Prevalence of schizophrenia in China between 1990 and 2010.
Bibliographic record
Abstract
BACKGROUND: Dramatic development and changes in lifestyle in many low and middle-income countries (LMIC) over the past three decades may have affected mental health of their populations. Being the largest country and having the most striking record of development, industrialization and urbanization, China provides an important opportunity for studying the nature and magnitude of possible effects. METHODS: We reviewed CNKI, WanFang and PubMed databases for epidemiological studies of schizophrenia in mainland China published between 1990 and 2010. We identified 42 studies that reported schizophrenia prevalence using internationally recognized diagnostic criteria, with breakdown by rural and urban residency. The analysis involved a total of 2 284 957 persons, with 10 506 diagnosed with schizophrenia. Bayesian methods were used to estimate the probability of case of schizophrenia ("prevalence") by type of residency in different years. FINDINGS: In urban China, lifetime prevalence was 0.39% (0.37-0.41%) in 1990, 0.57% (0.55-0.59%) in 2000 and 0.83% (0.75-0.91%) in 2010. In rural areas, the corresponding rates were 0.37% (0.34-0.40%), 0.43% (0.42-0.44%) and 0.50% (0.47-0.53%). In 1990 there were 3.09 (2.87-3.32) million people in China affected with schizophrenia during their lifetime. The number of cases rose to 7.16 (6.57-7.75) million in 2010, a 132% increase, while the total population increased by 18%. The contribution of cases from urban areas to the overall burden increased from 27% in 1990 to 62% in 2010. CONCLUSIONS: The prevalence of schizophrenia in China has more than doubled between 1990 and 2010, with rates being particularly high in the most developed areas of modern China. This has broad implications, as the ongoing development in LMIC countries may be increasing the global prevalence of schizophrenia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".