Targeted Estimation of the Relationship Between Childhood Adversity and Fluid Intelligence in a US Population Sample of Adolescents

Abstract Many studies have shown inverse associations between childhood adversity and intelligence, although most are based on small clinical samples and fail to account for the effects of multiple co-occurring adversities. Using data from the 2001–2004 National Comorbidity Survey Adolescent Supplem...

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Veröffentlicht in:American journal of epidemiology 2018-07, Vol.187 (7), p.1456-1466
Hauptverfasser: Platt, Jonathan M, McLaughlin, Katie A, Luedtke, Alex R, Ahern, Jennifer, Kaufman, Alan S, Keyes, Katherine M
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container_end_page 1466
container_issue 7
container_start_page 1456
container_title American journal of epidemiology
container_volume 187
creator Platt, Jonathan M
McLaughlin, Katie A
Luedtke, Alex R
Ahern, Jennifer
Kaufman, Alan S
Keyes, Katherine M
description Abstract Many studies have shown inverse associations between childhood adversity and intelligence, although most are based on small clinical samples and fail to account for the effects of multiple co-occurring adversities. Using data from the 2001–2004 National Comorbidity Survey Adolescent Supplement, a cross-sectional US population study of adolescents aged 13–18 years (n = 10,073), we examined the associations between 11 childhood adversities and intelligence, using targeted maximum likelihood estimation. Targeted maximum likelihood estimation incorporates machine learning to identify the relationships between exposures and outcomes without overfitting, including interactions and nonlinearity. The nonverbal score from the Kaufman Brief Intelligence Test was used as a standardized measure of fluid reasoning. Childhood adversities were grouped into deprivation and threat types based on recent conceptual models. Adjusted marginal mean differences compared the mean intelligence score if all adolescents experienced each adversity to the mean in the absence of the adversity. The largest associations were observed for deprivation-type experiences, including poverty and low parental education, which were related to reduced intelligence. Although lower in magnitude, threat events related to intelligence included physical abuse and witnessing domestic violence. Violence prevention and poverty-reduction measures would likely improve childhood cognitive outcomes.
doi_str_mv 10.1093/aje/kwy006
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Using data from the 2001–2004 National Comorbidity Survey Adolescent Supplement, a cross-sectional US population study of adolescents aged 13–18 years (n = 10,073), we examined the associations between 11 childhood adversities and intelligence, using targeted maximum likelihood estimation. Targeted maximum likelihood estimation incorporates machine learning to identify the relationships between exposures and outcomes without overfitting, including interactions and nonlinearity. The nonverbal score from the Kaufman Brief Intelligence Test was used as a standardized measure of fluid reasoning. Childhood adversities were grouped into deprivation and threat types based on recent conceptual models. Adjusted marginal mean differences compared the mean intelligence score if all adolescents experienced each adversity to the mean in the absence of the adversity. The largest associations were observed for deprivation-type experiences, including poverty and low parental education, which were related to reduced intelligence. Although lower in magnitude, threat events related to intelligence included physical abuse and witnessing domestic violence. Violence prevention and poverty-reduction measures would likely improve childhood cognitive outcomes.</description><identifier>ISSN: 0002-9262</identifier><identifier>EISSN: 1476-6256</identifier><identifier>DOI: 10.1093/aje/kwy006</identifier><identifier>PMID: 29982374</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Abuse ; Adolescent ; Adolescents ; Adverse childhood experiences ; Adverse Childhood Experiences - statistics &amp; numerical data ; Aggression ; Child Abuse - psychology ; Children ; Cognitive ability ; Cross-Sectional Studies ; Deprivation ; Domestic violence ; Female ; Humans ; Intelligence ; Intelligence tests ; Learning algorithms ; Likelihood Functions ; Machine learning ; Male ; Maximum likelihood estimation ; Mental Disorders - epidemiology ; Mental Disorders - psychology ; Nonlinear systems ; Original Contributions ; Population studies ; Poverty ; Risk Factors ; Teenagers ; United States - epidemiology ; Violence</subject><ispartof>American journal of epidemiology, 2018-07, Vol.187 (7), p.1456-1466</ispartof><rights>The Author(s) 2018. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 2018</rights><rights>The Author(s) 2018. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. 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Using data from the 2001–2004 National Comorbidity Survey Adolescent Supplement, a cross-sectional US population study of adolescents aged 13–18 years (n = 10,073), we examined the associations between 11 childhood adversities and intelligence, using targeted maximum likelihood estimation. Targeted maximum likelihood estimation incorporates machine learning to identify the relationships between exposures and outcomes without overfitting, including interactions and nonlinearity. The nonverbal score from the Kaufman Brief Intelligence Test was used as a standardized measure of fluid reasoning. Childhood adversities were grouped into deprivation and threat types based on recent conceptual models. Adjusted marginal mean differences compared the mean intelligence score if all adolescents experienced each adversity to the mean in the absence of the adversity. 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source MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford University Press Journals All Titles (1996-Current); Alma/SFX Local Collection
subjects Abuse
Adolescent
Adolescents
Adverse childhood experiences
Adverse Childhood Experiences - statistics & numerical data
Aggression
Child Abuse - psychology
Children
Cognitive ability
Cross-Sectional Studies
Deprivation
Domestic violence
Female
Humans
Intelligence
Intelligence tests
Learning algorithms
Likelihood Functions
Machine learning
Male
Maximum likelihood estimation
Mental Disorders - epidemiology
Mental Disorders - psychology
Nonlinear systems
Original Contributions
Population studies
Poverty
Risk Factors
Teenagers
United States - epidemiology
Violence
title Targeted Estimation of the Relationship Between Childhood Adversity and Fluid Intelligence in a US Population Sample of Adolescents
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