Increased prevalence and incidence of anemia among adults in transforming rural China: two cross-sectional surveys
Bibliographic record
Abstract
BACKGROUND: Anemia remains one of the serious nutrition-related diseases in China, but data on incidence of anemia were less available, especially in rural area which are experiencing rapid urbanization. Out study aimed to estimate both the prevalence and incidence of anemia in transforming rural China. METHODS: We conducted a combined study of rural adults 18-64 years of age with a repeated cross-sectional component (4456 in 2006 and 2184 in 2008) and a cohort component (1424) in rural Deqing, China. Anemia was diagnosed based on blood hemoglobin levels using the hemiglobincyanide (HiCN) method according to both the World Health Organization (WHO) and Chinese criteria. The prevalence and incidence of anemia and their 95% confidential intervals (95% CI) were calculated. RESULTS: The prevalence of anemia based on the WHO criteria was 51.5% in 2006 and 53.7% in 2008, and the 2-year cumulative incidence was 42.1%. Of the cases, over 95% had mild anemia. The prevalence was much lower when the Chinese criteria was used. Both the prevalence and incidence were higher in women than in men and significantly increased with age in men. In both sexes, the incidence sharply increased after 45 years of age. CONCLUSION: Our study showed a high prevalence and incidence of anemia among adults in rural Deqing, China. Monitoring and intervention were needed urgently, especially among individuals over 45 years of age.
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 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 | 0.000 | 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) | 0.001 | 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".