Monitoring sick leave data for early detection of influenza outbreaks.
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ABSTRACT: Background
Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks.Methods
Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place.Results
Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier.Conclusion
Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.
SUBMITTER: Duchemin T
PROVIDER: S-EPMC7799403 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesThe aim was to study whether a workplace-registered frequent short-term sick leave spell pattern was an early indicator of future disability pension or future long-term sick leave among municipal eldercare workers.SettingThe municipal healthcare sector in the city of Aarhus, which is the second largest city in Denmark.ParticipantsAll elder care employees who worked the entire year of 2004 in the municipality of Aarhus, Denmark (N=2774). The employees' sick leave days during 2004 were categorised into: 0-2 and 3-17 short (1-7 days) spells, 2-13 mixed short and long (8+ days) spells and long spells only. Student workers (n=180), employees who were absent due to maternal/paternal leave (n=536) and employees who did not work the entire year of 2004 (n=1218) were not included.Primary outcomeDisability pension and long-term sick leave (≥8 weeks) were subsequently identified in a National register. The cumulative incidence proportion as a function of follow-up weeks was estimated using the Kaplan-Meier curve. The relative cumulative incidence (RR) of experiencing events within 352 weeks was analysed in a generalised linear regression model using the pseudo values method adjusted for age, occupation, unfavourable work factors and sick leave length.ResultsA frequent short-term and a mixed sick leave pattern showed RRs of being granted a disability pension of 2.08 (95% CI 1.00 to 4.35) and 2.61 (95% CI 1.33 to 5.12) compared with 0-2 short spells. The risk of long-term sick leave was significantly increased for all sick leave patterns compared with 0-2 short spells. Adding sick leave length to the models attenuated all RRs and they became non-significant.ConclusionsSick leave length was a better indicator of future workability than spell frequency. Preventive actions should target employees engaged in homecare. The more sick leave days the greater the preventive potential seems, irrespective of spell frequency.
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Project description:PurposeThe study aimed to explore the prevalence of sick leave and the risk of work-related accidents among German social workers and to describe causes and time trends in sick leave and accident claims.MethodsA retrospective analysis of routine data was carried out. Aggregated sick leave data of 195,100 social workers from four health insurance funds and 3037 accident claims of social workers from an accident insurance institution were analysed. Causes of accidents were examined by statistics of the German Social Accident Insurance (DGUV). Sick leave rates per 100 insured person-years were calculated. Relative risks (RR) of accidents were calculated in a multivariate analysis for three occupational groups (social workers and therapists, caregivers in sheltered workshops and teachers in residential institutions) and compared to other health and welfare service workers.ResultsMental disorders caused about one-fifth of the sick leave days of social workers. Sick leave due to mental disorders slightly increased in 2015 compared to 2012 (+ 3% and + 18%). Among the three subgroups of social workers, caregivers in sheltered workshops (RR 1.30; 95% CI 1.14-1.49) and teachers in residential institutions (RR 1.41; 95% CI 1.17-1.70) were at an increased risk of accidents at the workplace. Accidents were mostly caused by slipping (30%) and by violence (22%).ConclusionsThis study confirms that sick leave of social workers is frequently caused by mental disorders. Future studies could further examine differences between practice fields, long-term effects of work hazards and effective workplace interventions.
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