What absence data should SMEs look at?
It’s time to go further than simply recording how may days off sick each member of staff takes.
One common measurement of sickness absence is the Bradford Factor. This highlights repeated instances of short-term sickness which can otherwise go unnoticed.
B = S² x D
To calculate an employee’s Bradford Factor (B) simply multiply the number of occasions they have been off (S) by the same amount (S) and then multiply that by the total days they’ve had off in that time (D).
Separate occurrences of absence carry more weighting than a single occasion even if they total the same amount of days away from the business. The theory being that it is more disruptive to a business to have three separate days off than have three days off in a row, for example. Therefore, employees who take one long period of sick leave will have a lower score than those who take lots of short periods of sick leave.
Once you have the full picture across your workforce, you can then set the score level at which you take action. This usually means asking the employee about why they are off or requesting a sick note for each subsequent absence. Make sure you are prepared to listen to the reasons why and not make assumptions. An ongoing medical condition can prompt a higher score, as can some mental health problems.
Depending on the size of your workforce, you might want to look at sick leave by team as a focus area. If one team seems to have more incidences of absence than the rest, this can be indicative of problems with stress, workload or even management style so it is worth clarifying.
It can also be insightful to determine when bouts of sick leave are most prevalent. If the winter months are a particular issue, you might want to consider offering your employees a flu jab. But apart from this, looking at sickness trends allows you to see if there are clear peaks a certain times of year. If so, you might like to take the opportunity to prepare ahead, perhaps with backup plans in place for staff or looking more closely at seasonal workload for example.