The Impact of COVID-19 on Psychotropic Medication Prescriptionsin Adolescents: Analysis of a Federated Research Network
Notice bibliographique
Résumé
Background: COVID-19 pandemic restrictions resulted in psychosocial stress and increased potential for psychiatric disorders in the adolescent population. Adolescent psychiatric disorders are increasingly managed with psychotropic medications. We aimed to evaluate the first-time prescription rates of psychotropic medications—antidepressants, antipsychotics, hypnotics, sedatives, mood stabilizers, and psychostimulants—in adolescent patients during the COVID-19 pandemic compared to the years immediately prior. Methods: We utilized electronic health records, claims data, and pharmaceutical data generated from 68 healthcare organizations stored within the TriNetX Research Network to conduct a retrospective matched cohort study. Adolescent patients aged 10-19 years presenting for outpatient evaluation were placed into two cohorts: 1) outpatient evaluation before (2017-2019) and 2) during (2020-2022) the COVID-19 pandemic. Patients with prior history of psychiatric disorders and/or prior use of psychotropic medications were excluded. The main outcome was first-time psychotropic medication prescription within 90 days of outpatient evaluation. We used propensity score matching with logistic regression to build cohorts of equal size based on covariates of interest. Results: A total of 1,612,283 adolescents presenting before the COVID-19 pandemic and 1,008,161 adolescents presenting during the COVID-19 pandemic were identified. After matching on age, race/ethnicity, smoking status, and obesity status, a total of 1,005,408 adolescents were included in each cohort, each with an average age of 14.7 ± 2.84 years and 52% female and 48% male. The standardized differences between propensity scores were less than 0.1, suggesting a minimal difference between the two groups. Prescription rates for antipsychotics and benzodiazepines were increased for adolescents presenting during the pandemic (Risk Ratio (RR): 1.58, 95% confidence intervals (CI) 1.48-1.69). However, this group had decreased prescription rates for antidepressants (RR: 0.6, 95% CI 0.57-0.63), anxiolytics (RR: 0.78, 95% CI 0.75-0.81), psychostimulants (RR: 0.26, 95% CI 0.25-0.27), and mood stabilizers (RR: 0.44, 95% CI 0.39-0.49). Conclusion: Adolescents presenting for outpatient evaluation during the COVID-19 pandemic were prescribed antipsychotics and benzodiazepines at an increased rate relative to the years immediately prior, suggesting an increased need for sedation in this patient population. Given reduced access to care during the COVID-19 pandemic, the decreased prescription rate observed for other psychotropic medication classes does not necessarily reflect a decreased incidence of the associated psychiatric disorders.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».