Secular Trends in Depressive Symptoms in Adolescents in Yunnan, Southwest China From Before COVID-19 to During the COVID-19 Pandemic: Longitudinal, Observational Study
Notice bibliographique
Résumé
BACKGROUND: Yunnan province borders Myanmar, Laos, and Vietnam, giving it one of the longest borders in China. We aimed to determine the trends in prevalence and impact of COVID-19 on depressive symptoms among adolescents (12-18 years) from 2018 to 2022 in Yunnan, southwest China. OBJECTIVE: We evaluated the impact of the COVID-19 epidemic on adolescents' mental health, with the aim of reducing the effect of psychological emergency syndrome and promoting healthy, happy adolescent growth. METHODS: This longitudinal, observational study used Students' Health Survey data on adolescents' depressive symptoms from 2018 to 2022 (before and during COVID-19) in Yunnan. We used multistage, stratified sampling in 3 prefectures in 2018 and 16 prefectures from 2019 to 2022. In each prefecture, the study population was classified by gender and residence (urban or rural), and each group was of equal size. Depressive symptoms were diagnosed based on Center for Epidemiological Studies Depression Scale (CES-D) scores. We used ANOVA to assess the differences in mean CES-D scores stratified by gender, age, residence, grade, and ethnicity. Chi-square tests were used to compare depressive symptoms by different variables. For comparability, the age-standard and gender-standard population prevalences were calculated using the 2010 China Census as the standard population. The association between COVID-19 and the risk of a standardized prevalence of depressive symptoms was identified using unconditional logistic regression analysis. RESULTS: The standardized prevalence of depressive symptoms for all participants was 32.98%: 28.26% in 2018, 30.89% in 2019, 29.81% in 2020, 28.77% in 2021, 36.33% in 2022. The prevalences were 30.49% before COVID-19,29.29% in early COVID-19, and 36.33% during the COVID-19 pandemic. Compared with before COVID-19, the risks of depressive symptoms were 0.793 (95% CI 0.772-0.814) times higher in early COVID-19 and 1.071 (95% CI 1.042-1.100) times higher than during COVID-19. The average annual increase in depressive symptoms was 1.61%. During the epidemic, the prevalence of depressive symptoms in girls (36.87%) was higher than that in boys (28.64%), and the acceleration rate of girls was faster than that of boys. The prevalences of depressive symptoms and acceleration rates by age group were as follows: 27.14% and 1.09% (12-13 years), 33.99% and 1.8% (14-15 years), 36.59% and 1.65% (16-18 years). Prevalences did not differ between Han (32.89%) and minority (33.10%) populations. However, the acceleration rate was faster for the former than for the latter. The rate for senior high school students was the highest (34.94%). However, the acceleration rate for vocational high school students was the fastest (2.88%), followed by that for junior high school students (2.32%). Rural residents (35.10%) had a higher prevalence and faster acceleration than urban residents (30.16%). CONCLUSIONS: From 2018 to 2022, there was a significant, continuous increase in the prevalence of depressive symptoms among adolescents in Yunnan, China, especially during the COVID-19 pandemic. This represents an emergency public health problem that should be given more attention. Effective, comprehensive psychological and lifestyle intervention measures should be used to reduce the prevalence of mental health issues in adolescents.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».