Occupational health impact of the 2009 H1N1 flu pandemic: surveillance of sickness absence
Bibliographic record
Abstract
OBJECTIVES: Workplace absences due to illness can disrupt usual operations and increase costs for businesses. This study of sickness absence due to influenza and influenza-related illness presents a unique opportunity to characterise and measure the impact of the 2009 (H1N1) pandemic, by comparing trends during the pandemic to those of previous years, and adding this information to that obtained by traditional epidemiological surveillance systems. METHODS: We compared the numbers of cases of sickness absence due to illness caused by influenza and influenza-related illness in 2007-2009, and in the first 3 months of 2010 in Catalonia (n=811 940) using a time series approach. Trends were examined by economic activity, age and gender. The weekly endemic-epidemic index (EEI) was calculated and its 95% CI obtained with the delta method, with observed and expected cases considered as independent random variables. RESULTS: Influenza activity peaked earlier in 2009 and yielded more cases than in previous years. Week 46 (in November 2009) had the highest number of new cases resulting in sickness absence (EEI 20.99; 95% CI 9.44 to 46.69). Women and the 'education, health and other social activities' sector were the most affected. CONCLUSIONS: Results indicate that the new H1N1 pandemic had a significant impact on business, with shifts in the timing of peak incidence, a doubling in the number of cases, and changes in the distribution of cases by economic activity sector and gender. Traditional epidemiological surveillance systems could benefit from the addition of information based on sickness absence data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".