Self-reported health problems and obesity predict sickness absence during a 12-month follow-up: a prospective cohort study in 21 608 employees from different industries

1 PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access Selfreported health problems and obesity predict sickness absence during a 12month followup: a prospective cohort study in 21 608 employees from different industries Minna Pihlajamäki,1,2 Jukka Uitti,1,3 Heikki Arola,2 Jyrki Ollikainen,4 Mikko Korhonen,4 Tapio Nummi,4 Simo Taimela 5,6 To cite: PihlajamäkiM, UittiJ, ArolaH, etal. Selfreported health problems and obesity predict sickness absence during a 12month followup: a prospective cohort study in 21 608 employees from different industries. BMJ Open 2019;9:e025967. doi:10.1136/ bmjopen-2018-025967 ►Prepublication history for this paper is available online. To view these files, please visit the journal online (http:// dx. doi. org/ 10. 1136/ bmjopen2018025967). Received 22 August 2018 Revised 30 August 2019 Accepted 27 September 2019 For numbered affiliations see end of article. Correspondence to Dr Simo Taimela; simo. taimela@ helsinki. fi Original research © Author(s) (or their employer(s)) 2019. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. ABSTRACT Objectives To study whether selfreported health problems predict sickness absence (SA) from work in employees from different industries. Methods The results of a health risk appraisal (HRA) were combined with archival data of SA of 21 608 employees (59% female, 56% clerical). Exposure variables were selfreported health problems, labelled as ‘work disability (WD) risk factors’ in the HRA, presence of problems with occupational wellbeing and obesity. Age, socioeconomic grading and the number of SA days 12 months before the survey were treated as confounders. The outcome measure was accumulated SA days during 12month followup. Data were analysed separately for males and females. A Hurdle model with negative binomial response was used to analyse zeroinflated count data of SA. Results The HRA results predicted the number of accumulated SA days during the 12month followup, regardless of occupational group and gender. The ratio of means of SA days varied between 2.7 and 4.0 among those with ‘WD risk factors’ and the reference category with no findings, depending on gender and occupational group. The lower limit of the 95% CI was at the lowest 2.0. In the Hurdle model, ‘WD risk factors’, SA days prior to the HRA and obesity were additive predictors for SA and/or the accumulated SA days in all occupational groups. Conclusion Selfreported health problems and obesity predict a higher total count of SA days in an additive fashion. These findings have implications for both management and the healthcare system in the prevention of WD. InTROduCTIOn The main goals of health surveillance are to prevent workrelated illnesses, to support workers’ health and work ability and to reduce absenteeism as defined in International directives (Council Directive 83/391/ EEC) and International Labour Office’s guidelines (ILO).1 Screening questionnaires are used as a part of targeted health surveillance to identify workers at risk. Some screening questionnaires have shown predictive value for identifying individuals with an increased risk of sickness absence (SA) or work disability (WD) due to health issues.2–5 In the present study, we used a health risk appraisal (HRA), which is widely used in Finland and The Netherlands as a part of preventive occupational health services (OHS) to recognise employees at WD risk and to target interventions for those in need. The HRA was able to identify employees with a high number of SA days in an earlier study.6 The previous study population (n=1341) were mainly blue collars (61%) and males (88%) from the construction industry. Age, gender, occupational grade and the selfassessment of future work ability were strong determinants of SA.6 SA is a complex and multifactorial phenomenon determined by personal, sociodemographical, lisfestylerelated and healthrelated factors as well as organisational determinants, healthcare management and legislation.7 8 Selfreported health problems predict SA and prolonged return to work.6 9 The key psychosocial predictors of SA include individuals’ own perceptions of health and work ability.10–13 On the other hand, SA serves as a measure of Strengths and limitations of this study ►Our study is based on prospectively collected extensive data from various fields of industries and occupations. ►The coverage, accuracy and consistency of the registrybased sickness absence outcomes is superior to selfreports. ►Our advanced statistical model is able to control key potential confounders. ►Generalisations can only be made to a working population. Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 2PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access Table 1 Criteria for classifying employees into the HRA categories Topic Criteria Work disability risk: at least one of the topics below Doubt of work ability Selfrated future work ability: uncertain of own ability (‘uncertain’), or quite sure (‘not able’) not being able to continue in the current job due to health reasons. Impairment due to musculoskeletal problems at work Numerical rating scale (0–10) score ≥5. Pain hampering work At least moderate pain that affects working ability at minimum three times a week. Sleep problems Problems in falling asleep or night awakenings AND daytime sleepiness daily or almost daily. Depressive symptoms DEPS score ≥11. Workrelated constant fatigue Feeling being squeezed empty. Workrelated constant stress Feeling tense, strained, nervous and/or anxious because workrelated issues are on one’s mind all the time. Health risks: at least one of the topics below Weight problems* BMI ≥30 or ≤18.5. Diabetes risk Diabetes risk score ≥11. Excess use of alcohol Males ≥350 mL/week, females ≥240 mL/ week (expressed as absolute alcohol). Some symptoms: at least one of the topics below Impairment due to musculoskeletal problems at work Numerical rating scale (0–10) score=4. Some depressive symptoms DEPS score between 8 and 10. Some sleep problems Problems in falling asleep or night awakenings AND daytime sleepiness 3–5 times a week. A chronic disease Selfreported chronic diseases. Symptoms Selfreported symptoms. Lifestyle issues: at least one of the topics below Smoking Smoking=yes. Physical inactivity No physical activity during leisure time nor while commuting to work. Overweight* BMI between 25 and 30. No findings Previous criteria are not met *Overweight and weight problems were not included in the HRA category but analysed separately in the fully adjusted model. BMI, body mass index; DEPS, Depression Scale; HRA, health risk appraisal. Table 2 Criteria for problems with occupational wellbeing Feature Criteria Insufficient job control High ‘job demands’ AND (low ‘decision authority’ OR low result concerning ‘job contents’). Worklife conflict The low result in ‘worklife balance’. Strain due to rewarding Any of the individual questions concerning rewarding (meaningfulness of work; appreciation; income; career opportunities) in a category ‘causes very much strain’. Lack of social support Bullying at workplace OR (no support from line manager AND no teamwork). Overloaded Always feeling squeezed empty due to work OR always feels stress. Dissatisfied Seldom enjoys life OR never content with the present job. Figure 1 The distribution of the responses by occupational group and standard industrial classification by statistics Finland. A=agriculture, forestry and fishing; B=mining and quarrying; C=manufacturing; D=electricity, gas, steam and air conditioning supply; E=water supply; sewerage, waste management and remediation activities; F=construction; G=wholesale and retail trade; repair of motor vehicles; H=transportation and storage; I=accommodation and food service activities; J=information and communication; K=financial and insurance activities; L=real estate activities; M=professional, scientific and technical activities; N=administrative and support service activities; O=public administration and defence; compulsory social security; P=education; Q=health and social work activities; R=arts, entertainment and recreation; S=other service activities; X=industry unknown. health in the working population when health is understood as a mixture of social, psychological and physiological functioning.14 15 Multiple studies have shown that psychosocial work factors and work characteristics predict ill health and eventually WD.16 Therefore, besides questions related to health and lifestyle risk factors, the HRA used in our study includes questions modified from the job demandcontrolsocial support (JDCS) model, the effortreward imbalance (ERI) model, the worklife conflict (WLC) theories, level of stress and work satisfaction.17–19 Recent studies have suggested that obesity may be a risk factor for SA,20–22 and that prevention of obesity may be costeffective.23 In some studies, body mass index (BMI) has been a predictor of SA in females but not in males.20 22 It has been estimated that obesity is associated with an Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 3 PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access Table 3 The prevalence of work disability (WD) risk factors, problems with occupational wellbeing (OWB) and obesity and characteristics of the distribution of the number of sickness absence (SA) days by gender, occupational group (OG) and age OG Gender Age Subjects, N WD risks (%) Problems with OWB (%) Obesity (BMI >30) (%) Days on sickness absence One Two or more % with zero SA Median Upper quartile Mean of all values Mean of nonzero values BC Female ≤30 701 19 13 25 13 29 5 13.0 12.7 17.9 30–40 829 20 14 25 17 24 5 15.0 13.8 18.1 40–50 1020 22 10 20 21 31 4 13.0 13.7 19.9 50–60 1067 21 17 22 19 28 5 16.0 15.1 21.1 >60 289 34 22 21 16 40 2 12.0 12.1 20.5 All 3906 22 14 23 18 29 4 15 13.8 19.5 Male ≤30 475 12 4 16 12 34 3 9 8.5 12.8 30–40 702 16 6 19 20 29 3.5 10 9.7 13.6 40–50 684 16 7 16 20 35 3 11 11.6 17.9 50–60 659 20 12 17 24 38 2 11 11.5 18.7 >60 177 25 13 11 24 53 0 8 9.9 21.1 All 2697 17 8 17 20 35 3 10 10.4 16.1 All 6603 28 12 20 19 32 4 12 12.4 18.2 C Female ≤30 577 15 6 16 5 45 2 6 6.5 11.7 30–40 1543 19 8 17 13 40 2 8 7.5 12.6 40–50 2036 19 9 19 18 44 2 7 7.9 14.0 50–60 2482 19 13 19 19 48 1 7 8.0 15.5 >60 696 23 17 15 20 54 0 5 7.7 16.9 All 7334 19 11 18 16 46 1 7 7.7 14.2 Male ≤30 391 8 2 6 8 67 0 2.5 3.8 11.5 30–40 1143 11 4 14 14 60 0 3 3.7 9.2 40–50 1179 13 6 13 18 65 0 3 4.4 12.3 50–60 1347 14 9 12 19 67 0 3 5.3 15.9 >60 495 17 8 10 16 72 0 2 5.3 19.2 All 4555 13 6 12 16 65 0 3 4.5 13.0 All 11 889 17 9 16 16 53 0 5 6.5 13.8 P/M Female ≤30 60 20 0 10 10 58 0 3 3.8 9.1 30–40 349 13 7 13 9 54 0 4 5.0 10.8 40–50 485 18 6 13 12 56 0 4 4.5 10.1 50–60 414 19 8 14 18 53 0 5 6.0 12.8 >60 139 23 12 15 17 61 0 4 6.5 16.8 All 1447 18 7 13 13 55 0 4 5.2 11.6 Male ≤30 79 6 3 10 5 66 0 2.5 2.5 7.4 30–40 457 11 3 11 10 64 0 2 2.4 6.8 40–50 543 10 2 8 14 71 0 2 3.4 11.7 50–60 464 11 4 9 18 70 0 2 3.8 12.8 >60 126 13 6 9 17 70 0 2 3.8 12.7 All 1669 11 3 9 14 68 0 2 3.2 10.3 All 3116 14 5 11 14 62 0 3 4.1 11.0 All Female 12 687 20 11 19 17 42 2 9 9.3 15.9 Male 8921 14 6 13 17 57 0 5 6.1 14.0 All 21 608 17 9 16 17 48 1 7 8.0 15.3 BC, blue collar; BMI, body mass index; C, clerical; P/M, professional/manager. Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 4PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access Table 4 Sickness absence (SA) by the HRA categories in different occupational groups by gender: means and the ratio of means Interpretation of the HRA Male Female SA days (N; mean; SD) Ratio of means (95% CI) SA days (N; mean; SD) Ratio of means (95% CI) Bluecollar workers No findings 274 5.18 10.50 Ref 198 6.95 17.83 Ref Some symptoms 1060 7.18 15.73 1.39 (1.07 to 1.87) 1471 9.31 20.24 1.34 (0.96 to 2.11) Health risk 691 8.33 16.56 1.61 (1.23 to 2.19) 843 12.04 22.42 1.73 (1.24 to 2.74) WD risk 672 19.88 41.05 3.84 (2.91 to 5.23) 1394 20.64 38.19 2.97 (2.15 to 4.67) Clerical employees No findings 532 2.43 12.20 Ref 425 4.48 16.6 Ref Some symptoms 2198 3.10 10.61 1.28 (0.87 to 2.26) 3390 5.18 13.49 1.15 (0.84 to 1.80) Health risk 953 4.08 11.57 1.68 (1.11 to 3.00) 1344 7.87 18.11 1.75 (1.26 to 2.75) WD risk 872 9.80 25.2 4.04 (2.69 to 7.19) 2175 12.27 26.43 2.74 (1.99 to 4.26) Professionals/managers No findings 232 1.77 4.71 Ref 95 2.00 3.76 Ref Some symptoms 913 2.86 11.39 1.61 (1.06 to 2.60) 774 4.15 12.73 2.08 (1.38 to 3.48) Health risk 292 3.89 12.15 2.20 (1.29 to 3.69) 220 5.98 14.81 2.99 (1.79 to 5.20) WD risk 232 5.40 17.07 3.05 (1.68 to 5.23) 358 7.82 18.14 3.91 (2.55 to 6.59) Bold values denote statistical significance at the p < 0.05 level. HRA, health risk appraisal; WD, work disability. increase in SA from 1.1 to 1.7 extra days missed annually compared with normalweight employees in the USA.21 It remains to be seen whether obesity acts as an additive risk factor for SA besides health problems. In the present study, we evaluated how the HRA results predict SA in respondents from various industries and occupations and assessed the potential additive roles of selfreported health problems, occupational wellbeing and obesity. Our hypothesis was that selfreported health problems predict future SA, irrespective of gender and occupational group and that obesity has an additive effect. MeThOdS Study design, ethics and setting The study design is a retrospective analysis of prospectively collected register data. The questionnaire data and SA register data were collected from one nationwide occupational health (OH) service provider’s registers. Data privacy was strictly followed. The study setting is OHS. Most Finnish employees use OHS for all their primary healthcare needs. In 2015, approximately 2.10 million Finnish employees (95% of the total workforce24) were covered by OHS. Besides 1.2 million preventive health examinations, OHS also performed 4.8 million illnessrelated visits.25 The Finnish public social insurance system includes all lawful residents of Finland and provides health services and social service benefits for all members of the scheme, administered by The Social Insurance Institution of Finland (KELA). The SA benefit programme provides coverage for lost income due to medically certified sickness or disease from day 1 until the person can work again up to 52 weeks. After that, longterm benefits from the disability benefits system provide coverage for lost income. Participants The study participants were workingage Finnish residents, aged 18–68 years, who had completed the HRA (n=22 515). An invitation to the HRA had been sent to 33 990 employees, of which 11 475 had not responded (response rate 66%). We used HRA results collected as a part of preventive OHS in 2012–2015 and archival data of SA covering 2011–2016. The inclusion criteria was a completed HRA. Exclusion criteria were >150 SA days in the 12 months preceding the HRA (n=119), granted disability benefit (n=689), missing data concerning occupational group (n=79) and loss to followup (n=77). Some respondents were in several exclusion categories. Finally, we analysed the data from 21 608 employees. Patient and public involvement Patients were not involved in the design, recruitment or conduct of the study. Measurements Explanatory variables The classified results of the HRA were used as the primary exposure variable. Other exposure variables included problems with occupational wellbeing and obesity. Gender, age, occupational group and SA days before the HRA were treated as confounding factors. Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 5 PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access Table 5 Predicting the odds for any sickness absence (SA) (logistic model) and the duration of SA, if any (zerotruncated negative binomial (NB) part) in the negative binomial Hurdle model: crude analysis OG Gender HRA result category N Logistic model (0 vs >0) Zerotruncated NB (>0) OR 95% CI RR 95% CI BC Female (Intercept) 1.08 (0.82 to 1.43) 10.28 (7.81 to 13.53) No findings 198 1.00 Ref 1.00 Ref Some symptoms 1471 1.78 (1.32 to 2.40) 1.07 (0.80 to 1.42) Health risk 843 2.05 (1.49 to 2.80)1.36 (1.01 to 1.82) WD risk, 1 risk factor 840 3.13 (2.27 to 4.33)1.58 (1.18 to 2.12) WD risk, 2 risk factors 335 4.91 (3.27 to 7.36)2.36 (1.72 to 3.25) WD risk, 3–5 risk factors 219 5.59 (3.49 to 8.96)3.43 (2.45 to 4.82) Male (Intercept) 1.23 (0.97 to 1.56) 6.64 (5.25 to 8.41) No findings 274 1.00 Ref 1.00 Ref Some symptoms 1060 1.37 (1.04 to 1.79) 1.27 (0.98 to 1.63) Health risk 691 1.44 (1.08 to 1.91)1.47 (1.13 to 1.92) WD risk, 1 risk factor 456 2.04 (1.49 to 2.79)2.89 (2.19 to 3.82) WD risk, 2 risk factors 147 2.34 (1.51 to 3.63)3.86 (2.70 to 5.51) WD risk, 3–5 risk factors 69 3.51 (1.83 to 6.71)5.32 (3.41 to 8.30) C Female (Intercept) 0.62 (0.51 to 0.76) 8.05 (6.35 to 10.20) No findings 425 1.00 Ref 1.00 Ref Some symptoms 3390 1.58 (1.28 to 1.94) 0.87 (0.68 to 1.11) Health risk 1344 1.94 (1.55 to 2.42) 1.28 (0.99 to 1.65) WD risk, 1 risk factor 1403 2.81 (2.24 to 3.51)1.48 (1.15 to 1.91) WD risk, 2 risk factors 500 3.32 (2.54 to 4.36)1.73 (1.30 to 2.30) WD risk, 3–5 risk factors 272 3.54 (2.56 to 4.88)3.05 (2.21 to 4.21) Male (Intercept) 0.34 (0.28 to 0.42) 5.96 (4.51 to 7.86) No findings 532 1.00 Ref 1.00 Ref Some symptoms 2198 1.36 (1.10 to 1.69) 1.03 (0.77 to 1.37) Health risk 953 1.46 (1.15 to 1.85) 1.36 (0.99 to 1.86) WD risk, 1 risk factor 590 2.59 (2.01 to 3.34)1.84 (1.34 to 2.54) WD risk, 2 risk factors 197 3.19 (2.27 to 4.49)3.22 (2.15 to 4.82) WD risk, 3–5 risk factors 85 4.16 (2.59 to 6.68)3.83 (2.29 to 6.41) P/M Female (Intercept) 0.58 (0.38 to 0.88) 2.77 (1.59 to 4.83) No findings 95 1.00 Ref 1.00 Ref Some symptoms 774 1.23 (0.79 to 1.92) 2.18 (1.26 to 3.78) Health risk 220 1.45 (0.89 to 2.38) 3.03 (1.65 to 5.58) WD risk, 1 risk factor 254 1.71 (1.06 to 2.78)3.09 (1.71 to 5.60) WD risk, 2 risk factors 69 2.36 (1.25 to 4.46)4.31 (2.09 to 8.90) WD risk, 3–5 risk factors 35 2.57 (1.16 to 5.69)5.00 (2.10 to 11.9) Male (Intercept) 0.39 (0.29 to 0.52) 2.95 (1.80 to 4.81) No findings 232 1.00 Ref 1.00 Ref Some symptoms 913 1.06 (0.77 to 1.46) 1.76 (1.12 to 2.78) Health risk 292 1.38 (0.95 to 2.01) 2.08 (1.23 to 3.51) WD risk, 1 risk factor 176 1.43 (0.94 to 2.18) 3.18 (1.76 to 5.73) WD risk, 2 risk factors 36 2.06 (1.00 to 4.21) 1.35 (0.54 to 3.28) WD risk, 3–5 risk factors 20 2.57 (1.02 to 6.46) 2.51 (0.80 to 7.82) Continued Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 6PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access OG Gender HRA result category N Logistic model (0 vs >0) Zerotruncated NB (>0) OR 95% CI RR 95% CI Logistic model refers to the model component for predicting membership to the subpopulation A with high propensity to zero absence, and zerotruncated NB to the component predicting the days on sick leave among the susceptible subpopulation B. To facilitate interpretation, for the zeroinflation part we have shown the ORs associated with the complementary propensity to having any sickness absence, that is, inclusion in subpopulation B. Bold values denote statistical significance at the p < 0.05 level. BC, blue collar; C, clerical; HRA, health risk appraisal; OG, occupational group; P/M, professional/manager; RR, risk ratio; WD, work disability. Table 5 Continued The HRA result categories in declining priority order are 1) WD risk, 2) health risk, 3) some symptoms, 4) lifestyle issues and 5) no findings (table 1). Within the category ‘WD risk’, the results were further subdivided by the number of risk factors (1–5). We constructed a dichotomous variable ‘problems with occupational wellbeing’, based on a series of questions modified from the JDCS model, the ERI model, the WLC theories and the presence of constant stress or dissatisfaction (table 2).17–19 If any of the criteria were met, the respondent was classified as having a problem with occupational wellbeing. BMI was categorised as underweight (<18.5), normal weight (18.5–25.0), overweight (25.0–30.0) and obese (>30.0). Normal weight was chosen as the reference class in the statistical models. Among males, underweight was combined with normal weight due to small numbers. Age was categorised into five classes: <30, 30–40, 40–50, 50–60 and >60 years. The age group 30–40 years was chosen the reference class in the statistical models. Occupational group was defined as bluecollar workers, clerical employees and professionals/managers. The number of SA days 12 months prior to the questionnaire was included as a continuous variable. Sickness absence We used SA days as the outcome variable. SA was operationalised as the accumulated number of days on sick leave during the 12month followup after the survey. It includes the number of days and periods absent because of sickness. Overlapping and consecutive SA were combined. Maternity/paternity leave and absence from work to care for a sick child are not included in the SA. The employer records the sick leave periods and dates when each SA starts and ends. If the SA is prescribed by the OH physician, the employer supplies the information to the OH care. In most cases, permanent employees are paid a full salary during their SA from the first day, up to 3 months. The employer receives sickness allowance from KELA after 10 working days, Sundays and other national holidays are excluded. The employee needs a medical certificate to qualify for sickness allowance. Mostly the bluecollar employees cannot complete their own certificates for any SA, while professional and manager employees must provide a written explanation for short SA and a medical certification for SA longer than 3 days. An employee may receive sickness allowance from KELA for <1 year of WD due to the same illness. If an employee is unfit for work because of an illness for longer than a year, it is possible to claim a disability benefit. The evaluation of eligibility for WD benefits is transferred to the pension insurance companies if the illness lasts longer than 1 year. Statistical methods We analysed how the number of future SA days vary based on the results of the HRA, taking into account other exposure variables and potential confounding factors. Missing values in the questionnairebased variables were imputed with the multiple imputation method MICE software with predictive mean matching. We are not aware of any systematic reasons or motives that would cause the nonresponse to be different in the HRA response categories. Based on our best knowledge, missing questionnaire data are missing at random. The following items were used as determinants when conducting the missing data imputation: gender, age, problems with occupational wellbeing, stress and fatigue, job satisfaction, BMI, allcause SA both 12 months prior to and after and the lifestyle questions in the HRA (alcohol consumption, exercise, smoking). There were complex interactions between gender and other variables in our data and we performed all analyses stratified by gender and occupational group as has been suggested earlier.26 Baseline characteristics are presented using descriptive statistics. Patterns with SA means and SD and the ratio of means with 95% CIs were calculated by the HRA result classes separately by gender and occupational group. When modelling SA data, a special challenge is that a large number of employees have no absenteeism due to sickness.6 Ordinary count data methods like Poisson or negative binomial regression models are not directly suitable for the analysis in case of the excess count of zero days. Our approach was to try mixture regression, zeroinflated negative binomial regression and the Hurdle model. The first two approaches yielded problems when estimating the model’s parameters. We chose the Hurdle model, which provides a combination of the two statistical models: a binary model determines whether the outcome is zero or positive (logistic regression) and a truncated at zero count model for the positive part of the count data. We used the truncated negative binomial regression model because it accounts for the overdispersion present in count data. The estimated ORs (with 95% CI) based Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 7 PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access on the binary part are reported in tabulated form. In the zerotruncated negative binomial part, the estimated risk ratios (RR) based on the regression coefficients of the HRA categories and covariates are reported with 95% CIs. The statistical analyses were performed using R V.3.4.4 software. ReSulTS The average age of the participants was 45.3 years (SD 11; range 19–68) and 59% (n=12 687) were female, 6603 (32%) were bluecollar workers, 11 889 (56%) were clerical employees and 3116 (15%) belonged to the professional or manager category (figure 1). The nonrespondents were slightly younger (average age 44.2 years, SD 12; t=−7.3; p<0.0001) than the respondents on the average. Also, males were less likely to respond than females with response rates 60% and 71%, respectively (χ2=425.5; p<0.0001). The response rates were almost identical among bluecollar workers (65%), clerical employees (67%) and experts/managers (66%) (χ2=14.3; p=0.0007). A total of 172 331 days of SA were recorded in the study population during the 12month followup. The distribution was heavily rightskewed in all age groups. Moreover, 48% had not been on SA at all, indicating a substantial zero component in the response distribution (table 3). The proportion of respondents with zero SA days was 35% in bluecollar males and 29% in females, 65% in clerical males and 46% in females, and 68% in professional/ manager males and 55% in females. The mean numbers of SA days among those with any SA were 18.2, 13.8, 11.0 days in blue collars, clericals and professionals/managers, respectively. An increasing trend of SA by age was observed among those with any SA. Females tend to have more SA days than males in all occupational groups. Twentysix per cent of subjects reported ‘WD risk factors’, but their share of the total number of SA was 47% (table 3). Belonging to the HRA category ‘health risk’ or ‘WD risk factors’ predicted higher mean values of SA during the followup, regardless of the occupational group or gender. The ratio of means of SA days varied between 2.7 and 4.0 among those with ‘WD risk factors’ and the reference category with no findings in the HRA, depending on gender and occupational group. The lower limit of the 95% CI was at the lowest 2.0. (table 4). The results from fitting the Hurdle model are displayed in the table 5 (unadjusted model) and table 6 (adjusted model). The analyses were performed stratified by gender and occupational group due to complex interactions. We included the result of the HRA (six categories for blue collars and clericals and five categories for professionals/managers) as a covariate. The categories ‘lifestyle issues and no findings” were combined as the reference class and the number of WD risk factors was analysed separately when possible. We excluded weight problems from the HRA ‘health risk category’ in the fully adjusted model. The average number of SA among the susceptible to any SA followed the pattern bluecollar workers>clerical employees>professionals/managers in both genders. There was some evidence of an overall decreasing trend in the susceptibility to SA by increasing age. In males in both clerical and professional and managerial positions, the number of SA days tended to increase by age, but not in females or bluecollar males. SA prior to the HRA predicted both susceptibility for SA and the number of SA days during the followup. The presence of ‘WD risk factors’ predicted susceptibility to and the mean number of days on SA in all occupational groups. In the unadjusted model, HRA category ‘WD risk factors’ predicted the probability of SA (OR at the lowest 1.7 with 95% CI at the lowest 1.1 by occupational groups) for both genders (table 5). HRA category ‘some symptoms’ (OR at the lowest 1.4 with 95% CI at the lowest 1.0) and ‘health risk’ (OR at the lowest 1.4 with 95% CI at the lowest 1.1) predicted the probability for SA in blue collars and clericals. When all covariates were included (table 6), the ORs and RRs decreased. The number of earlier SA days predicted the probability of SA (OR at the lowest 1.03 with 95% CI at the lowest 1.02 by occupational groups) in the adjusted model. The presence of multiple ‘WD risk factors’ increased both susceptibility and/or the number of SA days. Problems with wellbeing at work predicted SA in the professional and managerial group in both genders. Overweight and/or obesity predicted SA in all professional groups in both genders. All these effects are additive, that is, adjusted for each other within each stratum. dISCuSSIOn Selfreported health problems in the HRA—musculoskeletal problems, depressive symptoms, sleep problems, constant stress and feeling of exhaustion and doubts about work ability—predicted future SA in both genders, regardless of occupational group. Of note, the larger the number of these problems, labelled as ‘WD risk factors’, the higher were the odds for any SA and the larger the number of SA days, if any. In Finland, the two largest categories of the causes of SA and permanent WD are musculoskeletal disorders and mental and behavioural disorders.27 Also, problems with sleep,28 constant stress,29 exhaustion30 and attitudes towards work ability6 have predicted SA in earlier studies. It seems that using a questionnaire for selfrating of symptoms of the common causes of SA is a valid way to identify individuals at risk of SA, as the HR:s were relatively high in our study. Obesity and earlier sick leave days also predicted future SA in an additive fashion. The strengths of the study include the registrybased, prospectively collected extensive data from various industries. Recorded SA data have several advantages: the quality of the data in terms of coverage, accuracy and consistency over time is better to that achievable via selfreports.31 We were also able to control key potential Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 8PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access Table 6 Predicting the odds for any sickness absence (SA) (logistic model) and the duration of SA, if any (zerotruncated negative binomial (NB) part) in the negative binomial Hurdle model: all covariates included OG Gender Explanatory variable N Logistic model (0 vs >0) Zerotruncated NB (>0) OR 95% CI RR 95% CI BC Female (Intercept) 1.13 (0.82 to 1.56) 8.48 (6.48 to 11.10) No findings 198 1.00 Ref. 1.00 Ref. Some symptoms 1471 1.70 (1.25 to 2.31) 0.97 (0.74 to 1.27) Health risk 843 1.70 (1.23 to 2.37) 1.05 (0.79 to 1.40) WD risk, 1 risk factor 840 2.68 (1.91 to 3.75) 1.21 (0.91 to 1.61) WD risk, 2 risk factors 335 3.65 (2.37 to 5.61)1.51 (1.11 to 2.06) WD risk, 3–5 risk factors 219 3.46 (2.08 to 5.75)1.95 (1.39 to 2.74) BMI>18.5 and ≤25 1866 1.00 Ref. 1.00 Ref. BMI≤18.5 53 1.13 (0.58 to 2.21) 1.42 (0.94 to 2.17) BMI>25 and ≤30 1210 1.09 (0.92 to 1.29) 1.23 (1.10 to 1.39) BMI>30 684 1.27 (1.02 to 1.58)1.29 (1.12 to 1.49) Age>30 and ≤40 829 1.00 Ref. 1.00 Ref. Age≤30 701 0.84 (0.66 to 1.07) 1.06 (0.91 to 1.24) Age>40 and ≤50 1020 0.66 (0.53 to 0.82) 1.02 (0.88 to 1.17) Age>50 and ≤60 1067 0.71 (0.57 to 0.88) 1.08 (0.94 to 1.24) Age>60 289 0.40 (0.30 to 0.54) 1.02 (0.82 to 1.28) Problems in wellbeing 881 0.99 (0.82 to 1.20) 1.04 (0.92 to 1.17) Sick leaves before the questionnaire 1.048 (1.039 to 1.057)1.019 (1.016 to 1.022) Male (Intercept) 1.12 (0.83 to 1.51) 5.23 (4.05 to 6.75) No findings 274 1.00 Ref. 1.00 Ref. Some symptoms 1060 1.28 (0.97 to 1.69) 1.05 (0.82 to 1.34) Health risk 691 1.33 (0.98 to 1.79) 1.25 (0.97 to 1.61) WD risk, 1 risk factor 456 1.63 (1.17 to 2.28)2.06 (1.57 to 2.72) WD risk, 2 risk factors 147 1.70 (1.06 to 2.75)2.16 (1.53 to 3.07) WD risk, 3–5 risk factors 69 1.81 (0.89 to 3.68) 3.29 (2.13 to 5.08) BMI≤25 933 1.00 ref. 1.00 ref. BMI>25 and ≤30 1205 1.22 (1.01 to 1.47) 1.16 (1.00 to 1.35) BMI>30 527 1.57 (1.22 to 2.02) 1.15 (0.96 to 1.38) Age>30 and ≤40 702 1.00 Ref. 1.00 Ref. Age<30 475 0.88 (0.68 to 1.14) 1.04 (0.86 to 1.26) Age>40 and ≤50 684 0.74 (0.59 to 0.94)1.38 (1.16 to 1.64) Age>50 and ≤60 659 0.56 (0.44 to 0.71) 1.16 (0.97 to 1.39) Age>60 177 0.31 (0.22 to 0.44) 1.32 (0.96 to 1.82) Problems in wellbeing 447 1.28 (1.00 to 1.64) 1.04 (0.87 to 1.23) Sick leaves before the questionnaire 1.053 (1.042 to 1.065)1.020 (1.015 to 1.024) Continued Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from 9 PihlajamäkiM, etal. BMJ Open 2019;9:e025967. doi:10.1136/bmjopen-2018-025967 Open access OG Gender Explanatory variable N Logistic model (0 vs >0) Zerotruncated NB (>0) OR 95% CI RR 95% CI C Female (Intercept) 0.67 (0.54 to 0.83) 6.27 (4.94 to 7.96) No findings 425 1.00 Ref. 1.00 Ref. Some symptoms 3390 1.47 (1.19 to 1.82) 0.90 (0.72 to 1.14) Health risk 1344 1.72 (1.36 to 2.18) 1.17 (0.91 to 1.51) WD risk, 1 risk factor 1403 2.30 (1.82 to 2.91)1.34 (1.05 to 1.71) WD risk, 2 risk factors 500 2.47 (1.85 to 3.29) 1.27 (0.96 to 1.68) WD risk, 3–5 risk factors 272 2.45 (1.73 to 3.47)2.20 (1.60 to 3.03) BMI>18.5 and ≤25 3775 1.00 Ref. 1.00 Ref. BMI≤18.5 81 0.82 (0.52 to 1.30) 0.68 (0.43 to 1.07) BMI>25 and ≤30 2213 1.08 (0.96 to 1.20) 1.10 (0.99 to 1.22) BMI>30 1192 1.37 (1.18 to 1.59)1.23 (1.08 to 1.4) Age>30 and ≤40 1543 1.00 Ref. 1.00 Ref. Age<30 577 0.94 (0.77 to 1.14) 1.01 (0.84 to 1.21) Age>40 and ≤50 2036 0.80 (0.70 to 0.93) 1.07 (0.94 to 1.21) Age>50 and ≤60 2482 0.63 (0.55 to 0.72) 1.12 (0.99 to 1.27) Age>60 696 0.45 (0.37 to 0.55) 1.13 (0.93 to 1.36) Problems in wellbeing 1308 1.10 (0.96 to 1.25) 1.05 (0.94 to 1.19) Sick leaves before the questionnaire 1.053 (1.046 to 1.060)1.019 (1.016 to 1.022) Male (Intercept) 0.36 (0.29 to 0.45) 4.30 (3.16 to 5.85) No findings 532 1.00 Ref. 1.00 Ref. Some symptoms 2198 1.41 (1.13 to 1.76) 1.01 (0.76 to 1.33) Health risk 953 1.50 (1.16 to 1.93) 1.07 (0.78 to 1.46) WD risk, 1 risk factor 590 2.50 (1.91 to 3.27)1.46 (1.06 to 2.02) WD risk, 2 risk factors 197 2.70 (1.87 to 3.91)2.42 (1.61 to 3.65) WD risk, 3–5 risk factors 85 3.51 (2.11 to 5.86)2.85 (1.68 to 4.85) BMI≤25 1687 1.00 Ref. 1.00 Ref. BMI>25 and ≤30 211 1.12 (0.97 to 1.29) 1.18 (0.99 to 1.39) BMI>30 734 1.23 (1.01 to 1.49)1.35 (1.08 to 1.69) Age>30 and ≤40 1143 1.00 Ref. 1.00 Ref. Age<30 391 0.82 (0.64 to 1.05) 1.25 (0.93 to 1.69) Age>40 and ≤50 1179 0.75 (0.63 to 0.90)1.33 (1.08 to 1.62) Age>50 and ≤60 1347 0.64 (0.54 to 0.76)1.40 (1.13 to 1.73) Age>60 495 0.47 (0.37 to 0.60)1.80 (1.34 to 2.43) Problems in wellbeing 551 1.18 (0.97 to 1.44) 0.95 (0.76 to 1.18) Sick leaves before the questionnaire 1.040 (1.032 to 1.048)1.016 (1.010 to 1.022) Table 6 Continued Continued Hospital of Tampere. Protected by copyright. on November 29, 2019 at Tampere University and Universityhttp://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2018-025967 on 31 October 2019. Downloaded from