Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review.

BACKGROUND: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies. OBJECTIVE: The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home. METHODS: A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis. RESULTS: Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used. CONCLUSION: Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

Validating part of the social media infodemic listening conceptual framework using structural equation modelling.

Background: The literature has identified various factors that promote or hinder people's intentions towards COVID-19 vaccination, and structural equation modelling (SEM) is a common approach to validate these associations. We propose a conceptual framework called social media infodemic listening (SoMeIL) for public health behaviours. Hypothesizing parameters retrieved from social media platforms can be used to infer people's intentions towards vaccination behaviours. This study preliminarily validates several components of the SoMeIL conceptual framework using SEM and Twitter data and examines the feasibility of using Twitter data in SEM research. Methods: A total of 2420 English tweets in Toronto or Ottawa, Ontario, Canada, were collected from March 8 to June 30, 2021. Confirmatory factor analysis and SEM were applied to validate the SoMeIL conceptual framework in this cross-sectional study. Findings: The results showed that sentiment scores, the log-numbers of favourites and retweets of a tweet, and the log-numbers of a user's favourites, followers, and public lists had significant direct associations with COVID-19 vaccination intention. The sentiment score of a tweet had the strongest relationship, whereas a user's number of followers had the weakest relationship with the intention of COVID-19 vaccine uptake. Interpretation: The findings preliminarily validate several components of the SoMeIL conceptual framework by testing associations between self-reported COVID-19 vaccination intention and sentiment scores and the log-numbers of a tweet's favourites and retweets as well as users' favourites, followers, and public lists. This study also demonstrates the feasibility of using Twitter data in SEM research. Importantly, this study preliminarily validates the use of these six components as online reaction behaviours in the SoMeIL framework to infer the self-reported COVID-19 vaccination intentions of Canadian Twitter users in two cities. Funding: This study was supported by the 2023-24 Ontario Graduate Scholarship.

Health trends in Canada 1990-2019: An analysis for the Global Burden of Disease Study.

OBJECTIVE: Monitoring trends in key population health indicators is important for informing health policies. The aim of this study was to examine population health trends in Canada over the past 30 years in relation to other countries. METHODS: We used data on disability-adjusted life years (DALYs), years of life lost (YLL), years lived with disability, life expectancy (LE), and child mortality for Canada and other countries between 1990 and 2019 provided by the Global Burden of Disease Study. RESULTS: Life expectancy, age-standardized YLL, and age-standardized DALYs all improved in Canada between 1990 and 2019, although the rate of improvement has leveled off since 2011. The top five causes of all-age DALYs in Canada in 2019 were neoplasms, cardiovascular diseases, musculoskeletal disorders, neurological disorders, and mental disorders. The greatest increases in all-age DALYs since 1990 were observed for substance use, diabetes and chronic kidney disease, and sense organ disorders. Age-standardized DALYs declined for most conditions, except for substance use, diabetes and chronic kidney disease, and musculoskeletal disorders, which increased by 94.6%, 14.6%, and 7.3% respectively since 1990. Canada's world ranking for age-standardized DALYs declined from 9th place in 1990 to 24th in 2019. CONCLUSION: Canadians are healthier today than in 1990, but progress has slowed in Canada in recent years in comparison with other high-income countries. The growing burden of substance abuse, diabetes/chronic kidney disease, and musculoskeletal diseases will require continued action to improve population health.

RéSUMé: OBJECTIF: La surveillance des tendances des indicateurs clés de la santé de la population est importante pour éclairer les politiques de santé. Dans cette étude, nous avons examiné les tendances de la santé de la population au Canada au cours des 30 dernières années par rapport à d'autres pays. MéTHODES: Nous avons utilisé des données sur les années de vie ajustées en fonction de l'incapacité (DALY), les années de vie perdues (YLL), les années vécues avec un handicap, l'espérance de vie (LE) et la mortalité infantile pour le Canada et d'autres pays entre 1990 et 2019, fournies par l'Étude mondiale sur le fardeau de la maladie. RéSULTATS: L'espérance de vie, les YLL ajustées selon l'âge et les DALY ajustées selon l'âge ont tous connu une amélioration au Canada entre 1990 et 2019, bien que le taux d'amélioration se soit stabilisé depuis 2011. Les cinq principales causes des DALY pour tous les âges au Canada en 2019 étaient les néoplasmes, les maladies cardiovasculaires, les affections musculosquelettiques, les affections neurologiques et les troubles mentaux. Les plus fortes augmentations des DALY pour tous les âges depuis 1990 ont été observées pour l'usage de substances, le diabète et les maladies rénales chroniques, ainsi que les troubles des organes sensoriels. Les DALY ajustées selon l'âge ont diminué pour la plupart des conditions, à l'exception de l'usage de substances, du diabète et des maladies rénales chroniques, ainsi que des troubles musculosquelettiques, qui ont augmenté de 94,6 %, 14,6 % et 7,3 % respectivement depuis 1990. Le classement mondial du Canada pour les DALY ajustées selon l'âge est diminué de la 9ième place en 1990 à la 24ième place en 2019. CONCLUSION: Les Canadiens sont en meilleure santé aujourd'hui qu'en 1990, mais les progrès se sont ralentis ces dernières années par rapport à d'autres pays à revenu élevé. La croissance du fardeau lié à l'abus de substances, au diabète/maladies rénales chroniques et aux affections musculosquelettiques exigera des actions continues pour améliorer la santé de la population.

National burden of rheumatoid arthritis in Canada, 1990-2019: findings from the Global Burden of Disease Study 2019 - a GBD collaborator-led study.

OBJECTIVE: The objectives of this study were: (1) to describe burden of rheumatoid arthritis (RA) and trends from 1990 to 2019 using the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) data, (2) to describe age and sex differences in RA and (3) to compare Canada's RA burden to that of other countries. METHODS: Disease burden indicators included prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life-years (DALYs). GBD estimated fatal and non-fatal outcomes using published literature, survey data and health insurance claims. Data were analysed by Bayesian meta-regression, cause of death ensemble model and other statistical methods. DALYs for Canada were compared with DALYs of countries with similarly high Socio-Demographic Index values. RESULTS: In Canada, the RA prevalence rate increased by 27% between 1990 and 2019, mortality rate decreased by 27%, YLL rate decreased by 30%, YLD increased by 27% and DALY rate increased by 13%, all age standardised. The decline in RA mortality and YLL rates was especially pronounced after 2002. The disease burden was higher in females for all indicators, and DALY rates were higher among older age groups, peaking at age 75-79 years. Prevalence and DALYs were higher in Canada compared with global rates. CONCLUSION: Trends in RA burden indicators over time and differences by age and sex have important implications for Canadian policy-makers, researchers and care providers. Early identification and management of RA in women may help reduce the overall burden of RA in Canada.

Investigating Canadian Public Attitudes Toward COVID-19 Vaccine Mandates with a Nested Analysis Framework.

BACKGROUND: Social media is an important medium for studying public attitudes toward COVID-19 vaccine mandates in Canada, and Reddit network communities are a good source for this. METHODS: This study applied a "nested analysis" framework. We collected 20378 Reddit comments via the Pushshift API and developed a BERT-based binary classification model to screen for relevance to COVID-19 vaccine mandates. We then used a Guided Latent Dirichlet Allocation (LDA) model on relevant comments to extract key topics and assign each comment to its most relevant topic. RESULTS: There were 3179 (15.6%) relevant and 17199 (84.4%) irrelevant comments. Our BERT-based model achieved 91% accuracy trained with 300 Reddit comments after 60 epochs. The Guided LDA model had an optimal coherence score of 0.471 with four topics: travel, government, certification, and institutions. Human evaluation of the Guided LDA model showed an 83% accuracy in assigning samples to their topic groups. CONCLUSION: We develop a screening tool for filtering and analyzing Reddit comments on COVID-19 vaccine mandates through topic modelling. Future research could develop more effective seed word-choosing and evaluation methods to reduce the need for human judgment.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Monkeypox: a review of epidemiological modelling studies and how modelling has led to mechanistic insight.

Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.

Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study.

BACKGROUND: Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents. OBJECTIVE: This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada. METHODS: We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level. RESULTS: The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (-7.009; 95% CI -13.130 to -0.980) and for both males (-4.544; 95% CI -8.224 to -0.895) and females (-3.513; 95% CI -6.289 to -0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature. CONCLUSIONS: As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being.

Exploring the dynamic transitions of polysubstance use patterns among Canadian youth using Latent Markov Models on COMPASS data.

Background: Understanding what factors lead to youth polysubstance use (PSU) patterns and how the transitions between use patterns can inform the design and implementation of PSU prevention programs. We explore the dynamics of PSU patterns from a large cohort of Canadian secondary school students using machine learning techniques. Methods: We employed a multivariate latent Markov model (LMM) on COMPASS data, with a linked sample (N = 8824) of three-annual waves, Wave I (WI, 2016-17, as baseline), Wave II (WII, 2017-18), and Wave III (WIII, 2018-19). Substance use indicators, i.e., cigarette, e-cigarette, alcohol and marijuana, were self-reported and were categorized into never/occasional/current use. Outcomes: Four distinct use patterns were identified: no-use (S1), single-use of alcohol (S2), dual-use of e-cigarettes and alcohol (S3), and multi-use (S4). S1 had the highest prevalence (60.5%) at WI, however, S3 became the prominent use pattern (32.5%) by WIII. Most students remained in the same subgroup over time, particularly S4 had the highest transition probability (0.87) across the three-wave. With time, those who transitioned typically moved towards a higher use pattern, with the most and least likely transition occurring S2→S3 (0.45) and S3→S2 (<0.01), respectively. Among all covariates being examined, truancy, being measured by the # of classes skipped, significantly affected transition probabilities from any low→high (e.g., ORS2→S4 = 2.41, 95% CI [2.11, 2.72], p < 0.00001) and high→low (e.g., ORS3→S1 = 0.38, 95% CI [0.33, 0.44], p < 0.00001) use directions over time. Older students, blacks (vs. whites), and breakfast eaters were less likely to transition from low→high use direction. Students with more weekly allowance, with more friends that smoked, longer sedentary time, and attended attended school unsupportive to resist or quit drug/alcohol were more likely to transition from low→high use direction. Except for truancy, all other covariates had inconsistent effects on the transition probabilities from the high→low use direction. Interpretation: This is the first study to ascertain the dynamics of use patterns and factors in youth PSU utilizing LMM with population-based longitudinal health surveys, providing evidence in developing programs to prevent youth PSU. Funding: The Applied Health Sciences scholarship; the Microsoft AI for Good grant; the Canadian Institutes of Health Research, Health Canada, the Canadian Centre on Substance Abuse, the SickKids Foundation, the Ministère de la Santé et des Services sociaux of the province of Québec.

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning.

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.

Impact of direct-acting antivirals for HCV on mortality in a large population-based cohort study.

BACKGROUND & AIMS: We evaluated the effect of direct-acting antiviral (DAA)-induced sustained virologic response (SVR) on all-cause, liver- and drug-related mortality in a population-based cohort in British Columbia, Canada. METHODS: We used data from the British Columbia Hepatitis Testers Cohort, which includes people tested for HCV since 1990, linked with data on medical visits, hospitalizations, prescription drugs and mortality. We followed people who received DAAs and people who did not receive any HCV treatment to death or December 31, 2019. We used inverse probability of treatment weighting to balance the baseline profile of treated and untreated individuals and performed multivariable proportional hazard modelling to assess the effect of DAAs on mortality. RESULTS: Our cohort comprised 10,851 people treated with DAAs (SVR 10,426 [96%], no-SVR: 425) and 10,851 matched untreated individuals. Median follow-up time was 2.2 years (IQR 1.3-3.6; maximum 6.2). The all-cause mortality rate was 19.5/1,000 person-years (PY) among the SVR group (deaths = 552), 86.5/1,000 PY among the no-SVR group (deaths = 96), and 99.2/1,000 PY among the untreated group (deaths = 2,133). In the multivariable model, SVR was associated with significant reduction in all-cause (adjusted hazard ratio [aHR] 0.19; 95% CI 0.17-0.21), liver- (adjusted subdistribution HR [asHR] 0.22, 95% CI 0.18-0.27) and drug-related mortality (asHR 0.26, 95% CI 0.21-0.32) compared to no-treatment. Older age and cirrhosis were associated with higher risk of liver-related mortality while younger age, injection drug use (IDU), problematic alcohol use and HIV/HBV co-infections were associated with a higher risk of drug-related mortality. CONCLUSIONS: DAA treatment is associated with a substantial reduction in all-cause, liver- and drug-related mortality. The association of IDU and related syndemic factors with a higher risk of drug-related mortality calls for an integrated social support, addiction, and HCV care approach among people who inject drugs. LAY SUMMARY: We assessed the effect of treatment of hepatitis C virus infection with direct-acting antiviral drugs on deaths from all causes, liver disease and drug use. We found that treatment with direct-acting antiviral drugs is associated with substantial lowering in risk of death from all causes, liver disease and drug use among people with hepatitis C virus infection.

Understanding the Differential Impact of Vegetation Measures on Modeling the Association between Vegetation and Psychotic and Non-Psychotic Disorders in Toronto, Canada.

Considerable debate exists on whether exposure to vegetation cover is associated with better mental health outcomes. Past studies could not accurately capture people's exposure to surrounding vegetation and heavily relied on non-spatial models, where the spatial autocorrelation and latent covariates could not be adjusted. Therefore, a suite of five different vegetation measures was used to separately analyze the association between vegetation cover and the number of psychotic and non-psychotic disorder cases in the neighborhoods of Toronto, Canada. Three satellite-based and two area-based vegetation measures were used to analyze these associations using Poisson lognormal models under a Bayesian framework. Healthy vegetation cover was found to be negatively associated with both psychotic and non-psychotic disorders. Results suggest that the satellite-based indices, which can measure both the density and health of vegetation cover and are also adjusted for urban and environmental perturbations, could be better alternatives to simple ratio- and area-based measures for understanding the effect of vegetation on mental health. A strong dominance of spatially structured latent covariates was found in the models, highlighting the importance of adopting a spatial approach. This study can provide critical guidelines for selecting appropriate vegetation measures and developing spatial models for future population-based epidemiological research.

Biobehavioral Aspects of the COVID-19 Pandemic: A Review.

OBJECTIVE: This review highlights the scope and significance of the coronavirus disease 2019 (COVID-19) pandemic with a focus on biobehavioral aspects and critical avenues for research. METHODS: A narrative review of the published research literature was undertaken, highlighting major empirical findings emerging during the first and second waves of the COVID-19 pandemic. RESULTS: Interactions among biological, behavioral, and societal processes were prominent across all regions of the globe during the first year of the COVID-19 emergency. Affective, cognitive, behavioral, socioeconomic, and technological factors all played a significant role in the spread of infection, response precautions, and outcomes of mitigation efforts. Affective symptoms, suicidality, and cognitive dysfunction have been widely described consequences of the infection, the economic fallout, and the necessary public health mitigation measures themselves. The impact of COVID-19 may be especially serious for those living with severe mental illness and/or chronic medical diseases, given the confluence of several adverse factors in a manner that appears to have syndemic potential. CONCLUSIONS: The COVID-19 pandemic has made clear that biological and behavioral factors interact with societal processes in the infectious disease context. Empirical research examining mechanistic pathways from infection and recovery to immunological, behavioral, and emotional outcomes is critical. Examination of how emotional and behavioral factors relate to the pandemic-both as causes and as effects-can provide valuable insights that can improve management of the current pandemic and future pandemics to come.

Pattern discovery and disentanglement on relational datasets.

Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.

Elevated risk of colorectal, liver, and pancreatic cancers among HCV, HBV and/or HIV (co)infected individuals in a population based cohort in Canada.

INTRODUCTION: Studies of the impact of hepatitis C virus (HCV), hepatitis B virus (HBV) and HIV mono and co-infections on the risk of cancer, particularly extra-hepatic cancer, have been limited and inconsistent in their findings. METHODS: In the British Columbia Hepatitis Testers Cohort, we assessed the risk of colorectal, liver, and pancreatic cancers in association with HCV, HBV and HIV infection status. Using Fine and Gray adjusted proportional subdistribution hazards models, we assessed the impact of infection status on each cancer, accounting for competing mortality risk. Cancer occurrence was ascertained from the BC Cancer Registry. RESULTS: Among 658,697 individuals tested for the occurrence of all three infections, 1407 colorectal, 1294 liver, and 489 pancreatic cancers were identified. Compared to uninfected individuals, the risk of colorectal cancer was significantly elevated among those with HCV (Hazard ration [HR] 2.99; 95% confidence interval [CI] 2.55-3.51), HBV (HR 2.47; 95% CI 1.85-3.28), and HIV mono-infection (HR 2.30; 95% CI 1.47-3.59), and HCV/HIV co-infection. The risk of liver cancer was significantly elevated among HCV and HBV mono-infected and all co-infected individuals. The risk of pancreatic cancer was significantly elevated among individuals with HCV (HR 2.79; 95% CI 2.01-3.70) and HIV mono-infection (HR 2.82; 95% CI 1.39-5.71), and HCV/HBV co-infection. DISCUSSION/CONCLUSION: Compared to uninfected individuals, the risk of colorectal, pancreatic and liver cancers was elevated among those with HCV, HBV and/or HIV infection. These findings highlight the need for targeted cancer prevention and diligent clinical monitoring for hepatic and extrahepatic cancers in infected populations.

What social media told us in the time of COVID-19: a scoping review.

With the onset of the COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak from November, 2019, to November, 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social media platforms and COVID-19. These themes focused on: surveying public attitudes, identifying infodemics, assessing mental health, detecting or predicting COVID-19 cases, analysing government responses to the pandemic, and evaluating quality of health information in prevention education videos. Furthermore, our Review emphasises the paucity of studies on the application of machine learning on data from COVID-19-related social media and a scarcity of studies documenting real-time surveillance that was developed with data from social media on COVID-19. For COVID-19, social media can have a crucial role in disseminating health information and tackling infodemics and misinformation.

HCV reinfection rates after cure or spontaneous clearance among HIV-infected and uninfected men who have sex with men.

BACKGROUND & AIMS: Hepatitis C virus (HCV) reinfection among high-risk groups threatens HCV elimination goals. We assessed HCV reinfection rates among men who have sex with men (MSM) in British Columbia (BC), Canada. METHODS: We used data from the BC Hepatitis Testers Cohort, which includes nearly 1.7 million individuals tested for HCV or HIV in BC. MSM who had either achieved sustained virologic response (SVR) after successful HCV treatment, or spontaneous clearance (SC) and had ≥1 subsequent HCV RNA measurement, were followed from the date of SVR or SC until the earliest of reinfection, death, or last HCV RNA measurement. Predictors of reinfection were identified by Cox proportional modelling. The earliest study start date was 6 November 1997 and latest end date was 13 April 2018. RESULTS: Of 1349 HCV-positive MSM who met the inclusion criteria, 493 had SC while 856 achieved SVR. 349 (25.65%) had HIV coinfection. We identified 98 reinfections during 5203 person-years (PYs) yielding a reinfection rate of 1.88/100PYs. The reinfection rate among SC (2.74/100PYs) was more than twice that of those with SVR (1.03/100 PYs). Problematic alcohol use (aHR 1.73, 95% CI 1.003-2.92), injection drug use (aHR 2.60, 95% CI 1.57-4.29) and HIV coinfection (aHR 2.04, 95% CI 1.29-3.23) were associated with increased risk of HCV reinfection. Mental health counselling history (aHR 0.24, 95% CI 0.13-0.46) was associated with reduced HCV reinfection risk. CONCLUSIONS: There is the need to engage MSM in harm reduction and prevention services following treatment to reduce reinfection risk.

Concurrent Hepatitis C and B Virus and Human Immunodeficiency Virus Infections Are Associated With Higher Mortality Risk Illustrating the Impact of Syndemics on Health Outcomes.

BACKGROUND: Hepatitis C virus (HCV), hepatitis B virus (HBV), and human immunodeficiency virus (HIV) infections are associated with significant mortality globally and in North America. However, data on impact of concurrent multiple infections on mortality risk are limited. We evaluated the effect of HCV, HBV, and HIV infections and coinfections and associated factors on all-cause mortality in British Columbia (BC), Canada. METHODS: The BC Hepatitis Testers Cohort includes ~1.7 million individuals tested for HCV or HIV, or reported as a case of HCV, HIV, or HBV from 1990 to 2015, linked to administrative databases. We followed people with HCV, HBV, or HIV monoinfection, coinfections, and triple infections from their negative status to date of death or December 31, 2016. Extended Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for factors associated with all-cause mortality. RESULTS: Of 658 704 individuals tested for HCV, HBV, and HIV, there were 33 804 (5.13%) deaths. In multivariable Cox regression analysis, individuals with HCV/HBV/HIV (HR, 8.9; 95% CI, 8.2-9.7) infections had the highest risk of mortality followed by HCV/HIV (HR, 4.8; 95% CI, 4.4-5.1), HBV/HIV (HR, 4.1; 95% CI, 3.5-4.8), HCV/HBV (HR, 3.9; 95% CI, 3.7-4.2), HCV (HR, 2.6; 95% CI, 2.6-2.7), HBV (HR, 2.2; 95% CI, 2.0-2.3), and HIV (HR, 1.6; 95% CI, 1.5-1.7). Additional factors associated with mortality included injection drug use, problematic alcohol use, material deprivation, diabetes, chronic kidney disease, heart failure, and hypertension. CONCLUSIONS: Concurrent multiple infections are associated with high mortality risk. Substance use, comorbidities, and material disadvantage were significantly associated with mortality independent of coinfection. Preventive interventions, including harm reduction combined with coinfection treatments, can significantly reduce mortality.

Syndemic profiles of people living with hepatitis C virus using population-level latent class analysis to optimize health services.

BACKGROUND: Hepatitis C (HCV) affects diverse populations such as people who inject drugs (PWID), 'baby boomers,' gay/bisexual men who have sex with men (gbMSM), and people from HCV endemic regions. Assessing HCV syndemics (i.e.relationships with mental health/chronic diseases) among subpopulations using Latent Class Analysis (LCA) may facilitate targeted program planning. METHODS: The BC Hepatitis Testers Cohort(BC-HTC) includes all HCV cases identified in BC between 1990 and 2015, integrated with medical administrative data. LCA grouped all BC-HTC HCV diagnosed people(n = 73,665) by socio-demographic/clinical indicators previously determined to be relevant for HCV outcomes. The final model was chosen based on fit statistics, epidemiological meaningfulness, and posterior probability. Classes were named by most defining characteristics. RESULTS: The six-class model was the best fit and had the following names and characteristics: 'Younger PWID'(n =11,563): recent IDU (67%), people born >1974 (48%), mental illness (62%), material deprivation (59%). 'Older PWID'(n =15,266): past IDU (78%), HIV (17%), HBV (17%) coinfections, alcohol misuse(68%). 'Other Middle-Aged People'(n = 9019): gbMSM (26%), material privilege (31%), people born between 1965-1974 (47%). 'People of Asian backgrounds' (n = 4718): East/South Asians (92%), no alcohol misuse (97%) or mental illness (93%), people born <1945 (26%), social privilege (66%). 'Rural baby boomers' (n = 20,401): rural dwellers (32%), baby boomers (79%), heterosexuals (99%), no HIV (100%). 'Urban socially deprived baby boomers' (n = 12,698): urban dwellers (99%), no IDU (100%), liver disease (22%), social deprivation (94%). CONCLUSIONS: Differences between classes suggest variability in patients' service needs. Further analysis of health service utilization patterns may inform optimal service layout.