Mental health service use among Canadian Veterans and non-Veterans in Ontario: a population-based study exploring differences among males and females and by length of service
Notice bibliographique
Résumé
Veterans of the Canadian Armed Forces (CAF) and Royal Canadian Mounted Police (RCMP) have a higher prevalence of mental health (MH) conditions than members of the Canadian general population (non-Veterans). However, the limited availability of health-related data in Veteran and non-Veteran populations from a common data source, combined with the potential of a healthy worker effect, have historically hindered our ability to make direct comparisons of mental health service use between Veterans and non-Veterans, while the inclusion of small numbers of females in previous research has restricted our ability to explore the effect of sex within Veteran samples. In this dissertation, I used administrative healthcare data from ICES to compare rates of mental health service use (outpatient visits, emergency department visits, and hospitalizations) between Veterans and non-Veterans in Ontario, Canada overall and by sex and length of service.In my first study, I explored the impact of using different matching criteria on the association between Veteran status and an outpatient MH encounter within the first five years following release from the CAF or RCMP, comparing less stringent (hard-matching on age and sex) with more stringent matching criteria (matching on age, sex, region of residence, and median neighbourhood income quintile while restricting to non-Veterans without a history of long term care or rehabilitation stay, or disability or income support payments). I found that, on average, Veterans had a higher rate of outpatient MH visits than non-Veterans, and that the observed effect was stronger among female Veterans. I also found that the intensity of outpatient mental health service use varied over time, with higher rates being observed among Veterans within the first three years compared to years four and five. Last, I found that matching on age, sex, region of residence, and median neighbourhood income quintile resulted in larger relative differences in the effect estimates than the other matching approaches. In my second study, I estimated rates of MH-related emergency department (ED) use using Andersen-Gill recurrent time-to-event models. I found that Veterans had a higher rate of MH-related ED visits compared to non-Veterans, and that the effect was stronger among female Veterans and Veterans with fewer years of service. For my final study, I estimated rates of MH-related hospitalizations within the first ten years following release from the CAF or RCMP. Using a competing risk analysis, where the competing risk was all-cause mortality prior to a MH-related hospitalization, I found that Veterans had a higher rate of MH-related hospitalizations, and that the effect was once again more pronounced among female Veterans and Veterans with fewer years of service. The findings from these studies demonstrate that, on average, Veterans in Ontario, particularly female Veterans and Veterans with fewer years of service, have higher rates of mental health service use than non-Veterans in the years following transition to civilian life.
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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 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».