Seroprevalence of viral hepatitis A, B, C, D and E viruses in the Hormozgan province southern Iran
Bibliographic record
Abstract
BACKGROUND: Viral hepatitis is a global public health problem affecting millions of people worldwide, causing thousands of deaths due to acute and persistent infection, cirrhosis, and liver cancer. Providing updated serologic data can improve both surveillance and disease control programs. This study is aimed to determine the seroprevalence of markers for viral hepatitis (A, B, C, D and E) and the epidemiology of such infections in the general population of southern Iran's Hormozgan province. METHODS: Between 2016 and 2017, a total of 562 individuals with ages ranging from 1 to 86 years, who visited governmental public laboratories for routine check-ups, were tested for the presence of serological markers to hepatitis virus types A to E using enzyme-linked immunosorbent assays. RESULTS: The overall anti-hepatitis A virus (HAV) antibody seroprevalence was 93.2% (524/562). The prevalence of anti-hepatitis E virus (HEV) antibodies was 15.8% (89/562) among which 1.6% (9/562) of the seropositive individuals also had evidence of recent exposure to the virus (IgM positivity). Two and a half percent (14/562) were positive for hepatitis B surface (HBs) antigen, whereas 11.6% (65/562) tested positive for anti-hepatitis B core (HBc) antibodies. Among anti-HBc positive patients, 11% (7/65) had HBs Ag and 5% (3/65) were positive for anti-hepatitis D virus (HDV) antibodies. The prevalence of anti-hepatitis C virus (HCV) antibodies was 0.7% (4/562). The seroprevalence of anti-HAV, HEV IgG, anti-HBc antibodies, and HBs Ag increased with age. CONCLUSION: The present study confirms a high seroprevalence of HAV infection among the examined population and reveals high levels of endemicity for HEV in the region. Planned vaccination policies against HAV should be considered in all parts of Iran. In addition, improvements on public sanitation and hygiene management of drinking water sources for the studied area are recommended.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".