Lifestyle factors and risk of sickness absence from work: a multicohort study

Abstract

Background: Lifestyle factors influence the risk of morbidity and mortality, but the extent to which they are associated with employees' absence from work due to illness is unclear. We examined the relative contributions of smoking, alcohol consumption, high body-mass index, and low physical activity to diagnosis-specific sickness absence.

Methods: We did a multicohort study with individual-level data of participants of four cohorts from the UK, France, and Finland. Participants' responses to a lifestyle survey were linked to records of sickness absence episodes, typically lasting longer than 9 days; for each diagnostic category, the outcome was the total number of sickness absence days per year. We estimated the associations between lifestyle factors and sickness absence by calculating rate ratios for the number of sickness absence days per year and combining cohort-specific estimates with meta-analysis. The criteria for assessing the evidence included the strength of association, consistency across cohorts, robustness to adjustments and multiple testing, and impact assessment by use of population attributable fractions (PAF), with both internal lifestyle factor prevalence estimates and those obtained from European populations (PAF_external).

Findings: For 74 296 participants, during 446 478 person-years at risk, the most common diagnoses for sickness absence were musculoskeletal diseases (70·9 days per 10 person-years), depressive disorders (26·5 days per 10 person-years), and external causes (such as injuries and poisonings; 12·8 days per 10 person-years). Being overweight (rate ratio [adjusted for age, sex, socioeconomic status, and chronic disease at baseline] 1·30, 95% CI 1·21-1·40; PAF_external 8·9%) and low physical activity (1·23, 1·14-1·34; 7·8%) were associated with absences due to musculoskeletal diseases; heavy episodic drinking (1·90, 1·41-2·56; 15·2%), smoking (1·70, 1·42-2·03; 11·8%), low physical activity (1·67, 1·42-1·96; 19·8%), and obesity (1·38, 1·11-1·71; 5·6%) were associated with absences due to depressive disorders; heavy episodic drinking (1·64, 1·33-2·03; 11·3%), obesity (1·48, 1·27-1·72; 6·6%), smoking (1·35, 1·20-1·53; 6·3%), and being overweight (1·20, 1·08-1·33; 6·2%) were associated with absences due to external causes; obesity (1·82, 1·40-2·36; 11·0%) and smoking (1·60, 1·30-1·98; 10·3%) were associated with absences due to circulatory diseases; low physical activity (1·37, 1·25-1·49; 12·0%) and smoking (1·27, 1·16-1·40; 4·9%) were associated with absences due to respiratory diseases; and obesity (1·67, 1·34-2·07; 9·7%) was associated with absences due to digestive diseases.

Interpretation: Lifestyle factors are associated with sickness absence due to several diseases, but observational data cannot determine the nature of these associations. Future studies should investigate the cost-effectiveness of lifestyle interventions aimed at reducing sickness absence and the use of information on lifestyle for identifying groups at risk.

Funding: NordForsk, British Medical Research Council, Academy of Finland, Helsinki Institute of Life Sciences, and Economic and Social Research Council.

Figure 1

Rate ratio from meta-analyses for association between lifestyle factors and diagnosis-specific sickness absence, adjusted for age, sex, socioeconomic status and chronic disease Error bars denote 95% CI.

Figure 2

Heat map of evidence of association between lifestyle factors and diagnosis-specific sickness absence Strength of association: rate ration (RR) lower than 1·1 (low), 1·1–1·49 and significant (moderate), and 1·5 or higher and significant (high). Consistency: I² values greater than 50% and significant (low), 25–50% (moderate), and lower than 25% (high). Robustness to serial adjustments and multiple testing: RR not robust to adjustments (low); robust to adjustments, but not to multiple testing (moderate); and robust to adjustments and multiple testing (high). Population attributable fractions (PAF) on the basis of exposure prevalence estimates obtained from European countries (PAF_external): greater than 10% (high), 5–10% and significant (moderate), and lower than 5% (low). Although causal associations can be strong and weak, strong multivariable-adjusted associations are less likely to be confounded than weak associations. For example, an RR of 1·3 between a single confounder and sickness absence could explain a weak 1·05 times increase in risk of sickness absence associated with the lifestyle factor; the corresponding RR required to explain a strong 1·5 times increased association between the lifestyle factor and sickness absence would be as high as 2·4. Details of PAF calculations are provided in the appendix (p 6). *Overall rating is indicated as: 0 (at least one low rating in strength of association, consistency, or robustness), + (high or moderate rating for strength of association, consistency and robustness, and moderate PAF); or ++ (high or moderate rating for strength of association, consistency and robustness, and high PAF). †Data available from Finnish Public Sector study and Health and Social Support study. Not estimated=non-significant association or negative PAF.