Mapping Geographic Trends in Early Childhood Social, Emotional, and Behavioural Difficulties in Glasgow: 2010–2017

Samantha Ofili (), Lucy Thompson, Philip Wilson, Louise Marryat, Graham Connelly, Marion Henderson and Sarah J. E. Barry
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Samantha Ofili: Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XQ, UK
Lucy Thompson: Centre for Rural Health, Centre for Health Science, University of Aberdeen, Inverness IV2 3JH, UK
Philip Wilson: Centre for Rural Health, Centre for Health Science, University of Aberdeen, Inverness IV2 3JH, UK
Louise Marryat: School of Health Sciences, University of Dundee, Dundee DD1 4HJ, UK
Graham Connelly: School of Social Work and Social Policy, University of Strathclyde, Glasgow G4 0LT, UK
Marion Henderson: School of Social Work and Social Policy, University of Strathclyde, Glasgow G4 0LT, UK
Sarah J. E. Barry: Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XQ, UK

IJERPH, 2022, vol. 19, issue 18, 1-14

Abstract: Measuring variation in childhood mental health supports the development of local early intervention strategies. The methodological approach used to investigate mental health trends (often determined by the availability of individual level data) can affect decision making. We apply two approaches to identify geographic trends in childhood social, emotional, and behavioural difficulties using the Strengths and Difficulties Questionnaire (SDQ). SDQ forms were analysed for 35,171 children aged 4–6 years old across 180 preschools in Glasgow, UK, between 2010 and 2017 as part of routine monitoring. The number of children in each electoral ward and year with a high SDQ total difficulties score (≥15), indicating a high risk of psychopathology, was modelled using a disease mapping model. The total difficulties score for an individual child nested in their preschool and electoral ward was modelled using a multilevel model. For each approach, linear time trends and unstructured spatial random effects were estimated. The disease mapping model estimated a yearly rise in the relative rate (RR) of high scores of 1.5–5.0%. The multilevel model estimated an RR increase of 0.3–1.2% in average total scores across the years, with higher variation between preschools than between electoral wards. Rising temporal trends may indicate worsening social, emotional, and behavioural difficulties over time, with a faster rate for the proportion with high scores than for the average total scores. Preschool and ward variation, although minimal, highlight potential priority areas for local service provision. Both methodological approaches have utility in estimating and predicting children’s difficulties and local areas requiring greater intervention.

Keywords: child development; disease mapping; multilevel modelling; SDQ; spatiotemporal; children; social and emotional difficulties; early years (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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