Identifying Gaps and Challenges in Acute Hepatitis B Surveillance in the Country of Georgia: Comprehensive Surveillance System Evaluation
Bibliographic record
Abstract
Background: In 2012, the country of Georgia established an electronic integrated disease surveillance system (EIDSS) for acute hepatitis B virus (HBV) infection. All medical facilities must report suspected and confirmed acute HBV cases to the regional public health centers within 24 hours, which are subsequently registered in the EIDSS. Objective: This study aims to evaluate the acute hepatitis B surveillance system in Georgia in order to identify areas for improvement and develop recommendations that enhance its capacity to inform prevention and response efforts, supporting the elimination of viral hepatitis. Methods: For the evaluation of the acute HBV surveillance system from 2015 to 2020, we used the US Centers for Disease Control and Prevention updated guidelines. We assessed data quality by calculating the percentage of missing values for key variables. We assessed simplicity, acceptability, and flexibility by describing surveillance processes and by surveying public health center epidemiologists. We evaluated representativeness by comparing cases registered in EIDSS with cases registered in hospital discharges. We assessed timeliness by calculating the number of days from the date of diagnosis to the date of notification in EIDSS. We calculated the positive predictive value as the proportion of cases notified during 2018-2020 having documentation of confirmatory tests in their medical records, meeting the confirmed case definition. Results: During 2015-2020, 270 cases of acute viral hepatitis B were reported to the EIDSS. All notified cases were hepatitis B surface antigen positive. However, only 10 of the 19 (53%) key variables were complete. Hepatitis B test results were missing in most reported cases, despite 82% (223/270) being classified as "confirmed." Simplicity and acceptability of the system were affected by 30% (31/104) of the respondents experiencing challenges with the EIDSS reporting form. The system had limited flexibility due to cumbersome procedures to implement any changes. Representativeness was limited, as only 41% (270/657) of confirmed cases recorded in the hospital discharge database were reported to the EIDSS. The average notification delay was 72 hours. Among 104 cases notified in 2018-2020, 66 met the case definition, leading to a positive predictive value of 63%. Conclusions: The surveillance system for acute HBV infection was timely, although only 51% (139/270) of the cases were reported within the 24-hour notification target. The system was not representative and did not correctly ascertain cases. We recommend reconsidering the statutory notification time of 24 hours, revising notification forms and providing clear guidelines for data entry, and reporting all test results needed for adequate case classification to enhance data completeness and reliability of case classification.
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 | 0.137 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".