Secular Trends in Depressive Symptoms in Adolescents in Yunnan, Southwest China From Before COVID-19 to During the COVID-19 Pandemic: Longitudinal, Observational Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".