AB011. Disease burden, and temporal trends in schizophrenia in low- and middle-income countries: a global analysis from 1990 to 2019
Bibliographic record
Abstract
Background: Schizophrenia is a psychiatric syndrome that presents with delusions, disorganized speech, hallucinations, and impaired executive functioning. This study aims to evaluate the disease burden of schizophrenia in low- and middle-income countries (LMICs), considering genders and 11 age groups, assessing disability adjusted life-years (DALYs) rate, incidence rate, prevalence rate, and temporal trends. Methods: This study utilized data from the Global Burden of Disease (GBD) databases to extract DALYs, incidence rate, and prevalence rate associated with schizophrenia across 173 LMICs or territories from 1990 to 2019. The countries were grouped based on the income classification of the World Bank. Results: The prevalence rates of schizophrenia for both genders in upper-middle income countries (male: 285 to 359; female: 271 to 341) consistently remained higher than the other two income tiers from 1990 to 2019. Prevalence rates showed a mostly positive correlation with time during this period. The prevalence rate of schizophrenia for males was consistently higher than for females in LMICs from 1990 to 2019. While most trends were increasing, the incidence rates of schizophrenia for both genders in upper-income countries decreased significantly from 2009 to 2019 (male: 20.41 to 18.72; female: 18.40 to 16.32). Burn et al. [2013] found that countries with higher income inequality tend to have a higher incidence rate of schizophrenia. The burden of schizophrenia for all measures is positively correlated with the country’s income tier, and male burden is always higher than female burden regardless of the measure method and income tier. Countries in East and Southeast Asia, and Eastern Europe tend to have the highest burden of schizophrenia, while African countries have the lowest burden. Social isolation is highly related to schizophrenia, so creating a more inclusive and equal society may help reduce its burden. Further research is needed to investigate risk factors and treatments for schizophrenia. Conclusions: Schizophrenia is a mental health burden that is prevalent in LMICs in East and Southeast Asia, and East Europe, and is positively correlated with the country’s income tier, with males experiencing a consistently higher burden than females, while income inequality and social isolation are contributing factors, necessitating the need for further investigation and a more inclusive society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".