Sickness absence as a global measure of health: evidence from mortality in the Whitehall II prospective cohort study.
OBJECTIVE
To examine the association between sickness absence and mortality compared with associations between established health indicators and mortality.


METHODS
Prospective cohort study. Medical examination and questionnaire survey conducted in 1985-8; sickness absence records covered the period 1985-98.


METHODS
20 civil service departments in London.


METHODS
6895 male and 3413 female civil servants aged 35-55 years.


METHODS
All cause mortality until the end of 1999.


RESULTS
After adjustment for age and grade, men and women who had more than five medically certified absences (spells > 7 days) per 10 years had a mortality 4.8 (95% confidence interval 3.3 to 6.9) and 2.7 (1.5 to 4.9) times greater than those with no such absence. Poor self rated health, presence of longstanding illness, and a measure of common clinical conditions comprising diabetes, diagnosed heart disease, abnormalities on electrocardiogram, hypertension, and respiratory illness were all associated with mortality--relative rates between 1.3 and 1.9. In a multivariate model including all the above health indicators and additional health risk factors, medically certified sickness absence remained a significant predictor of mortality. No linear association existed between self certified absence (spells 1-7 days) and mortality, but the findings suggest that a small amount of self certified absence is protective.


CONCLUSIONS
Evidence linking sickness absence to mortality indicates that routinely collected sickness absence data could be used as a global measure of health differentials between employees. However, such approaches should focus on medically certified (or long term) absences rather than self certified absences.
Kivimäki
Mika
M
Finnish Institute of Occupational Health, Topeliuksenkatu 41 aA, FIN-00250 Helsinki, Finland. mika.kivimaki@ttl.fi
Head
Jenny
J
Ferrie
Jane E
JE
Shipley
Martin J
MJ
Vahtera
Jussi
J
Marmot
Michael G
MG
D000328:Adult; D017677:Age Distribution; D015331:Cohort Studies; D005260:Female; D006304:Health Status; D006801:Humans; D008131:London / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D009026:Mortality; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D017678:Sex Distribution; D018582:Sick Leave / Q000706:statistics & numerical data*