Gender and regional disparities of tuberculosis in Hunan, China
Bibliographic record
Abstract
INTRODUCTION: Major efforts have been made to improve the health care system in Hunan province, China. The aims of this study were to assess whether and to what extent these efforts have impacted on gender and regional disparities of Tuberculosis (TB) incidence in recent years, especially for less developed areas. METHODS: We obtained data from the 2005-2009 China Information System for Disease Control and Prevention (CISDCP)to conduct this study in Hunan province. Counties within the province were divided into four regions according to quartiles based on the 2007 per capita GDP. Index of Disparity (ID) and Relative Index of Inequality (RII) were used to measure the disparities of TB incidence in relation to gender and region. Bootstrap technique was used to increase the precision. RESULTS: The average annual incidence of TB was 111.75 per 100,000 in males and 43.44 per 100 000 in females in Hunan. The gender disparity was stable, with ID from 42.34 in 2005 to 43.92 in 2009. For regional disparity, ID, RII (mean) and RII (ratio) decreased significantly from 2005 to 2009 in males (P < 0.05) but remained stable among the female population. CONCLUSIONS: As interventions such as introduction of the New Rural Cooperative Scheme put in place to reduce health disparities in China, regional disparity in relation to incidence of TB decreased significantly, but the gender disparity remains in the Hunan province.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".