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  • Japan Epidemiology Collaboration on Occupational Health (J-ECOH) Study is an ongoing, large-scale study among workers in Japan. According to standard procedure of the study, researchers obtained several types of worker health data including those of periodic health checkup (2008 and thereafter), cardiovascular event (myocardial infarction and stroke), death from all causes, and long-term sick leave (1 month or longer) from participating companies. Additional researches including case-control study on cardiovascular event and nutritional survey were performed in selected companies. In Japan, employees are obliged to undergo general health examination at least once a year under the health and safety law. Of the 11 participating companies of the J-ECOH Study, 9 provided data on periodic health checkup that was performed during the period between 2008 and 2011, which was then combined to create an analytic database. Of these, data with the earliest date of examination (mostly in 2008) were selected for the present cross-sectional analysis; however, if 2008 dataset for a company contained a much greater number of missing information or much fewer subjects than other datasets, 2009 or 2010 dataset were used instead, giving a total sample size of 80,469 (67,472 men and 12,997 women aged 15–84 years) for cross-sectional analysis. Prior to the collection of data, the conduct of the J-ECOH Study was announced in each company by using posters that explained the purpose and procedure of the study. Participants did not provide their verbal or written informed consent to join the study but were allowed to refuse their participation. This procedure conforms to the Japanese Ethical Guidelines for Epidemiological Research [19], where informed consent is not necessarily required for observational studies using existing data. The study protocol was approved by the Ethics Committee of the National Center for Global Health and Medicine, Japan. Most participating companies provided data in either anonymized or de-identified form, but a few other companies provided data including identifiable information, which was removed from analytic database. The data are hosted in the National Center for Global Health and Medicine. Currently, the data cannot be widely shared because the research group has not obtained permission from participating companies to provide the data on request. However, the data can be requested by other researchers for the purpose of academic, non-commercial research; inquiries and applications can be made to Department of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan. We extracted checkup data for 64,434 individuals in 4 companies where overtime work data were available from the original database. Of these, we excluded data for 1,809 participants who reported a history of cardiovascular disease (n = 490), cerebrovascular disease (n = 182), or psychiatric disease (n = 1,160). After this manipulation, we further excluded 21,764 subjects who had missing data on overtime work (n = 7,604), HbA1c (n = 12,768), blood glucose (n = 11,871), current use of anti-diabetic drug (n = 1,261), body mass index (BMI) (n = 138), smoking status (n = 6,032), and fasting condition (n = 1,449); and who received health checkup in non-fasting condition (n = 4,592). Some participants met more than one of the exclusion criteria. Finally, 40,861 participants (35,170 men and 5,691 women) remained for analysis. Body height and body weight were measured according to a standard protocol in each company. BMI was calculated as weight in kilograms divided by squared height in meters. History of disease and health-related lifestyle were ascertained using a questionnaire, the content of which differs considerably among participating companies. Biochemical measurements included plasma glucose and HbA1c. HbA1c was measured according to a method used by the Japan Diabetes Society, thus we converted it to the National Glycohemoglobin Standardization Program (NGSP) equivalent value (%) using the formula: HbA1c (%)  = 1.02×HbA1c (Japan Diabetes Society) (%) +0.25% [20]. In two companies, employees self-reported overtime work hours at health checkup (<45, 45 to <60, 60 to <80, 80 to <100, or ≥100 hours per month in the last 2 to 3 months; <45, 45 to <80, 80 to <100, or ≥100 hours per month, respectively). In another company, employees self-reported average total working hours per day at the timing of health checkup and monthly overtime were calculated using the following formula: (daily hours worked – 8 h) ×20 days. In the remaining company, employees were asked to self-report their overtime work hours in the last month with 11 response options (from “0 to 10” to “>100 hours” per month) at annual survey, not health checkup, in September. We classified these data on overtime work hours into 4 categories using cutoff point of 45, 80, and 100 hours per month for statistical analysis. In one company, 41–50 hours and 71–80 hours of overtime was categorized into 45–79 hours, and >100 hours into ≥100 hours for statistical analysis. Diabetes was diagnosed according to the American Diabetes Association criteria [21] as a fasting plasma glucose level of ≥126 mg/dl (≥7.0 mmol/L), HbA1c of ≥6.5% (≥48 mmol/mol), or the current use of anti-diabetic drug. Smoking status (never, past, or current) and, if current smoker, number of cigarettes smoked per day were asked at the time of health check-up. Detailed information on job, lifestyle, and family history of disease was available in one of these companies and used for adjustment as a sensitivity analysis. The information on shift work, job position, type of department, alcohol consumption, sleep duration, physical activity, and family history of diabetes was assessed using a questionnaire at the time of health check-up. Shift work was categorized as shift worker (rotating or night shift) or non-shift worker. Job position was categorized as high position (department chief, department director, or more) or low position (others). We classified 12 departments into two categories; one was termed “field work” for 4 departments and the rest was termed “non-field work” for 8 departments. Averaged daily ethanol consumption from alcohol beverage was calculated as drinking frequency multiplied by ethanol consumption per drinking day. Average sleep duration were assessed with 4 options (<5 hours, 5–<6 hours, 6–<7 hours, or ≥7 hours). Total weekly minutes of leisure time physical activity were calculated as frequency of physical activity or sports activity multiplied by duration of the activity. The difference of age, sex, smoking, and BMI between those who were included in the present analysis and those who were excluded was tested by using t-test for continuous variables and χ2 test for categorical variables. Means (standard error) and percentages across overtime category were presented for continuous and categorical variables with adjustment for age and sex, respectively. Trend association was assessed using linear regression analysis for continuous variable and using logistic regression for categorical variables by assigning 23, 62, 90, and 100 to categories of overtime work hours, respectively. Multiple logistic regression analysis was performed to calculate odds ratio and its 95% confidence interval of having diabetes across categories of overtime work hours. Trend association was assessed by assigning 23, 62, 90, and 100 to each category of overtime. To test quadratic trend, we used Stata contrast command after running multiple logistic regression. Model 1 was adjusted for age (continuous, year), sex, and company (4 companies). Model 2 was additionally adjusted for smoking (never, past, or current) and model 3 for BMI (continuous, kg/m2). In one company (n = 33,807) from which detailed information on work and lifestyle was obtained, we additionally adjusted for other potential confounders, including alcohol use (non-drinker, drinker consuming >0 to <23 g, 23 to <46 g, or ≥46 g of ethanol per day), family history of diabetes (yes or no), shift work (yes or no), department (field work or non-field work), and job position (high or low) in model 2. We further adjusted for sleep duration (<6 hours, 6 to <7 hours, or ≥7 hours per day) in model 3 and physical activity (<150 min or ≥150 min per week) in model 4. We examined the effect modification by shift work (yes or no), type of department (field work or non-field work), smoking habits (non-smoker or smoker), alcohol use (<23 g or ≥23 g of ethanol/day), physical activity (<150 min or ≥150 min per week), and sleep duration (<6 hours or ≥6 hours) on the association between overtime work and diabetes using likelihood ratio test comparing models with and without interaction terms in the fully adjusted model treating overtime work as a categorical variable. We repeated the above analyses after exclusion of subjects under medication for diabetes to minimize the possibility of reverse causality. Two-sided P values of less than 0.05 were considered as statistically significant. All analyses were performed using Stata version 12.1 (StataCorp, College Station, Texas, USA).
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