Melissa J Green1,2, Stacy Tzoumakis2,3, Kristin R Laurens1,2,4, Kimberlie Dean1,2,5, Maina Kariuki1,2, Felicity Harris1,2, Nicole O'Reilly1, Marilyn Chilvers6, Sally A Brinkman7,8, Vaughan J Carr1,2,9. 1. 1 UNSW Research Unit for Schizophrenia Epidemiology, School of Psychiatry, University of New South Wales, Darlinghurst, NSW, Australia. 2. 2 Neuroscience Research Australia (NeuRA), Sydney, NSW, Australia. 3. 3 School of Social Sciences, University of New South Wales, Sydney, NSW, Australia. 4. 4 School of Psychology, Australian Catholic University, Brisbane, QLD, Australia. 5. 5 Justice Health & Forensic Mental Health Network, Matraville, NSW, Australia. 6. 6 NSW Department of Family and Community Services, Sydney, NSW, Australia. 7. 7 Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia. 8. 8 School of Population Health, The University of Adelaide, Adelaide, SA, Australia. 9. 9 Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, VIC, Australia.
Abstract
OBJECTIVE: Detecting the early emergence of childhood risk for adult mental disorders may lead to interventions for reducing subsequent burden of these disorders. We set out to determine classes of children who may be at risk for later mental disorder on the basis of early patterns of development in a population cohort, and associated exposures gleaned from linked administrative records obtained within the New South Wales Child Development Study. METHODS: Intergenerational records from government departments of health, education, justice and child protection were linked with the Australian Early Development Census for a state population cohort of 67,353 children approximately 5 years of age. We used binary data from 16 subdomains of the Australian Early Development Census to determine classes of children with shared patterns of Australian Early Development Census-defined vulnerability using latent class analysis. Covariates, which included demographic features (sex, socioeconomic status) and exposure to child maltreatment, parental mental illness, parental criminal offending and perinatal adversities (i.e. birth complications, smoking during pregnancy, low birth weight), were examined hierarchically within latent class analysis models. RESULTS: Four classes were identified, reflecting putative risk states for mental disorders: (1) disrespectful and aggressive/hyperactive behaviour, labelled 'misconduct risk' ( N = 4368; 6.5%); (2) 'pervasive risk' ( N = 2668; 4.0%); (3) 'mild generalised risk' ( N = 7822; 11.6%); and (4) 'no risk' ( N = 52,495; 77.9%). The odds of membership in putative risk groups (relative to the no risk group) were greater among children from backgrounds of child maltreatment, parental history of mental illness, parental history of criminal offending, socioeconomic disadvantage and perinatal adversities, with distinguishable patterns of association for some covariates. CONCLUSION: Patterns of early childhood developmental vulnerabilities may provide useful indicators for particular mental disorder outcomes in later life, although their predictive utility in this respect remains to be established in longitudinal follow-up of the cohort.
OBJECTIVE: Detecting the early emergence of childhood risk for adult mental disorders may lead to interventions for reducing subsequent burden of these disorders. We set out to determine classes of children who may be at risk for later mental disorder on the basis of early patterns of development in a population cohort, and associated exposures gleaned from linked administrative records obtained within the New South Wales Child Development Study. METHODS: Intergenerational records from government departments of health, education, justice and child protection were linked with the Australian Early Development Census for a state population cohort of 67,353 children approximately 5 years of age. We used binary data from 16 subdomains of the Australian Early Development Census to determine classes of children with shared patterns of Australian Early Development Census-defined vulnerability using latent class analysis. Covariates, which included demographic features (sex, socioeconomic status) and exposure to child maltreatment, parental mental illness, parental criminal offending and perinatal adversities (i.e. birth complications, smoking during pregnancy, low birth weight), were examined hierarchically within latent class analysis models. RESULTS: Four classes were identified, reflecting putative risk states for mental disorders: (1) disrespectful and aggressive/hyperactive behaviour, labelled 'misconduct risk' ( N = 4368; 6.5%); (2) 'pervasive risk' ( N = 2668; 4.0%); (3) 'mild generalised risk' ( N = 7822; 11.6%); and (4) 'no risk' ( N = 52,495; 77.9%). The odds of membership in putative risk groups (relative to the no risk group) were greater among children from backgrounds of child maltreatment, parental history of mental illness, parental history of criminal offending, socioeconomic disadvantage and perinatal adversities, with distinguishable patterns of association for some covariates. CONCLUSION: Patterns of early childhood developmental vulnerabilities may provide useful indicators for particular mental disorder outcomes in later life, although their predictive utility in this respect remains to be established in longitudinal follow-up of the cohort.
Entities:
Keywords:
Early childhood; children; mental health; record linkage; risk profiles
Authors: Magdalena Janus; Caroline Reid-Westoby; Noam Raiter; Barry Forer; Martin Guhn Journal: Int J Environ Res Public Health Date: 2021-03-25 Impact factor: 3.390