Individual measurement series can be hidden, displayed or highlighted by clicking the descriptions of the graphs.
Monthly distribution of absence days and diagnosis classes
The graph shows a maximum of five of the most common diagnosis classes. Diagnoses with fewer than five absence days are included in the “Other diagnoses” class to ensure privacy protection.
all sectors:Respiratory system
Musculoskeletal system and connective...
Certain infectious and parasitic dis
Symptoms, signs and abnormal clinical and ...
Mental and behavioural disorders
Other diagnoses
Food services:Respiratory system
Musculoskeletal system and connective...
Certain infectious and parasitic dis
Symptoms, signs and abnormal clinical and ...
Mental and behavioural disorders
Other diagnoses
Weekday distribution of absence days and diagnosis classes
The graph shows a maximum of five of the most common diagnosis classes. Diagnoses with fewer than five absence days are included in the “Other diagnoses” class to ensure privacy protection.
all sectors:Respiratory system
Musculoskeletal system and connective...
Certain infectious and parasitic dis
Symptoms, signs and abnormal clinical and ...
Mental and behavioural disorders
Other diagnoses
Food services:Respiratory system
Musculoskeletal system and connective...
Certain infectious and parasitic dis
Symptoms, signs and abnormal clinical and ...
Mental and behavioural disorders
Other diagnoses
Most common diagnosis classes of absence days
The graph shows a maximum of ten of the most common diagnosis classes. Diagnoses with fewer than five absence days are excluded to ensure privacy protection.
Respiratory diseases were the most common reason for short sickness absence in all of the six sectors studied. Browse and compare, for example, the second and third most common reasons for absences in different sectors and how they are distributed at an annual or weekly level.
Description
The extensive, anonymised and masked longitudinal materials from employers and occupational health care organizations were collected from the Kokkola and Jakobstad areas in 2017–2020. The total number of absence periods in six different sectors was 17,088. Data was collected from the sectors of early childhood education, teaching and expert work, food services, the food industry and light and heavy factory work. The absence data were processed with an AI-based system that used information about diagnoses and dates. Based on the AI analysis, predictions about the timing of absences in different sectors were made.
What the graphs describe
The graphs show the most common sector-specific reasons for short sickness absences (1–10 days) and when they occurred. The data can be browsed and compared accordingly:
The overall data of all of the six sectors can be browsed as a single package
The data of a sector can be compared to another sector or all sectors
The diagnosis data of a single sector can be browsed or compared to another sector or all of the sectors
Annual sector-specific absence data can be compared to another year or all years of the study
Monthly absence data can be browsed on a sector-specific basis or compared to the data of another sector or all of the sectors
Weekly absence data can be browsed on a sector-specific basis or compared to another sector or all of the sectors
The data were collected and analysed in the LYHTY project aimed to prevent short absences that ended on 31 December 2021. LYHTY was a project funded by the European Social Fund and the Finnish Work Environment Fund and executed by Centria University of Applied Sciences.
Long sickness absences have been studied widely, but there is little research data available on short sickness absences even though the impacts of short sickness absences on daily life, coping at work and expenses are significant.
Mental well-being is one of the cornerstones of work ability, but mental health disorders remain a significant challenge for work life. In the Work-Life Knowledge service, you can find diverse information on the prevalence of mental health disorders, risk factors and resource factors.
