Epidemiological characteristics of hepatitis A before and after inclusion of hepatitis A vaccine in expanded program on immunization in Dalian
Bibliographic record
Abstract
Objective To compare the epidemiological characteristics of hepatitis A before and after the inclusion of hepatitis A vaccine in Expanded Program on Immunization (EPI) in Dalian. Methods The data of hepatitis A cases from 1998 to 2017 in Dalian were collected. Changes in temporal, spartial and populational distributions of hepatitis A were analyzed. Results A total of 34 693 hepatitis A cases were reported in Dalian from 1998 to 2017. The average annual incidence of hepatitis A was 28.60/100 000. The annual incidence rates decreased from 55.97/100 000 before EPI inclusion to 5.42/100 000 after EPI inclusion (P<0.01). Before EPI inclusion, the proportion of hepatitis A in the second quarter was the highest (44.98%) and the incidence in urban area was 81.31/100 000. Of all occupations, the proportion of unemployed was the highest, accounting for 24.31% of all cases. After EPI inclusion, the proportion of hepatitis A in the first quarter was the highest, accounting for 40.97% and the incidence in rutal area was 7.70/100 000. Of all the occupations, the proportion of farmer was the highest, accounting for 37.88% of all cases. Conclusions After the Hepatitis A vaccine was included in the EPI in Dalian, epidemic of hepatitis A was well controlled. Surveillance should be enhanced and integrated control strategy should be formulated for vulnerable population. Key words: Hepatitis A; Epidemiological characteristics; Immunization program
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".