Substantial impact of the COVID-19 pandemic on the reported number of diagnosed chronic hepatitis C virus infections in the Netherlands, 2019–2021
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has widespread consequences for health facilities, social contacts, and health-seeking behaviour, affecting the incidence, diagnosis and reporting of other infectious diseases. We examined trends in reported chronic hepatitis C virus (HCV) infections and associated transmission routes in the Netherlands to identify the potential impact of COVID-19 on access to healthcare (testing) services. METHODS: We analysed notification data of patients with chronic HCV reported to the National Notifiable Disease Surveillance System from January 2019 until December 2021 in the Netherlands. Rates of newly reported chronic cases per 100,000 population with 95% confidence intervals (CI) were calculated, and we compared proportional changes in transmission routes for chronic HCV between 2019, 2020 and 2021. RESULTS: During the study period, a total of 1,521 chronic HCV infections were reported, 72% males, median age 52 years, and an overall rate of 8.8 (95%CI 8.4-9.2) per 100,000 population. We observed an overall decline (-41.9%) in the number of reported chronic HCV in 2020 compared to 2019, with the sharpest decline in men who have sex with men (MSM)-related transmission (-57.9% in 2020, p = 0.005). CONCLUSIONS: Reported cases of chronic HCV strongly declined during the COVID-19 pandemic when healthcare services were scaled down. Between February and June 2021, reported chronic HCV cases increased again, indicating a recovery of healthcare services. MSM showed the largest decline compared to other groups. Further research is needed to fully understand the impact of access to healthcare, health seeking behaviour, and (sexual) transmission risks of HCV during the COVID-19 pandemic.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".