Global Trends in the Incidence of Anxiety Disorders From 1990 to 2019: Joinpoint and Age-Period-Cohort Analysis Study
Bibliographic record
Abstract
BACKGROUND: Anxiety disorders (ADs) are the most common mental illness with high prevalence, chronicity, and comorbidity. Despite rapid economic and cultural development, the global incidence of ADs continues to increase, with predominance in male individuals. OBJECTIVE: To address the above issues, we analyzed the dynamic trends of the global incidence and disease burden of ADs from 1990 to 2019 and their different effects on age, period, and birth cohort and predicted the future trend of AD incidence. METHODS: The data were obtained from the Global Burden of Disease study in 2019. A joinpoint regression model was used to calculate the annual percent change in AD incidence, and age-period-cohort analysis was used to estimate the independent effects of age, period, and cohort. Nordpred age-period-cohort analysis was used to predict the incidence of ADs from 2020 to 2044. RESULTS: The age-standardized incidence rate of ADs increased by 1.06% for both sexes, and the age-standardized disability-adjusted life-year (DALY) rate (ASDR) decreased by 0.12%. Joinpoint regression indicated that increments in average annual percent changes in the age-standardized incidence rate (0.068 vs 0.012) and ASDR (0.035 vs -0.015) for ADs globally were higher among male individuals than female individuals. The age-period-cohort analyses revealed that the relative risk (RR) of the incidence and DALYs of ADs among people of different sexes increased with age in adolescence and middle age and then decreased. For the period effect, the RR of incidence decreased, whereas the RR of DALYs increased in both sexes. Moreover, the RR of the incidence gradually increased and DALYs slowly decreased with birth year for both male and female individuals. New cases of ADs in male individuals are predicted to increase in the coming 25 years. CONCLUSIONS: This study provided the changing trend of the global incidence and disease burden of ADs in the past 3 decades, indicating that early prevention and effective control cannot be ignored. We analyzed the age-period-cohort effect of potential trends in ADs and predicted future incidence trends. The results suggest that we should take active intervention measures, focusing on high-risk groups and developing effective management and control policies to reduce the global burden of disease.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".