Infectious Disease and Prevention and Control Branch, Public Health Agency of Canada, Ottawa, Ontario, Canada

Statistics Canada, Ottawa, Ontario, Canada

Abstract

Background

As many respiratory viruses are responsible for influenza like symptoms, accurate measures of the disease burden are not available and estimates are generally based on statistical methods. The objective of this study was to estimate absenteeism rates and hours lost due to seasonal influenza and compare these estimates with estimates of absenteeism attributable to the two H1N1 pandemic waves that occurred in 2009.

Methods

Key absenteeism variables were extracted from Statistics Canada's monthly labour force survey (LFS). Absenteeism and the proportion of hours lost due to own illness or disability were modelled as a function of trend, seasonality and proxy variables for influenza activity from 1998 to 2009.

Results

Hours lost due to the H1N1/09 pandemic strain were elevated compared to seasonal influenza, accounting for a loss of 0.2% of potential hours worked annually. In comparison, an estimated 0.08% of hours worked annually were lost due to seasonal influenza illnesses. Absenteeism rates due to influenza were estimated at 12% per year for seasonal influenza over the 1997/98 to 2008/09 seasons, and 13% for the two H1N1/09 pandemic waves. Employees who took time off due to a seasonal influenza infection took an average of 14 hours off. For the pandemic strain, the average absence was 25 hours.

Conclusions

This study confirms that absenteeism due to seasonal influenza has typically ranged from 5% to 20%, with higher rates associated with multiple circulating strains. Absenteeism rates for the 2009 pandemic were similar to those occurring for seasonal influenza. Employees took more time off due to the pandemic strain than was typical for seasonal influenza.

Background

As many viruses can cause similar respiratory symptoms and laboratory confirmation is not routine, data specific to influenza is limited. Statistical estimates of the influenza burden identify a significant morbidity

Methods

The estimation of the number of deaths (or hospital admissions) attributable to influenza involves establishing a seasonal baseline for the weekly time-series of deaths (or respiratory admissions) to account for seasonality and secular trends, and then matching the weekly pattern of a proxy variable for influenza activity to the pattern of excess deaths (or admissions)

Data Sources

Monthly time series were extracted from Statistics Canada's Labour Force Survey (LFS) for key variables related to absenteeism rates and the proportion of potential hours worked that were lost due to illness or disability and employee characteristics

The weekly number of laboratory confirmations for influenza A and B were obtained from the

Statistical Analysis

Combining these data sources, the full study period available includes 15 seasons from September 1995 to February 2010. As the number of laboratory tests reported to

Absenteeism rates (# of employed persons who were absent due to own illness or disability/# of employed persons) and the proportion of potential hours worked that were lost due to own illness or disability were modeled separately as a function of seasonality (month), secular trend, and the level of influenza activity corresponding to the reference week. The regression model was fit using SAS Enterprise Guide

where _{1 }parameters account for the baseline seasonality with monthly indicator variables (_{m}
_{2 }parameters account for a general trend with indicator variables for each influenza season or flu year (_{y}
_{3 }parameters account for hours lost due to influenza A infection, potentially varying by _{4 }accounts for the increase in hours lost due to influenza B infection (_{5 }accounts for any change in absenteeism behavior once the pandemic was announced that was not related to the level of influenza activity, that is an influenza infection (for example, staying home because of concern that a respiratory infection was due to the pandemic strain even though the employee would have otherwise reported for work and the infection was due to another respiratory virus); and β_{6 }accounts for hours lost due to the H1N1/2009 pandemic strain (

A similar model was fit for the absenteeism rates, with the proportion of employed persons who took time off work due to their own illness or disability in the reference week as the dependant variable. Absences and hours lost for care of others were considered for separate analysis, however, variation in these monthly time series were found to be minimal and not associated with influenza activity.

There are various models that have been used to estimate influenza-attributable events or excess mortality. All regression models include variables to explain the weekly or monthly seasonality and secular trends of the dependent variable and to account for the impact of influenza activity. The percent of tests positive for influenza is a convenient choice of proxy variable to account for the impact of influenza activity which easily normalises for differences in testing over time, while the use of the number of influenza A positive tests along with separate parameters for each season, in addition to providing a slightly better model fit, provided a redundancy that helped illustrate model robustness in previous work _{3 }parameters) would provide face validity of model results and was one criterion considered in guiding model development.

A regression model approach facilitated the simultaneous estimation of the effects of influenza activity while controlling for other factors. Baseline rates were calculated from the fitted model by setting the proxy variables for influenza activity to zero. The difference between the model-predicted number of hours lost and the baseline is an estimate of the number of hours lost due to influenza. The excess hours lost (defined as actual less baseline) includes unexplained variation that may be due to (unknown) events unrelated to influenza. The unexplained variation will average out over each season due to the nature of the regression model. A linear link function was chosen to maintain a linear relationship between viral activity and absenteeism due to influenza. Confidence intervals for estimates of the proportion of hours lost due to influenza were calculated from the coefficient of variation of the corresponding parameter for the proxy variable for influenza activity. The dispersion parameter was included in the model estimation to account for additional variation due to events not captured by the choice of explanatory variables.

Annual absenteeism rates attributable to influenza were calculated by summing the predicted monthly absenteeism rates for each month within the indicated time period. Repeat influenza infections in one employee (and in different months) though rare, would be counted as two absences. The absenteeism rate was pro-rated to the full month based on the number of work days in the month with an adjustment for variation in the level of influenza throughout the month. The proportion of hours lost due to influenza was calculated by summing the estimated monthly hours lost due to influenza and the potential hours worked over the specified period and then dividing. The number of hours lost per absence due to influenza was calculated from the estimated number of hours lost due to influenza divided by the estimated number of absences attributed to influenza per season.

Differences in the annual estimates of absenteeism rates and the proportion of hours lost due to influenza were compared with the number and subtypes of the circulating strains from national year-end summary reports

The proportion of potential hours worked that were lost due to influenza was also estimated by age group, sex, and employment characteristics (full-time/part-time; permanent/temporary; public/private sector; union coverage; and urban or rural residency) in separate regression models. As hours lost for the care of others had very little seasonality and the limited seasonal variation that was present was not associated with influenza activity, model estimates were not produced for the care of others from this data set and absenteeism due to influenza for the care of others could be considered negligible.

Ethical Statement

This study was conducted in accordance with the principles expressed in the Declaration of Helsinki. Data provided by Statistics Canada were collected under the Statistics Canada Act and are available to the public through their cost recovery program. Data provided by the Public Health Agency of Canada were collected under the Public Health Agency of Canada Act and were used in agreement with policy and regulations related to the publication of information related to public health. Identifying information was not available to this study. Hence, ethics approval was not required.

Results

An estimated 13% of employed persons in Canada took time off from work as a result of their own illness associated with the H1N1/2009 pandemic strain. Absenteeism rates for seasonal influenza averaged 12% over the 1997/98 to 2008/09 seasons. Typically 3% of potential hours worked are lost due to the employee's own illness or disability annually, though this figure varies with age and other employment characteristics. An average of 0.08% (95% CI: 0.06-0.10) of hours worked were lost annually due to seasonal influenza, while the proportion of potential hours worked that were lost due to influenza over the two pandemic waves was 0.19% (95% CI: 0.15-0.23) when pro-rated to an annual bases for comparison (Table

Absenteeism and percent of hours lost due to own illness or disability attributed to influenza in employed persons 15 years of age and older

**Season/Wave**

**Length of Period**

**% of Hours Worked that were Lost Due to Own Illness or Disability**

**% of Potential Hours Worked that were Lost Due to Own Illness and Attributed to Influenza ^{1}**

**% of Hours Lost that were Attributable to Influenza**

**Estimated % of Employees Absent due to Influenza per Period (Wave/Season) ^{1}**

Seasonal

Annual (12 months)

2.9%

0.08%

3%

11.5%

H1N1/09

Pro-rated to annual

(May09-April10)

3.1%

0.19%

6%

13.4%

H1N1/09

Spring

4 months (May-Aug09)

2.8%

0.12%

4%

2.9%

Fall

4 months (Sept-Dec09)

3.2%

0.47%

15%

10.5%

Oct-09

1 month

3.2%

0.59%^{2}

18%

3.3%

Nov-09

1 month

3.9%

1.25%^{2}

32%

6.7%^{2}

Dec-09

1 month

3.0%

0.05%

2%

0.3%

^{1 }The attribution to influenza was estimated on an annual basis, as described in the methods sections. Monthly estimates were calculated as the model-predicted estimate less estimated baseline and agree with the influenza-attributed time series in Figure 4.

^{2 }The number of hours lost or number of absences reported for the indicated reference weeks were significantly above the estimated baseline (95% confidence level). The excess for these months is not shown in this table, though can be seen in Figures 4 and 5.

Annual Estimates

The annual estimates of absenteeism and proportion of hours lost were correlated, though rates varied significantly from season to season (Figure ^{th }and 90^{th }percentiles are 5%-20% and 0.05%-0.13%, respectively. As estimates for the 2005/06 (A/California/7/2004) season were not statistically significant, the full range is uncertain and the minimum absenteeism rate may be significantly lower than 5%. Higher rates were associated with seasons where more than one distinct antigenic strain circulated. This correlation reflects a consistency of the estimates, as the impact of influenza on absenteeism and hours lost was estimated separately for each season. The pandemic waves were more remarkable for the hours lost than the number of employees taking time off from work (Figure

Workplace absenteeism attributed to influenza: seasonal, 1997/98-2008/09, and pandemic 2009

**Workplace absenteeism attributed to influenza: seasonal, 1997/98-2008/09, and pandemic 2009**. Influenza seasons differed by predominant subtype (H1N1 vs H3N2), the co-circulation of influenza B strains and the number of antigenic strains. The pandemic waves were more remarkable for the number of hours lost than the number of employees taking time off work. Estimates of the seasonal absenteeism rate attributable to influenza and proportion of hours lost due to influenza infection were based on separate models, though a strong association between these estimates is evident.

By age and other employment characteristics

The proportion of hours lost due to one's own illness or disability increases significantly with age (Figure

Percent of potential hours worked annually that were lost due to influenza illness by age group

**Percent of potential hours worked annually that were lost due to influenza illness by age group**. The dashed line shows the increase in the proportion of hours lost due to own illness or disability with increasing age. The estimated proportion of hours lost due to an infection with the pandemic strain was similar for all age groups.

Estimated percent of potential hours worked that were lost due to influenza per season

**Estimated percent of potential hours worked that were lost due to influenza per season**. The proportion of potential hours worked annually that were lost due to influenza over the two pandemic waves was 0.19% (95% CI 0.15-0.23) compared to 0.08% (0.06-0.10) for seasonal influenza. Confidence intervals were estimated based on the coefficient of variation of the corresponding parameter for the proxy variable for influenza activity and includes excess variation estimated by the inclusion of a dispersion parameter. As a result, the CIs were quite broad and differences by employment characteristics were not statistically significant. The CIs for the Urban/Rural split were included to illustrate. While, the proportion of hours lost varied significantly with the specific employment characteristics, these employment characteristics had less impact on hours lost due to influenza.

Model Fit

The overall model fit is shown in Figure

Model fit showing the estimated baseline and attribution to influenza

**Model fit showing the estimated baseline and attribution to influenza**. The actual data, a) the proportion of hours lost, and b) absenteeism rates are plotted against the reference week along with the model predicted values, the model estimated baseline and the attribution to influenza. Influenza is responsible for much of the seasonal variation and contributes significantly to peak absenteeism rates.

Comparison of the attribution of hours lost to influenza: assessing model fit

**Comparison of the attribution of hours lost to influenza: assessing model fit**. The actual hours lost less baseline (excess) is compared with the model predicted hours lost less baseline. The difference between the two curves are known as model residuals (and equal to actual - baseline). Residuals represent the variation not explained by the model. The influenza-attributed curve is smoother as the residuals, or unexplained variation, are not included in this time series. The residuals will average out over a season. The model fit is reasonable, though the model seems to miss the occasional dip in hours lost over the summer period.

A Comparison of the seasonal baseline for absenteeism rates and percent of potential hours worked that were lost due to own illness or disability

**A Comparison of the seasonal baseline for absenteeism rates and percent of potential hours worked that were lost due to own illness or disability**. The seasonal baselines in the absence of influenza activity for the two measures of time off work: absenteeism rate and hours lost due to own illness or disability, were estimated statistically. The baseline curves are distinct, with considerably more seasonal variation in the absenteeism rate than in the proportion of hours lost due to own illness or disability.

The expectation was that β_{5 }(change in behavior during the pandemic) would be positive due to intensive public health messaging during the pandemic period reminding the public to stay home if sick. The β_{5 }parameter was actually negative in the hours lost model, and not significant in the absenteeism model. This can be confirmed visually in Figure

Discussion

This study confirms that estimates of absenteeism due to seasonal influenza typically ranged from 5% to 20%; higher absenteeism rates were associated with mixed seasons. These results are in reasonable agreement with general assumptions on the clinical attack rate for influenza, though it is noted that not everyone with symptoms consistent with an influenza like illness (ILI)

Four special questions were added to the labour force survey in December 2009 through to February 2010 in order to estimate the impact of influenza on hours worked. Labour force survey participants were asked how many hours they took off work as a result of the 'flu' in the previous month due to their own illness as well as for the care of others. An estimated 9.0%, 4.4% and 3.5% of employed people were absent from work as a result of the 'flu' for November 2009, December 2009, and January 2010 respectively

Economic studies of the benefits of influenza vaccination programs in the workplace avoid the costly process of directly measuring absenteeism due to influenza by comparing the number of days lost due to ILI in vaccinated and unvaccinated workers

The inclusion of a scale parameter in the model inflated the confidence intervals of the estimated parameters, so it is unlikely that the level of statistical significance is overstated, however, the less than ideal model fit still suggests caution in the interpretation of model results. As this is a population-level study design, other explanations than those included in the model may be possible. The effect of public health messaging advising the public to stay home if sick is uncertain, as the proportion of hours lost was actually lower in the summer months of 2009 than for previous years. Would employees have taken less time off for other reasons in anticipation of possibly needing additional sick days due to a future infection with the pandemic strain? Absenteeism rates were not statistically significant for all seasons; it is not clear whether the lower peak absenteeism rates for the 2005/06 season were due to limited illnesses related to influenza that season as the model suggests, or due to other causes not included in the model. The robustness of annual estimates of disease burden is known to be less than ideal.

Despite higher vaccination coverage in recent years (increasing from approximately 10 to 25% of the working age Canadian population

Conclusions

These estimates of absenteeism and the range of year-to-year variation should be a valuable contribution to the study of the economic burden of influenza and of potential use to cost-benefit analyses of workplace vaccination programs. At the community level, 50% of the cases were found to occur within a 4 to 5 week period

Abbreviations

95% CI: 95% confidence interval; ILI: influenza-like illness; LFS: Labour Force Survey; PHAC: Public Health Agency of Canada; RVS: Respiratory Syncytial Virus

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

DS conceived the study, performed the analysis and drafted the manuscript. DS, HZ and JG contributed to the study design. HZ and JG contributed to the interpretation of study results. All authors revised the manuscript critically, and all approved the final version that was submitted.

Acknowledgements

The authors acknowledge the support of the National

Pre-publication history

The pre-publication history for this paper can be accessed here: