OBJECTIVE: To investigate the associations between demographic variables and sick leave use. METHODS: We analyzed sick leave use among civil servants at a federal agency (FA) from 2004 to 2012 by demographic and FA-specific variables. We used a mixed methods approach and type III analysis to build a descriptive model of sick leave proportions and demographic variables. RESULTS: Sick absenteeism usage varied significantly (variation of greater than one sick day per year) by sex, Emergency Operations Center response tier, length of service at the FA, age, and general schedule pay grade level. Our final descriptive model contained age, sex, response tier and an interaction term between age and sex. CONCLUSIONS: Future studies should examine these associations on smaller time scales, perhaps breaking the data down by month or day of the week.

OBJECTIVE: This study evaluates the predictability in temporal absences trends due to all causes (total absenteeism) among employees at a federal agency. The objective is to determine how leave trends vary within the year, and determine whether trends are predictable. METHODS: Ten years of absenteeism data from an attendance system were analyzed for rates of total absence. RESULTS: Trends over a 10-year period followed predictable and regular patterns during a given year that correspond to major holiday periods. Temporal trends in leave among small, medium, and large facilities compared favorably with the agency as a whole. CONCLUSIONS: Temporal trends in total absenteeism rates for an organization can be determined using its attendance system. The ability to predict employee absenteeism rates can be extremely helpful for management in optimizing business performance and ensuring that an organization meets its mission.

OBJECTIVE: To determine whether trends of sickness in employees at a federal agency are predictable, and whether the variance was minimal enough to detect unusual levels of employee illness for further investigation. METHODS: Ten years of absenteeism data from an attendance system were analyzed for rates of sickness absence. Specifically, week of year and day of week were used to describe temporal trends. RESULTS: This study evaluates the predictability in temporal absence trends due to sickness among employees at a federal agency. Trends follow regular patterns during a given year that correspond to seasonal illnesses. Temporal trends in sick leave have been proven to be very predictable. CONCLUSION: The minimal variance allows the detection of sick leave anomalies that may be ascribable to specific causes, allowing the business or agency to follow-up and develop interventions.