Neonatal Herpes Simplex Virus Infection Among Medicaid-Enrolled Children: 2009–2015
Bibliographic record
Abstract
OBJECTIVES: To examine the incidence, mortality, and health care use related to neonatal herpes simplex virus (HSV) infection. METHODS: A retrospective longitudinal cohort study using a multistate Medicaid claims database. We identified neonates hospitalized with HSV infection from 2009 to 2015 by using discharge diagnosis codes and managed them for 6 months after discharge. Incidence rates were corrected for the imperfect sensitivity and specificity of thediagnosis codes for identifying HSV infection. RESULTS: Of 2 107 124 births from 2009 to 2015, 900 neonates were identified with HSV infection, with a corrected incidence rate of 4.5 (95% confidence interval [CI]: 4.2–4.8) per 10 000 births. The yearly disease incidence increased by 56%, from 3.4 (95% CI: 2.8–4.2) per 10 000 births (or 1 in 2941 births) in 2009 to 5.3 (95% CI: 4.6–6.1) per 10 000 births (or 1 in 1886 births) in 2015 (P < .001). Of the 900 neonates with HSV infection, 54 (6.0% [95% CI: 4.4%–7.6%]) died during the index hospitalization; there was no increase in the yearly mortality rate. Of the 692 (81.2%) infants with follow-up data, 316 (45.7%) had an emergency department visit, and 112 (16.2%) had a hospital readmission. Total payments at 6 months amounted to $60 620 431, a median of $87 602 per case of neonatal HSV infection. CONCLUSIONS: We observed an increase in neonatal HSV infection incidence over a recent 7-year period in a Medicaid population. Associated health care use and payments were substantial. Public health interventions targeting disease prevention and early diagnosis are needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".