224. Evaluating the Epidemiology of Bloodstream Infections: A Population-Based Study
Bibliographic record
Abstract
Abstract Background Bloodstream infections (BSI) are a major cause of morbidity, mortality, and health care costs worldwide. Population-based studies are key to assess BSI epidemiology over time while minimizing selection bias but remain limited. Therefore, we aimed to assess the incidence of BSI in a large Canadian health region in a contemporary period. We hypothesized that there would be significant age and sex-based differences including over time. Methods We conducted a retrospective cohort study from 2011 through 2018 using a population-based microbiology database to determine the annual age- and sex-specific BSI testing and case rates with the census as the population reference. BSI was defined as a positive blood culture for a pathogen. Episodes > 30 days apart were included for analysis. Incidence rate ratios (IRR) for testing and case rates including by sex were calculated to assess changes over time. All analyses were run at a two-sided α of 0.05 and were conducted with R 4.0.4. Results A total of 154,147 distinct individuals (49.9% male) were analyzed and 22,869 (14.8%) had a BSI at the first encounter in the study period. Overall BSI testing incidence ranged from 1529 to 1707 per 100,000 person-years and case incidence ranged from 180 to 292 per 100,000 person-years. Testing and case incidence for BSI was greatest in the 0-4 and 75+ years age groups (p < 0.01). Males compared to females had greater testing and case incidence rates in young and old age groups, but females had greater rates in the 15-44 years groups (p < 0.01). Overall IRR for cases comparing 2018 to 2011 was 0.62 (95% CI 0.59-0.65) reflecting a significant decrease over time. Testing also decreased over the study period with an IRR of 0.90 (95% CI 0.88-0.91). Testing and case IRRs were not significantly different stratified by sex. Incidence rates (per 100,000 person-years) of BSI testing and cases by sex from 2011 through 2018 in a Canadian health region Conclusion In our large population-based study of BSI, we identified that BSI remain frequent and the youngest and oldest age groups as well as males in these age groups have the greatest BSI incidence rates which may reflect both biological sex and gender-based differences. Encouragingly, BSI incidence rates have decreased over time at a greater increment relative to testing rates. Future studies of BSI should focus on pathogen and outcome-based evaluations. Disclosures All Authors: No reported disclosures
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".