Liu et al. BMC Public Health 2011, 11:931 http://www.biomedcentral.com/1471-2458/11/931 consistently less than 1% for both high and low pools. No significant laboratory drift was detected during 10 years [9]. At baseline and the first follow-up examination, the lipids were analyzed by using fresh blood samples. At the second follow-up examination (1993-1994), serum sam- ples were frozen and analyzed 6 months later. The mean value from frozen samples was on average 2.2 mg/dl higher than that from fresh samples evaluated from 124 persons [9]. Hence, we performed additional data analyses adjusting for these small shifts in the 1993-1994 individual TC values. Hyperlipidemia was defined as having a TC ≥220 mg/dl and/or LDL-C ≥160 mg/dl. Statistical methods The student’s t-test and Chi-square test (Fisher’s exact test if appropriate) was used to compare mean and proportion between workers and farmers within the same gender. The longitudinal analyses were per- formed using the Generalized Estimating Equation (GEE) method [18] to examine the time trends of var- ious measures (e.g., blood pressure, BMI, lipids) and factors related to these changes, accounting for the effect of aging and the correlations of repeated mea- sures on the same person. The model for each gender- occupation group is as follows: Yit = β0 + β1t + β2Xit + β3Zi0 + β4 Zit + eit Where for t = 0,4,10, Yit is the dependent variable (e.g., SBP) for the ith person at time t; Xit is a time-dependent covariate for the ith person at time t (i.e., age, use of anti- hypertensive medication); Zi0 is the baseline value of a time-independent covariate (i.e., baseline education, BMI, number of cigarettes smoked per day, and alcohol intake per day); ΔZit = Zit-Zi0, that is the change in a covariate (i.e., BMI, number of cigarettes smoked per day, and alcohol intake per day) between time t and baseline for the ith person; and eit is the error term. The coefficient b1 measures the average annual change in a dependent variable (e.g., SBP) adjusting for all other covariates in the model and the longitudinal relation- ship. In this report we multiply b1 by 10; hence, it mea- sures the average 10-year change of a dependent variable during the follow-up. This model also simulta- neously examines the cross-sectional relationship between each of the independent variables (e.g., BMI) and a dependent variable (e.g., SBP) and the relationship between changes in these independent variables and longitudinal changes in the dependent variable. Because cigarette smoking was uncommon among females, this covariate was not included in the model for females. Page 3 of 9 Results Baseline characteristics of the cohorts Characteristics of the four gender-occupation groups at baseline are shown in Table 1. The mean age of workers and farmers was similar for both males and females. The education levels were rather low and lower in farmers than in workers (P < 0.01). Smoking was very common among males but was uncommon among females. Farm- ers smoked more than workers within the same gender. Likewise alcohol drinking was much more common among males than females and more common among farmers than workers (P < 0.01). Except for SBP among males, average blood pressure levels were higher among workers than farmers within the same gender (P < 0.01). Prevalence of hypertension was lower than 11% and likewise higher among workers than farmers (P < 0.01). Only a small percent of persons with hypertension were taking antihypertensive medica- tion, especially among farmers. Average BMI and prevalence of overweight (including obesity) were rather low (ranging from 1.0% to 11.8%) and higher among workers (especially female workers) than farmers (P < 0.01). Workers had significantly higher TC, Low density lipoprotein cholesterol (LDL-C), and triglycerides levels than did farmers (P < 0.01). Changes in the levels of cardiovascular risk factors during follow-ups Table 2 presents the prevalence of current smoking, hypertension, overweight (including obesity), and hyperli- pidemia at baseline (year 0) and during follow-ups (year 4 and year 10). The prevalence estimates and the average change (linear trends) in 10 years were derived by the GEE method adjusting for age and the correlations of repeated measures. Prevalence of current smoking decreased significantly in every gender-occupation group. However, at year 10, age-adjusted smoking rate was still very high among males (60.5% in workers and 72.0% in farmers). There was a significant increase in the preva- lence of hypertension in all cohorts. Correspondingly, prevalence of antihypertensive medication use also increased. The 10-year average percentage point increase in medication use in the cohorts was 1.4 among male workers (p = 0.016), 0.6 among male farmers (p = 0.076), 3.6 among female workers (p < 0.001), and 0.4 among female farmers (p = 0.091). The percentage of persons with hypertension who reported taking antihypertensive medication increased significantly among workers but not farmers (data not shown). The treatment rate remained low at 10 years, i.e., 21.9% among male work- ers, 5.0% among male farmers, 28.6% among female workers, and 9.3% among female farmers. Although the prevalence of overweight (including obesity) remained