Under-reporting of TB cases and associated factors: a case study in China
Bibliographic record
Abstract
BACKGROUND: Tuberculosis is a leading cause of death worldwide and has become a high global health priority. Accurate country level surveillance is critical to ending the pandemic. Effective routine reporting systems which track the course of the epidemic are vital in addressing TB. China, which has the third largest TB epidemic in the world and has developed a reporting system to help with the control and prevention of TB, this study examined its effectiveness in Eastern China. METHODS: The number of TB cases reported internally in two hospitals in Eastern China were compared to the number TB cases reported by these same hospitals in the national reporting systems in order to assess the accuracy of reporting. Qualitative data from interviews with key health officials and researcher experience using the TB reporting systems were used to identify factors affecting the accuracy of TB cases being reported in the national systems. RESULTS: This study found that over a quarter of TB cases recorded in the internal hospital records were not entered into the national TB reporting systems, leading to an under representation of national TB cases. Factors associated with underreporting included unqualified and overworked health personnel, poor supervision and accountability at local and national levels, and a complicated incohesive health information management system. CONCLUSIONS: This study demonstrates that TB in Eastern China is being underreported. Given that Eastern China is a developed province, one could assume similar problems may be found in other parts of China with fewer resources as well as many low- and middle-income countries. Having an accurate account of the number of national TB cases is essential to understanding the national and global burden of the disease and in managing TB prevention and control efforts. As such, factors associated with underreporting need to be addressed in order to reduce underreporting.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".