Fairness and bias correction in machine learning for depression prediction across four study populations.

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases.

Association between COVID-19 and subsequent vascular events in primary care patients in Germany.

Objectives: The aim of this study was to investigate the relationship between COVID-19 diagnosis and the risk of developing a first-ever vascular event (VE) compared with the same risk in those with respiratory tract infection (RTI).

Study Design: This was a retrospective cohort study.

Methods: This study using data from Disease Analyzer Database (IQVIA) included patients aged ≥18 years with at least one visit to a German practice during the index period.

LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases.

The current epidemics of cardiovascular and metabolic noncommunicable diseases have emerged alongside dramatic modifications in lifestyle and living environments. These correspond to changes in our "modern" postwar societies globally characterized by rural-to-urban migration, modernization of agricultural practices, and transportation, climate change, and aging. Evidence suggests that these changes are related to each other, although the social and biological mechanisms as well as their interactions have yet to be uncovered.