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Using Worker Absenteeism to Track the Flu

Posted on by Matthew R. Groenewold, PhD

Is flu on the rise among workers? Those working in public health track the number of flu-related hospital and doctor visits, but many people suffer symptoms and don’t seek medical treatment. So, how do we know how many people are sick with the flu during a flu pandemic or a seasonal epidemic?

Each year, the Centers for Disease Control and Prevention (CDC) uses a mathematical model to estimate the total number of flu illnesses in the United States, but this is not done until the end of the flu season. Conventional flu surveillance relies on healthcare data such as lab test results, hospitalizations and doctor’s office visits. Tracking absenteeism trends in workplaces during the flu season is an important supplement to conventional flu surveillance because, often, those who are sick will stay home from work, but they may not see a doctor.

The National Institute for Occupational Safety and Health (NIOSH) is monitoring health-related workplace absenteeism among full-time workers in the U.S. using data received monthly from the Current Population Survey. The results are made available online using a Tableau dashboard. These data are useful for assessing the occurrence of illnesses like the flu since the amount of health-related absences is strongly related to the amount of flu-like illness occurring at about the same time (1). Therefore, workplace absenteeism provides additional information to measure the overall impact of seasonal flu epidemics or pandemics.

Results from the first year of surveillance analyses are available in a new report published in MMWR, “Health-Related Workplace Absenteeism Among Full-Time Workers — United States, 2017–2018 Influenza Season”.

The first year of data found that during the high-severity influenza season of 2017-2018:

  • Absenteeism:
    • increased sharply in November
    • peaked in January
    • at its peak, was significantly higher than the average during the previous five seasons
  • Workers most affected included:
    • men
    • workers aged 45–64 years
    • workers living in Department of Health and Human Services Region 6 (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) and Region 9 (Arizona, California, Hawaii, and Nevada )
  • Jobs most affected included:
    • management, business, and financial
    • installation, maintenance, and repair
    • production-related jobs

The findings are consistent with other surveillance from the 2017-2018 flu season. Learn more about last year’s workplace absence trends, and view the most recent trends.

We would like to hear from you. Does your workplace have a plan for protecting workers while maintaining operations during an influenza pandemic?

 

Matthew R. Groenewold, PhD, is an epidemiologist in the NIOSH Division of Field Studies and Engineering.

 

Reference

  1. Groenewold MR, Konicki DL, Luckhaupt SE, Gomaa A, Koonin LM. Exploring national surveillance for health-related workplace absenteeism: Lessons learned from the 2009 influenza A pandemic. Disaster Med Public 2013;7:160–6.
Posted on by Matthew R. Groenewold, PhD

5 comments on “Using Worker Absenteeism to Track the Flu”

Comments listed below are posted by individuals not associated with CDC, unless otherwise stated. These comments do not represent the official views of CDC, and CDC does not guarantee that any information posted by individuals on this site is correct, and disclaims any liability for any loss or damage resulting from reliance on any such information. Read more about our comment policy ».

    It was interesting to see the numbers for 2017-2018. However an explanation why in this season the number in January was significantly higher than the average during the previous five seasons was needed. Was only the peak month number so high or the numbers for November and December were also somewhat higher than previous years? What was particular about the month of January? Relatively older males (45-64) were at high risk. Was this expected? Region 6 and Region 9 had higher numbers. However, Florida is not in these regions. In terms of jobs, are these same jobs always at high risk of flu? Comparison of these findings with previous years would add more value to the data shared here.

    Hi Hasanat,
    Thank you for your interest in our absenteeism surveillance work. The prevalence of health-related workplace absenteeism was not higher than expected during the months of November and December 2017. It was higher than expected in February of 2018, but not significantly so. In technical terms, the point estimate for February’s absenteeism prevalence was higher than the epidemic threshold, but the margin of error for that estimate included the epidemic threshold.

    As you have noted, absenteeism prevalence was significantly elevated in the month of January 2018. As for the reason why, our assumption is that absenteeism peaked in January because that is when influenza activity in the United States was at its peak.

    Similarly, the higher than expected absenteeism in HHS Region 6 in January and February 2018 and in Region 9 in December 2017 and March 2018 corresponded with peaks in influenza-like illness occurring in those regions. So our assumption is that the regional peaks in absenteeism were driven by high levels of influenza activity occurring at the same time in those regions.

    The associations of influenza-like illness and workplace absenteeism within demographic subgroups are complex and mediated by factors such as vaccination coverage and access to paid sick leave. More study using additional data sources will be needed to fully understand the reasons for increases in absenteeism related to sex, age, or specific occupations that were identified in our surveillance analyses.

    The 2017 – 2018 season was the first full year of operation for our absenteeism surveillance system, so we cannot say yet whether the absenteeism patterns we observed among occupational groups will persist in subsequent seasons. But we agree with you that comparisons of surveillance findings across years will add value to the data. We look forward to being able to provide the ability to make such comparisons as we accumulate data over the coming seasons.
    Thank you again for your interest.

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