The first aim of the current study was to estimate differences in diagnosis‐specific sickness absence diagnosis between abstainers, former, persistent and new at‐risk and low‐risk drinkers. We focused on mental disorders, musculoskeletal disorders, diseases of the circulatory system, digestive disease and respiratory diseases, as well as external causes, as these diagnostic groups have been found to underlie alcohol‐related sickness absence 12 , 13 . In addition, we studied whether the alcohol use–sickness absence associations differed between men and women.

Differences in social insurance systems, policies for sickness absence and cultural differences might affect the association between alcohol use and sickness absence 7 - 10 . To obtain generalizable, rather than particular evidence, in our previous study we used data from three countries (Finland, the United Kingdom and France), and demonstrated a U‐shaped association between alcohol use and sickness absence among men 11 . In that study, both men and women abstaining from alcohol for 4–6 years had a higher risk of all‐cause sickness absence compared to those who had been moderate drinkers during the same time. To date, however, it is still unclear which specific diagnoses are associated with the excess risk of sickness absence among abstainers, and whether they correspond to those observed among at‐risk drinkers.

Previous studies have shown that both abstainers and at‐risk drinkers are at an increased risk of sickness absence compared with low‐risk drinkers 1 - 5 . As former at‐risk drinkers also have a higher risk of sickness absence than low‐risk drinkers 6 , part of the excess sickness absence in abstainers may be attributable to health selection, i.e. at‐risk drinkers may reduce their drinking due to health problems, and thus the higher risk of sickness absence among abstainers is due at least partly to prior alcohol use.

Methods

Design In this prospective multi‐cohort study, we used individual‐level data from four well‐characterized cohort studies: (a) a population‐based sample of Finnish working‐age adults participating in the Health and Social Support (HeSSup) study, Finland 14, 15; (b) the Whitehall II study of British civil servants 16; (c) the employees of the national gas and electricity company participating in the GAZEL study, France 17; and (d) the municipal employees of the Finnish Public Sector (FPS) study, Finland 1. In all these studies, alcohol use was assessed twice, and diagnosis‐specific sickness absences were followed for 4–7 years after the latter survey via linkage to electronic health records. Ethical approval was obtained from Turku University Central Hospital Ethics committee for the HeSSup study, from the University College London Medical School committee on the ethics of human research for the Whitehall II study, from the Inserm Ethics committee for GAZEL and from the Ethics committee of the Hospital District of Helsinki and Uusimaa for FPS. From all four cohorts, we included respondents who were alive, not retired before the start of the follow‐up and had data on all studied variables from the surveys that were included in this study design. The eligible population in each study comprised the respondents of a baseline and follow‐up questionnaire survey. In the HeSSup study, the survey years were 1998 and 2003 (n = 10 667), in the GAZEL study 1993 and 1997 (n = 8107), in the Whitehall II study phases 1 (1985–8) and 3 (1991–94) (n = 3730) and in the FPS 2000–02 and 2004 (n = 25 016). The attrition rates between the two measurement points were acceptable: 24% were lost to follow‐up in HeSSup, 7% in GAZEL, 13% in Whitehall II and 34% in FPS. The follow‐up time (time at‐risk for sickness absence) in all studies was until disability or old‐age pension, death or end of follow‐up, whichever came first. Time at‐risk for sickness absence was defined with person‐years in the labour force. In the Whitehall II cohort, we were able exclude possible periods of unemployment, parental leave or other transient episodes of not working from time at risk. In the GAZEL, HeSSup and FPS cohorts we were unable to exclude these episodes of not working.

Measurement of alcohol use Alcohol use was assessed by questions on average weekly consumption. One drink/alcohol unit was estimated as 12 g of alcohol (EUR unit) except in Whitehall, where a unit was 8 g (UK unit). Alcohol intake was categorized into ‘no alcohol use’, ‘moderate use’ (a maximum of 140 g or 1–11 EUR units or 1–17 UK units for women and 280 g or 1–23 EUR units or 1–34 UK units for men per week), and ‘heavy use’ (> 140 g or > 11 EUR units or > 17 UK units for women and > 280 g or > 23 EUR units or > 34 UK units for men per week). The cut‐points of at‐risk drinking were based on Finnish Current Care Guidelines 18. Alcohol use was measured twice (two survey responses). Based on these two measurements, we classified the respondents as ‘abstainers’ (no alcohol use in either survey), ‘low‐risk’ (moderate use reported in both surveys), ‘former at‐risk’ (heavy use reported at baseline survey, but no or moderate use in the follow‐up survey), ‘persistent at‐risk’ (heavy use reported in both surveys) and ‘new at‐risk’ (heavy use at follow‐up survey only). Those who changed between abstinence and moderate use between the two time‐points were omitted from the study (pooled n = 4924, 10%). This was conducted to ensure the ‘cleanliness’ reference group, i.e. low‐risk drinkers. Changing between abstinence and moderate use could be a sign of health problems intervening with our study design. Classification of alcohol use was consistent with our previous study 11.

Ascertainment of diagnosis‐specific sickness absence Sickness absence was measured as number of sickness absence days per follow‐up year. In HeSSup and FPS, register information on the dates of sickness absence exceeding 9 days was retrieved from the Social Insurance Institution of Finland. These were followed‐up from 1 January 2004 to 31 December 2010 in HeSSup and from 1 January 2005 to 31 December 2011 in FPS. In Whitehall II, information on all days of sickness absence was from the Civil Service (employer) records for those employees who gave consent to monitor their sickness absence for a follow‐up period from phase 3 until the end of 1998. In GAZEL, the information on annual days of sickness absence was obtained from the employer's records for a follow‐up period from 1 anuary 1998 to 31 December 2004. Diagnoses of sickness absence were coded according to International Classification of Diseases (ICD‐10) in HeSSup, GAZEL and FPS. We used the following diagnosis groups: codes F00‐F99 for mental and behavioural disorder; I00–I99 for diseases of the circulatory system; J00–J99 for diseases of the respiratory system; K00–K93 for diseases of the digestive system; M00–M99 for diseases of the musculoskeletal system and connective tissue; and S00–T98 for injury, poisoning and certain other consequences of external causes. In HeSSup and FPS, the data included all absence episodes lasting for at least 10 days, from the date that illness began (the first days of absence form work) until the sickness absence benefit ended. In GAZEL, a medical certificate is required from day 1, and the data included all episodes of sickness absence irrespective of length. However, the cause of sickness absence was missing for 50% of absences of fewer than 7 days, 17% of those of 8–28 days and 3% of absences of more than 28 days 12. In Whitehall II, a medical certificate was required for absences longer than 7 calendar days. The coding was based on ICD‐8 classification, which was converted to a smaller number of disease categories using the morbidity coding system of the Royal College of General Practitioners (RCGP), as described elsewhere in detail 19, 20. We used codes 5 (psychoses), 40 (neurosis) and 41 (neurosis ill‐defined) to define sickness absence due to mental and behavioural disorder‐related sickness absence; codes 9 (cardiovascular diseases), 10 (cerebrovascular diseases) and 11 (peripheral vascular diseases) to define sickness absence due to diseases of the circulatory system; code 12 (diseases of the respiratory system); code 13 (diseases of the digestive system); code 17 (diseases of the musculoskeletal system and connective tissue); and code 23 (injury and poisoning). Thus, our outcome measures best capture sickness absence due to chronic long‐term illness rather than transient/short‐term illness, such as respiratory infections, headaches or migraine.

Covariates Covariates, measured at T2, were socio‐economic status (SES), age, sex, smoking and body mass index (BMI). SES was based on occupational class except for HeSSup, where information on occupational class was unavailable, and SES was based on vocational education. In FPS and GAZEL, SES was based on register data and in HeSSup and Whitehall, it was based on self‐reports. High SES included administrators, managers, experts and specialists, and in HeSSup, those with university/polytechnic education. Intermediate SES included skilled non‐manual occupations, such as office work, customer service, sales work and hospital nurses, and in HeSSup, those with college‐level education. Low SES included manual workers, such as construction workers, manufacturing, transportation (FPS, GAZEL), clerical and office support work (in Whitehall II) and those with vocational school, vocational course, apprenticeship training or no vocational education (HeSSup). Age was treated as a continuous variable in the analyses, except for sickness absence due to musculoskeletal diagnoses, where we observed a curvilinear association with age among women. There, we used age as categorized into < 40, < 50 and ≥ 50 years. Smoking was self‐reported in all studies, and was dichotomized into current smoker or non‐smoker (including never and ex‐smokers). BMI (= weight in kg divided by height in m2) was self‐reported in HeSSup, GAZEL and FPS. In the Whitehall II study, BMI was derived from measures taken at clinical examinations. BMI was categorized as less than 18.5 (underweight), 18.5–25 (normal weight), 25–29 (overweight) and 30 or more (obesity).