Unintentional Injuries Among Children of Parents with Diagnosed Mental Health Conditions in Alberta, Canada
Notice bibliographique
Résumé
Background: Unintentional Injuries (UI) are a leading cause of hospitalization among children in Alberta, Canada. A small body of global research has found the risk of UI to be higher among children of parents with mental health and addiction conditions (MHAC) as compared to children with parents without MHAC. However, the scope of research related to this phenomenon is limited and has yet to be conducted in Canada. Objectives: This thesis includes three studies each with different objectives. Study 1 descriptively assessed the different types and causes of UIs among children of parents with mood and anxiety disorders (MAD) as compared to children in the general population of Alberta. Study 2 quantified and compared the risk of UI among children of parents with MAD as compared to children with parents without MAD in Alberta. Study 3 examined whether the risk of UI in children with parents with MAD is greater in the acute period following parental diagnosis and if the risk changes over time. Methods: Study 1 used administrative health data to identify a study population of children with parents with MAD and children in the general Alberta population between the ages of 0-9 who had sustained an incident UI. Descriptive analyses identified the most common types and causes of injuries between the populations. Further analysis identified whether the different frequencies were statistically significant between the two populations. An analysis was also conducted to determine if injury types differed depending on how many parents in the household had MAD. Studies 2 and 3 were retrospective case control studies which used administrative health data to identify cases of infant, pre-school and school-aged children aged 0-9 with a UI and age-sex matched controls without a history of UI. Cases and controls were linked to their parental guardians, and parental MHAC status was identified. In study 2 conditional logistic regression was used to determine the odds ratios (ORs) of the risk of UI given parental status of MAD compared to the risk of UI given parental status of no MAD. Unadjusted and adjusted ORs were calculated and reported with 95% confidence intervals (CI). In study 3 the time from parental diagnosis of MAD to the time of UI was calculated and categorized as being within 0-90 days and more than 90 days. Conditional logistic regression was used to determine ORs for the risk of UI given parental status of MAD compared to the risk of UI given parental status of no MAD. ORs were calculated separately for both time periods. Results: Study 1 found children of parents with MAD to be prone to different types of injuries that are more severe and costly in nature. The frequency of injury types among children with parents with MAD was found to be similar regardless of the number of parents with MAD in the household. The causes of injuries were also found to be similar across children with parents with MAD and children in the general population. Study 2 found the crude, unadjusted risk of UI to be elevated among children whose parents had been diagnosed with anxiety disorders (infant OR=1.24; preschool OR=1.25; school-age OR=1.18) and mood disorders (infant OR=1.32; preschool OR=1.15; school-age OR=1.22) compared to children with parents with no MAD. The ORs remained positive, even following propensity score adjustment and adjustment for presence of other MHACs. Study 3 found the risk of UI among children with parents with anxiety and mood disorders was highest during the 0-90 -day period (OR = 1.23; OR=1.40), and decreased but remained positive following the 90-day period (OR=1.18; OR = 1.20). Conclusion: Consistent with global research, children in Alberta with parents with MAD face a higher risk of UI compared to children with parents without MAD. However, a novel finding of this study is that not only is this risk of UI higher during the acute period following parental diagnosis, but children in this population continue to face an elevated risk of UI in the long-term compared to children whose parents do not have a history of MAD. In conclusion, while interventions to support families of parents with MAD are important during the acute period following parental diagnosis, they should also be available long-term.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».