Population vs Individual Prediction of Poor Health From Results of Adverse Childhood Experiences Screening

Baldwin, Jessie; Caspi, Avshalom; Meehan, Alan J.; Ambler, Antony; Arseneault, Louise; Fisher, Helen L.; Harrington, HonaLee; Matthews, Tim; Odgers, Candice L.; Poulton, Richie; Ramrakha, Sandhya; Moffitt, Terrie E.; Danese, Andrea

doi:10.1001/jamapediatrics.2020.5602

Cited by 137 publications

(100 citation statements)

References 37 publications

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“…More research using large, population-representative samples will be necessary to accurately estimate the magnitude of exposure-brain associations ( 57 ). Moreover, as we have recently shown, ACEs scores can forecast mean group differences in later health problems but have poor accuracy in identifying individuals at high risk for future health problems ( 58 ). However, small effect sizes may be consequential over the long term, either because effects accumulate over time or because many individuals are affected ( 59 ).…”

Section: Discussionmentioning

confidence: 99%

Long-term Neural Embedding of Childhood Adversity in a Population-Representative Birth Cohort Followed for 5 Decades

Gehred

Knodt

Ambler³

et al. 2021

Biological Psychiatry

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Section: Discussionmentioning

confidence: 99%

Long-term Neural Embedding of Childhood Adversity in a Population-Representative Birth Cohort Followed for 5 Decades

Gehred

Knodt

Ambler³

et al. 2021

Biological Psychiatry

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“…However, we did not find evidence for moderation of the effect of ACEs by deprivation. Our findings offer further evidence to support the need for structural interventions, reducing modifiable socioeconomic inequalities, and the early identification and evidence-based intervention for all children who have experienced ACEs [ 68 , 69 ]; models such as the Building Community Resilience (BCR) have been viewed as positive [ 69 ]; however, interventions using ACE screening require further research to establish their effectiveness [ 70 ].…”

Section: Discussionmentioning

confidence: 99%

Adverse childhood experiences and child mental health: an electronic birth cohort study

Lowthian

Anthony

Evans

et al. 2021

BMC Med

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Background Adverse childhood experiences (ACEs) are negatively associated with a range of child health outcomes. In this study, we explored associations between five individual ACEs and child mental health diagnoses or symptoms. ACEs included living with someone who had an alcohol-related problem, common mental health disorder or serious mental illness, or experienced victimisation or death of a household member. Methods We analysed data from a population-level electronic cohort of children in Wales, UK, (N = 191,035) between the years of 1998 and 2012. We used Cox regression with discrete time-varying exposure variables to model time to child mental health diagnosis during the first 15 years of life. Child mental health diagnoses include five categories: (i) externalising symptoms (anti-social behaviour), (ii) internalising symptoms (stress, anxiety, depression), (iii) developmental delay (e.g. learning disability), (iv) other (e.g. eating disorder, personality disorders), and (v) any mental health diagnosis, which was created by combining externalising symptoms, internalising symptoms and other. Our analyses were adjusted for social deprivation and perinatal risk factors. Results There were strong univariable associations between the five individual ACEs, sociodemographic and perinatal factors (e.g. gestational weight at birth) and an increased risk of child mental health diagnoses. After adjusting for sociodemographic and perinatal aspects, there was a remaining conditional increased risk of any child mental health diagnosis, associated with victimisation (conditional hazard ratio (cHR) 1.90, CI 95% 1.34–2.69), and living with an adult with a common mental health diagnosis (cHR 1.63, CI 95% 1.52–1.75). Coefficients of product terms between ACEs and deprivation were not statistically significant. Conclusion The increased risk of child mental health diagnosis associated with victimisation, or exposure to common mental health diagnoses, and alcohol problems in the household supports the need for policy measures and intervention strategies for children and their families.

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“…21,22 These results are in line with previous studies. 20,23 This nding highlights that association doesn't necessarily means prediction, 11 and that proximal risk factors of STB may be better than distal one for predicting one-year STB.…”

Section: Discussionmentioning

confidence: 98%

Predicting suicidal thoughts and behaviors among college students: a machine learning approach

Macalli

Navarro

Orri

et al. 2021

Preprint

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Suicidal thoughts and behaviors are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviors among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviors. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviors in a community sample of college students.

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Population vs Individual Prediction of Poor Health From Results of Adverse Childhood Experiences Screening

Cited by 137 publications

References 37 publications

Long-term Neural Embedding of Childhood Adversity in a Population-Representative Birth Cohort Followed for 5 Decades

Long-term Neural Embedding of Childhood Adversity in a Population-Representative Birth Cohort Followed for 5 Decades

Adverse childhood experiences and child mental health: an electronic birth cohort study

Predicting suicidal thoughts and behaviors among college students: a machine learning approach

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