Acute care utilization due to hospitalizations for pediatric lower respiratory tract infections in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Pediatric LRTI hospitalizations are a significant burden on patients, families, and healthcare systems. This study determined the burden of pediatric LRTIs on hospital settings in British Columbia and the benefits of prevention strategies as they relate to healthcare resource demand. METHODS: LRTI inpatient episodes for patients <19 years of age during 2008-2010 were extracted from the BC Discharge Abstract Database. The annual number of acute care beds required to treat pediatric LRTIs was estimated. Sub-analyses determined the burden due to infants <1 year of age and high-risk infants. Population projections were used to forecast LRTI hospitalizations and the effectiveness of public health initiatives to reduce the incidence of LRTIs to 2020 and 2030. RESULTS: During 2008-2010, LRTI as the primary diagnosis accounted for 32.0 and 75.9% hospitalizations for diseases of the respiratory system in children <19 years of age and infants <1 year of age, respectively. Infants <1 year of age accounted for 47 and 77% hospitalizations due to pediatric LRTIs and pediatric LRTI hospitalizations specifically due to respiratory syncytial virus (RSV), respectively. The average length of stay was 3.1 days for otherwise healthy infants <1 year of age and 9.1 days for high-risk infants (P <0.0001). 73.1% pediatric LRTI hospitalizations occurred between November and April. Over the study timeframe, 19.6 acute care beds were required on average to care for pediatric LRTIs which increased to 64.0 beds at the peak of LRTI hospitalizations. Increases in LRTI bed-days of 5.5 and 16.2% among <19 year olds by 2020 and 2030, respectively, were predicted. Implementation of appropriate prevention strategies could cause 307 and 338 less LRTI hospitalizations in <19 year olds in 2020 and 2030, respectively. CONCLUSION: Pediatric LRTI hospitalizations require significant use of acute care infrastructure particularly between November and April. Population projections show the burden may increase in the next 20 years, but implementation of effective public health prevention strategies may contribute to reducing the acute care demand and to supporting efforts for overall pediatric healthcare sustainability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".