Electronic notifiable disease reporting system from primary care health centres in Qatar: a comparison of paper-based versus electronic reporting
Bibliographic record
Abstract
Communicable disease outbreaks can spread rapidly, causing enormous losses to individual health, national economies and social well-being. Therefore, communicable disease surveillance is essential for protecting public health. In Qatar, electronic reporting from primary health centres was proposed as a means of improving disease notification, replacing a paper-based method of reporting (via internal mail, facsimile, email or telephone), which has disadvantages and requires active cooperation and engagement of staff. This study is a predescriptive and postdescriptive analysis, which compared disease notifications received from electronic and paper-based systems during 3-month evaluation periods (quarter 2 in 2016 and quarter 2 in 2018 for paper-based and electronic reporting, respectively) in terms of comprehensiveness, timeliness and completeness. For the 23 notifiable diseases included in this study, approximately twice as many notifications were received through the electronic reporting system as from the paper-based reporting system, demonstrating it is more comprehensive. An overall increase in notifications is likely to have a positive public health impact in Qatar. 100% of electronic notifications were received in a timely manner, compared with 28% for paper-based notifications. Findings of the study show that electronic reporting presents a revolutionary opportunity to advance public health surveillance. It is recommended that electronic reporting be rolled out more widely to improve the completeness, stability and representativeness of the national public health surveillance system in Qatar as well as other countries.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".