Correlation of school absenteeism and laboratory results for Flu A in Alberta, Canada
Bibliographic record
Abstract
ObjectiveTo assess the correlations between weekly rates of elementaryschool absenteeism due to illness (SAi) and percent positivity forinfluenza A from laboratory testing (PPFluA) when conducted at acity level from September to December over multiple years.IntroductionRates of student absenteeism in schools have been mainly used todetect outbreaks in schools and prompt public health action to stoplocal transmission1,2. A report by Mogto et al.3stated that aggregatedcounts of school absenteeism (SAi) were correlated with PPFluA, butthe sample may have been biased. The purpose of this study was toassess the correlation between aggregated rates of SAi and PPFluAfor two cities, Calgary and Edmonton, in Alberta. In such situations,SAi could potentially be used as a proxy for PPFluA when there arenot enough samples for stable laboratory estimates.MethodsThe Alberta Real-Time Syndromic Surveillance Net (ARTSSN)4collects elementary SA data from the two major school boards intwo cities in Alberta with populations >800,000. Since reasons forSA are stated, rates of SAi can be calculated. Data were obtained forthree years, 2012 to 2014, for each city. Laboratory data on tests ofrespiratory agents using a standardized protocol were obtained fromAlberta’s Provincial Laboratory for Public Health for the same timeperiod and locations. The dates of the specimens being received bythe laboratory were used in this analysis. For each data source, therelative proportions (SAi and PPFluA) were calculated. Data forthe first week of school in September and for the last two weeks ofDecember were removed for each year due to the SAi rates beingunstable. Linear regression models were constructed, with rates ofSAi predicted by PPFluA. Separate models were run for each cityand for each year, resulting in a total of 6 models. Percent positivityfor entero-rhinoviruses (PPERV) was added to see if it improved themodel. The regression models were created using Excel and checkedin the statistical programs, SAS and R. An analysis to assess theinfluence of a lag period was assessed using R.ResultsFor each city, the provincial lab tested between 4,000 and 6,000specimens each fall and SAi rates were based on denominators ofbetween 20,000 and 36,000 children. The R2, betas, and p-valuesfor all 6 regression models are shown in Table 1. The minimumcorrelation value was 0.693 and the maximum was 0.935. Dueto the strong negative correlations between PPERV and PPFluA,PPERV was not retained in the models. Looking at the lag periods,the maximum correlations occurred at a zero week lag in two years(2012 and 2014) and at a -1 week lag in 2013. The two years with azero lag were both dominated by a H3N2 strain while the year withmainly a H1N1 strain showed a lag of -1. Only one year of H1N1 datawas available for analysis.ConclusionsWe observed strong correlations between the weekly rates ofelementary SAi and PPFluA at the city level over three years, fromSeptember to December. The reasons for the difference in lag timesbetween the H1N1 and H3N2 seasons are being investigated.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| 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".