Multidimensional analysis of costs and outcomes in a Colombian cohort of pediatric <i>Streptococcus pneumoniae</i> meningitis cases (2017–2022)
Bibliographic record
Abstract
meningitis poses a significant public health challenge, imposing substantial burdens on healthcare systems. This study analyzed the costs and outcomes of pneumococcal meningitis in a Colombian cohort of children from 2017 to 2022, using correspondence analysis and cost assessment to explore the impact of vaccination. A retrospective cohort study was conducted, analyzing children under 18 years diagnosed with pneumococcal meningitis. A total of 55 cases were analyzed in this cohort study, which, while representative of confirmed cases in the country, reflects the rarity of the disease and poses statistical limitations. Direct healthcare costs were calculated in 2023 USD, and data were assessed using cluster and multiple correspondence analyses (MCA) to evaluate relationships between vaccination status, serotypes, clinical outcomes, and costs. The MCA revealed two main dimensions that collectively explained 31.2% of the total variance. Median direct healthcare costs were ~$2,900 (range ~$500-$21,000), with ICU admission as the primary cost driver. PCV13 vaccination correlated with lower overall patient costs and better clinical outcomes. MCA revealed clustering of vaccinated patients with favorable outcomes and reduced costs, while unvaccinated patients aligned with severe outcomes and higher expenses. This study explores the clinical and economic impact of pneumococcal meningitis in pediatric patients, highlighting the potential protective role of PCV13 vaccination. MCA provided an exploratory framework for examining relationships between vaccination status, serotypes, costs, and outcomes.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".