Nursing absenteeism--one determining factor for the staffing plan

Prevalence and Economic Costs of Absenteeism in an Aging Population—A Quasi-Stochastic Projection for Germany

Demographic change is leading to the aging of German society. As long as the baby boom cohorts are still of working age, the working population will also age—and decline as soon as this baby boom generation gradually reaches retirement age. At the same time, there has been a trend toward increasing absenteeism (times of inability to work) in companies since the zero years, with the number of days of absence increasing with age. We present a novel stochastic forecast approach that combines population forecasting with forecasts of labor force participation trends, considering epidemiological aspects. For this, we combine a stochastic Monte Carlo-based cohort-component forecast of the population with projections of labor force participation rates and morbidity rates. This article examines the purely demographic effect on the economic costs associated with such absenteeism due to the inability to work. Under expected future employment patterns and constant morbidity patterns, absenteeism is expected to be close to 5 percent by 2050 relative to 2020, associated with increasing economic costs of almost 3 percent. Our results illustrate how strongly the pronounced baby boom/baby bust phenomenon determines demographic development in Germany in the midterm.

[Absenteeism in the nursing team]

The absence of elements of nursing staff jeopardizes the work organization causing overload on this staff and reduction of quality on the nursing assistance. The present study characterizes the type and distribution of absences of the nursing staff in a general far-reaching hospital, purposing a Technical Safety Rate to cover the absences from the reality investigated. Eight types of absences were studied between foreseen or non-foreseen. The highest number of absence types found were those ones referred to the foreseen absences. In sequence we found maternity leave representing the highest number of non-foreseen absences.