Occupational health care identifies risk for type 2 diabetes and cardiovascular disease

p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 95–102

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Primary Care Diabetes journal homepage: http://www.elsevier.com/locate/pcd

Original research

Occupational health care identifies risk for type 2 diabetes and cardiovascular disease Katriina Viitasalo a,∗ , Jaana Lindström b , Katri Hemiö b , Sampsa Puttonen c , Anja Koho a , Mikko Härmä c , Markku Peltonen b a b c

Finnair Health Services, HEL-IF/67, 01053 Finnair, Finland National Institute for Health and Welfare, P.O. Box 30, FI-00271 Helsinki, Finland Finnish Institute of Occupational Health, Topeliuksenkatu 41 a A, FI-00250 Helsinki, Finland

a r t i c l e

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a b s t r a c t

Article history:

Aims: To assess the risk for type 2 diabetes (T2D) and cardiovascular disease (CVD) among

Received 3 September 2011

employees of a Finnish airline; to study the association of shift work with T2D and CVD risk;

Received in revised form

and to test the feasibility of risk screening in occupational health care setting.

6 January 2012

Methods: Altogether 4169 employees were invited for a health check-up and 2312 participated

Accepted 13 January 2012

in this study. The check-up included physical examinations, questionnaires on working

Available online 4 February 2012

hours, sleep, and lifestyle, diabetes risk score FINDRISC, and blood tests. Lifestyle counselling was offered for those with increased T2D risk.

Keywords:

Results: Altogether 15% of participants had a high T2D risk (FINDRISC ≥ 15 and/or elevated,

Cardiovascular disease

but non-diabetic blood glucose), and a further 15% had a moderate T2D risk (FINDRISC 10–14

Lifestyle intervention

and normal blood glucose). Of those 60% agreed to attend lifestyle counselling. Metabolic

Risk factor

syndrome was more common, lipid profile more unfavorable and hsCRP higher by increasing

Shift work

FINDRISC score category. Risk factor profiles linked to shift work status were not self-evident.

Type 2 diabetes

Conclusions: The renewed health check-up process effectively identified those employees with increased T2D and CVD risk who would benefit from lifestyle intervention. The use of FINDRISC questionnaire was a feasible first-step screening method in occupational health care setting. © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Occupational health care (OHC) is an important part of the Finnish primary health service system, encompassing about 80% of the workforce. The traditional duty of OHC has been the prevention of work-related illnesses and injuries and health

∗

examinations have focused on exposures of work processes and other occupational threats to working capacity. Recently, the role of OHC in health promotion and evaluation of individual health risks has been emphasized by the Finland’s Ministry of Social Affairs and Health [1]. Shift work and related sleeping problems have become more common during the past decades [2]. In Europe 20%

Corresponding author. Tel.: +358 40 5519228; fax: +358 9 818 4824. E-mail addresses: katriina.viitasalo@fimnet.fi (K. Viitasalo), jaana.lindstrom@thl.fi (J. Lindström), katri.hemio@thl.fi (K. Hemiö), sampsa.puttonen@ttl.fi (S. Puttonen), anja.koho@finnair.fi (A. Koho), mikko.harma@ttl.fi (M. Härmä), markku.peltonen@thl.fi (M. Peltonen). 1751-9918/$ – see front matter © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.pcd.2012.01.003

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p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 95–102

and in Finland 24% of the total workforce work in shifts [3]. In Finland’s major airline Finnair up to two-thirds of the 7000 employees work other than regular day work schedule (08:00–18:00). Shift workers’ health habits are irregular compared with those of day workers [4–6]. They also sleep fewer hours during working days and their sleep is more disturbed. Epidemiological studies have indicated that both short sleep and insomnia predict weight gain, obesity, and type 2 diabetes (T2D) [7–9]. In addition, sleep deprivation activates the immune system [10] and influences glucose metabolism [11]. Shift work increases the risk of cardiovascular disease (CVD), and possibly also the risks of metabolic syndrome (MetS) and obesity [4,12,13]. MetS is known to predispose to the development of T2D and CVD [14]. People with traditional CVD risk factors, such as smoking, overweight, and high cholesterol, are more likely to be debilitated by shift work. On the other hand, irregular working hours or shift work may predispose employees to smoking and unhealthy dietary habits [5,6]. T2D is becoming more common in general and also among working-age people [15]. Half of the cases are estimated to be undiagnosed [16]. Diabetes and its co-morbidities are costly diseases [17]. The most important risk factors for T2D are obesity, sedentary lifestyle and unhealthy diet [18–20]. Smoking is also associated with increased T2D risk [21]. As cardiovascular diseases and T2D share common risk factors, initiatives to prevent CVD and T2D support each other. The Finnish Diabetes Prevention Study (DPS) and other clinical trials have shown that T2D can be efficiently prevented by lifestyle intervention in high-risk individuals [22,23]. Finland was one of the first countries in the world to establish a national programme for prevention of T2D [24,25]. Consequently, Finnair OHC services renewed the health check-up process to include also risk screening and preventive activities for T2D and CVD. The aims of the present study were to assess the risk for T2D and CVD among Finnair’s employees, to study the association of shift work or varying working hours with the risk of T2D and CVD, and to test the feasibility of risk screening in occupational health care setting.

2.

Methods

2.1.

Study population and procedures

The health check-up process of Finnair OHC services was renewed in 2006. Each year one fifth of Finnair employees who are predominantly (over 95%) white Finns are invited for a check-up. The target group during the years 2006–2008 was 4169 persons. Employees without previously diagnosed diabetes and non-pregnant were asked to participate in this study. Participants gave a written informed consent and the study was approved by the local ethics committee. The study protocol is depicted in Fig. 1. Altogether 2762 people attended the health check–up (66% of the target group) and of these 2312 (84%) were eligible and willing to participate in the study. The participation rates by age categories for men and women were 40% and 60% (<35 years), 50% and 60% (35–44 years), 57% and 68%

(45–54 years), and 56% and 62% (≥55 years), respectively. Of all participants 36% were regular day workers, 35% nonflight shift workers, and 29% in-flight workers, in the target group these per cents were 33%, 39%, and 28%, respectively.

2.2.

Clinical measurements and questionnaires

The health check-up was completed by an occupational nurse or a physician and included clinical examination with measurements of blood pressure, height, weight, and waist circumference, questionnaires (with items of work and working hours, sleep, diseases, medication, and lifestyle), and the diabetes risk score FINDRISC [26], which comprises 8 categorized and rated questions (age, body mass index (BMI), waist circumference, physical activity, consumption of fruit and vegetables, hypertension medication, history of high blood glucose, and family history of diabetes) and gives an estimate of the subject’s probability to get diabetes in 10 years.

2.3.

Laboratory measurements

Laboratory tests included plasma glucose, total cholesterol, high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, triglycerides, and hsCRP. Additionally, a 75-g oral glucose tolerance test (OGTT) was offered to those participants whose diabetes risk was estimated high either based on the FINDRISC score (≥15 points) or elevated fasting plasma glucose (≥6.1 mmol/l). Blood tests were taken between 07:30 and 10:00 after a 12-h fast. The participants were advised not to arrive to the health check-up after a night shift and to avoid smoking and vigorous physical activity at least half an hour before the laboratory tests and clinical measurements. Blood samples were centrifuged within 2 h and analyzed on the same day (high sensitive C-reactive protein (hsCRP) twice a week) in a certified laboratory according to standard procedures using a Konelab 60i clinical chemistry analyzer (Thermo Fisher Scientific, Ltd, Vantaa, Finland). Blood pressure was measured twice with Omron® M4-1 device, (OMRON Healthcare Europe BV, Hoofddorp, Netherlands) after a 5-min rest in a sitting position.

2.4. Definitions of risk scores, glucose tolerance and metabolic syndrome FINDRISC score below 10 with fasting plasma glucose <6.1 mmol/l was considered to denote low diabetes risk. Score value between 10 and 14 with fasting plasma glucose <6.1 mmol/l was considered to denote moderate risk. Individuals with either score value ≥15, fasting plasma glucose ≥6.1 but <7.0 mmol/l or 2-h glucose in the OGTT ≥7.8 but <11.1 mmol/l were considered to have high diabetes risk. Glucose tolerance was classified according to the WHO 1999 criteria [27]. Individuals with fasting plasma glucose <6.1 and 2 h plasma glucose <7.8 mmol/l were classified as normoglycaemic. Individuals with fasting plasma glucose ≥6.1 but <7.0 mmol/l, and 2 h plasma glucose <7.8 mmol/l were classified as having impaired fasting glucose (IFG). Those with 2 h plasma glucose ≥7.8 but <11.1 mmol/l, and fasting plasma

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Extended health check-up: Questionnaires, clinical examinations and laboratory test Low risk < 10 points

Moderate risk = 10-14 points

High risk > 14 points OGTT

Counselling by occupational health nurse or physician

Normal blood glucose

IFG or DM*

DM*

Normal blood glucose, IFG or IGT

Normal blood glucose

Blood glucose measurement to confirm diabetic value

OGTT

DM IFG or IGT Lifestyle counselling

Referred to a diabetes specialist (excluded from the study) No intervention Fig. 1 – Screening for type 2 diabetes risk at occupational health care. Screening and counselling according to the diabetes risk score (FINDRISC) and fasting glucose value or oral glucose tolerance test. Normal glucose (mmol/l): 0 h: <6.1, 2 h: <7.8. IFG = impaired fasting glucose (mmol/l): 0 h: 6.1–6.9, 2 h: <7.8. IGT = impaired glucose tolerance (mmol/l): 0 h: <7.0, 2 h: 7.8–11.0. DM* = (mmol/l): 0 h: ≥7.0, 2 h: ≥11.1. Diabetes (DM) was diagnosed if blood glucose was diabetic in two separate measurements.

glucose <7.0 mmol/l were classified as having impaired glucose tolerance (IGT). In this study individuals with either fasting plasma glucose ≥7.0 mmol/l or with 2-h plasma glucose ≥11.1 mmol/l were classified as having diabetes (T2D). For clinical diagnosis, confirmatory measurement with a diabetic value was required. Furthermore, IFG, IGT, and T2D were combined to form a group dysglycaemia. Metabolic syndrome (MetS) was defined according to the IDF criteria [14] as central obesity (waist circumference ≥80 cm in women and ≥94 cm in men) plus any two: (a) raised triglycerides (≥1.7 mmol/l) or specific treatment for this lipid abnormality, (b) reduced HDL–cholesterol (<1.03 mmol/l in men and <1.29 mmol/l in women) or specific treatment for this lipid abnormality, (c) raised systolic blood pressure (≥130 mm Hg) or raised diastolic blood pressure (≥85 mm Hg) or treatment of previously diagnosed hypertension, (d) raised fasting plasma glucose (≥5.6 mmol/l) or previously diagnosed diabetes. Predicted 10-year risk of CVD (morbidity and mortality) was calculated using the Finnish FINRISK estimator [28] and the Framingham risk equation [29]. The FINRISK function estimates the risk of fatal or non-fatal coronary heart events and strokes in the next ten years. In the Framingham risk equation also angina pectoris is an end point. In this study the risk predictors for both equations were age, total and HDL cholesterol, systolic blood pressure, diabetes and smoking.

2.5. Grouping of working times, education and lifestyle habits Working times were classified as regular day work (working hours between 08:00 and 18:00), in-flight work (working on aviation duties as pilots or cabin attendants with diverse working hours), and non-flight shift work (neither regular day work nor in-flight work). Education was divided into two classes (college, polytechnic or academic vs. lower education). Alcohol consumption was measured as the number of 12 g units/week. Smoking status was coded as current smoker or non-smoker. Active lifestyle was defined as practicing non-sweat physical activity at least 4 h weekly and sedentary lifestyle less than 4 h weekly.

2.6.

Lifestyle intervention

During the health check-up a physician or a nurse discussed the results with the person. An individualized written health promotion plan was agreed upon and given to the person with educational leaflets and other health material. A special diabetes prevention website was launched to support lifestyle changes. The website provided information on physical exercise and healthy food choices with a special attention to shift work and its influence on sleep, lifestyle, and health.

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Table 1 – Characteristics and risk factors of participants (mean ± SD) divided by sex. Menn = 1199 (51.9%) Age, years Education, high levelb , % Smoking (%) Alcohol, units/week Sedentary lifestylec , % Body Mass Index, kg/m2 Waist circumference, cm Systolic blood pressure, mmHg Diastolic blood pressure, mm Hg BP medication, % fP-glucose, mmol/l Dysglycaemia, %d Diabetes, % Total cholesterol, mmol/l HDL cholesterol, mmol/l LDL cholesterol, mmol/l Triglycerides, mmol/l Cholesterol medication, % hsCRPe , mg/l MetS IDFf , % FINDRISC diabetes risk score Framingham functiong Framingham function ≥20% FINRISKI functionh FINRISKI function ≥10% a

b c d e f g h

44.9 (9.2) 29.7 22.7 6.9 (7.1) 18.7 26.9 (3.7) 95.3 (11.0) 142 (17) 88 (11) 8.1 5.6 (0.7) 19.0 2.6 5.2 (1.0) 1.41 (0.37) 3.23 (0.88) 1.31 (0.68) 5.0 1.66 (3.15) 34.1 6.9 (4.5) 11.2 (9.2) 16.6 3.7 (4.2) 7.2

44.0 (8.8) 65.5 18.6 3.3 (3.6) 11.8 24.4 (4.2) 81.0 (10.9) 132 (18) 83 (10) 4.9 5.2 (0.5) 6.3 0.9 5.1 (0.9) 1.83 (0.47) 2.81 (0.84) 0.93 (0.48) 2.0 1.81(3.88) 17.4 6.2 (4.6) 4.7 (5.6) 2.9 1.3 (1.8) 0.8

p-Valuea 0.021 <0.001 0.016 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.002 <0.001 <0.001 0.003 <0.001 <0.001 <0.001 <0.001 <0.001 0.475 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

F-test for continuous and chi-square test for categorical variables. Log-transformed values for triglycerides and high sensitivity C-reactive protein were used in analyses. Education high level: college-level training, polytechnic or academic degree of education. Sedentary lifestyle: less than 4 hours weekly leisure time non-sweat physical exercise. Dysglycaemia: IFG, IGT, and T2D. High sensitivity C-reactive protein among those with no reported infection during the past week, n = 1713. Metabolic Syndrome according to the IDF criteria. Framingham function. FINRISKI function: probability to get ill or die in CVD during the next 10 years according to Framingham and FINRISKI algorithms.

Additional lifestyle counselling by a dietician and/or a diabetes nurse (1–3 sessions) was offered to the participants with a moderate or high diabetes risk. The counselling included evaluation of dietary and lifestyle habits, an individualised lifestyle change plan based on personalised targets and self-selected practical goals, and a follow-up plan. Also small-group counselling sessions with varying themes were offered.

2.7.

Statistical methods

We tested gender differences in CVD risk factors with chi-square and t-tests. ANCOVAs were used to assess the differences in CVD risk factor between the FINDRISC diabetes risk score groups (the low risk category as the reference group) and working time groups (day workers as the reference group). All p-values given are two-sided and p < 0.05 was considered as statistically significant. SPSS (version 15.0) was used for all statistical analyses.

3.

Womenn = 1113

Results

The participants’ characteristics and risk factors are presented separately for men and women in Table 1. Men and

women differed in many risk factors, in women’s favour (blood pressure, blood glucose, total, HDL and LDL–cholesterol, and triglycerides, anti-hypertensive and cholesterol-lowering medication, MetS, dysglycaemia, FINDRISC score) also after adjustment for age. The proportion of overweight (body mass index BMI ≥ 25) was 66.3% among men and 35.8% among women. Of men 18.5% and of women 9.8% were obese (BMI ≥ 30). The estimated risk for a CVD event in 10 years was 11.2% for men and 4.7% for women according to the Framingham function. The FINRISK function gave somewhat lower CVD risk predictions for both sexes, however the correlation between the two CVD risk functions was high (r = 0.92). In Table 2 the parameters are given for low, moderate and high diabetes risk categories based on the FINDRISC score. Of all participants 75.7% (74.7% of men and 76.8% of women) had low, 18.4% (19.1% of men and 17.6% of women) moderate, and 5.9% (6.2% of men and 5.6% of women) high diabetes risk score. Education associated inversely with diabetes risk score. However, adjustment for education did not substantially affect the results shown in Table 2. As expected, diabetes, dysglycaemia and sedentary lifestyle were more common and fasting blood glucose and blood pressure higher by increasing risk score category. Also MetS was more common, lipid profile more unfavorable and hsCRP higher by increasing FINDRISC

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Table 2 – Characteristics and risk factors of participants (mean ± SD) divided by diabetes risk score (FINDRISC). Low risk score <10 n = 1698 (75.7%) Men, % Age, years Education, high levelb , % Smoking, % Alcohol, units/week Sedentary lifestylec , % Body Mass Index, kg/m2 Waist circumference, women, cm Waist circumference, men, cm Systolic blood pressure, mmHg Diastolic blood pressure, mmHg BP medication, % fP-glucose, mmol/l Dysglycaemiad , % Diabetes, % Total cholesterol, mmol/l HDL cholesterol, women, mmol/l HDL cholesterol, men, mmol/l LDL cholesterol, mmol/l Triglycerides, mmol/l Cholesterol medication, % hsCRPe , mg/l MetS IDFf , % Framingham functiong Framingham function ≥20% FINRISKI functionh FINRISKI function ≥10% a

b c d e f g h

51.4 43.1 (8.8) 49.2 19.6 5.0 (5.6) 11.4 24.6 (3.4) 77.7 (8.1) 92.0 (9.1) 135 (18) 84 (11) 3.2 5.3 (0.6) 9.0 0.7 5.1 (0.9) 1.89 (0.46) 1.42 (0.38) 2.97 (0.87) 1.05 (0.58) 2.0 1.42 (3.02) 16.6 6.6 (7.1) 6.1 1.9 (2.7) 2.2

Moderate risk score 10–14 n = 412 (18.4%)

High risk score >14 n = 132 (5.9%)

54.1 48.9 (7.6)*** 39.1*** 22.1 5.6 (6.1) 27.0*** 28.9 (4.5)*** 90.8 (10.7)*** 103.8 (10.3)*** 143 (19)*** 90 (10)*** 13.3*** 5.6 (0.6)*** 18.7*** 2.9** 5.3 (1.0)*** 1.67 (0.44)*** 1.36 (0.37)* 3.22 (0.94)*** 1.32 (0.66)*** 7.3*** 2.54 (4.36)*** 51.0*** 12.0 (9.7)*** 19.9*** 3.9 (4.2)*** 8.8***

54.5 51.0 (7.0)*** 40.5*** 22.5 6.9 (9.6)** 29.9*** 30.6 (4.5)*** 96.4 (12.8)*** 109.2 (9.1)*** 147 (19)*** 92 (12)*** 32.6*** 5.9 (0.9)*** 45.5*** 12.1*** 5.2 (0.9)* 1.55 (0.46)*** 1.27 (0.36)** 3.17 (0.83)* 1.52 (0.73)*** 13.6*** 3.33 (5.35)*** 70.5*** 15.4 (10.3)*** 29.4*** 5.7 (6.2)*** 13.5***

p-Valuea

0.504 <0.001 <0.001 0.448 0.008 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

F-test for continuous and chi-square test for categorical variables. Low risk score group was compared pairwise with moderate and high risk score groups (***p < 0.001, **p < 0.01, and *p < 0.05). Log-transformed values for triglycerides and high sensitivity C-reactive protein were used in analyses. Education high level: college-level training, polytechnic or academic degree of education. Sedentary lifestyle: less than 4 hours weekly leisure time non-sweat physical exercise. Dysglycaemia: IFG, IGT, and T2D. High sensitivity C-reactive protein among those with no reported infection during the past week, n = 1713. Metabolic Syndrome according to the IDF criteria. Framingham function. FINRISKI function: probability to get ill or die in CVD during the next 10 years according to Framingham and FINRISKI algorithms.

score category. Among the high-risk category 29.4% and 13.5% of employees had a notable probability of CVD events during the next ten years calculated by the Framingham (≥20%) and FINRISK (≥10%) functions, respectively. The distributions of most risk factors between the diabetes risk categories were equal in both genders (data not shown). However, alcohol consumption was higher in the high diabetes risk category only in men (p = 0.004), and LDL–cholesterol was higher only in women with moderate (p < 0.001) or high diabetes risk (p = 0.001) compared with the low FINDRISC score group. Table 3 shows the parameters in the working time groups for men and women separately. Compared with regular day workers, male non-flight shift workers were younger. Adjusting for age, they had lower level of education, were more often smokers and had slightly higher hsCRP but fewer of them used anti-hypertensive or cholesterol-lowering drugs. After adjustment for education the difference in antihypertensive and cholesterol-lowering drug use and hsCRP remained statistically significant.

Also male in-flight workers were younger than regular day workers. After adjusting for age, they had higher education, and significantly lower BMI and waist circumference. Also their systolic blood pressure, triglyceride concentration, and FINDRISC score were lower and they used less often antihypertensive medication. Further adjustment for education did not notably change the associations. Also among females, regular day workers were the oldest and in-flight workers the youngest. After adjusting for age, compared with regular day workers, female non-flight shift workers had lower education level. They also had lower systolic blood pressure but clearly higher hsCRP, which association remained after further adjustment for education. Compared with regular day workers female in-flight workers were, after adjustment for age, more educated. They were less often smokers, antihypertensive drug users or had a sedentary lifestyle, dysglycaemia or the MetS. Furthermore, their HDL–cholesterol was higher, BMI, waist circumference, systolic blood pressure, triglycerides, hsCRP and also FINRDISC score and values of Framingham risk function were

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Table 3 – Characteristics and risk factors of participants (mean ± SD) according to sex and working time groups. Men

a

b c d e f g h i

Non-flight shift work n = 581 (48.5%)

46.8 (9.3) 61.8 20.0 7.6 (8.6) 18.2 27.1 (3.8) 96.1 (11.2) 143 (17) 89 (12) 12.3 5.6 (0.8) 19.5 2.6 5.2 (0.9) 1.43 (0.37) 3.22 (0.84) 1.34 (0.74) 7.9 1.52 (2.68) 36.6 7.4 (4.7) 12.2 (9.4) 18.4 4.2 (4.7) 7.6

43.9 (8.6)††† 12.7*** 26.2* 6.4 (6.0) 21.2 27.1 (4.0) 95.4 (11.1) 143 (16) 88 (11) 5.7*,†† 5.5 (0.6) 17.5 2.9 5.2 (1.0) 1.40 (0.37) 3.24 (0.88) 1.32 (0.69) 2.9**,†† 1.85 (3.64)*,† 32.9 6.8 (4.3),†† 11.1 (9.2) 16.8 3.5 (3.9) 7.8

In-flight work n = 147 (12.3%) 42.6 (8.6) ††† 69.6*** 17.1 6.1 (5.5) 10.5 25.8 (3.3)**,†† 91.3 (9.6)*,† ,† 137(15)** 85 (10) 4.1*,† 5.7 (0.5) 23.2 1.4 5.2 (1.2) 1.42 (0.37) 3.26 (1.02) 1.18 (0.57)* 4.1 1.33 (2.23) 30.5 5.6 (4.3)*,† 8.4 (8.1) 10.0 2.7 (3.2) 3.6

<0.001 0.019 0.058 0.014 0.002 0.016 <0.001 0.020 0.012 0.087 0.106 0.601 0.695 0.474 0.595 0.043 0.030 0.011 0.937 0.035 0.004 0.039 0.254 0.028

p-Valueb

Regular day work n = 351 (31.5%)

<0.001

46.6 (8.3) 62.5 22.3 3.5 (3.7) 17.8 25.6 (4.6) 83.5 (11.9) 135 (19) 84 (11) 8.5 5.3 (0.6) 9.6 0.9 5.1 (0.9) 1.76 (0.46) 2.93 (0.88) 0.96 (0.49) 2.3 2.01 (4.19) 22.8 7.6 (4.8) 6.2 (6.1) 2.7 1.6 (2.0) 0.9

0.239 0.072 0.051 0.012 0.035 0.008 0.144 0.008 0.086 0.214 0.790 0.823 0.806 0.514 0.199 0.023 0.008 0.823 0.037 0.158 0.438 0.401 0.126

Non-flight shift work n = 229 (20.6%)

In-flight work n = 533 (47.9%)

43.3 (8.7) ††† 48.8** 26.8 3.2 (4.2) 15.7 25.5 (4.9) 83.7 (12.5) 130 (17)** 83 (11) 4.4 5.2 (0.5) 7.3 0.9 5.0 (0.9) 1.76 (0.46) 2.81 (0.80) 1.01 (0.52) 2.6 2.74 (5.53)*,† 20.5 7.0 (4.8) 4.7 (5.6) 3.3 1.3 (1.8) 1.4

42.6 (8.8) ††† 74.9*** 12.7**,†† 3.2 (3.3) 6.2***,††† 23.1 (3.1)***,††† 78.2 (8.4)***,††† ,†† 129(18)** 81 (10) 2.8*,†† 5.2 (0.5) 3.7**,†† 1.0 5.0 (0.9) 1.92 (0.47)***,††† 2.73 (0.82) 0.87 (0.46)*,† 1.5 1.38 (2.78)*,† 12.3**,†† 4.9 (4.0)***,††† 3.7 (5.0) **,†† 2.8 1.0 (1.8)* 0.4

p-Valuea

p-Valueb

<0.001 <0.001 <0.001 0.998 <0.001 <0.001 <0.001 0.022 0.090 0.035 0.660 0.019 0.871 0.637 <0.001 0.210 0.001 0.600 <0.001 0.003 <0.001 0.001 0.251 0.042 0.104

<0.001 1.000 <0.001 <0.001 <0.001 0.027 0.090 0.032 0.190 0.047 0.847 0.906 <0.001 0.097 0.003 0.742 <0.001 0.012 <0.001 0.006 0.310 0.115 0.194

Age-adjusted difference between the groups, regular day work was compared pairwise with non-flight shift work and in-flight work (***p < 0.001, **p < 0.01, and *p < 0.05). Log-transformed values for triglycerides and high sensitivity CRP were used in analyses. Age- and education-adjusted difference between the groups, regular day was compared pairwise with non-flight shift work and in-flight work (††† p < 0.001, †† p < 0.01, and † p < 0.05). Education high level: college-level training, polytechnic or academic degree of education. Sedentary lifestyle: less than 4 h weekly leisure time non-sweat physical exercise. Dysglycaemia: IFG, IGT, and T2D. High sensitivity C-reactive protein among those with no reported infection during the past week, n = 1713. Metabolic Syndrome according to the IDF criteria. Framingham function. FINRISKI function: probability to get ill or die in CVD during the next 10 years according to Framingham and FINRISKI algorithms.

p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 95–102

Age, years Education, highc , % Smoking, % Alcohol, units/week Sedentary lifestyled , % BMI, kg/m2 Waist circumference, Systolic BP, mm Hg Diastolic BP, mm Hg BP medication, % fP-glucose, mmol/l Dysglycaemiae , % Diabetes, % Total chol, mmol/l HDL chol, mmol/l LDL chol., mmol/l Triglycerides, mmol/l Cholesterol medication, % hsCRPf , mg/l MetS IDFg , % FINDRISC score Framingham functionh Framingham function ≥20% FINRISKI functioni FINRISKI function ≥10%

Regular day work n = 471 (39.3%)

Women p-Valuea

p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 95–102

significantly lower. Further adjustment for education did not notably change the results. Employees with IFG, IGT or FINDRISC score ≥15 were considered high-risk (15.1%) and those with normoglycaemia but FINDIRSC score 10–14 moderate risk individuals (14.9%). Of these 60% and 59%, respectively, agreed to participate in the offered lifestyle counselling provided by diabetes nurse and/or dietician. Individual counselling was preferred to small-group counselling: during the first year only 18 employees participated in the group sessions and hence group interventions were discontinued.

4.

Discussion

OHC encompassing most of the Finnish workforce is an important part of the primary health service system and welfare policy in Finland. Traditionally, OHC has focused on workrelated health risks and maintaining the working capacity of employees [30]. Recently, the role of OHC in the prevention of chronic diseases such as CVD and T2D has been recognized. The new extended health check-up process initiated by the Finnair OHC proved to be feasible and well-accepted and it successfully identified groups of employees that would benefit from lifestyle intervention. The attendance at the health check-up was compulsory only for in-flight workers. However, the participation in the study was voluntary for all. The interest to participate in voluntary health checks may be lower for people who feel themselves healthy. The slightly differing participation rates of regular day workers and non-flight shift workers probably arise from the fact that it was easier for day workers to attend the health checks at office hours. The overall participation rate in the study was, however, comparable to other voluntary health checks in Finnair and to Finnish population screenings [31]. Obesity is an emerging health problem worldwide and increasing especially among working-age people in Finland [32,33]. Finnair’s employees, particularly women, were slightly thinner than the general Finnish population (mean BMI 27.2 for men and 26.5 for women in 2007) [31]. Even so, altogether 34.1% of men and 17.4% of women in Finnair had the MetS. Diabetes and other disturbances of glucose metabolism shown in Table 1 were rare among Finnair’s employees compared to general Finnish population. In the age group of 45–64 years, the prevalence of undiagnosed T2D was 3.4–2.3% for men and 0.6–2.0% for women in Finnair, whereas the corresponding numbers were 5.9–8.7% and 2.5–8.0% in Finnish population, respectively. In the same age group the prevalence of dysglycaemia was 19.1–20.9% for men and 6.7–9.2% for women in Finnair, and 26.4–44.3% and 20.5–31.7% in Finnish population, respectively [16]. The healthy worker effect and easily available health care services of the company probably accounts for some part of this difference. The study revealed that the risk factor profiles linked to differing working hours are not self-evident and depend on related background factors. The observed differences in CVD risk factors were minor between regular day workers and nonflight shift workers; however in-flight workers stood out from the rest. Evidently the health risks related to shift work can

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be attenuated by lifestyle choices. The occupational selection, the health standards of aviation occupations, and the regular health examinations controlled by aviation authority probably influence the observed findings. On the other hand, the categories “regular day workers” and “non-flight shift workers” are heterogenic, and require further exploring. Some of the regular day workers probably are ex-shift workers, who for health reasons have changed the working time. The FINDRISC diabetes risk score was originally developed to identify individuals with increased risk for T2D, but it has proven to be a valid predictor for coronary heart disease, stroke, and total mortality, too [34]. In our study 51.0% and 70.5% of all participants with moderate and high FINDRISC, respectively, had the MetS. Also other studied CVD risk factors correlated well with the FINDRISC score. Therefore, evaluation of the risks for both T2D and CVD in parallel is warranted, and in this the FINDRISC seems to be a feasible, low-cost, non-invasive first-step screening method. Optimally, the test result should be checked and interpreted by a health-care professional. Our protocol of screening and referring individuals with elevated risk of T2D to lifestyle interventions (Fig. 1) is in congruent with the European IMAGE guideline for diabetes prevention [35]. Our study revealed that the interest to attend lifestyle counselling was high among individuals with elevated T2D risk. However, combining shift work and group counselling is a challenge, which apparently requires new and innovative means. The Finnish development strategy for OHC outlines the measures of promoting employees’ health and working capacity [1]. The Finnair employees have special strains as the majority of them are shift workers operating in aviation duties with the requirement of good health and high vigilance. The present OHC development project serves both the national strategy and the airlines’ requirements. The new healthcheck-up process has been implemented in Finnair OHC services. The future challenge will be increasing the participation rate in voluntary health checks to cover all employees. The health and economic effects of the renewed health check process will be assessed after the follow-up phase of the study. In conclusion, extended health check-up process is effective in identifying workers with an increased risk of T2D and other CVD risk factors. A simple questionnaire, FINDRISC score, is a feasible first step in risk screening. The relationship of shift work with CVD risk factors may be modified by past changes in working conditions and should thus be studied prospectively.

Conflict of interest The authors declare that they have no competing interests.

Authors’ contributions All authors have participated in the design of the study and helped to draft the manuscript. K.V., S.P. and M.P. performed the statistical analysis. K.V., K.H. and A.K. have coordinated

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the study. All authors have read and approved the final manuscript.

Acknowledgements The study was supported by the Academy of Finland (SALVE consortium, grant 129518) and the Finnish Work Environment Fund (grant 108320). Parts of this study have been published in Finnish in the Finnish Medical Journal (Suomen Lääkärilehti 2010;65:33–42).

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