Occupation-specific frequency of sick-leave due to cardiovascular diseases. (19th October 2011)

• Record Type: Journal Article
• Authors: Brendler, Claudia; Liebers, Falk; Latza, Ute
• Is Part Of: Occupational and environmental medicine. Volume 68(2011)Supplement 1
• Page Start: A105
• Page End: A105
• Publication Date: 2011-10-19
• Subjects: Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oemed-2011-100382.350
• Languages: English
• ISSNs: 1351-0711

Abstract

Abstract : Objectives: Cardiovascular diseases (CVD) contribute to a large share of morbidity among workers in industrialised countries. The aim was to identify occupations with elevated sick-leave due to arteriosclerotic CVD for recognition of potentials for prevention and research needs. Methods: Aggregated data for the German working population aged 15–64 years pooled from nearly all statutory health insurances (SHI) for the year 2008 were stratified by gender, occupation (KldB 1988), age group, and 6 frequent ICD-10 diagnoses. Standardised morbidity ratios (SMR) with 99.99% CIs of sick-leave cases were calculated (reference: office workers). Estimates with a SE <0.2 were considered. Results: Data from 26.2 million employees were retrieved. The most frequent diagnoses were chronic ischaemic heart disease (I25), angina pectoris (I20), and acute myocardial infarction (I21). Significantly higher frequencies of sick-leave for 5 of the selected 6 diagnoses were observed for metal workers (SMR 1.31–1.96), street cleaners/ garbagemen (SMR 1.30–2.68), storage/ transport workers (SMR 1.17–1.63), and watchmen (SMR 1.29–2.11) among males and for cleaners (SMR 1.21–1.95), retail/ trade workers (SMR 1.29-1.92), assistant nurses (SMR 1.54–1.75), and domestic caregivers (SMR 1.23–1.97) among females. Conclusions: The data are representative for employees with SHI comprising about 2/3 of the German working population. No causal inference is possible due to the cross-sectional design and the secondary data analysis with missing data on occupational history/risk factors, life style, and mortality due to CVD. Prevention should focus on high-risk occupations. Risk profiles among these occupations need to be investigated.