Prevnar 7 Childhood Immunization Program and Serotype Replacement: Changes in Pneumococcal Incidence and Resulting Impact on Health Care Costs in Alberta (2003–2008)
Bibliographic record
Abstract
OBJECTIVE: Acute respiratory tract infections caused by Streptococcus pneumoniae are a leading cause of morbidity and mortality in young children and the elderly. In 2002, Alberta introduced a pneumococcal universal immunization program for children, using Pfizer's Prevnar 7, a 7-valent pneumococcal conjugate vaccine (PCV7). In this study, we explored the impact of the immunization program on the burden of disease and related health care costs in Alberta, in the context of serotype replacement. METHODS: Using surveillance data from Alberta, we examined the change in costs averted as a result of a decline in invasive pneumococcal disease (IPD) cases caused by PCV7 serotypes, as well as the increase in costs due to serotype replacement. We also calculated the magnitude of positive externalities (indirect effects) in terms of costs averted. RESULTS: We found that following the introduction of PCV7 (2003-2008), the number of cases of IPD caused by vaccine serotypes declined significantly across all ages. Non-PCV7 IPD cases, on the other hand, increased. Net costs were averted as a result of the implementation of PCV7 universal vaccination in Alberta, after accounting for serotype replacement. CONCLUSION: On the basis of the analysis of serotype-specific pneumococcal data, the impact of the Prevnar public immunization program on direct health costs averted in Alberta as a result of reducing IPD cases caused by PCV7 strains amounted to $5.5 million (in 2008 Canadian dollars). However, the unintended effects of serotype replacement resulted in costs incurred of nearly $1.9 million. As a result, on net, the total cost savings for Alberta amounted to about $3.6 million. Irrespective of serotype replacement, the PCV7 immunization program has had a positive impact in terms of health benefits, which translates into health service costs averted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".