A Bio-Mathematical Model's Fatigue-Risk Scores Predict Sickness Absence in Hospital Nurses: A One-Year Retrospective Cohort Study

Background: Sick days are often taken by fatigued employees. In healthcare settings that require 24/7 coverage, one person’s sickness absence cascades into more work days and longer shift durations for those that remain. A major challenge for nurse administrators is how to monitor for nurse fatigue that is a major safety-concern in the workplace. Bio-mathematical fatigue models present a practical solution by identifying high-risk work shifts and patterns, and generating fatigue-risk scores from the input of work schedules. These fatigue-safety tools have been integrated in safety-critical industries but not in healthcare.

Purpose: To explore the associations between fatigue-risk scores and sickness absence in 12-hour shift hospital nurses from a pediatric hospital over 12 months of follow-up.

Methods: The study used retrospective cohort design. The demographic data of 40 female nurses from an intervention study were linked to electronic work schedules and absence data using the hospital’s attendance (clock-in, clock-out hours) system. Fatigue-risk scores were generated for work days using the Fatigue Audit InterDyne (FAID) software program with higher scores indicating greater fatigue-risk, and then linked to sickness absence. In STATA 14.1, generalized linear mixed models were used to test the associations between fatigue-risk scores and sickness absence, while accounting for the non-independency of repeated measures and possible personal confounders.

Results: The study period resulted in 6057 work shifts of which 5.2% were sickness absence episodes. Worked 12-hour night shifts were slightly higher (51.2%) than day shifts. The sample was predominantly white (79.0%), single (72.5%) with a mean age of 30.90 years (SD=7.9). Nurses on average had 6.15 years (SD=6.77) of work experience, and one in four reported having health problems. FAID fatigue-risk scores ranged from 9 to 154; fatigue scores for a standard 9-5 work schedule range from 7-40. Nurses with high FAID scores were more likely to be absent from work when compared to the standard FAID group (scores 41-79, OR=1.74, 95%CI=1.17-2.58, p=.006 and scores ≥ 80, OR=1.91, 95%CI=1.24-2.95, p=.004). No individual level variables (age, BMI, marital status) significantly predicted the outcome. Nurses with a history of SA were 1.43 times more likely to be absent from work when compared to their co-workers with no absences in the past month (95%CI=1.04-1.97, p=.028).

Conclusion: Fatigue-risk scores significantly predicted nurses’ sickness absence. Bio-mathematical fatigue software programs such as the FAID can help optimize high-risk work schedules for hospital nurses, lower absenteeism rates on nursing units, and therefore promote a healthier workplace.