Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.

Breaking Down Bias: A Methodological Primer on Identifying, Evaluating, and Mitigating Bias in Cardiovascular Research.

Systematic error, often referred to as bias is an inherent challenge in observational cardiovascular research, and has the potential to profoundly influence the design, conduct, and interpretation of study results. If not carefully considered and managed, bias can lead to spurious results, which can misinform clinical practice or public health initiatives and compromise patient outcomes. This methodological primer offers a concise introduction to the identification, evaluation, and mitigation of bias in observational cardiovascular research studies assessing the causal association of an exposure (or treatment) on an outcome.

Setting the balance of care for older adults at risk of hospitalization and delayed discharge: A mixed-methods research protocol.

Introduction: Delayed hospital discharge is a persistent care quality issue experienced across health systems worldwide and remains a priority area to be addressed in Canada. Often associated with a decrease in services while waiting to leave the hospital, delayed discharge from hospital can lead to increased frailty, physical and cognitive decline, and caregiver burnout. Optimizing availability of and timely access to community-based health and social care are avenues that could reduce initial admissions to the hospital and length of hospital stay, and facilitate hospital discharges.

Using a causal decomposition approach to estimate the contribution of employment to differences in mental health profiles between men and women.

Background: Mental health disorders are known to manifest differently in men and women, however our understanding of how gender interacts with mental health and well-being as a broader construct remains limited. Employment is a key determinant of mental health and there are historical differences in occupational roles among men and women that continue to influence working lives (Bonde, 2008; Cabezas-Rodríguez, Utzet, & Bacigalupe, 2021; Drolet, 2022; Gedikli, Miraglia, Connolly, Bryan, & Watson, 2023; Moyser, 2017; Niedhammer, Bertrais, & Witt, 2021; Stier & Yaish, 2014; Van der Doef & Maes, 1999). This study aims to explore differences in multidimensional mental health between men and women, and to quantify how these differences may change if women had the same employment characteristics as men.

High-cost users after sepsis: a population-based observational cohort study.

Background: High-cost users (HCU) represent important targets for health policy interventions. Sepsis is a life-threatening syndrome that is associated with high morbidity, mortality, and economic costs to the healthcare system. We sought to estimate the effect of sepsis on being a subsequent HCU.