Canada


Sleep is a crucial determinant of health, yet many older Canadians experience inadequate sleep, particularly those facing socioeconomic disadvantages. This study investigates the relationship between sleep deprivation and socioeconomic status (SES) in predicting cognitive decline and cardiometabolic illness among older adults in Canada. We conducted a secondary analysis of seven Canadian datasets, the Canadian Health Measures Survey (CHMS), the Canadian Community Health Survey (CCHS), the Canadian Longitudinal Study on Aging (CLSA), the Canadian Dementia Imaging Protocol (CDIP), Sleep Rest Inequality (SRI) Dataset, and the Wearable Sleep Inequity Dataset. Adults aged 60 and above were evaluated using self-reported and objective sleep indicators (duration, fragmentation, waking after sleep onset), SES measures (income quintile, education), and health outcomes (cardiometabolic illness, BMI, cognition). T-tests, chi-square tests, linear and logistic regression, and unsupervised K-means clustering were used to examine associations and predict health outcomes. Older adults in the lowest income quintile reported significantly shorter sleep (6.0 hrs vs. 6.6 hrs), higher cardiometabolic disease rates (33% vs. 19%), and greater cognitive impairment (28% vs. 14%). Both the risk of mood disorders (RR = 1.45) and cardiometabolic disease (RR = 1.33) were strongly predicted by SES and sleep duration, according to regression models. Cognitive scores and sleep length had a positive correlation, and clustering identified different sleep health profiles by socioeconomic status (SES), with a Silhouette Score of 0.61. Sleep inequality is a measurable and powerful predictor of chronic illness in aging Canadians.

Aging Powerfully: AI Blueprint for Chronic Disease Management and Social Determinants of Longevity

Sleep Inequality and Chronic Illness in Aging Canadians

cardiometabolic disease

socioeconomic status

sleep inequality

cognitive decline

older adults

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technical paper

2025 National Inter-University Health Data & AI Inquiry Program - this program is a unique project-based inquiry and science communication program that brings together students from diverse academic backgrounds and research interests. Presented research projects explore how generative AI is being applied to medication management, mental health screening, and chronic disease monitoring to align with the future of pharmacy practice.

2025 National Inter-University Health Data & AI Inquiry Program is a unique project-based inquiry and science communication program that brings together students from diverse academic backgrounds and research interests. 

Chronic conditions may develop as a result of a variety of reasons, such as genetics, lifestyle choices, or a combination of factors. Socioeconomic status has been found to be a major factor in the development of multiple chronic diseases. This study aimed to examine the socio- economic factors linked with cardiovascular and musculoskeletal chronic diseases and hypertension using multi-component analysis (MCA). Also, we probed further into the relationship between socioeconomic factors and chronic musculoskeletal diseases (CMDs). In contrast to other similar studies conducted with respect to Canadian seniors, which had used inferential analysis techniques, this study focused on the impact of socioeconomic factors through the use of logistic regression and random forest models. Both models found education level and mental health to be the primary predictors of CMDs in seniors. While education level could be the cause, mental health status can be both a cause and effect, as the diseases’ development might affect seniors’ mental well-being, which can have adverse effects on pre-existing symptoms. Also, lower education levels were found to be a key predictor for all three categories. These findings lead us to recommend policies which: address financial disparities in accessing treatment, support senior immigrants in culturally-aware ways, and encourage mental-wellbeing through counselling and community-building activities.

Socio-economic Predictors of Chronic Musculoskeletal Diseases

Air pollution is a significant contributor to adverse health outcomes, particularly among older adults. Chronic exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO₂), and ozone (O₃) has been linked to elevated risks of respiratory diseases, impaired lung function, and premature mortality. This study investigates the relationship between long-term pollutant exposure and respiratory health in aging populations by integrating data from the National Health and Nutrition Examination Survey (NHANES), the U.S. Environmental Protection Agency (EPA), and satellite-based monitoring systems. A combination of statistical modelling, machine learning algorithms, and geospatial analysis in R and Python was applied to identify regions where older individuals are most at risk. The findings aim to support targeted public health policies and improve understanding of environmental health disparities across geographic and demographic lines.

Spatial and Demographic Disparities in Respiratory Disease Risk among Older Adults

Background: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder primarily affecting adults 60 years of age and older. It is typically diagnosed after motor symptoms appear by which point extensive neuronal damage has often already occurred. Earlier detection is key to improve prognosis and enable preventative strategies. Objective: This research study aims to develop a diagnostic process focused on factors like motor and non-motor symptoms, cardiometabolic factors, demographic factors, and medical history to detect early manifestations of PD or establish strong risk associations for the development of PD to aid in early treatment. Methods: A Random Forest classifier was applied to a dataset of individuals aged 50-90 years, using features including motor symptoms, non-motor symptoms, cardiometabolic indicators, and demographic features. Results: The model achieved high predictive accuracy (ROC AUC = 0.75) with motor and non- motor features being the strongest individual predictors. Models trained soley on demographic, lifestyle, or cardiometabolic factors performed poorly (ROC AUC = 0.51-0.53). Non-motor symptoms such as sleep and cognitive decline demonstrated moderate standalone predictive value, supporting their role in early PD screening. Conclusion: Machine learning models integrating symptom-based data outperform those based on demographic or risk factor profiles alone. While the findings suggest valuable utility of non- motor features, limitations such as the use of a single dataset may affect generalizability. Future work should validate these findings across diverse populations to support real-world clinical applications.

The Best Predictors of Parkinson’s Disease: A Subgroup Analysis Using Random Forests

Non-Alcoholic Fatty Liver Disease (NAFLD) is a critical contributor to aging worker health concerns. While metabolic and lifestyle factors are recognized influences, psychosocial stress factors such as job strain are inadequately explored. This meta-analysis studies the association between job strain and NAFLD risk in adults aged 30 and older. Ten peer-reviewed studies were selected through a thorough database screening. Five studies with adjusted odds ratios were incorporated in a quantitative synthesis using a random-effects model. The findings demonstrate a positive association between job strain and increased NAFLD risk. Job strain is measured through factors such as long working hours, shift work, and occupational stress. Odds ratios across studies ranged from 1.12 to 2.33, indicating elevated risk in job-strain-exposed individuals. Older adults were particularly more vulnerable, suggesting that aging increases the consequences of liver damage. Funnel and forest plot analyses showed minimal publication bias and a statistically significant pooled effect (p = 0.0236), though high heterogeneity (I² = 78.17%) was observed. These results highlight job strain as an important risk factor for NAFLD in aging populations. This understanding of the likelihood of NAFLD chances in aging workers offers an opportunity for intervention strategies. Public health efforts should consider job-related stress in NAFLD risk assessments and promote workplace health policies to mitigate this hidden burden.

The Hidden Burden of Work: Job Strain and NAFLD in Aging Populations

Alzheimer’s disease is a progressive neurodegenerative disorder and the leading cause of dementia, marked by cognitive decline, emotional disturbances, and neuropathological changes. Despite extensive research, accurately predicting Alzheimer progression remains challenging. This study introduces a tri-model machine learning framework that explores how music features, electroencephalography (EEG) signals, and cognitive measures could forecast disease progression. Four publicly available datasets were analyzed: EEG responses during music listening tasks, resting state EEG recordings and Mini-Mental State Examination (MMSE) scores, longitudinal cognitive assessments with dementia status classifications, and EEG paired with working memory tasks under varying music conditions. Three sequential models were created. Model A tested if acoustic features from music could predict EEG activity, finding no link and showcasing the difficulty of decoding music driven neural responses without disease specific markers. Model B assessed the relationship between EEG features and cognitive status using MMSE, presenting limited predictive power, with non-linear models like Random Forest only marginally outperforming linear approaches. Model C predicted Alzheimer’s progression using longitudinal cognitive and demographic data, showing moderate success in predicting cognitive decline and dementia status change. Overall, these findings emphasize both the potential and limitations of using multimodal data and machine learning approaches in Alzheimer research. While predictive accuracy was modest, this study provides a foundation for future research aiming to enhance early detection and support the development of personalized, non-pharmacological interventions for Alzheimer’s disease management.

Tri-model Forecasting of Alzheimer’s Progression using Music, EEG, and Cognition

Polypharmacy, commonly defined as the concurrent use of five or more medications, is frequently observed in older adults and may be associated with adverse health outcomes. This study aimed to describe patterns of polypharmacy and related characteristics in a nationally representative sample of adults aged 65 and older using data from the 2017–2018 cycle of the National Health and Nutrition Examination Survey (NHANES). Demographic, clinical, and prescription drug data were extracted and merged using participant identifiers. Continuous variables were standardized using the StandardScaler. Categorical variables such as sex, race and ethnicity, and medical conditions were one-hot encoded to ensure appropriate treatment in clustering. Prescription medications were mapped to therapeutic category codes based on the Multum Lexicon system. Each participant’s drug profile was embedded into a 16-dimensional vector using a skip-gram model trained on categorical co-occurrence. Unsupervised clustering was conducted using the KMeans algorithm. The optimal number of clusters was selected as three based on elbow plot and silhouette score analysis. Cluster labels were reassigned post hoc according to the average number of medications per participant. Descriptive analyses were performed to compare demographic and clinical features across clusters. Independent t-tests were conducted on the standardized income-to-poverty ratio to assess variation in economic status between groups. Additional categorical comparisons were examined for patterns related to income, education, chronic conditions, and depressive symptoms. This approach combines demographic, clinical, and medication data to describe distinct groupings of older adults based on polypharmacy and associated variables.

Uncovering Polypharmacy Patterns in Older Adults: A Cluster Analysis of NHANES Data

This study explores how immigration affects the aging experience in Canada by examining the role of social determinants of health (SDOHs) in shaping physical and mental health outcomes among older adults. With Canada’s aging population and a growing proportion of immigrants, understanding these influences is vital for developing equitable health and social policies. Using data from a national health survey, we applied structural equation modeling to analyze relationships between immigrant status, SDOHs, chronic physical and mental health conditions, and overall quality of life. The analysis included over 26,000 participants and revealed that immigrants were more likely to experience adverse social conditions, which were linked to higher rates of chronic illnesses and mental health challenges. Physical health problems also contributed to increased mental health burden. Machine learning methods were used to further assess how social and health factors impact quality of life, highlighting important predictors specific to immigrant populations. These findings demonstrate that social and economic factors significantly influence the health and well-being of aging immigrants. The results emphasize the need for policies and interventions that address social inequalities to promote healthier aging in diverse communities. Overall, this research advances knowledge on the complex interplay between immigration, social determinants, and aging, offering valuable insights to support healthier and more inclusive aging experiences in Canada.

Understanding the Influence of Immigration on the Aging Experience in Canada

Nowadays, Canada is facing a serious threat, with the quickly aging population, the risk of cognitive decline largely impacts the public-health area and economic area. Moreover, the protective value of continued work after retirement is still unclear. We studied 22,374 adults aged 65 and older who were part of the 2022 RAND Health and Retirement Study. The study tracked every two years across nine survey waves. Brain health was measured in three ways: the Mental Status Index, the Recall Index, and whether someone showed signs of serious cognitive trouble. We compared those scores with job status, job type, weekly hours, pay rate, and how much physical or mental effort each job demanded, while also adjusting for age, income, lifestyle, and medical factors. Mixed-effects and GEE models let us follow changes over time. The results showed that individuals in service-sector jobs, particularly those in household and cleaning, food service, and personal care, had significantly lower mental health scores and experienced faster cognitive decline compared to individuals in managerial or professional roles. In the Recall Index model, main effects of all occupational categories were negative, and the skilled trades group experienced significantly faster memory decline relative to the retired group. Meanwhile, mental health scores were positively associated with moderate alcohol use and being in a close personal relationship, suggesting that lifestyle and social context also shape later-life well-being. Overall, the impact of post-retirement occupational engagement on cognitive and mental health varies by job type and should be interpreted contextually.

Unlocking Cognitive Resilience: Post-Retirement Careers That Protect the Aging Mind

Arrhythmias are a common cardiovascular disease afflicting primarily the elderly population which often have severe symptoms including death. As populations age, the case count of arrhythmias are expected to rise and the primary method of detecting them, manual electrocardiogram interpretation, may require large amounts of resources dedicated that may be better allocated elsewhere in the healthcare system. Therefore, it is pertinent to provide an alternative method of interpreting electrocardiograms that is more automated and still maintains high standards of care. In this work, we provide our ideal solution. Herein, we describe the creation, testing, and use simulation of a multilayer perceptron deep-learning artificial intelligence model for the detection and characterization of arrhythmias based on standard ECG measurement data provided by MIT. Two models were created, with 94% and 96% accuracy rates on detecting and identifying ventricular ectopic beats arrhythmia and supraventricular ectopic beats arrhythmia. A facile and accessible data input methodology was developed for the trained model and a use case simulation using 100 datapoints was conducted, which yielded 100% accuracy.

Use of Artificial Intelligence in Detection of Arrhythmia from Electrocardiograms

Cardiovascular disease is one of the most common aging diseases and is the leading cause of mortality worldwide. Therefore, new diagnostic and therapeutic strategies are necessary to treat this type of disease. Drug discovery, a research process that identifies and develops a new medication, is time-consuming and costly. Artificial Intelligence (AI) can potentially revolutionize the drug discovery process by improving its efficiency, accuracy, and speed. Binding affinity is an important parameter in drug design. This paper used data from Kaggle's competition “NeurIPS 2024 - Predict New Medicines with BELKA” to train three machine learning models -- a Logistic Regression model, a Sequential Neural Network built with TensorFlow, and an MLPClassifier from Scikit-Learn -- to predict the probability that a molecule will bind to three proteins: BRD4, HSA, and sEH. MLPClassifier achieved the highest public score of 0.04353, and Logistic Regression achieved the highest private score of 0.02994. Future research can enhance the performance of these models and apply transfer learning to extend their use to predicting binding affinity between drugs and cardiovascular disease–related proteins. By leveraging machine learning to predict molecular binding in out-of-distribution protein targets, this research could pave the way for future breakthroughs in developing new treatments for cardiovascular and other diseases.

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