through examples how they can contribute to informed decisions, with a focus on population health applications. We first introduce core concepts needed to understand modern uses of AI and then describe its sub-fields. Finally, we examine four sub-fields of AI most relevant to population health along with examples of available tools and frameworks. Artificial intelligence is a broad and complex field, but the tools that enable the use of AI techniques are becoming more accessible, less expensive, and easier to use than ever before. Applications of AI have the potential to assist clinicians, health system managers, policy-makers, and public health practitioners in making more precise, and potentially more effective, decisions.

Public health is critical to a healthy, fair, and sustainable society. Realizing this vision requires imagining a public health community that can maintain its foundational core while adapting and responding to contemporary imperatives such as entrenched inequities and ecological degradation. In this commentary, we reflect on what tomorrow’s public health might look like, from the point of view of our collective experiences as researchers in Canada who are part of an Applied Public Health Chairs program designed to support “innovative population health research that improves health equity for citizens in Canada and around the world.” We view applied public health research as sitting at the intersection of core principles for population and public health: namely sustainability, equity, and effectiveness. We further identify three attributes of a robust applied public health research community that we argue are necessary to permit contribution to those principles: researcher autonomy, sustained intersectoral research capacity, and a critical perspective on the research-practice-policy interface. Our intention is to catalyze further discussion and debate about why and how public health matters today and tomorrow, and the role of applied public health research therein.

One Health surveillance for antimicrobial resistance has been promoted by the scientific community and by international organizations for more than a decade. In this article, we highlight issues that need to be addressed to improve the understanding of the effectiveness of One Health surveillance for antimicrobial resistance. We also outline the evidence needed to support countries planning to increase the level of integration of their surveillance system. Based on experience in Canada and other countries, we argue that more effort is needed to understand and measure the added value of One Health for antimicrobial resistance surveillance and to identify the most effective integration strategies. To date, guidelines for the development of One Health surveillance have focused mainly on the types of data that should be integrated. However, it may be necessary to apply the concept of One Health to surveillance tasks beyond data integration to realize the full value of the approach. Integration can be enhanced across different surveillance activities (data collection, analysis, interpretation and dissemination), taking account of the different skills and perspectives of experts and stakeholders involved. More research is needed to investigate the mechanisms through which a One Health approach to surveillance can increase the performance of antimicrobial resistance surveillance and, ultimately, improve health outcomes.Cela fait plus de dix ans que la communauté scientifique et les organisations internationales préconisent l'application de l'approche «Un monde, une santé» à la surveillance de la résistance aux antimicrobiens. Cet article souligne les éléments à considérer pour mieux comprendre l'efficacité d'une surveillance fondée sur cette approche. Nous y évoquons également les données requises pour éclairer les pays dans la définition de leurs plans nationaux afin d'améliorer le niveau d'intégration de leur système de surveillance. À partir de l’expérience du Canada et d'autres pays, nous estimons que des efforts doivent encore être faits pour comprendre et mesurer la véritable valeur ajoutée de l'approche «Un monde, une santé» dans le cadre de la surveillance de la résistance aux antimicrobiens et afin d'identifier les stratégies d'intégration les plus efficaces. À ce jour, les lignes directrices pour l'établissement d'une surveillance fondée sur cette approche se sont principalement axées sur les types de données qui devraient être intégrées. Néanmoins, pour exploiter toute la valeur de cette approche, il pourrait être utile d'appliquer le concept «Un monde, une santé» aux activités de surveillance au-delà de la simple intégration des données. Une meilleure intégration peut être obtenue au niveau des différentes activités de surveillance (collecte, analyse, interprétation et diffusion des données) en tenant compte des différentes compétences et des différents points de vue des experts et des parties prenantes. De nouvelles recherches sont nécessaires pour comprendre les mécanismes par lesquels l'approche «Un monde, une santé» appliquée à la surveillance peut améliorer les performances de la surveillance de la résistance aux antimicrobiens et, en fin de compte, améliorer les résultats de santé.La comunidad científica y las organizaciones internacionales han promovido durante más de una década la vigilancia sanitaria de la resistencia a los antimicrobianos. En este artículo, destacamos las cuestiones que deben abordarse para mejorar la comprensión de la eficacia de la vigilancia de la resistencia a los antimicrobianos de One Health. También esbozamos las pruebas necesarias para apoyar a los países que planean aumentar el nivel de integración de su sistema de vigilancia. Basándonos en la experiencia de Canadá y de otros países, sostenemos que se necesitan más esfuerzos para comprender y medir el valor agregado de One Health para la vigilancia de la resistencia a los antimicrobianos y para identificar las estrategias de integración más eficaces. Hasta la fecha, las directrices para el desarrollo de vigilancia de One Health se han centrado principalmente en los tipos de datos que deben integrarse. Sin embargo, puede ser necesario aplicar el concepto de One Health a tareas de vigilancia que van más allá de la integración de datos para aprovechar todo el valor del enfoque. La integración puede mejorarse en las diferentes actividades de vigilancia (recopilación, análisis, interpretación y difusión de datos), teniendo en cuenta las diferentes competencias y perspectivas de los expertos y las partes interesadas. Se necesita más investigación para estudiar los mecanismos mediante los cuales un enfoque de vigilancia de One Health puede aumentar el rendimiento de la vigilancia de la resistencia a los antimicrobianos y, en última instancia, mejorar los resultados sanitarios.تم الترويج لمراقبة One Health لمكافحة مضادات الميكروبات بواسطة المجتمع العلمي ومنظمات دولية منذ ما يزيد عن عقد من الزمان. نسلط في هذا المقال الضوء على القضايا التي بحاجة للتناول لتحسين فهم فعالية مراقبة One Health لمكافحة مضادات الميكروبات. كما نوضح الأدلة اللازمة لدعم الدول التي تخطط لزيادة مستوى التكامل في نظام المراقبة الخاص بها. وبناءً على التجربة الكندية وتجارب بلدان أخرى، فإننا نزعم أننا بحاجة إلى بذل المزيد من الجهد لفهم وقياس القيمة المضافة لنظام One Health لمراقبة مكافحة مضادات الميكروبات، ولتحديد أكثر استراتيجيات التكامل من حيث الفعالية. وحتى الآن، ركزت إرشادات تطوير نظام One Health للمراقبة بشكل أساسي على أنواع البيانات التي ينبغي إحداث التكامل فيما بينها. ومع ذلك، قد يكون من الضروري تطبيق مفهوم One Health على مهام المراقبة التي تتجاوز تكامل البيانات، وذلك لإدراك القيمة الكاملة لهذا النهج. يمكن تعزيز التكامل عبر أنشطة المراقبة المختلفة (جمع البيانات وتحليلها وتفسيرها ونشرها)، مع الأخذ في الاعتبار المهارات المختلفة ووجهات نظر الخبراء وأصحاب المصلحة المعنيين. هناك حاجة إلى مزيد من الأبحاث للتحقيق في الآليات التي يمكن من خلالها لنهج One Health للمراقبة أن يزيد من أداء مراقبة مكافحة مضادات الميكروبات، مما يؤدي في النهاية إلى تحسين النتائج الصحية.十多年来，科学界和国际组织一直致力于对抗菌素耐药性进行“唯一健康”监测。在本文中，我们强调了需要解决的问题，旨在提高“唯一健康”监测对抗菌素耐药性有效性的认识。我们还对所需的证据进行了概述，以支持那些计划提高其监测系统整合水平的国家。根据加拿大及他国经验，我们认为需要付出更多努力来理解和衡量“唯一健康”监测对抗菌素耐药性的附加价值，并确定最有效的整合策略。迄今为止，制定“唯一健康”监测的指南主要侧重于应整合的数据类型。然而，可能有必要将“唯一健康”概念应用于数据整合之外的监测任务，以实现该方法的全部价值。可以通过不同的监测活动（数据收集、分析、解释和传播）加强整合，同时考虑到所涉专家和利益相关者的不同技能和观点。需进一步研究以调查提高抗菌素耐药性监测表现的“唯一健康”方法，并最终改善健康结果。.Система надзора за резистентностью к антимикробным препаратам One Health находит поддержку научного сообщества и международных организаций уже в течение более десяти лет. В этой статье освещаются проблемы, которые следует решить для улучшения понимания эффективности надзора за резистентностью к антимикробным препаратам One Health. Авторы также описывают характер подтверждений, необходимых для поддержки стран, планирующих повысить уровень интеграции своих надзорных систем. На основании опыта Канады и других стран оспаривается утверждение о том, что нужно больше усилий для понимания и измерения ценности системы надзора за резистентностью к антимикробным препаратам One Health и для выявления наиболее эффективных стратегий интеграции. На сегодня рекомендации по разработке системы надзора One Health в основном сосредотачивались на типах данных, которые следует интегрировать. Однако, чтобы оценить всю ценность подхода, может понадобиться применение концепции One Health к надзорным задачам, не ограничивающимся интеграцией данных. Интеграция может быть усилена между разного рода надзорной деятельностью (сбор данных, анализ, интерпретация и распространение) с учетом различия в навыках и точках зрения специалистов и партнеров, участвующих в работе системы. Требуются дополнительные исследования для изучения механизмов, посредством которых подход One Health к вопросам надзора может улучшить качество работы систем надзора за антимикробной резистентностью и, в конце концов, улучшить результаты работы системы здравоохранения.

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.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.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.Price discounting is an important driver of CED consumption and has a varying impact across community education and income.RéSUMé: OBJECTIFS: La consommation de boissons énergisantes contenant de la caféine (BEC) a augmenté malgré leurs effets néfastes sur la santé. L’escompte sur les prix fait rarement l’objet d’une étude, mais serait potentiellement une puissante tactique de marketing encourageant l’achat de BEC. À l’aide des registres de transactions d’épiceries montréalaises, nous avons étudié la corrélation entre les rabais et les ventes de BEC selon la situation socio-économique − scolarité et revenu − du quartier. MéTHODES: Les ventes hebdomadaires de BEC en magasins ont régressé en fonction du pourcentage de remise, des facteurs de confusion et d’une interaction entre la remise et le niveau de scolarité et revenu d’un quartier, séparément des transactions des grandes surfaces, pharmacies, supermarchés et dépanneurs. RéSULTATS: Il y a eu 18 743, 12 437, 3 965 et 49 533 semaines de vente BEC dans les grandes surfaces, pharmacies, supermarchés et dépanneurs. Le pourcentage d’actualisation était positivement associé aux ventes de BEC. L’interaction avec l’éducation au tercile était importante dans les supercentres : −0,039 [intervalle de confiance (IC) à 95% : −0,051, −0,028] et −0,039 [IC à 95% : −0,057, −0,021], pour les quartiers au niveau d’éducation moyenne et élevée par rapport aux quartiers au niveau d’éducation faible, respectivement. L’interaction avec le revenu dans les supercentres était de 0,022 [IC à 95% : 0,010, 0,033] et de 0,015 (IC à 95% : −0,001, 0,031) pour le quartier avec tertile à revenu moyen et élevé, respectivement. CONCLUSION: L’actualisation augmente les achats de BEC avec un impact variable selon l’éducation et le revenu d’une communauté.

Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and an NPI stringency index across Canada before the accelerated vaccine roll-out.We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed-effects model accounting for stringency period, province, and mobility in public domains.Case growth declined rapidly by 20-60% and plateaued within the first month of the first wave, irrespective of the starting values of the stringency index. When stringency periods increased, changes in case growth were not immediate and were faster in the first wave than in the second. In the first wave, the largest decreasing trends from our mixed effects model occurred in both early and late stringency periods, depending on the province, at a geometric mean index value of 30⋅1 out of 100. When compared with the first wave, the stringency periods in the second wave possessed little association with case growth.The minimal association in the first wave, and the lack thereof in the second, is compatible with the hypothesis that NPIs do not, per se, lead to a decline in case growth. Instead, the correlations we observed might be better explained by a combination of underlying behaviors of the populations in each province and the natural dynamics of SARS-CoV-2. Although there exist alternative explanations for the equivocal relationship between NPIs and case growth, the onus of providing evidence shifts to demonstrating how NPIs can consistently have flat association, despite incrementally high stringency.

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. Materials and Methods: We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. Results: We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. Availability and implementation: MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS

The papers in this special section focus on explainable artificial intelligence (AI) in healthcare services. Recent advances in AI, precision health, and medicine have paved the way for the accelerated adaptation and use of intelligent tools and systems in decision-making processes across the healthcare spectrum. Insights and knowledge derived from complex analytics are used to implement diagnostic and therapeutic solutions and targeted interventions in individuals and communities across the globe. Given the complexity of the current multi-dimensional clinical and public health data landscape, providing explainability in the context of socio-environmental and technical systems is a key to revealing pathways from socio-economic disadvantages to health disparities and implementing equitable interventions. As the complexity of the underlying data sets and AI-based algorithms increases, the explainability and justifiability of the insights generated decrease. Humans need to understand the underlying mechanism behind these insights to know whether they are sound, correct, trustable, and justifiable to make informed decisions. Lack of understandability and explainability in the biomedical domain often leads to poor transparency and accountability and ultimately lower quality of care and suboptimal and unfair health policies. Explainability is considered one of the prerequisites for deep medicine, where AI is meant to provide composite, panoramic views of individuals’ medical data.

ObjectiveTo assess the efficacy of policies designed to reduce the risk of international travelers importing SARS-CoV-2 into a country.MethodWe developed a simulation model and compared mandatory quarantine, testing, and combined quarantine and testing. We assessed the sensitivity of policy effectiveness to the timing of testing, compliance with quarantine and isolation, and other factors.ResultsIn the base scenario, a 2-day quarantine reduced more risk than testing alone. The effectiveness of a 5-day quarantine requirement with perfect compliance was similar to a 14-day quarantine with moderate compliance. Testing 72h before arrival reduced less than 10% of in-country transmission risk across all scenarios. The addition of testing to quarantine added value for shorter quarantine lengths, when testing compliance was enforced, and when testing was performed near the end of quarantine.ConclusionsQuarantine is more effective at preventing SARS-CoV-2 transmission from arriving travelers than testing alone, but testing combined with quarantine can add value if longer quarantine requirements are infeasible. Enforcing compliance with quarantine and isolation is critical. Requiring a negative test up to 72h before arrival may have limited effectiveness.

Deep medicine, which aims to push the boundaries of artificial intelligence to reshape the health and medical intelligence and decision making, is a promising concept that is gaining attention over traditional EMR-based medical information management systems. The success of intelligent solutions in health and medicine depends on the degree to which they support interoperability, to allow consistent integration of different systems and data sources, and explainability, to make their decisions understandable, interpretable, and justifiable by humans.

IntroductionThe epidemic of non-communicable diseases including cardiovascular diseases and type 2 diabetes is attributable in large part to unhealthy eating and physical inactivity. In the fall of 2016, the Québec government launched its first-ever Government Health Prevention Policy (Politique gouvernementale de prévention en santé (PGPS)) to influence factors that lead to improved health status and quality of life as well as reduced social inequalities in health in the population of Québec. NutriQuébec is a web-based prospective open cohort study whose primary aim is to provide essential data for the evaluation of the PGPS on the Québec population’s eating and other lifestyle behaviours over time.Methods and analysisOver a first phase of 3 years, NutriQuébec will enrol 20 000 adults living in the province of Québec in Canada through a multimedia campaign designed to reach different segments of the population, including subgroups with lower socioeconomic status. Participants will be invited to complete on a web platform nine core questionnaires on a yearly basis. Questionnaires will assess several dimensions related to lifestyle, including eating and physical activity behaviours, as well as a large number of personal characteristics and global health status. Temporal trends in eating and lifestyle behaviours will be analysed in relation to the implementation of the PGPS to provide essential data for its evaluation at a population level. Data analyses will use sociodemographic weights to adjust responses of participants to achieve, so far as is possible, representativeness of the adult Québec population.Ethics and disseminationUniversité Laval Research Ethics Board approved the NutriQuébec project. Data analysis, presentations in conferences and publication of manuscripts are scheduled to start in 2020.Trial registration numberNCT04140071.

International audience; The article "Why public health matters today and tomorrow: the role of applied public health research," written by Lindsay McLaren et al., was originally published Online First without Open Access.

BackgroundThe Canadian epidemics of COVID-19 exhibit distinct early trajectories, with Québec bearing a very high initial burden. Thesemaine de relâche, or March break, took place two weeks earlier in Québec as compared to the rest of Canada. This event may have played a role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to examine the role of case importation in the early transmission dynamics of SARS-CoV-2 in Québec.MethodsUsing detailed surveillance data, we developed and calibrated a deterministic SEIR-type compartmental model of SARS-CoV-2 transmission. We explored the impact of altering the number of imported cases on hospitalizations. Specifically, we investigated scenarios without case importation after March break, and as scenarios where cases were imported with the same frequency/timing as neighboring Ontario.ResultsA total of 1,544 and 1,150 returning travelers were laboratory-confirmed in Québec and Ontario, respectively (with symptoms onset before 2020-03-25). The cumulative number of hospitalizations could have been reduced by 55% (95%CrI: 51-59%) had no cases been imported after Québec’s March break. However, had Québec experienced Ontario’s number of imported cases, cumulative hospitalizations would have only been reduced by 12% (95%CrI: 8-16%).InterpretationOur results suggest that case importation played an important role in the early spread of COVID-19 in Québec. Yet, heavy importation of SARS-CoV-2 in early March could be insufficient to resolve interprovincial heterogeneities in cumulative hospitalisations. The importance of other factors-public health preparedness, responses, and capacity-should be investigated.

The idea of "precision public health" (PPH) was proposed as an alternative to a one-size-fits-all approach to improving population health, which is not always effective. PPH aims to develop and apply interventions in a customized way, taking into account the detailed information about the target group. To enable the implementation of PPH in practice, we are developing an ontology-driven software platform that provides: a) access to detailed up-to-date information about population health, b) a structured machine-readable repository of evidence about public health interventions, and c) a set of intelligent tools to facilitate the assessment of evidence transferability, i.e. to determine how well certain interventions fit a given population.

Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods. Keywords: Weighted networks; Null model; Comorbidity; Disease networks; Centrality

Introduction Price discounting is a marketing tactic used frequently by food industries and retailers, but the extent to which education modifies the effect of discounting on the purchasing of unhealthy foods has received little attention. We investigated whether there was a differential association of price discounting of soda with store-level soda purchasing records between 2008 and 2013 by store-neighbourhood education in Montreal, Canada. Methods Using data on grocery purchase transactions from a sample of supermarkets, pharmacies, supercentres and convenience stores, we performed an ecological time-series analysis, modelling weekly store-level sales of soda as a function of store-level price discounting, store- and neighbourhood-level confounders and an interaction term between discounting and categorical education in the neighbourhood of each store. Results Analysis by store type (n = 18 743, 12 437, 3965 and 49 533 store-weeks for superstores, pharmacies, supercentres and convenience stores, respectively) revealed that the effect measure modification of discounting by neighbourhood education on soda purchasing was lower in stores in the more educated neighbourhoods, most notably in pharmacies: -0.020 [95% confidence interval (CI): -0.028, -0.012] and -0.038 (95% CI: -0.051, -0.025), for middle- and high-education categories, respectively). Weaker effect modification was observed in convenience stores. There was no evidence of effect modification in supercentres or superstores. Conclusions Price discounting is an important environmental risk factor for soda purchasing and can widen education inequalities in excess sugar intake across levels of education. Interventions to regulate price discounting warrant further investigation as a public health strategy to improve population nutrition, particularly in lower-education neighbourhoods.

Purpose To assess the accuracy of using diagnostic codes from administrative data to infer treatment indications for antidepressants prescribed in primary care. Methods Validation study of administrative diagnostic codes for 13 plausible indications for antidepressants compared with physician‐documented treatment indications from an indication‐based electronic prescribing system in Quebec, Canada. The analysis included all antidepressant prescriptions written by primary care physicians between January 1, 2003 and December 31, 2012 using the electronic prescribing system. Patient prescribed antidepressants were linked to physician claims and hospitalization data to obtain all diagnoses recorded in the past year. Results Diagnostic codes had poor sensitivity for all treatment indications, ranging from a high of only 31.2% (95% CI, 26.8%‐35.9%) for anxiety/stress disorders to as low as 1.3% (95% CI, 0.0%‐5.2%) for sexual dysfunction. Sensitivity was notably worse among older patients and patients with more chronic comorbidities. Physician claims data were a better source of diagnostic codes for antidepressant treatment indications than hospitalization data. Conclusions Administrative diagnostic codes are poor proxies for antidepressant treatment indications. Future work should determine whether the use of other variables in administrative data besides diagnostic codes can improve the ability to predict antidepressant treatment indications.

OBJECTIVES: This study examined Twitter for public health surveillance during a mass gathering in Canada with two objectives: to explore the feasibility of acquiring, categorizing and using geolocated Twitter data and to compare Twitter data against other data sources used for Pan Parapan American Games (P/PAG) surveillance. METHODS: Syndrome definitions were created using keyword categorization to extract posts from Twitter. Categories were developed iteratively for four relevant syndromes: respiratory, gastrointestinal, heat-related illness, and influenza-like illness (ILI). All data sources corresponded to the location of Toronto, Canada. Twitter data were acquired from a publicly available stream representing a 1% random sample of tweets from June 26 to September 10, 2015. Cross-correlation analyses of time series data were conducted between Twitter and comparator surveillance data sources: emergency department visits, telephone helpline calls, laboratory testing positivity rate, reportable disease data, and temperature. RESULTS: The frequency of daily tweets that were classified into syndromes was low, with the highest mean number of daily tweets being for ILI and respiratory syndromes (22.0 and 21.6, respectively) and the lowest, for the heat syndrome (4.1). Cross-correlation analyses of Twitter data demonstrated significant correlations for heat syndrome with two data sources: telephone helpline calls (r = 0.4) and temperature data (r = 0.5). CONCLUSION: Using simple syndromes based on keyword classification of geolocated tweets, we found a correlation between tweets and two routine data sources for heat alerts, the only public health event detected during P/PAG. Further research is needed to understand the role for Twitter in surveillance. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-018-0059-0) contains supplementary material, which is available to authorized users.

IntroductionThe main harm reduction interventions for people who inject drugs (PWID) are supervised injection facilities, needle and syringe programmes and opioid agonist treatment. Current evidence supporting their implementation and operation underestimates their usefulness by excluding skin, soft tissue and vascular infections (SSTVIs) and anoxic/toxicity-related brain injury from cost-effectiveness analyses (CEA). Our goal is to conduct a comprehensive CEA of harm reduction interventions in a setting with a large, dispersed, heterogeneous population of PWID, and include prevention of SSTVIs and anoxic/toxicity-related brain injury as measures of benefit in addition to HIV, hepatitis C and overdose morbidity and mortalities averted.Methods and analysisThis protocol describes how we will develop an open, retrospective cohort of adult PWID living in Québec between 1 January 2009 and 31 December 2020 using administrative health record data. By complementing this data with non-linkable paramedic dispatch records, regional monthly needle and syringe dispensation counts and repeated cross-sectional biobehavioural surveys, we will estimate the hazards of occurrence and the impact of Montréal’s harm reduction interventions on the incidence of drug-use-related injuries, infections and deaths. We will synthesise results from our empirical analyses with published evidence to simulate infections and injuries in a hypothetical population of PWID in Montréal under different intervention scenarios including current levels of use and scale-up, and assess the cost-effectiveness of each intervention from the public healthcare payer’s perspective.Ethics and disseminationThis study was approved by McGill University’s Institutional Review Board (Study Number: A08-E53-19B). We will work with community partners to disseminate results to the public and scientific community via scientific conferences, a publicly accessible report, op-ed articles and open access peer-reviewed journals.

Objective:To comprehensively assess recurrent traumatic brain injury (rTBI) risk and risk factors in the general population.Methods:We systematically searched MEDLINE, EMBASE, and the references of included studies until January 16, 2017, for general population observational studies reporting rTBI risk or risk factors. Estimates were not meta-analyzed due to significant methodologic heterogeneity between studies, which was evaluated using meta-regression.Results:Twenty-two studies reported recurrence risk and 11 reported on 27 potential risk factors. rTBI risk was heterogeneous and varied from 0.43% (95% confidence interval [CI] 0.19%–0.67%) to 41.92% (95% CI 34.43%–49.40%), with varying follow-up periods (3 days–55 years). Median time to recurrence ranged from 0.5 to 3.8 years. In studies where cases were ascertained from multiple points of care, at least 5.50% (95% CI 4.80%–6.30%) of patients experienced a recurrence after a 1-year follow-up. Studies that used administrative data/self-report surveys to ascertain cases tended to report higher risk. Risk factors measured at time of index traumatic brain injury (TBI) that were significantly associated with rTBI in more than one study were male sex, prior TBI before index case, moderate or severe TBI, and alcohol intoxication. Risk factors reported in a single study that were significantly associated with rTBI were epilepsy, not seeking medical care, and multiple factors indicative of low socioeconomic status.Conclusions:rTBI is an important contributor to the general population TBI burden. Certain risk factors can help identify individuals at higher risk of these repeated injuries. However, higher quality research that improves on rTBI surveillance methodology is needed.

Background and Objective Many countries require hospitals to implement medication reconciliation for accreditation, but the process is resource-intensive, thus adherence is poor. We report on the impact of prepopulating and aligning community and hospital drug lists with data from population-based and hospital-based drug information systems to reduce workload and enhance adoption and use of an e-medication reconciliation application, RightRx. Methods The prototype e-medical reconciliation web-based software was developed for a cluster-randomized trial at the McGill University Health Centre. User-centered design and agile development processes were used to develop features intended to enhance adoption, safety, and efficiency. RightRx was implemented in medical and surgical wards, with support and training provided by unit champions and field staff. The time spent per professional using RightRx was measured, as well as the medication reconciliation completion rates in the intervention and control units during the first 20 months of the trial. Results Users identified required modifications to the application, including the need for dose-based prescribing, the role of the discharge physician in prescribing community-based medication, and access to the rationale for medication decisions made during hospitalization. In the intervention units, both physicians and pharmacists were involved in discharge reconciliation, for 96.1% and 71.9% of patients, respectively. Medication reconciliation was completed for 80.7% (surgery) to 96.0% (medicine) of patients in the intervention units, and 0.7% (surgery) to 82.7% of patients in the control units. The odds of completing medication reconciliation were 9 times greater in the intervention compared to control units (odds ratio: 9.0, 95% confidence interval, 7.4-10.9, P Conclusion High rates of medication reconciliation completion were achieved with automated prepopulation and alignment of community and hospital medication lists.

BACKGROUND: Using different indicators and definitions, the present study aimed to describe population antimicrobial use, as well as prevalence and incidence of clinically relevant antimicrobial resistances found in respiratory cultures performed in intensive care unit (ICU) patients. Results obtained with the various methodologies were then compared. METHODS: The present retrospective cohort study included all patients admitted to nine ICUs between April 2006 and March 2010. Prevalence and incidence of clinically relevant resistances in respiratory cultures were described and population antimicrobial use was measured using 10 different indicators based on dosage, duration of treatment, or exposure to antimicrobials. RESULTS: Indicators had variable sensitivity to detect time trends and differences among ICU types. However, the highest prevalence and incidence rates in respiratory isolates were in Staphylococcus aureus resistance to oxacillin (0.52% of ICU admissions and 6.57 acquisitions/10,000 patient-days) and coliforms resistance to piperacillin-tazobactam (0.44% and 7.80 acquisitions/10,000 patient-days). Cephalosporins, penicillins, and aminoglycosides were the most frequently prescribed antimicrobials, according to most indicators. CONCLUSIONS: Given the observed heterogeneity among indicators, one should consider referring to sets of indicators, allowing for the selection of indicators representing different aspects of antimicrobial use, resistance levels, and of patient case mix.

Background Neighborhood food cues have been inconsistently related to residents’ health, possibly due to variations in residents’ sensitivity to such cues. This study sought to investigate the degree to which children’s predisposition to eat upon exposure to food environment and food cues (external eating), could explain differences in strength of associations between their food consumption and the type of food outlets and marketing strategies present in their neighborhood. Methods Data were obtained from 616 6–12 y.o. children recruited into a population-based cross-sectional study in which food consumption was measured through a 24-h food recall and responsiveness to food cues measured using the external eating scale. The proportion of food retailers within 3 km of residence considered as “healthful” was calculated using a Geographical Information System. Neighborhood exposure to food marketing strategies (displays, discount frequency, variety, and price) for vegetables and soft drinks were derived from a geocoded digital marketing database. Adjusted mixed models with spatial covariance tested interaction effects of food environment indicators and external eating on food consumption. Results In children with higher external eating scores, healthful food consumption was more positively related to vegetable displays, and more negatively to the display and variety of soft drinks. No interactions were observed for unhealthful food consumption and no main effects of food environment indicators were found on food consumption. Conclusions Children differ in their responsiveness to marketing-related visual food cues on the basis of their external eating phenotype. Strategies aiming to increase the promotion of healthful relative to unhealthful food products in stores may be particularly beneficial for children identified as being more responsive to food cues. Electronic supplementary material The online version of this article (doi:10.1186/s12966-017-0540-9) contains supplementary material, which is available to authorized users.

Background A system for monitoring vaccine-related media content was previously developed and studied from an international perspective. This monitoring approach could also have value at a regional level, but it has yet to be evaluated at this scale. We examined regional patterns of vaccine-related media topics and sentiment in the US and Canada. Methods We extracted vaccine-relevant US and Canadian online media reports between June 2012 and October 2014 from the Vaccine Sentimeter, a HealthMap-based automated media monitoring system for news aggregators and blogs. We analyzed regional distributions of reports about vaccines, categories (i.e., topics), sentiment, and measles outbreaks. Findings The Vaccine Sentimeter captured 10,715 reports during the study period. Negative sentiment was highest in reports about vaccine safety (47%), Hepatitis B (19%), and Vermont (18%). Analyses of measles outbreaks revealed geographical variation in media content. For example, religious beliefs were mentioned in 27% of measles reports in Texas and 22% of British Columbia reports, but there were no references to religion in media on measles from California. Interpretations A regional analysis of online sentiment towards vaccine can provide insights that may give US and Canadian public health practitioners a deeper understanding of media influences on vaccine choices in their regions and consequently lead to more effective public health action.

Public health surveillance is the ongoing systematic collection, analysis and interpretation of data, closely integrated with the timely dissemination of the resulting information to those responsible for preventing and controlling disease and injury. With the rapid development of data science, encompassing big data and artificial intelligence, and with the exponential growth of accessible and highly heterogeneous health-related data, from healthcare providers to user-generated online content, the field of surveillance and health monitoring is changing rapidly. It is, therefore, the right time for a short glossary of key terms in public health surveillance, with an emphasis on new data-science developments in the field.

Background A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations. Methods The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups. Results From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were ‘long-term care’, ‘palliative’, ‘major acute’, ‘major chronic’, ‘major cancer’, ‘major newborn’, ‘major mental health’ and ‘moderate chronic’. Both ‘major acute’ and ‘major cancer’ health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, ‘major chronic’, ‘moderate chronic’, ‘major newborn’ and ‘other mental health’, a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use. Interpretation Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.

Summary Background Labelling outbreaks in surveillance data is necessary to train advanced analytical methods for outbreak detection, but there is a lack of software tools dedicated to this task. Aim To evaluate the usability of a web-based tool by infection control practitioners for labelling potential outbreaks. Methods A mixed methods design was used to evaluate how 25 experts from France and Canada interacted with a web-based application to identify potential outbreaks. Each expert used the application to retrospectively review 11–12 1-year incidence time series from 23 different types of micro-organism. The interactions between the users and the application were recorded and analysed using mixed effect models. The users' comments were analysed via qualitative methods. Findings From the 240 reviews completed, 439 potential outbreaks were labelled, approximately half with a high probability. Significant heterogeneity was observed between users regarding their answers and behaviours (evaluation time, usage of the different options). A significant learning effect was also observed for the experts' interactions with the tool, but this did not seem to impact their answers. The content analysis of the comments highlighted the difficulty of early outbreak identification for practitioners, but also the potential utility of web applications such as that evaluated for routine surveillance. Conclusion The interactive web application was both usable and useful for infection control practitioners. Its implementation in routine practice could help professionals to identify potential outbreaks while creating data to train automated detection algorithms.

Background Admission to hospital provides the opportunity to review patient medications; however, the extent to which the safety of drug regimens changes after hospitalization is unclear. Objective To estimate the number of potentially inappropriate medications (PIMs) prescribed to patients at hospital discharge and their association with the risk of adverse events 30 days after discharge. Design Prospective cohort study. Setting Tertiary care hospitals within the McGill University Health Centre Network in Montreal, Quebec, Canada. Participants Patients from internal medicine, cardiac, and thoracic surgery, aged 65 years and older, admitted between October 2014 and November 2016. Measures Abstracted chart data were linked to provincial health databases. PIMs were identified using AGS (American Geriatrics Society) Beers Criteria®, STOPP, and Choosing Wisely statements. Multivariable logistic regression and Cox models were used to assess the association between PIMs and adverse events. Results Of 2,402 included patients, 1,381 (57%) were male; median age was 76 years (interquartile range [IQR] = 70-82 years); and eight discharge medications were prescribed (IQR = 2-8). A total of 1,576 (66%) patients were prescribed at least one PIM at discharge; 1,176 (49%) continued a PIM from prior to admission, and 755 (31%) were prescribed at least one new PIM. In the 30 days after discharge, 218 (9%) experienced an adverse drug event (ADE) and 862 (36%) visited the emergency department (ED), were rehospitalized, or died. After adjustment, each additional new PIM and continued community PIM were respectively associated with a 21% (odds ratio [OR] = 1.21; 95% confidence interval [CI] = 1.01-1.45) and a 10% (OR = 1.10; 95% CI = 1.01-1.21) increased odds of ADEs. They were also respectively associated with a 13% (hazard ratio [HR] = 1.13; 95% CI = 1.03-1.26) and a 5% (HR = 1.05; 95% CI = 1.00-1.10) increased risk of ED visits, rehospitalization, and death. Conclusions Two in three hospitalized patients were prescribed a PIM at discharge, and increasing numbers of PIMs were associated with an increased risk of ADEs and all-cause adverse events. Improving hospital prescribing practices may reduce the frequency of PIMs and associated adverse events. J Am Geriatr Soc 68:1184-1192, 2020.

Over 3 million hospitalizations and 17 million ER visits occur in Canada each year. A substantial proportion of these encounters are preventable and attributable to medication non- adherence. Non-adherence to medication changes during discharge increases the risk of adverse events post-discharge. A mobile application was developed to improve medication management of post-discharge patients. A pilot randomized controlled trial was conducted to assess the application's usability and efficacy in decreasing non-adherence to medication changes made at discharge.

Sugar Sweetened Beverages (SSB) are the primary source of artificially added sugar and have a casual association with chronic diseases. Taxation of SSB has been proposed, but limited evidence exists to guide this public health policy. Grocery transaction data, with price, discounting and other information for beverage products, present an opportunity to evaluate the likely effects of taxation policy. Sales are often non-linearly associated with price and are affected by the prices of multiple competing brands. We evaluated the predictive performance of Boosted Decision Tree Regression (B-DTR) and Deep Neural Networks (DNN) that account for the non-linearity and competition across brands, and compared their performance to a benchmark regression, the Least Absolute Shrinkage and Selection Operator (LASSO). B-DTR and DNN showed a lower Mean Squared Error (MSE) of prediction in the sales of most major SSB brands in comparison to LASSO, indicating a superior accuracy in predicting the effectiveness of SSB taxation. We demonstrated the application of machine learning methods and large transactional data from grocery stores to forecast the effectiveness food taxation.