The Effect of Coarse Ambient Particulate Matter on First, Second, and Overall Hospital Admissions for Respiratory Disease Among the Elderly
Bibliographic record
Abstract
The objective of this article is to examine differences in the effect of ambient particulate matter on first, second, and overall hospital admissions for respiratory disease among the elderly. We studied 8989 adults 65 yr of age or older living in the greater Vancouver area who were admitted to hospital for any acute respiratory disease (ICD-9 codes 460-519) between June 1, 1995, and March 31, 1999. Time-series analysis was used to evaluate the association between respiratory admissions and daily measures of particulate matter (PM10, PM2.5, and PM10 - 2.5) in urban air, after adjustment for gaseous copollutants (CO, O3, NO2, and SO2) and meteorological variables. Repeated admissions for respiratory disease were common among the elderly. Approximately 30% of the subjects were readmitted to hospital after the first admission; 9% had more than 2 admissions for respiratory disease during the 4-yr study period. PM10 - 2.5 was significantly associated with the second and overall admissions for respiratory disease, but not with the first admission. The adjusted relative risks for an increment of 4.2 microg/m(3) in -day average PM10 - 2.5 concentrations were 1.03 (95% confidence interval: 0.98-1.09) for the first admission, 1.22 (1.10-1.36) for the second admission, and 1.06 (1.02, 1.11) for overall admissions. There was no significant association between PM2.5 and hospital admissions for respiratory disease among the elderly. Our data suggest that (1) people with a history of respiratory admissions are at a higher risk of respiratory disease in relation to particulate air pollution in urban areas, (2) analyses based on overall rather than repeated hospital admissions lead to lower estimates of the risk of respiratory disease associated with particulate air pollution, and (3) PM10 - 2.5 has a larger effect on respiratory admissions than PM2.5.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".