Congenital Cytomegalovirus Infection Burden and Epidemiologic Risk Factors in Countries With Universal Screening
Bibliographic record
Abstract
Importance: Congenital cytomegalovirus (cCMV) infection is the most common congenital infection and the leading acquired cause of developmental disabilities and sensorineural deafness, yet a reliable assessment of the infection burden is lacking. Objectives: To estimate the birth prevalence of cCMV in low- and middle-income countries (LMICs) and high-income countries (HICs), characterize the rate by screening methods, and delineate associated risk factors of the infection. Data Sources: MEDLINE/PubMed, Scopus, and Cochrane Database of Systematic Reviews databases were searched from January 1, 1960, to March 1, 2021, and a total of 1322 studies were identified. Study Selection: Studies that provided data on the prevalence of cCMV derived from universal screening of infants younger than 3 weeks were included. Targeted screening studies were excluded. Data Extraction and Synthesis: Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed. Extraction was performed independently by 3 reviewers. Quality was assessed using the Newcastle-Ottawa Scale for cohort studies. Random-effects meta-analysis was undertaken. Metaregression was conducted to evaluate the association of sociodemographic characteristics, maternal seroprevalence, population-level HIV prevalence, and screening methods with the prevalence of cCMV. Main Outcomes and Measures: Birth prevalence of cCMV ascertained through universal screening of infants younger than 3 weeks for CMV from urine, saliva, or blood samples. Results: Seventy-seven studies comprising 515 646 infants met the inclusion criteria from countries representative of each World Bank income level. The estimated pooled overall prevalence of cCMV was 0.67% (95% CI, 0.54%-0.83%). The pooled birth prevalence of cCMV was 3-fold greater in LMICs (1.42%; 95% CI, 0.97%-2.08%; n = 23 studies) than in HICs (0.48%; 95% CI, 0.40%-0.59%, n = 54 studies). Screening methods with blood samples demonstrated lower rates of cCMV than urine or saliva samples (odds ratio [OR], 0.38; 95% CI, 0.23-0.66). Higher maternal CMV seroprevalence (OR, 1.19; 95% CI, 1.11-1.28), higher population-level HIV prevalence (OR, 1.22; 95% CI, 1.05-1.40), lower socioeconomic status (OR, 3.03; 95% CI, 2.05-4.47), and younger mean maternal age (OR, 0.85; 95% CI, 0.78-0.92, older age was associated with lower rates) were associated with higher rates of cCMV. Conclusions and Relevance: In this meta-analysis, LMICs appeared to incur the most significant infection burden. Lower rates of cCMV were reported by studies using only blood or serum as a screening method.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".