A Novel Municipal-Level Approach to Uncover the Hidden Burden of Hepatitis C: A Replicable Model for National Elimination Strategies
Bibliographic record
Abstract
BACKGROUND: Hepatitis C Virus (HCV) remains a global health challenge as WHO elimination targets are not achievable in most countries, mainly due to the high number of undiagnosed individuals. In Italy, where national elimination efforts are ongoing, regional disparities further hinder progress. This study aimed to characterize the hidden burden of chronic HCV infection across t he territory of the Province of Salerno, Southern Italy, to suggest a novel municipal-level screening approach, with implications for national strategies. METHODS: We analyzed records of residents diagnosed with chronic HCV infection and linked to care between 2015 and 2022. Data included age, sex, municipality of residence, HCV genotype, and fibrosis stage. Observed prevalence was compared with expected prevalence derived from national/regional benchmarks. Municipalities were categorized as urban or rural based on the resident population. RESULTS: A total of 3528 cases were identified across 139 municipalities. Patients had a mean age of 63 years, and 54% were male. Half were diagnosed at an advanced stage (F3-F4), with genotype 1b being predominant. The hidden burden increased with age and showed a higher prevalence in rural areas compared to urban ones, with values of about 7 vs. 3 per 1000 inhabitants respectively. Logistic regression analysis identified age, male sex, urban residence, and genotype 1b as factors associated with advanced fibrosis or cirrhosis. CONCLUSIONS: This is the first Italian study to apply a standardized municipal-level classification to quantify the hidden burden of HCV. The model identifies underdiagnosed areas, highlights urban-rural disparities (a higher degree of underdiagnosis in rural areas versus a higher frequency of late diagnosis in urban ones), and provides a replicable tool for precision public health. Its adoption could enhance national HCV elimination efforts by supporting targeted screening, optimized resource allocation, and equitable access to care.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".