Adverse Childhood Experiences: Expanding the Concept of Adversity

Am J Prev Med. 2015 Sep;49(3):354-61. doi: 10.1016/j.amepre.2015.02.001.

Abstract

Introduction: Current knowledge of Adverse Childhood Experiences (ACEs) relies on data predominantly collected from white, middle- / upper-middle-class participants and focuses on experiences within the home. Using a more socioeconomically and racially diverse urban population, Conventional and Expanded (community-level) ACEs were measured to help understand whether Conventional ACEs alone can sufficiently measure adversity, particularly among various subgroups.

Methods: Participants from a previous large, representative, community-based health survey in Southeast Pennsylvania who were aged ≥18 years were contacted between November 2012 and January 2013 to complete another phone survey measuring ACEs. Ordinal logistic regression models were used to test associations between Conventional and Expanded ACEs scores and demographic characteristics. Analysis was conducted in 2013 and 2014.

Results: Of 1,784 respondents, 72.9% had at least one Conventional ACE, 63.4% at least one Expanded ACE, and 49.3% experienced both. A total of 13.9% experienced only Expanded ACEs and would have gone unrecognized if only Conventional ACEs were assessed. Certain demographic characteristics were associated with higher risk for Conventional ACEs but were not predictive of Expanded ACEs, and vice versa. Few adversities were associated with both Conventional and Expanded ACEs.

Conclusions: To more accurately represent the level of adversity experienced across various sociodemographic groups, these data support extending the Conventional ACEs measure.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Adult Survivors of Child Abuse / statistics & numerical data*
  • Adult Survivors of Child Adverse Events / statistics & numerical data*
  • Aged
  • Continental Population Groups / statistics & numerical data
  • Female
  • Health Surveys
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Pennsylvania
  • Socioeconomic Factors
  • Urban Population / statistics & numerical data*
  • Young Adult