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Fact or artefact? Childhood adversity and adulthood trauma in the U.S. population-based Health and Retirement Study.

David BürginCyril BoonmannMarc SchmidPaige TrippAoife O'Donovan
Published in: European journal of psychotraumatology (2020)
Background: Despite the well-known deleterious health effects of childhood adversity (CA) and adulthood trauma (AT) and ageing of the global population, little is known about self-reported CA and AT in older populations. Existing findings are mixed due to methodological and sampling artefacts, in particular, recall and selection biases, and due to age-period-cohort effects. Objectives: We aim to first, provide data on the prevalence of retrospective self-reported CA and AT in a large population-based sample of older adults and, second, to discuss the data in the context of major methodological and sampling artefacts, and age-period-cohort effects. Method: Data are derived from the U.S. population-based Health and Retirement Study (N = 19,547, mean age = 67.24 ± 11.33, 59% female). Seven birth-cohorts were included (<1924, 1924-1930, 1931-1941, 1942-1947, 1948-1953, 1954-1959, >1959). Results: Overall, 35% of participants reported CA and 62% AT, with strong variability among birth-cohorts. Opposing trends were observed regarding prevalence of CA and AT. As age of cohorts increased, prevalence of CAs decreased while that of ATs increased. Investigating the distributions of incidence of specific ATs across age and period per cohort revealed incidence of exposure was associated with (1) age (e.g. having lost a child), (2) time-period (e.g. major disaster), and (3) cohort (e.g. military combat). Conclusions: Retrospective self-reported CA and AT in older samples should be interpreted with caution and with regard to major methodological challenges, including recall and selection biases. Untangling fact from artefact and examining age, period, and cohort effects will help elucidate profiles of lifetime exposures in older populations.
Keyphrases
  • risk factors
  • healthcare
  • public health
  • mental health
  • early life
  • physical activity
  • big data
  • electronic health record
  • middle aged
  • artificial intelligence
  • air pollution
  • single cell
  • health promotion
  • childhood cancer