1397. Long term impact of the 13-valent pneumococcal conjugate vaccine use in infant immunization program on all-cause pneumonia hospitalizations in British Columbia, Canada: a time series analysis
Bibliographic record
Abstract
Abstract Background Pneumonia is a leading cause of hospitalization and in-patient mortality globally. We determined the impact of 13-valent pneumococcal conjugate vaccine (PCV13) use on all-cause pneumonia hospitalization rates eight years after the vaccine was introduced in British Columbia, Canada. Methods Routine administrative databases, such as, hospital discharge abstract databases, registry and demographics were used to build the cohort. Overall and age-specific all-cause pneumonia hospital admissions per month (Jan 2000 to Dec 2018) for those aged < 2 years, 2-5 years, 6-17 years, 18-64 years and ≥ 65 years were obtained using International Classification of Diseases 9 and 10 codes (480-486, J12-J18). Changes in the all-cause pneumonia hospitalization incidence rates before and after the PCV13 vaccine program introduction were evaluated using a negative binomial regression and time-series modelling while adjusting for seasonality, influenza-likeness illnesses, 7-valent pneumococcal conjugate vaccine (PCV7) program and pre-PCV13 vaccine secular trends. Results Long term use of the PCV13 vaccine in the infant immunization program was associated with significant declines in all-cause pneumonia hospitalization rates among all children, < 2 years (IRR: 0.63; 95% Confidence Interval (CI): 0.59-0.67), 2-5 years (IRR: 0.82; 95%CI: 0.77-0.87) and 6-17 years (IRR: 0.73; 95%CI: 0.69-0.78). All-cause pneumonia rates did not change significantly in those aged 18-64 years (IRR: 0.98; 95%CI: 0.96-1), whereas a modest increase was observed in those 65 years and over (IRR: 1.05; 95%CI: 1.02-1.07). Consequently, we did not observe significant change in the overall rate (IRR: 1.02; 95%CI: 1-1.02). Conclusion Significant reduction in all-cause pneumonia hospitalization rates in children demonstrates long term beneficial effect of PCV13 use. A modest increase in all-cause pneumonia hospitalization rates in adults aged 65 years and over indicates a need for further microbial investigation. Disclosures Nirma Khatri Vadlamudi, BA, BS, MPH, Pfizer Inc (Research Grant or Support) Fawziah Marra, BSc (Pharm), PharmD, Pfizer Inc (Research Grant or Support)
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".