Sick Leave & the Bradford Factor: Measuring BGM Metrics Correctly

Why Metrics Are the Foundation of Every BGM Program

No data, no BGM — at least not one with lasting impact. Many companies launch health programs based on gut feeling: a sense that morale is low, a perceived high number of sick days, rumors about turnover. What's missing is a reliable data foundation that shows where the real problem lies and whether measures are actually working.

The three most important BGM metrics are the **sick-leave rate**, the **turnover rate**, and the **Bradford Factor**. The sick-leave rate results from illness-related absent days relative to scheduled working time — expressed as a percentage. According to the official statistics of the German Federal Ministry of Health (KM1), the German national average was **5.66%** in 2025. If your figure is consistently above that, it's a clear signal that action is needed. If it's consistently below that, it would be naive to assume everything is running optimally — it may simply mean that people who should be calling in sick aren't (presenteeism).

Bradford Factor: Frequency Beats Duration

The Bradford Factor reveals what a plain absence rate conceals: someone who's briefly sick three times puts more strain on the company than someone who is out once for two weeks. The formula is:

**B = S² × D**

Here, **S** stands for the number of absence episodes within the observation period (e.g., one year) and **D** for the total number of absent days. Example: three sickness episodes of two days each result in B = 3² × 6 = **54**. A single illness lasting six days results in B = 1² × 6 = **6**. There are no universally valid limits; literature and practice set the bands differently. Our metrics analysis places each person's score over the last twelve months in four fixed bands: **below 50** normal, **50–124** elevated, **125–399** high, **400 and above** critical. These are guide values – they say nothing about the cause and do not replace a conversation.

Important: the Bradford Factor is not a disciplinary tool, but an early-warning signal. It helps identify potential strain situations early — before they escalate. Scores per person are based on sick notes and are therefore health data (Art. 9 GDPR). In Germany, a works council, where one exists, has a say in such an analysis (Section 87(1) No. 6 of the German Works Constitution Act, BetrVG). That is why the Bradford Factor is switched off by default here; it is switched on deliberately in the metrics data entry once the basis has been clarified.

From Spreadsheet to Automated Analysis

The most common mistake in practice: sick-leave rates and the Bradford Factor are maintained in Excel — with all the familiar problems. Manual entry errors, outdated export data from the HR system, no historical comparability. Anyone who wants to produce monthly reports ends up spending more time maintaining data than analyzing it.

Better: the raw data comes directly from the HR system — Personio, DATEV, or rexxHR — and is analyzed automatically. This produces trend curves across quarters, department comparisons, and early outlier alerts, without anyone having to type in a single number by hand. Management reporting then takes a few clicks instead of half a working day.