A Geographically Weighted Cost-effectiveness Analysis of Newborn Cytomegalovirus Screening
Bibliographic record
Abstract
Background: Early identification of newborns with congenital cytomegalovirus (CMV) is necessary to provide antiviral therapy and other interventions that can improve outcomes. Prior research demonstrates that universal newborn CMV screening would be the most cost-effective approach to identifying newborns who are infected. CMV is not uniformly prevalent, and it is uncertain whether universal screening would remain cost-effective in lower-prevalence neighborhoods. Our aim was to identify geographic heterogeneity in the cost-effectiveness of universal newborn CMV screening by combining a geospatial analysis with a preexisting cost-effectiveness analysis. Methods: This study used the CMV testing results and zip code location data of 96 785 newborns in 7 metropolitan areas who had been tested for CMV as part of the CMV and Hearing Multicenter Screening study. A hierarchical bayesian generalized additive model was constructed to evaluate geographic variability in the odds of CMV. The zip code-level odds of CMV were then used to weight the results of a previously published model evaluating universal CMV screening vs symptom-targeted screening. Results: The odds of CMV were heterogeneous over large geographic scales, with the highest odds in the southeastern United States. Universal screening was more cost-effective and afforded more averted cases of severe hearing loss than targeted testing. Universal screening remained the most cost-effective option even in areas with the lowest CMV prevalence. Conclusions: Universal newborn CMV screening is cost-effective regardless of underlying CMV prevalence and is the preferred strategy to reduce morbidity from congenital CMV.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".