Related Experiment Video
Updated: Mar 1, 2026

Rapid Fractionation and Isolation of Whole Blood Components in Samples Obtained from a Community-based Setting
Published on: November 30, 2015
Non-random patterns in the co-occurrence and accumulation of adverse life events in two national panel datasets
Kyra Evers1, Denny Borsboom2, Eiko Fried3
1Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands. kyra.c.evers@gmail.com.
Abstract:
Adverse life events (ALEs), such as illness, bereavement, and accidents, can have profound consequences for physical and mental health. Although existing research highlights risk factors for ALEs, such as personality and socioeconomic status, less is known about patterns in ALEs themselves. How do events cluster and accumulate over time? Using generalized linear mixed-effects models, we study yearly self-reported ALEs in two panel datasets, the Swiss Household Panel (n = 16,946, 210,031 person-years) and the Household, Income and Labour Dynamics in Australia (n = 25,803, 113,605 person-years). We identify widespread contemporaneous and lag-1 associations between ALEs. The twenty-year accumulation of ALE counts deviates substantially from a random process and is better described by a self-reinforcing process, in which ALEs increase the risk of future ALEs. For all analyses, differences between individuals and households were stronger predictors of event occurrence than concurrent or prior adverse life events. Non-random patterns in ALEs should inform our conceptual and statistical models, as well as our prevention strategies.
More Related Videos
04:20Author Spotlight: Exploring Microglial Interactions with Stress-Response Circuitry Using the Limited Bedding and Nesting Model
Published on: July 12, 2024
08:25Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
Published on: December 6, 2024
Related Concept Videos
Bias in Epidemiological Studies
Data Collection by Observations
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Applications of Life Tables
Censoring Survival Data
Confounding in Epidemiological Studies
Assumptions of Survival Analysis