Underreporting of hepatitis A in non-endemic countries: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Information on reporting completeness of passive surveillance systems can improve the quality of and public health response to surveillance data and better inform public health planning. As a result, we systematically reviewed available literature on reporting completeness of hepatitis A in non-endemic countries. METHODS: We searched Medline, EMBASE and grey literature sources, restricting to studies published in English between 1997 and 21 May 2015. Primary studies on hepatitis A surveillance and underreporting in non-endemic countries were included, and assessed for risk of bias. A pooled proportion of reporting completeness was estimated using a DerSimonian-Laird random-effects model. RESULTS: Diagnosed hepatitis A cases identified through positive laboratory tests, physician visits, and inpatient hospital discharges were underreported to public health in all eight included studies. Reporting completeness ranged from 4 to 97 % (pooled proportion 59 %, 95 % confidence interval = 32 %, 84 %). Substantial heterogeneity was observed, which may be explained by differences in the referent data sources used to identify diagnosed cases and in case reporting mechanisms and/or staffing infrastructure. Completeness was improved in settings where case reporting was automated or where dedicated staff had clear reporting responsibilities. CONCLUSIONS: Future studies that evaluate reporting completeness should describe the context, components, and operations of the surveillance system being evaluated in order to identify modifiable characteristics that improve system sensitivity and utility. Additionally, reporting completeness should be assessed across high risk groups to inform equitable allocation of public health resources and evaluate the effectiveness of targeted interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".