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We propose a multivariate GARCH model for non‐stationary health time series by modifying the observation‐level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state‐space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non‐stationary time series data. Model comparison can then be easily performed using the WAIC. [ABSTRACT FROM AUTHOR]

Introduction: There is increasing interest for patient-to-provider telemedicine in pediatric acute care. The suitability of telemedicine (virtualizability) for visits in this setting has not been formally assessed. We estimated the proportion of in-person pediatric emergency department (PED) visits that were potentially virtualizable, and identified factors associated with virtualizable care. Methods: This was a retrospective analysis of in-person visits at the PED of a Canadian tertiary pediatric hospital (02/2018–12/2019). Three definitions of virtualizable care were developed: (1) a definition based on "resource use" classifying visits as virtualizable if they resulted in a home discharge, no diagnostic testing, and no return visit within 72 h; (2) a "diagnostic definition" based on primary ED diagnosis; and (3) a stringent "combined definition" by which visits were classified as virtualizable if they met both the resource use and diagnostic definitions. Multivariable logistic regression was used to identify factors associated with telemedicine suitability. Results: There were 130,535 eligible visits from 80,727 individual patients during the study period. Using the most stringent combined definition of telemedicine suitability, 37.9% (95% confidence interval (CI) 37.6%–38.2%) of in-person visits were virtualizable. Overnight visits (adjusted odds ratio (aOR) 1.16–1.37), non-Canadian citizenship (aOR 1.10–1.18), ethnocultural vulnerability (aOR 1.14–1.22), and a consultation for head trauma (aOR 3.50–4.60) were associated with higher telemedicine suitability across definitions. Discussion: There is a high potential for patient-to-provider telemedicine in the PED setting. Local patient and visit-level characteristics must be considered in the design of safe and inclusive telemedicine models for pediatric acute care. [ABSTRACT FROM AUTHOR]

Background: Needle and syringe programs (NSP) are effective harm-reduction strategies against HIV and hepatitis C. Although skin, soft tissue, and vascular infections (SSTVI) are the most common morbidities in people who inject drugs (PWID), the extent to which NSP are clinically and cost-effective in relation to SSTVI in PWID remains unclear. The objective of this study was to model the clinical- and cost-effectiveness of NSP with respect to treatment of SSTVI in PWID. Methods: We performed a model-based, economic evaluation comparing a scenario with NSP to a scenario without NSP. We developed a microsimulation model to generate two cohorts of 100,000 individuals corresponding to each NSP scenario and estimated quality-adjusted life-years (QALY) and cost (in 2022 Canadian dollars) over a 5-year time horizon (1.5% per annum for costs and outcomes). To assess the clinical effectiveness of NSP, we conducted survival analysis that accounted for the recurrent use of health care services for treating SSTVI and SSTVI mortality in the presence of competing risks. Results: The incremental cost-effectiveness ratio associated with NSP was $70,278 per QALY, with incremental cost and QALY gains corresponding to $1207 and 0.017 QALY, respectively. Under the scenario with NSP, there were 788 fewer SSTVI deaths per 100,000 PWID, corresponding to 24% lower relative hazard of mortality from SSTVI (hazard ratio [HR] = 0.76; 95% confidence interval [CI] = 0.72–0.80). Health service utilization over the 5-year period remained lower under the scenario with NSP (outpatient: 66,511 vs. 86,879; emergency department: 9920 vs. 12,922; inpatient: 4282 vs. 5596). Relatedly, having NSP was associated with a modest reduction in the relative hazard of recurrent outpatient visits (HR = 0.96; 95% CI = 0.95–0.97) for purulent SSTVI as well as outpatient (HR = 0.88; 95% CI = 0.87–0.88) and emergency department visits (HR = 0.98; 95% CI = 0.97–0.99) for non-purulent SSTVI. Conclusions: Both the individuals and the healthcare system benefit from NSP through lower risk of SSTVI mortality and prevention of recurrent outpatient and emergency department visits to treat SSTVI. The microsimulation framework provides insights into clinical and economic implications of NSP, which can serve as valuable evidence that can aid decision-making in expansion of NSP services. [ABSTRACT FROM AUTHOR]

Background In Canada's largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Methods Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420 319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Results Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Conclusions Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation. [ABSTRACT FROM AUTHOR]

Objectives Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. Materials and methods We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. Results The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. Discussion and conclusion The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM. [ABSTRACT FROM AUTHOR]

Summary: Non‐parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyse survival data that have been collected under different study designs. We review non‐parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) to clarify the differences in the model assumptions and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta‐analysis of survival data obtained from different types of study, and to the modern era of electronic health records. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach." The correction adds a missing co-author, W Alton Russell, to the author group. The original article has been corrected accordingly. The correction notice is published in the Harm Reduction Journal. [Extracted from the article]

Information integration across multiple event-based surveillance (EBS) systems has been shown to improve global disease surveillance in experimental settings. In practice, however, integration does not occur due to the lack of a common conceptual framework for encoding data within EBS systems. We aim to address this gap by proposing a candidate conceptual framework for representing events and related concepts in the domain of public health surveillance. [ABSTRACT FROM AUTHOR]

Background: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. Methods: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARSCoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. Results: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. Interpretation: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Objective: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions.Design: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transforms store-level sales into area-level purchasing, we developed small-area public health indicators of food purchasing for neighbourhood districts. We generated the area-level indicators measuring per-resident purchasing quantity for soda, diet soda, flavoured (sugar-added) yogurt and plain yogurt purchasing. We then provided an illustrative public health application of these indicators as covariates for an ecological spatial regression model to estimate spatially correlated small-area risk of type 2 diabetes mellitus (T2D) obtained from the public health administrative data.Setting: Greater Montreal, Canada in 2012.Participants: Neighbourhood districts (n 193).Results: The indicator of flavoured yogurt had a positive association with neighbourhood-level risk of T2D (1·08, 95 % credible interval (CI) 1·02, 1·14), while that of plain yogurt had a negative association (0·93, 95 % CI 0·89, 0·96). The indicator of soda had an inconclusive association, and that of diet soda was excluded due to collinearity with soda. The addition of the indicators also improved model fit of the T2D spatial regression (Watanabe-Akaike information criterion = 1765 with the indicators, 1772 without).Conclusion: Store-level grocery sales data can be used to reveal micro-scale geographic disparities and trends of food selections that would be masked by traditional survey-based estimation. [ABSTRACT FROM AUTHOR]

Background: The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time.Methods and Findings: We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period.Conclusions: In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic. [ABSTRACT FROM AUTHOR]

Measuring and monitoring non-pharmaceutical interventions is important yet challenging due to the need to clearly define and encode non-pharmaceutical interventions, to collect geographically and socially representative data, and to accurately document the timing at which interventions are initiated and changed. These challenges highlight the importance of integrating and triangulating across multiple databases and the need to expand and fund the mandate for public health organizations to track interventions systematically. [ABSTRACT FROM AUTHOR]

Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessing the accuracy to conduct rTBI surveillance is not available. Accurate surveillance information on recurrent injuries is needed to justify the allocation of resources to rTBI prevention and to conduct high quality epidemiological research on interventions that mitigate this injury burden. This study evaluates the accuracy of administrative health data (AHD) surveillance case definitions for rTBI and estimates the 1-year rTBI incidence adjusted for measurement error. Methods: A 25% random sample of AHD for Montreal residents from 2000 to 2014 was used in this study. Four widely used TBI surveillance case definitions, based on the International Classification of Disease and on radiological exams of the head, were applied to ascertain suspected rTBI cases. Bayesian latent class models were used to estimate the accuracy of each case definition and the 1-year rTBI measurement-error-adjusted incidence without relying on a gold standard rTBI definition that does not exist, across children (<18 years), adults (18-64 years), and elderly (> =65 years). Results: The adjusted 1-year rTBI incidence was 4.48 (95% CrI 3.42, 6.20) per 100 person-years across all age groups, as opposed to a crude estimate of 8.03 (95% CrI 7.86, 8.21) per 100 person-years. Patients with higher severity index TBI had a significantly higher incidence of rTBI compared to patients with lower severity index TBI. The case definition that identified patients undergoing a radiological examination of the head in the context of any traumatic injury was the most sensitive across children [0.46 (95% CrI 0.33, 0.61)], adults [0.79 (95% CrI 0.64, 0.94)], and elderly [0.87 (95% CrI 0.78, 0.95)]. The most specific case definition was the discharge abstract database in children [0.99 (95% CrI 0.99, 1.00)], and emergency room visits claims in adults/elderly [0.99 (95% CrI 0.99, 0.99)]. Median time to rTBI was the shortest in adults (75 days) and the longest in children (120 days). Conclusion: Conducting accurate surveillance and valid epidemiological research for rTBI using AHD is feasible when measurement error is accounted for. [ABSTRACT FROM AUTHOR]

Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous‐time process potentially as a function of time‐varying covariates. We develop a continuous‐time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation‐maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the hypothesis that nonadherence to medication changes made at hospital discharge is associated with an increased risk of adverse events in the 30 days postdischarge.Study Setting: Patients admitted to hospitals in Montreal, Quebec, between 2014 and 2016.Study Design: Prospective cohort study.Data Collection: Nonadherence to medication changes was measured by comparing medications dispensed in the community with those prescribed at hospital discharge. Patient, health system, and drug regimen-level covariates were measured using medical services and pharmacy claims data as well as data abstracted from the patient's hospital chart. Multivariable Cox models were used to determine the association between nonadherence to medication changes and the risk of adverse events.Principal Findings: Among 2655 patients who met our inclusion criteria, mean age was 69.5 years (SD 14.7) and 1581 (60%) were males. Almost half of patients (n = 1161, 44%) were nonadherent to at least one medication change, and 860 (32%) were readmitted to hospital, visited the emergency department, or died in the 30 days postdischarge. Patients who were not adherent to any of their medication changes had a 35% higher risk of adverse events compared to those who were adherent to all medication changes (1.41 vs 1.27 events/100 person-days, adjusted hazard ratio: 1.35, 95% CI: 1.06-1.71).Conclusions: Almost half of all patients were not adherent to some or all changes made to their medications at hospital discharge. Nonadherence to all changes was associated with an increased risk of adverse events. Interventions addressing barriers to adherence should be considered moving forward. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Antidepressants increase the risk of falls and fracture in older adults. However, risk estimates vary considerably even in comparable populations, limiting the usefulness of current evidence for clinical decision making. Our aim was to apply a common protocol to cohorts of older antidepressant users in multiple jurisdictions to estimate fracture risk associated with different antidepressant classes, drugs, doses, and potential treatment indications. DESIGN: Retrospective (2009–2014) cohort study. SETTING: Five jurisdictions in the United States, Canada, United Kingdom, and Taiwan. PARTICIPANTS: Older antidepressant users—subjects were followed from first antidepressant prescription or dispensation to first fracture or until the end of follow‐up. MEASUREMENTS: The risk of fractures with antidepressants was estimated by multivariable Cox proportional hazards models using time‐varying measures of antidepressant dose and use vs nonuse, adjusting for patient characteristics. RESULTS: Between 42.9% and 55.6% of study cohorts were 75 years and older, and 29.3% to 45.4% were men. Selective serotonin reuptake inhibitors (SSRIs) (48.4%‐60.0%) were the predominant class used in North America compared with tricyclic antidepressants (TCAs) in the United Kingdom and Taiwan (49.6%‐53.6%). Fracture rates varied from 37.67 to 107.18 per 1,000. The SSRIs citalopram (hazard ratio [HR] = 1.23; 95% confidence interval [CI] = 1.11‐1.36 to HR = 1.43; 95% CI = 1.11‐1.84) and sertraline (HR = 1.36; 95% CI = 1.10‐1.68), the SNRI duloxetine (HR = 1.41; 95% CI = 1.06‐1.88), TCAs doxepin (HR = 1.36; 95% CI = 1.00‐1.86) and imipramine (HR = 1.16; 95% CI = 1.05‐1.28), and atypicals (HR = 1.34; 95% CI = 1.14‐1.58) increased fracture risk in some but not all jurisdictions. In the United States and the United Kingdom, fracture risk with all classes was higher when prescribed for depression than chronic pain, a trend that is likely explained by drug choice. CONCLUSION: The fracture risk for patients may be reduced by selecting paroxetine, an SSRI with lower risk than citalopram, the SNRI venlafaxine over duloxetine, and the TCA amitriptyline over imipramine or doxepin. There is uncertainty about the risk associated with the atypical antidepressants. J Am Geriatr Soc 68:1494‐1503, 2020. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Measurement of neighborhood dietary patterns at high spatial resolution allows public health agencies to identify and monitor communities with an elevated risk of nutrition-related chronic diseases. Currently, data on diet are obtained primarily through nutrition surveys, which produce measurements at low spatial resolutions. The availability of store-level grocery transaction data provides an opportunity to refine the measurement of neighborhood dietary patterns. We used these data to develop an indicator of area-level latent demand for soda in the Census Metropolitan Area of Montreal in 2012 by applying a hierarchical Bayesian spatial model to data on soda sales from 1,097 chain retail food outlets. The utility of the indicator of latent soda demand was evaluated by assessing its association with the neighborhood relative risk of prevalent type 2 diabetes mellitus. The indicator improved the fit of the disease-mapping model (deviance information criterion: 2,140 with the indicator and 2,148 without) and enables a novel approach to nutrition surveillance. [ABSTRACT FROM AUTHOR]

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). BDTR 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. [ABSTRACT FROM AUTHOR]

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). BDTR 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. [ABSTRACT FROM AUTHOR]

Objectives We used an international pharmacosurveillance network to estimate the rate and characteristics of antidepressant use in older adults in countries with more conservative (UK) and liberal depression guidelines (Canada, USA). Setting Electronic health records and population-based administrative data from six jurisdictions in four countries (UK, Taiwan, USA and Canada). Participants A historical cohort of older adults (=65 years) who had a new episode of antidepressant use between 2009 and 2014. Outcome measures The age and sex-standardised cumulative incidence of new episodes of antidepressant use in older adults was measured. Descriptive statistics were used to compare the proportion of new users by the antidepressant prescribed, therapeutic class, potential treatment indication and country, as well as the characteristics of the first treatment episode (standardised daily doses, duration and changes). Results The incidence of antidepressant use between 2009 and 2014 varied from 4.7% (Montreal and Quebec City) to 18.6% (Taiwan). Tricyclic antidepressants (TCAs) were the most commonly used class in the UK (48.8%) and Taiwan (52.4%) compared with selective serotonin reuptake inhibitors (SSRIs) in North American jurisdictions (42.3%-53.3%). Chronic pain was the most common potential treatment indication (41.2%-68.2%). Among users with chronic pain, TCAs were used most frequently in the UK and Taiwan (55.2%-60.4%), whereas SSRIs were used most frequently in North America (33.5%-46.4%). Treatment was longer (252-525 vs 169-437 days), standardised doses were higher (0.7-1.3 vs 0.5-1.0) and treatment was more likely to be changed (31%-46% vs 21%-34%) among patients with depression (9.1%-43%) than those with chronic pain. Conclusion Antidepressant use in older adults varied 24-fold by country, with the UK, which has the most conservative treatment guidelines, being among the lowest. Chronic pain was the most common potential treatment indication. Evaluation of real-world risks of TCAs is a priority for future research, given high rates of use and the potential for increased toxicity in older adults because of potent anticholinergic effects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

Estimation of the overall effect of a vaccine program is essential, but the effect is typically estimated for a whole program. We estimated the overall effect of the Quebec rotavirus vaccine program, launched in November 2011, and the effect for each 10% increase in rotavirus vaccine coverage on pediatric hospitalizations for all-cause acute gastroenteritis. We implemented negative binomial regressions adjusted for seasonality, long-term trends, and infection dynamics, to estimate the effect of the vaccine program as: 1) a dichotomous variable, representing program presence/absence, and linear term to account for changes in trend in the period after the program began; and 2) a continuous variable, representing rotavirus vaccine coverage. Using exposure 1, the vaccine program was associated with a 51.2% (95% confidence interval (CI): 28.5, 66.7) relative decline in adjusted weekly hospitalization rates for all-cause acute gastroenteritis as of December 28, 2014. Using exposure 2, a 10% increase in rotavirus ≥1-dose coverage was associated with a 7.1% (95% CI: 3.5, 10.5) relative decline in adjusted weekly rates, with maximum coverage of 87.0% associated with a 47.2% (95% CI: 26.9, 61.9) relative decline. Estimation of the overall effect attributable to a change in vaccine coverage might be a useful addition to standard measurement of the overall effect. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Real‐world data for observational research commonly require formatting and cleaning prior to analysis. Data preparation steps are rarely reported adequately and are likely to vary between research groups. Variation in methodology could potentially affect study outcomes. This study aimed to develop a framework to define and document drug data preparation and to examine the impact of different assumptions on results. Methods: An algorithm for processing prescription data was developed and tested using data from the Clinical Practice Research Datalink (CPRD). The impact of varying assumptions was examined by estimating the association between 2 exemplar medications (oral hypoglycaemic drugs and glucocorticoids) and cardiovascular events after preparing multiple datasets derived from the same source prescription data. Each dataset was analysed using Cox proportional hazards modelling. Results: The algorithm included 10 decision nodes and 54 possible unique assumptions. Over 11 000 possible pathways through the algorithm were identified. In both exemplar studies, similar hazard ratios and standard errors were found for the majority of pathways; however, certain assumptions had a greater influence on results. For example, in the hypoglycaemic analysis, choosing a different variable to define prescription end date altered the hazard ratios (95% confidence intervals) from 1.77 (1.56‐2.00) to 2.83 (1.59‐5.04). Conclusions: The framework offers a transparent and efficient way to perform and report drug data preparation steps. Assumptions made during data preparation can impact the results of analyses. Improving transparency regarding drug data preparation would increase the repeatability, reproducibility, and comparability of published results. [ABSTRACT FROM AUTHOR]

Objective: (1) To describe the usage of medication data from the Health Information Exchange (HIE) at the health care system level in the province of Quebec; (2) To assess the accuracy of the medication list obtained from the HIE.Methods: A descriptive study was conducted utilizing usage data obtained from the Ministry of Health at the individual provider level from January 1 to December 31, 2015. Usage patterns by role, type of site, and tool used to access the HIE were investigated. The list of medications of 111 high risk patients arriving at the emergency department of an academic healthcare center was obtained from the HIE and compared with the list obtained through the medication reconciliation process.Results: There were 31 022 distinct users accessing the HIE 11 085 653 times in 2015. The vast majority of pharmacists and general practitioners accessed it, compared to a minority of specialists and nurses. The top 1% of users was responsible of 19% of access. Also, 63% of the access was made using the Viewer application, while using a certified electronic medical record application seemed to facilitate usage. Among 111 patients, 71 (64%) had at least one discrepancy between the medication list obtained from the HIE and the reference list.Conclusions: Early adopters were mostly in primary care settings, and were accessing it more frequently when using a certified electronic medical record. Further work is needed to investigate how to resolve accuracy issues with the medication list and how certain tools provide different features. [ABSTRACT FROM AUTHOR]

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 < .0001) after adjusting for differences in patient characteristics.Conclusion: High rates of medication reconciliation completion were achieved with automated prepopulation and alignment of community and hospital medication lists. [ABSTRACT FROM AUTHOR]

Objective: Physicians commonly prescribe antidepressants for indications other than depression that are not evidence-based and need further evaluation. However, lack of routinely documented treatment indications for medications in administrative and medical databases creates a major barrier to evaluating antidepressant use for indications besides depression. Thus, the aim of this study was to derive a model to predict when primary care physicians prescribe antidepressants for indications other than depression and to identify important determinants of this prescribing practice. Methods: Prediction study using antidepressant prescriptions from January 2003-December 2012 in an indication-based electronic prescribing system in Quebec, Canada. Patients were linked to demographic files, medical billings data, and hospital discharge summary data to create over 370 candidate predictors. The final prediction model was derived on a random 75% sample of the data using 3-fold cross-validation integrated within a score-based forward stepwise selection procedure. The performance of the final model was assessed in the remaining 25% of the data. Results: Among 73,576 antidepressant prescriptions, 32,405 (44.0%) were written for indications other than depression. Among 40 predictors in the final model, the most important covariates included the molecule name, the patient's education level, the physician's workload, the prescribed dose, and diagnostic codes for plausible indications recorded in the past year. The final model had good discrimination (concordance (c) statistic 0.815; 95% CI, 0.787-0.847) and good calibration (ratio of observed to expected events 0.986; 95% CI, 0.842-1.136). Conclusion: In the absence of documented treatment indications, researchers may be able to use health services data to accurately predict when primary care physicians prescribe antidepressants for indications other than depression. Our prediction model represents a valuable tool for enabling researchers to differentiate between antidepressant use for depression versus other indications, thus addressing a major barrier to performing pharmacovigilance research on antidepressants. [ABSTRACT FROM AUTHOR]

Introduction: Prospective pharmacovigilance aims to rapidly detect safety concerns related to medical products. The exposure model selected for pharmacovigilance impacts the timeliness of signal detection. However, in most real-life pharmacovigilance studies, little is known about which model correctly represents the association and there is no evidence to guide the selection of an exposure model. Different exposure models reflect different aspects of exposure history, and their relevance varies across studies. Therefore, one potential solution is to apply several alternative exposure models simultaneously, with each model assuming a different exposure-risk association, and then combine the model results.Methods: We simulated alternative clinically plausible associations between time-varying drug exposure and the hazard of an adverse event. Prospective surveillance was conducted on the simulated data by estimating parametric and semi-parametric exposure-risk models at multiple times during follow-up. For each model separately, and using combined evidence from different subsets of models, we compared the time to signal detection.Results: Timely detection across the simulated associations was obtained by fitting a set of pharmacovigilance models. This set included alternative parametric models that assumed different exposure-risk associations and flexible models that made no assumptions regarding the form/shape of the association. Times to detection generated using a simple combination of evidence from multiple models were comparable to those observed under the ideal, but unrealistic, scenario where pharmacovigilance relied on the single 'true' model used for data generation.Conclusions: Simulation results indicate that, if the true model is not known, an association can be detected in a more timely manner by first fitting a carefully selected set of exposure-risk models and then generating a signal as soon as any of the models considered yields a test statistic value below a predetermined testing threshold. [ABSTRACT FROM AUTHOR]

The AAAI-17 workshop program included 17 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 4-5, 2017, at the Hilton San Francisco Union Square in San Francisco, California, USA. [ABSTRACT FROM AUTHOR]

Objective. To estimate the incidence rate of clinically apparent arterial thrombotic events and associated comorbidities in patients with primary systemic vasculitis. Methods. Using large cohort administrative data from Quebec, Canada, we identified patients with vasculitis, including polyarteritis nodosa (PAN) and granulomatosis with polyangiitis (GPA). Incident acute myocardial infarctions (AMIs) and cerebrovascular accidents (CVAs) after the diagnosis of vasculitis were ascertained in the PAN and GPA group via billing and hospitalization data. These were compared to rates of a general population comparator group. The incidences of comorbidities (type 2 diabetes mellitus, dyslipidemia, and hypertension) were also collected. Results. Among the 626 patients identified with vasculitis, 19.7% had PAN, 2.9% had Kawasaki disease, 23.8% had GPA, 52.4% had GCA, and 1.3% had Takayasu arteritis. The AMI rate was substantially higher in males aged 18–44 with PAN, with rates up to 268.1 events per 10,000 patient years [95% CI 67.1–1070.2], approximately 30 times that in the age- and sex-matched control group. The CVA rate was also substantially higher, particularly in adults aged 45–65. Patients with vasculitis had elevated incidences of diabetes, dyslipidemia, and hypertension versus the general population. Conclusion. Atherothrombotic rates were elevated in patients identified as having primary systemic vasculitis. While incident rates of cardiovascular comorbidities were also increased, the substantial elevation in AMIs seen in young adults suggests a disease-specific component which requires further investigation. [ABSTRACT FROM AUTHOR]

Objectives: Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. Methods: We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Results: Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Conclusion: Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results. [ABSTRACT FROM AUTHOR]

Most chronic diseases are a result of a complex web of causative and correlated factors. As a result, effective public health or clinical interventions that intend to generate a sustainable change in these diseases most often use a combination of strategies or programs. To optimize comparative effectiveness evaluations and select the most efficient intervention(s), stakeholders (i.e. public health institutions, policy-makers and advocacy groups, practitioners, insurers, clinicians, and researchers) need access to reliable assessment methods. Building on the theory of Evidence-Based Public Health (EBPH) we introduce a knowledgebased framework for evaluating the consistency and effectiveness of public health programs, interventions, and policies. We use a semantic inference model that assists decision-makers in finding inconsistencies, identifying selection and information biases, and with identifying confounding and hidden dependencies in different public health programs and interventions. The use of formal ontologies for automatic evaluation and assessment of public health programs improves program transparency to stakeholders and decision makers, which in turn increases buy-in and acceptance of methods, connects multiple evaluation activities, and strengthens cost analysis. [ABSTRACT FROM AUTHOR]

Within population health information systems, indicators are commonly presented as independent, cross-sectional measures, neglecting the multivariate, longitudinal nature of disease progression, health care use, and profiles of performance. We use administrative claims data of Montreal, Canada to identify patterns across indicators and over time in chronic obstructive pulmonary disease patients. We first cluster regions based on four health service indicators. Our second approach discovers individual-level trajectories based on a hidden Markov model using the same four indicators. Both approaches offer additional insights by facilitating the discovery and interpretation of indicators, such as a dual interpretation of low use of general practitioner services. These approaches to the analysis and visualization of health indicators can provide a foundation for information displays that will help decision makers identify areas of concern, predict future disease burden, and implement appropriate policies. [ABSTRACT FROM AUTHOR]

Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics. [ABSTRACT FROM AUTHOR]