Invasive Pneumococcal Disease in Alaskan Children
Bibliographic record
Abstract
BACKGROUND: Alaska Native (AN) children, especially those in the Yukon-Kuskokwim region (YK-AN children), suffer some of the highest rates of invasive pneumococcal disease (IPD) in the world. Rates of IPD declined after statewide introduction of the 7-valent pneumococcal conjugate vaccine (PCV7) in 2001, but increased in subsequent years. METHODS: Population-based laboratory surveillance data (1986-2007) for invasive Streptococcus pneumoniae infection in Alaskan children <5 years old were used to evaluate the association of IPD rates and serotype distribution with immunization, socioeconomic status, and in-home water service. RESULTS: Introduction of PCV7 vaccine resulted in elimination of IPD caused by vaccine serotypes, but was followed by increasing rates of IPD caused by nonvaccine serotypes. Among YK-AN children IPD rates dropped by 60%, but then rose due to non-PCV7 serotypes to levels 5- to 10-fold higher than rates in non-YK-AN children and non-AN children. IPD rates in YK-AN children were twice as high in villages where <10% of houses had in-home piped water compared with villages where more than 80% of houses had in-home piped water (390 cases/100,000 vs. 146 cases/100,000, P = 0.008). CONCLUSIONS: High IPD rates in Alaska are associated with lack of in-home piped water (controlling for household crowding and per capita income). The effect of in-home piped water is most likely mediated through reduced water supply leading to limitations on handwashing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".