Rate My Professor Kim Betts

Dr. Kim Betts is an Associate Professor in the Curtin School of Population Health, Faculty of Health Sciences, at Curtin University. He also serves as Discipline Lead for Health Economics and Data Analytics. Betts obtained his PhD in Epidemiology from the University of Queensland in November 2014. He holds a Master of Biostatistics (MBiostats) and a Master of Public Health (MPH). Prior to his current appointment, he worked as a Postdoctoral Research Fellow and Research Academic, with experience at the University of Queensland School of Public Health. Additionally, he is affiliated with the Life Course Centre as an affiliate researcher.

Betts specializes in epidemiology and biostatistics applied to psychiatric disorders. His research examines the intergenerational effects of maternal and paternal perinatal exposures, including cannabis use disorder, depression, and smoking, on offspring neurodevelopmental and mental health outcomes such as autism spectrum disorder, attention deficit hyperactivity disorder, anxiety disorders, and depression. He employs longitudinal cohort studies, systematic reviews, meta-analyses, and machine learning techniques to develop predictive models for psychiatric risks. Notable publications include 'Development and validation of a machine learning-based tool to predict autism among children' (Betts et al., 2023, Autism); 'Paternal Depression and Risk of Depression Among Offspring: A Systematic Review and Meta-analysis' (Lawrence et al., 2023, JAMA Network Open); 'Prenatal cannabis use and the risk of attention deficit hyperactivity disorder: A systematic review and meta-analysis' (Duko et al., 2024, Journal of Affective Disorders); 'The association between prenatal cannabis use and congenital birth defects: A systematic review and meta-analysis' (Betts and Alati, 2024); 'Associations of maternal perinatal depressive disorders with autism spectrum disorder in offspring: A systematic review and meta-analysis' (Ayano et al., 2025, Australian & New Zealand Journal of Psychiatry); and 'Developing a machine learning prediction model for postpartum psychiatric admission' (2020). His body of work has received over 2,074 citations according to Scopus.