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Social assistance and trajectories of child mental health problems in Canada: evidence from the National Longitudinal Survey of Children and Youth

Abstract

Objectives

To examine the link between stability and change in social assistance (SA) use and children’s mental health trajectories to better understand whether social policies targeted at low-income families might be an effective population-based mechanism for preventing mental health problems among children at risk.

Methods

The National Longitudinal Survey of Children and Youth (N = 8981) is used to classify children into 5 categories based on their family’s pattern of SA use from age 4–5 to 10–11: always or never on SA, a single transition on or off SA, or fluctuations on and off SA. Latent growth modelling is used to compare trajectories of emotional and behavioural problems among children with different patterns of SA exposure to their counterparts never on SA over this same time period.

Results

Child emotional and behavioural problems are exacerbated over time in accordance with patterns of SA use: chronic SA use (behavioural) and moving onto SA (emotional and behavioural). These differential rates of change result in mental health disparities at age 10–11 that were not present at age 4–5. Children exposed to SA when they were age 4–5 but subsequently moved off continue to demonstrate elevated levels of emotional and behavioural problems at age 10–11.

Conclusions

Successful social policies and interventions will require understanding the specific mechanisms through which SA undermines child mental health and how programs can be modified to reduce its negative consequences.

Résumé

Objectifs

Examiner le lien entre la stabilité et le changement dans le recours à l’aide sociale (AS) et les trajectoires de santé mentale des enfants pour mieux comprendre si les politiques sociales qui ciblent les familles à faible revenu pourraient constituer un mécanisme populationnel efficace pour prévenir les troubles de santé mentale chez les enfants à risque.

Méthode

L’Enquête longitudinale nationale sur les enfants et les jeunes (N = 8 981) a servi à classer les enfants en 5 catégories fondées sur les tendances de recours à l’AS de leurs familles entre l’âge de 4 et 5 ans et de 10 et 11 ans : toujours ou jamais dépendu de l’AS; transition unique (AS commencée ou quittée une seule fois); ou fluctuations (AS commencée et quittée plusieurs fois). La modélisation à courbe de croissance latente a servi à comparer les trajectoires des problèmes affectifs et comportementaux d’enfants ayant différentes tendances d’exposition à l’AS à leurs homologues n’ayant jamais dépendu de l’AS sur le même intervalle.

Résultats

Les problèmes affectifs et comportementaux des enfants sont exacerbés en fonction des tendances de recours à l’AS : recours chronique à l’AS (problèmes comportementaux) et accession à l’AS (problèmes affectifs et comportementaux). Ces taux de changement différentiels entraînent des disparités d’état de santé mentale à l’âge de 10 ou 11 ans qui n’étaient pas présents à l’âge de 4 ou 5 ans. Les enfants exposés à l’AS vers 4 ou 5 ans, mais qui en sont sortis continuent néanmoins de manifester des niveaux élevés de problèmes affectifs et comportementaux à l’âge de 10 ou 11 ans.

Conclusions

Pour que les politiques et les interventions sociales soient efficaces, il faut comprendre les mécanismes spécifiques par lesquels l’AS nuit à la santé mentale des enfants et les modifications à apporter aux programmes pour en réduire les conséquences négatives.

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Correspondence to Jinette Comeau.

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Comeau, J., Duncan, L., Georgiades, K. et al. Social assistance and trajectories of child mental health problems in Canada: evidence from the National Longitudinal Survey of Children and Youth. Can J Public Health 111, 585–593 (2020). https://doi.org/10.17269/s41997-020-00299-1

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Keywords

  • Children
  • Mental health disparities
  • Poverty
  • Social assistance
  • Latent growth modelling

Mots-clés

  • Enfants
  • Disparités d’état de santé mentale
  • Pauvreté
  • Aide sociale
  • Modélisation à courbe de croissance latente