Estimated cytomegalovirus seroprevalence in the general population of the United States and Canada
Bibliographic record
Abstract
Seroprevalence data for cytomegalovirus (CMV), a widespread virus causing lifelong infection, vary widely, and contemporary data from the United States (US) and Canada are limited. Utilizing a modeling approach based on a literature review (conducted August, 2022) of data published since 2005, we determine age-, sex-, and country-specific CMV seroprevalence in the general US and Canadian populations. Sex-specific data were extracted by age categories, and a random-effects meta-regression model was used to fit the reported data (incorporating splines for the US). Seven studies reported US CMV seroprevalence (both sexes, aged 1‒89 years); all used National Health and Nutrition Examination Survey data. Due to limited population-based studies, Canadian estimates were modeled using other limited country data. In both countries, modeled seroprevalence estimates increased with age and were higher in females versus males (US: 49.0% vs. 41.6% at 18‒19 years; 61.5% vs. 50.0% at 38‒39 years; Canada: 23.7% vs. 13.7% at 18‒19 years; 32.6% vs. 22.6% at 38‒39 years). Notably, by young adulthood, one-half of US and one-quarter of Canadian females have acquired CMV. The observed differences in CMV seroprevalence in the US and Canada may partially reflect variations in general population characteristics.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".