The epidemiology of aseptic meningitis in New Zealand children from 1991 to 2020
Bibliographic record
Abstract
AIM: Aseptic meningitis, including culture negative and viral meningitis, contributes a significant health-care burden, including unnecessary antibiotic use and hospitalisation to treat possible bacterial meningitis. This study analysed aseptic meningitis hospitalisations in New Zealand (NZ) children over 29 years. METHODS: In this population-based study, aseptic meningitis hospitalisations in NZ children <15 years old were analysed from 1991 to 2020. Incident rate ratios were calculated using Poisson regression models. Variations in hospitalisations by age, year, sex, ethnicity, geographical region and socio-economic deprivation were analysed. RESULTS: There were 5142 paediatric aseptic meningitis hospitalisations from 1991 to 2020. Most were unspecified viral meningitis (64%), followed by enterovirus (29%). Hospitalisation rates varied annually with a median of 18.4/100 000 children including a peak in 2001 of 56.4/100 000 (51.7-61.6). From 2002 to 2019, rates increased by 8.4%/year (7.2-9.5%) in infants <90 days old but decreased in all other age groups. In 2020, a reduction in hospitalisations to 9.6/100 000 (7.9-11.8) occurred, and in infants <90 days old were 0.37 times expected. Hospitalisations were 1.50 times (1.49-1.68) higher in males than females; higher in children of Māori (P < 0.001) and Pacific (P < 0.001) versus European ethnicity; and higher for children living in the most (2.44 times, (2.16-2.75)) versus least deprived households; and in northern versus southern NZ. CONCLUSIONS: Aseptic meningitis hospitalisations increased in young infants during 29 years of surveillance, apart from 2020 when admissions reduced during the COVID-19 pandemic. In contrast, hospitalisations decreased in children aged >1 year. Further investigation into reasons for higher admissions by ethnic group, geographical location and increased deprivation are required.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".