Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities.

Introduction: There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions. Methods: This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021. Results: Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021. Discussion: The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

Breast Cancer and Bone Mineral Density in a U.S. Cohort of Middle-Aged Women: Associations with Phosphate Toxicity.

Breast cancer is associated with phosphate toxicity, the toxic effect from dysregulated phosphate metabolism that can stimulate tumorigenesis. Phosphate toxicity and dysregulated phosphate metabolism are also associated with bone mineral abnormalities, including excessive bone mineral loss and deposition. Based on shared associations with dysregulated phosphate metabolism and phosphate toxicity, a hypothesis proposed in the present mixed methods-grounded theory study posits that middle-aged women with incidence of breast cancer had a greater magnitude of changes in bone mineral density over time compared with women who remained cancer-free. To test this hypothesis, a mixed-effects model was used to analyze the associations of breast cancer incidence with spinal bone mineral density changes in the U.S. Study of Women's Health Across the Nation. Compared with women in the cohort who remained cancer-free, women who self-reported breast cancer had higher bone mineral density at baseline, but had more rapid losses in bone mineral density during follow-up visits. These findings agree with the hypothesis that a greater magnitude of changes in bone mineral density over time is associated with breast cancer in a cohort of middle-aged women. The findings also have implications for studies investigating dysregulated phosphate metabolism and phosphate toxicity as causative factors of bone metastasis in metastatic breast cancer. Additionally, the authors previously found increased breast cancer risk associated with high dietary phosphate intake in the same cohort of middle-aged women, and more studies should investigate a low-phosphorus diet to reduce bone mineral abnormalities and tumorigenesis in breast cancer patients.

High Dietary Phosphorus Is Associated with Increased Breast Cancer Risk in a U.S. Cohort of Middle-Aged Women.

Research has shown that high amounts of dietary phosphorus that are twice the amount of the U.S. dietary reference intake of 700 mg for adults are associated with all-cause mortality, phosphate toxicity, and tumorigenesis. The present nested case-control study measured the relative risk of self-reported breast cancer associated with dietary phosphate intake over 10 annual visits in a cohort of middle-aged U.S. women from the Study of Women's Health Across the Nation. Analyzing data from food frequency questionnaires, the highest level of daily dietary phosphorus intake, >1800 mg of phosphorus, was approximately equivalent to the dietary phosphorus levels in menus promoted by the United States Department of Agriculture. After adjusting for participants' energy intake, this level of dietary phosphorus was associated with a 2.3-fold increased risk of breast cancer incidence compared to the reference dietary phosphorus level of 800 to 1000 mg, which is based on recommendations from the U.S. National Kidney Foundation, (RR: 2.30, 95% CI: 0.94-5.61, p = 0.07). Despite the lack of statistical significance, likely due to the small sample size of the cohort, the present nested case-control study's clinically significant effect size, dose-response, temporality, specificity, biological plausibility, consistency, coherence, and analogy with other research findings meet the criteria for inferred causality in observational studies, warranting further investigations. Furthermore, these findings suggest that a low-phosphate diet should be tested on patients with breast cancer.

Examining the Association Between Household Enrollment in the Pantawid Pamilyang Pilipino Program (4Ps) and Wasting and Stunting Status Among Children Experiencing Poverty in the Philippines: A Cross-Sectional Study.

This study assessed whether enrollment in a national conditional cash transfer program was associated with wasting and stunting among children experiencing extreme poverty in the Philippines. Data were drawn from cross-sectional surveys collected from 10 regional areas in the Philippines between April 2018 and May 2019. A total of 2945 children aged between six months and 12 years comprised the analytical sample. Multilevel logistic regression was conducted to estimate the association between enrollment in Pantawid Pamilyang Pilipino Program (4Ps) and stunting and wasting, controlling for sociodemographic factors and clustering by region. There was no meaningful association between household enrollment in 4Ps and the wasting status of children, but enrollment in 4Ps was associated with lower odds of stunting and differed by geography type. Findings suggest that the current design of 4Ps may not address sudden shocks that contribute to wasting, but may address the underlying socioeconomic risk factors associated with stunting.

Examining the use of decision trees in population health surveillance research: an application to youth mental health survey data in the COMPASS study. / Utilisation des arbres décisionnels dans la recherche en surveillance de la santé de la population : application aux données d'enquête sur la santé mentale des jeunes de l'étude COMPASS.

INTRODUCTION: In population health surveillance research, survey data are commonly analyzed using regression methods; however, these methods have limited ability to examine complex relationships. In contrast, decision tree models are ideally suited for segmenting populations and examining complex interactions among factors, and their use within health research is growing. This article provides a methodological overview of decision trees and their application to youth mental health survey data. METHODS: The performance of two popular decision tree techniques, the classification and regression tree (CART) and conditional inference tree (CTREE) techniques, is compared to traditional linear and logistic regression models through an application to youth mental health outcomes in the COMPASS study. Data were collected from 74 501 students across 136 schools in Canada. Anxiety, depression and psychosocial well-being outcomes were measured along with 23 sociodemographic and health behaviour predictors. Model performance was assessed using measures of prediction accuracy, parsimony and relative variable importance. RESULTS: Decision tree and regression models consistently identified the same sets of most important predictors for each outcome, indicating a general level of agreement between methods. Tree models had lower prediction accuracy but were more parsimonious and placed greater relative importance on key differentiating factors. CONCLUSION: Decision trees provide a means of identifying high-risk subgroups to whom prevention and intervention efforts can be targeted, making them a useful tool to address research questions that cannot be answered by traditional regression methods.

Using Decision Trees to Examine Environmental and Behavioural Factors Associated with Youth Anxiety, Depression, and Flourishing.

Modifiable environmental and behavioural factors influence youth mental health; however, past studies have primarily used regression models that quantify population average effects. Decision trees are an analytic technique that examine complex relationships between factors and identify high-risk subgroups to whom intervention measures can be targeted. This study used decision trees to examine associations of various risk factors with youth anxiety, depression, and flourishing. Data were collected from 74,501 students across Canadian high schools participating in the 2018-2019 COMPASS Study. Students completed a questionnaire including validated mental health scales and 23 covariates. Decision trees were grown to identify key factors and subgroups for anxiety, depression, and flourishing outcomes. Females lacking both happy home life and sense of connection to school were at greatest risk for higher anxiety and depression levels. In contrast with previous literature, behavioural factors such as diet, movement and substance use did not emerge as differentiators. This study highlights the influence of home and school environments on youth mental health using a novel decision tree analysis. While having a happy home life is most important in protecting against youth anxiety and depression, a sense of connection to school may mitigate the negative influence of a poor home environment.

Do school characteristics, based on the Comprehensive School Health Framework, contribute to youth meeting national physical activity recommendations over time? / Est-ce que certaines caractéristiques des écoles, classées selon l'Approche globale de la santé en milieu scolaire, aident les adolescents à mettre en pratique les recommandations nationales en matière d'activité physique au fil du temps?

INTRODUCTION: Canadian youth are insufficiently active, and schools may play a role in promoting student physical activity (PA). Based on the Comprehensive School Health (CSH) framework, this study examined whether school characteristics are associated with secondary school students meeting national PA recommendations over time. METHODS: We used COMPASS survey data from 78 schools in Ontario and Alberta and 9870 Grade 9 and 10 students attending those schools. Students who provided two years of linked PA data (2013/14 and 2015/16) and gender were included. Multilevel analysis was conducted by gender, evaluating the relationship of school-level characteristics (guided by CSH) with students achieving all three PA recommendations after two years (≥ 60 min/day of moderate-to-vigorous PA, vigorous PA ≥ 3 days/week, strengthening activities ≥ 3 days/week). RESULTS: More than half (56.9%) of students achieving the PA recommendations at baseline were no longer achieving them after two years, and just a quarter (25.6%) of students not achieving the recommendations at baseline achieved them after two years. School-level factors were significantly associated with students achieving the recommendations, but these factors differed by student strata (i.e. by gender and baseline PA status). Generally, student access to equipment, public health partnerships and staff time for health were associated with increased odds of achieving the PA recommendations for certain students. CONCLUSION: Modifications to school characteristics within CSH may play a role in supporting students in achieving or continuing to achieve the PA recommendations after two years. Further research is needed to better understand the underlying dynamics of the observed relationships.

A Comprehensive School Health Approach to Student Physical Activity: A Multilevel Analysis Examining the Association between School-Level Factors and Student Physical Activity Behaviors.

BACKGROUND: The comprehensive school health (CSH) framework has four components: social and physical environment; partnerships and services; teaching and learning; and policy. This study examines associations between CSH and student physical activity (PA). METHODS: Using 2015/2016 COMPASS study survey data of 37,397 students (grades 9-12) from 80 secondary schools in Ontario and Alberta, Canada, associations between school-level factors within CSH and student PA outcomes (weekly moderate-to-vigorous PA [MVPA] minutes and achieving the national PA recommendations of ≥60 min of MVPA daily, vigorous PA ≥3 days/week, strengthening activities ≥3 days/week) were analyzed using multilevel regression models stratified by gender and grade. RESULTS: Factors within all four CSH components were associated with student PA. Four student subgroups were more likely to achieve the recommendations if their school had youth organization partnerships (Range of AORs:1.15-1.33, p <.05) and female students were less likely if their school had low prioritization of PA (AOR = 0.77, 95% CI: [0.65-0.92]). Grade 9 students had higher MVPA when provided non-competitive PA opportunities (ß = 100.4, 95%CI: [30.0-170.9]). All student subgroups had better PA outcomes when schools provided access to equipment during non-instructional time. CONCLUSION: There is opportunity to improve student PA through CSH-guided interventions, but different strategies may be more effective for each gender/grade.

NextGen Public Health Surveillance and the Internet of Things (IoT).

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

School-Level Factors within Comprehensive School Health Associated with the Trajectory of Moderate-to-Vigorous Physical Activity over Time: A Longitudinal, Multilevel Analysis in a Large Sample of Grade 9 and 10 Students in Canada.

(1) The majority of Canadian youth are insufficiently active, and moderate-to-vigorous physical activity (MVPA) decreases substantially during secondary school. School factors within the comprehensive school health (CSH) framework may help attenuate this decline. This study aimed to examine how youth MVPA changes over a three-year period and evaluate the school characteristics associated with preventing the decline in MVPA over time, guided by the CSH framework. (2) This study uses COMPASS survey data from 78 secondary schools in Ontario and Alberta that participated in Year 2 (2013/14), Year 3 (2014/15), and Year 4 (2015/16), and 17,661 students attending these schools. Multilevel (linear mixed effects) models were used to determine the association between school-level factors and student MVPA (weekly minutes) over time, stratified by gender. (3) Both male and female students had a significant decline in MVPA across the 3 years, with a greater decrease observed among female students. Within the CSH framework, the school's social environment, partnerships, and policies were associated with student MVPA over time, however the specific school factors and directions of associations varied by gender. (4) School-based interventions (e.g., public health partnerships) may help avoid the decline in MVPA observed in this critical period and support student health.

Low-frequency exercise training improves cardiovascular fitness and strength during treatment for breast cancer: a single-arm intervention study.

Aerobic and resistance exercise during and after cancer treatment are important for health-related outcomes, however treatment-specific barriers may inhibit adherence. We explored the effect of lower-frequency exercise training on fitness, body composition, and metabolic markers (i.e. glucose and lipids) in a group of recently diagnosed breast cancer patients. Fifty-two females ≥ 18 years with stage I-IIIB breast cancer were instructed to attend 2 cardiovascular and strength training sessions/week over 12 weeks, but program length was expanded as needed to accommodate missed sessions. Pre- and post-intervention, we measured: (1) cardiovascular fitness, (2) isometric strength, (3) body composition (dual-energy X-ray absorptiometry), and (4) fasting glucose, insulin, c-peptide, and lipids. Pre-intervention, participants were 53 ± 10 years old (mean ± SD) and overweight (BMI: 27.5 ± 5.4 kg m-2, 40.1 ± 6.5% body fat). Forty participants completed the program over a median 20 weeks (range: 13-32 weeks, median frequency: 1.2 sessions/week), over which predicted VO2peak improved by 7% (2.2[0.1-4.4] mL/kg/min) (delta[95% CI]), and strength increased by 7-9% (right arm: 2.3[0.1-4.5] N m; right leg: 7.9[2.1-13.7] N m; left leg: 7.8[1.9-13.7] N m). Body composition and metabolic markers were unchanged. An exercise frequency of 1.2 sessions/week stimulated significant improvements in fitness, and may represent a practical target for patients during active treatment.

Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients.

APACHE IVa provides typically useful and accurate predictions on in-hospital mortality and length of stay for patients in critical care. However, there are factors which may preclude APACHE IVa from reaching its ceiling of predictive accuracy. Our primary aim was to determine which variables available within the first 24 h of a patient's ICU stay may be indicative of the APACHE IVa scoring system making occasional but potentially illuminating errors in predicting in-hospital mortality. We utilized the publicly available multi-institutional ICU database, eICU, available since 2018, to identify a large observational cohort for our investigation. APACHE IVa scores are provided by eICU for each patient's ICU stay. We used Lasso logistic regression in an aim to build parsimonious final models, using cross-validation to select the penalization parameter, separately for each of our two responses, i.e., errors, of interest, which are APACHE falsely predicting in-hospital death (Type I error), and APACHE falsely predicting in-hospital survival (Type II error). We then assessed the performance of the models with a random holdout validation sample. While the extremeness of the APACHE prediction led to dependable predictions for preventing either type of error, distinct variables were identified as being strongly associated with the two different types of errors occurring. These included a primary set of predictors consisting of mean SpO2 and worst lactate for predicting Type I errors, and worst albumin and mean heart rate for Type II. In addition, a secondary set of predictors including changes recorded in care limitations for the patient's treatment plan, worst pH, whether cardiac arrest occurred at admission, and whether vasopressor was provided for predicting Type I error; age, whether the patient was ventilated in day 1, mean respiratory rate, worst lactate, worst blood urea nitrogen test, and mean aperiodic vitals for Type II. The two models also differed in their performance metrics in their holdout validation samples, in large part due to the lower prevalence of Type II errors compared to Type I. The eICU database was a good resource for evaluating our objective, and important recommendations are provided, particularly identifying key variables that could lead to APACHE prediction errors when APACHE scores are sufficiently low to predict in-hospital survival.

More-2-Eat implementation demonstrates that screening, assessment and treatment of malnourished patients can be spread and sustained in acute care; a multi-site, pretest post-test time series study.

BACKGROUND: Malnutrition in medical and surgical inpatients is an on-going problem. More-2-Eat (M2E) Phase 1 demonstrated that improved detection and treatment of hospital malnutrition could be embedded into routine practice using an intensive researcher-facilitated implementation process. Yet, spreading and sustaining new practices in diverse hospital cultures with minimal researcher support is unknown. AIMS: To demonstrate that a scalable model of implementation can increase three key nutrition practices (admission screening; Subjective Global Assessment (SGA); and medication pass (MedPass) of oral nutritional supplement) in diverse acute care hospitals to detect and treat malnutrition in medical and surgical patients. METHODS: Ten hospitals participated in this pretest post-test time series implementation study from across Canada, including 21 medical or surgical units (Phase 1 original units (n = 4), Phase 1 hospital new units (n = 9), Phase 2 new hospitals and units (n = 8)). The scalable implementation model included: training champions on implementation strategies and providing them with education resources for teams; creating a self-directed audit and feedback process; and providing mentorship. Standardized audits of all patients on the study unit on an audit day were completed bi-monthly to track nutrition care activities since admission. Bivariate comparisons were performed by time period (initial, mid-term and final audits). Run-charts depicted the trajectory of change and qualitatively compared to Phase 1. RESULTS: 5158 patient charts were audited over the course of 18-months. Admission nutrition screening rates increased from 50% to 84% (p < 0.0001). New Phase 1 units more readily implemented screening than Phase 2 sites, and the original Phase 1 units generally sustained screening practices from Phase 1. SGA was a sustained practice at Phase 1 hospitals including in new Phase 1 units. The new Phase 2 units improved completion of SGA but did not reach the levels of Phase 1 units (original or new). MedPass almost doubled over the time periods (7%-13% of all patients p < 0.007). Other care practices significantly increased (e.g. volunteer mealtime assistance). CONCLUSION: Nutrition-care activities significantly increased in diverse hospital units with this scalable model. This heralds the transition from implementation research to sustained changes in routine practice. Screening, SGA, and MedPass can all be implemented, improve nutrition care for all patients, spread within an organization, and for the most part, sustained (and in the case of original Phase 1 units, for over 3 years) with champion leadership.

The impact of an alcohol policy change on developmental trajectories of youth alcohol use: examination of a natural experiment in Canada.

OBJECTIVES: In 2015, the Liquor Control Board of Ontario (LCBO) authorized sale of alcohol in some Ontario grocery stores. This research evaluates the impact of the new policy on alcohol use patterns of youth in a quasi-experimental setting with two control groups. METHODS: The sample consists of 2267 grade 9 students attending 60 secondary schools across Ontario (n = 56) and Alberta (n = 4), who provided 4-year linked longitudinal data (2013-2014 to 2016-2017) in the COMPASS study. The study used the frequency of drinking and the frequency of binge drinking to characterize alcohol use behaviours. RESULTS: Latent transition analysis found four statuses of alcohol use: abstainer, periodic drinker, low-risk drinker, and high-risk regular drinker. The new policy had no negative impact among periodic and low-risk drinkers, but the risk of transitioning from the abstainer (lowest risk status) to high-risk regular drinker (highest risk status) among the exposed cohort was 1.71 times greater post-policy than pre-policy change, compared with those of Ontario-unexposed (0.50) and Alberta-unexposed cohorts (1.00). The probability of sustaining high-risk drinking among the exposed cohort increased by a factor of 1.76, compared with 1.13-fold and 0.89-fold among the Ontario-unexposed and Alberta-unexposed cohorts, respectively. CONCLUSION: Youth are more likely to transition from abstinence to high-risk regular drinking, and high-risk regular drinkers are more likely to maintain their behaviours in the jurisdictions exposed to the latest change in LCBO policy authorizing grocery stores to sell alcohol. When formulating policy interventions, youth access to alcohol should be considered in order to reduce their harmful alcohol consumption.

RéSUMé: OBJECTIFS: Depuis 2015, la Régie des alcools de l'Ontario (LCBO) autorise la vente d'alcool dans certaines épiceries de la province. Nous évaluons ici l'incidence de la nouvelle politique sur les habitudes de consommation d'alcool des jeunes dans un milieu quasi-expérimental avec deux groupes témoins. MéTHODE: Notre échantillon est constitué de 2 267 élèves de 9e année, fréquentant 60 écoles secondaires en Ontario (n = 56) et en Alberta (n = 4), qui ont fourni des données longitudinales couplées sur une période de quatre ans (2013-2014 à 2016-2017) dans le cadre de l'étude COMPASS. L'étude utilise la fréquence de consommation et la fréquence d'hyperalcoolisation rapide pour caractériser les comportements de consommation d'alcool. RéSULTATS: Une analyse de transition latente a permis de répartir la consommation d'alcool en quatre catégories : non-consommation, consommation ponctuelle, consommation à faible risque et consommation régulière à haut risque. La nouvelle politique n'a pas eu d'effet nuisible chez les consommateurs ponctuels et à faible risque, mais le risque de passer de la catégorie de la non-consommation (risque minimal) à celle de la consommation régulière à haut risque (risque maximal) dans la cohorte exposée était 1,71 fois supérieur après l'instauration de la politique qu'avant le changement d'orientation, comparativement aux cohortes non exposées de l'Ontario (0,50) et de l'Alberta (1,00). La probabilité de maintenir une consommation d'alcool à haut risque dans la cohorte exposée a été de 1,76 fois supérieure, contre 1,13 fois et 0,89 fois dans les cohortes non exposées de l'Ontario et de l'Alberta, respectivement. CONCLUSIONS: Dans les administrations exposées au changement récent de la politique de la LCBO, qui autorise les épiceries à vendre de l'alcool, les jeunes sont plus susceptibles de passer de l'abstinence à une consommation régulière à haut risque, et les consommateurs réguliers à haut risque sont plus susceptibles de maintenir leur comportement. Lors de l'élaboration de politiques, il faudrait tenir compte de l'accès des jeunes à l'alcool pour réduire la consommation nocive dans cette population.

Identifying subpopulations of septic patients: A temporal data-driven approach.

Sepsis is one of the deadliest diseases in North America and in spite of the vast amount of research on this topic there is still uncertainty in the outcome of sepsis treatments. This study aimed at investigating the informativeness of temporal electronic health records (EHR) in stratifying septic patients and identifying subpopulations of septic patients with similar trajectories and clinical needs. We performed hierarchical clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analyses using data from septic patients in the MIMIC III intensive care unit database. The t-Distributed Stochastic Neighbor Embedding (t-SNE) method was utilized to map patients to a two-dimensional space. We utilized silhouette index and cluster-wise stability assessment by resampling to investigate the validity of the clusters. The hierarchical clustering with Euclidean metric identified twelve clinically recognizable subgroups that demonstrated different characteristics in spite of sharing common conditions. Our results demonstrated that data-driven approaches can help in customizing care platforms for septic patients by identifying similar clinically relevant groups.

Handgrip strength predicts length of stay and quality of life in and out of hospital.

BACKGROUND: Handgrip strength (HGS) is a practical measure of strength and physical function that can be used to identify frailty among hospitalized patients, but its utility in this setting is unclear. To be considered useful, any functional measure needs to provide pertinent information on the patient and predict relevant outcomes such as health-care utilization (e.g., length of stay (LOS)) and patient-reported quality of life (QOL). The purpose of this study was to determine if HGS predicted LOS and QOL. A second aim was to examine the best sensitivity (SE) and specificity (SP) for predicting length of stay (>7 or >13 days) using previously published cut-points for HGS. METHODOLOGY: HGS was measured on 1136 medical patients shortly after admission with a Lafayette dynamometer. QOL was assessed with the self-reported SF-12 completed with an interviewer during hospitalization and 30- days after discharge via telephone. Physical (PCS) and mental (MCS) component scores of SF-12 were calculated. A variety of covariates were assessed (e.g., nutritional status). Multivariate analyses stratified by sex were completed. RESULTS: The mean LOS was 12.71 days (median = 8.00; SD = 13.20), 12.88 days (SD = 13.82) for males, and 12.58 days (SD = 12.68) for females. Lower admission HGS scores were associated with longer LOS (male X2 = 7.85, p < 0.05; female X2 = 14.9, p < 0.0001). The average quality of life scores were as follows: in hospital PCS: 34.66, MCS: 46.49; post discharge PCS: 36.17; MCS: 51.22. HGS predicted PCS during hospitalization (male X2 = 36.22, p < 0.0001; female X2 = 19.87, p < 0.0001) and post hospitalization (male X2 = 6.98, p < 0.01; female X2 = 10.99, p < 0.01). Various reference cut-points for HGS were tested against LOS, with none being considered appropriate (e.g., SE and SP both < 70) when adjusting for age and sex. CONCLUSION: Admission HGS adds predictive value for both LOS and physical components of QOL and is worth pursuing in practice to identify potential frailty and the need for proactive steps to mitigate further functional decline during hospitalization. However, HGS cut-points for LOS specific to acute care patients need to be defined and tested.

Identifying patterns of alcohol use among secondary school students in Canada: A multilevel latent class analysis.

INTRODUCTION: Harm from alcohol use depend not only on the volume of consumption but also on drinking patterns. This study identifies patterns of alcohol consumption in youth and investigates how these patterns vary across schools and whether individual- and school-level factors are associated with engagement in patterns of alcohol consumption. METHODS: The sample consists of 45,298 grade 9 to 12 students attending 89 secondary schools across Ontario and Alberta (Canada), who participated in the COMPASS study during the school year 2013-14. The frequency of drinking, the frequency of binge drinking, and age of alcohol-use initiation were used to characterize alcohol use patterns. RESULTS: The multilevel latent class analysis identified 4 student-level latent groups and 2 school-level latent groups. Student-level groups of youth were characterized as non-drinkers (44.2%), light drinkers (41.8%), regular drinkers (11.1%), and heavy drinkers (2.9%). Two groups of schools were characterized as either low-use (44.9%) or high-use (55.1%) schools, with significantly different probability of membership in each student-level group. Male students (OR 1.30) and upper grades (OR 1.93) were significantly associated with membership in higher use groups of individuals. The median household income and the number of off-premise alcohol outlets had no significant association with patterns of alcohol consumption within schools. CONCLUSIONS: A large proportion of students reported a level of drinking, suggesting that, in addition to delaying the onset of alcohol use, interventions need to encourage drinker students to quit drinking or lower their consumption. Schools may need to select and/or alter external interventions according to the dominant patterns of alcohol use among their students.

Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study.

BACKGROUND:  Little is known about whether off-the-shelf wearable sensor data can contribute to fall risk classification or complement clinical assessment tools such as the Resident Assessment Instrument-Home Care (RAI-HC). OBJECTIVE:  This study aimed to (1) investigate the similarities and differences in physical activity (PA), heart rate, and night sleep in a sample of community-dwelling older adults with varying fall histories using a smart wrist-worn device and (2) create and evaluate fall risk classification models based on (i) wearable data, (ii) the RAI-HC, and (iii) the combination of wearable and RAI-HC data. METHODS:  A prospective, observational study was conducted among 3 faller groups (G0, G1, G2+) based on the number of previous falls (0, 1, ≥2 falls) in a sample of older community-dwelling adults. Each participant was requested to wear a smart wristband for 7 consecutive days while carrying out day-to-day activities in their normal lives. The wearable and RAI-HC assessment data were analyzed and utilized to create fall risk classification models, with 3 supervised machine learning algorithms: logistic regression, decision tree, and random forest (RF). RESULTS:  Of 40 participants aged 65 to 93 years, 16 (40%) had no previous falls, whereas 8 (20%) and 16 (40%) had experienced 1 and multiple (≥2) falls, respectively. Level of PA as measured by average daily steps was significantly different between groups (P=.04). In the 3 faller group classification, RF achieved the best accuracy of 83.8% using both wearable and RAI-HC data, which is 13.5% higher than that of using the RAI-HC data only and 18.9% higher than that of using wearable data exclusively. In discriminating between {G0+G1} and G2+, RF achieved the best area under the receiver operating characteristic curve of 0.894 (overall accuracy of 89.2%) based on wearable and RAI-HC data. Discrimination between G0 and {G1+G2+} did not result in better classification performance than that between {G0+G1} and G2+. CONCLUSIONS:  Both wearable data and the RAI-HC assessment can contribute to fall risk classification. All the classification models revealed that RAI-HC outperforms wearable data, and the best performance was achieved with the combination of 2 datasets. Future studies in fall risk assessment should consider using wearable technologies to supplement resident assessment instruments.

Identifying trajectories of alcohol use in a sample of secondary school students in Ontario and Alberta: longitudinal evidence from the COMPASS study. / Établissement des trajectoires de consommation d'alcool chez un échantillon d'élèves du secondaire de l'Ontario et de l'Alberta : données probantes longitudinales tirées de l'étude COMPASS.

INTRODUCTION: Despite evidence indicating a rapid progression in use of alcohol during adolescence, little is known about the ways patterns of drinking develop over time. This study investigated patterns of alcohol use within a cohort of youth in Ontario and Alberta and the probability of changes between patterns. METHODS: The sample consists of two-year linked longitudinal data (school year 2013/14 to 2014/15) from 19 492 students in Grades 9 to 12 in 89 secondary schools across Ontario and Alberta, Canada, who participated in the COMPASS study. The latent class analysis used two self-reported items about the frequency of drinking (measured as none, monthly, weekly, or daily use) and the frequency of binge drinking (measured as none, less than or once a month, 2-4 times a month, or more than once week) to characterize patterns of alcohol use. The effects of gender, ethnicity and cannabis and cigarette use on alcohol use patterns were examined. RESULTS: The study identified four drinking patterns: non-drinker, periodic drinker (reported monthly drinking and no binge drinking), low-risk drinker (reported monthly drinking and limited binge drinking) and high-risk regular drinker (reported drinking 1-3 times a week and binge drinking 2-4 times a month). Non-drinker was the most prevalent pattern at baseline (55.1%) and follow-up (39.1%). Periodic drinkers had the highest likelihood of an increase in alcohol consumption, with 40% moving to the low-risk pattern. A notable proportion of participants returned to a lower severity pattern or transitioning out of drinking. CONCLUSION: There are four distinct youth alcohol-use patterns. The high probability of transitioning to drinking during the secondary school years suggests the need for preventive interventions in earlier stages of use, before drinking becomes habitual.