Canada

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.

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

Understanding the Influence of Immigration on the Aging Experience in Canada

aging experience

social determinants of health

immigration

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. 

technical paper

One of the most popular and affordable inpatient operations used to treat osteoarthritis in the older population in Canada is hip and knee replacement. However, due to their higher risk of complications and associated costs, revision procedures pose substantial challenges. Using the Canadian Joint Replacement Registry (CJRR) Annual Reports (2017–2019), a recent study on revision predictors indexed by PubMed, and preliminary findings from the open-source Kaggle dataset Too Hip to be Cool, this narrative review summarizes national trends, revision risks, and cost implications for hip and knee arthroplasty in Canada. Descriptive analysis of the Kaggle dataset was conducted in conjunction with a review of pertinent national registry reports and peer-reviewed literature. Surgery numbers, demographic trends, revision causes, and related inpatient expenses were among the important outcomes. The average number of hip and knee replacements performed in Canada is over 130,000 per year, and over 20% more have been performed in the past five years. Degenerative arthritis is the main cause of this number. The primary causes of revision rates, which are still low but costly, are infection, aseptic loosening, and instability. Exploratory Kaggle data analysis suggests potential routes for artificial intelligence risk prediction, which is consistent with registry patterns. This analysis emphasizes the need to track revision risks to inform optimal practices and the ongoing high demand for hip and knee replacements. Data-driven decision-making can be improved, and patient outcomes for Canadians can be optimized by connecting registries with open patient-level statistics and PROMs.

A Critical Review of Canadian Hip and Knee Replacement Trends and Revisions

Cardiovascular diseases remain the leading global cause of death, with aging populations facing disproportionate burden. While automated ECG interpretation using convolutional neural networks (CNNs) shows promise for democratizing cardiac diagnostics, age- related performance bias has not been systematically evaluated. We developed a low-resource CNN-based system using continuous wavelet transform (CWT) scalograms from single-lead ECG signals (Lead II) for three-class rhythm classification: Sinus Rhythm (SR), Sinus Bradycardia (SB), and Atrial Fibrillation/Flutter (AFIB). The transfer learning approach adapted AlexNet architecture trained on 45,152 expert-annotated 12-lead ECGs from the Chapman University database. Age-stratified evaluation was conducted across three demographic groups: young adults (0-40 years), middle-aged (40-65 years), and elderly (65+ years). On the test dataset, the model achieved 96.88% overall accuracy, outperforming existing literature on multi-class arrhythmia classification. However, significant age-related bias was identified, particularly affecting elderly patients. AFIB classification precision declined by 4 percentage points from middle aged (98.5%) to elderly patients (94.5%), while SB classification demonstrated remarkable robustness across all age groups (>99.2% specificity). Overall accuracy decreased from 96.9% to 96.0% between young and elderly cohorts. This study represents the first comprehensive age- bias analysis in CNN-based ECG classification, revealing systematic performance degradation in elderly populations. The findings have critical implications for healthcare equity and highlight the need for age-aware training strategies to ensure equitable deployment of automated cardiac diagnostics in aging global populations.

Age Group Robustness in CNN-Based ECG Rhythm Classification Using Wavelet Scalograms

Osteoporosis is a slow, often undetected condition that leads to decreased bone density and increased fracture risk, particularly among the elderly. Despite its prevalence, most people are unaware of its risk until the condition gets severe, making early detection both critical and challenging. This project supports early screening and personalized risk communication through artificial intelligence (AI) and natural language processing (NLP) to develop a method that predicts the likelihood of osteoporosis in a patient from their health records and explains the associated risks in simple and understandable language. Due to the scarcity of reliable publicly available datasets, a synthetic dataset was created based on a medically reviewed questionnaire having 50 questions varying by gender and including both binary, numerical, and alphanumerical inputs. Containing the key clinical attributes such as bone mineral density, age, symptom history, sex, calcium/vitamin D levels, and lifestyle factors. The classification pipeline utilizes transformer-based models like DistilBERT, and RoBERTa to assess osteoporosis risk, while LLaMA 3 8B, LLaMA 3.1 405B parameters, and DeepSeek LLM 7B are employed for natural language summarization and explanation. By integrating advanced machine learning with NLP, this work offers a dual benefit. Model performance is evaluated using ROUGE score for summarization accuracy, and perplexity score for language fluency. It's designed as a universal tool that everyone can use. The main goal is contributing to earlier diagnosis, improving public health, and longevity-focused healthcare by taking precautionary measures as soon as possible.

AI-Based Early Osteoporosis Risk Detection Using Questionnaire Responses

Alzheimer’s disease (AD) is the most common cause of dementia in older adults, posing a major public health and economic burden worldwide. While early detection of AD is critical to improving patient outcomes and delaying disease progression, existing diagnostic methods, such as cognitive testing, spinal fluid assays, and advanced brain imaging, are often costly, invasive, or inaccessible in community settings. Our project explores the role of artificial intelligence (AI) to enable scalable, non-invasive screening by jointly analyzing retinal images and electroencephalography (EEG) recordings. The retina, as an extension of the central nervous system, reflects structural changes such as nerve fiber thinning and microvascular abnormalities, while EEG captures functional brain activity that can reveal disruptions in neural connectivity and cognitive processing. Our research involves implementing a reproducible Jupyter- Notebook pipeline: retinal optical coherence tomography (OCT) scans are normalized via the min-max scaling technique, fed into a convolutional neural network (CNN) pretrained on large public image collections, then fine-tuned on expert-labeled fundus photographs to focus on disease-relevant features. Simultaneously, EEG signals undergo band-pass and notch filtering, segmentation into fixed-length windows, and z-score standardization via TensorFlow’s data before being classified by a compact EEGNet architecture. By integrating structural and functional outputs, our multimodal model substantially outperforms single-modality approaches, demonstrating enhanced sensitivity in detecting early AD and mild cognitive impairment. This research has the potential to inform the development of affordable, accessible diagnostic tools that can be deployed in clinical and community settings, particularly for early intervention in aging populations at risk of dementia.

AI-Driven Diagnosis and Early Detection of Alzheimer's Disease Using Retinal Imaging and EEG

Cardiovascular diseases (CVDs) are a leading cause of death in older adults. Early and accurate diagnosis is vital but challenging due to complex clinical and physiological data. This study presents an AI framework using knowledge graphs and graph neural networks (GNNs) to improve CVD detection in elderly patients. We constructed feature-based knowledge graphs from three clinical datasets and modeled ECG signals as graphs using the PTB-XL dataset. Multiple GNN architectures—including GCN, GraphSAGE, GAT, and RGCN—were trained and compared. Our results show that GCN achieves the highest accuracy (ROC AUC: 0.78) for classifying CVD risk. The approach demonstrates the value of integrating graph-based methods for early, interpretable, and accurate CVD prediction in older adults.

AI-Driven Early Detection of Cardiovascular Diseases Using Knowledge Graphs and GNNs for Elderly

Polycystic Ovary Syndrome (PCOS) is a common, multifactorial endocrine disorder affecting 8–13% of women of reproductive age. It is associated with chronic metabolic, cardiovascular, and reproductive complications that contribute to accelerated biological aging. Due to its heterogeneity, standard diagnostic and treatment approaches often fail to capture the diverse pathophysiological mechanisms involved. To address this, we developed an integrative artificial intelligence (AI) framework to stratify PCOS patients into clinically meaningful subgroups and predict individualized treatments based on genetic, hormonal, and metabolic profiles. Clinical and biochemical data were sourced from the NHANES database, while SNP associations were derived from the GWAS Catalog and dbSNP. Pathway enrichment was conducted using Enrichr. Unsupervised clustering identified three phenotypic subtypes linked to inflammatory, metabolic, and neuroendocrine features, each with distinct pathway enrichments such as NF-κB signaling, nitric oxide regulation, and synaptic transmission. Supervised machine learning models, including Random Forest and XGBoost, were used to predict optimal treatments—such as metformin for insulin resistance and oral contraceptives for hyperandrogenism—with high performance (accuracy = 0.99, precision = 0.95, recall = 0.96). SHAP analysis confirmed biological interpretability, highlighting fasting glucose, insulin, BMI, and androgen biomarkers as key predictors. This study demonstrates the utility of AI-based stratification and treatment matching to enhance PCOS management and promote healthier aging in women.

AI-Driven Multi-Omics Approach for PCOS Subtyping and Drug Prediction to Advance Precision Women’s Healthy Aging

Falls present significant health consequences for older adults, increasing the risks of severe injury, hospitalization, and loss of independence. In recent years, artificial intelligence (AI) powered wearable devices have emerged as a promising fall detection and prevention technology, particularly for older adults living with chronic mobility impairments. The objective of this narrative review is to synthesize current evidence on AI-powered wearable systems for the detection and prevention of falls, with a focus on performance in real-world settings. PubMed, Google Scholar, and Omni were searched for studies published between 2018 and 2025. A total of 17 peer-reviewed articles were included in this review, meeting the following inclusion criteria: (1) study of adults 55 years and older, (2) utilization of AI tools with potential for real-world clinical translation, and (3) reporting of patient-relevant outcomes. The majority of evaluated technologies integrated inertial measurement units (IMU) and deep learning models (e.g., convolutional neural networks [CNN], long short-term memory [LSTM], or hybrid models). Edge computing based and multimodal sensor integrated systems reported the best performance with real-time results. Systems tested in realistic environments produced the strongest results. Commercially available products were notably poorer in performance in terms of sensitivity. Furthermore, several investigated studies did not account for the impacts of transparency and fairness. In conclusion, wearable technologies powered by AI show promise for fall detection and prevention for older adults, particularly in real-world environments. Prioritizing accessibility, equity, and incorporation into geriatric care should be the focus of future studies.

AI-Powered Wearable Sensors for Fall Detection in Older Adults

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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