Design and procedure
This analysis was part of a series of studies (June 2012 to December 2013) investigating different population groups in northeast Germany (general practice patients, job agency clients, and health insurance members) within a stepwise examination program addressing cardiovascular health [10, 11]. The first part of the study, an in-office SP, included a self-administered computerized assessment of cardiovascular risk factors. The opportunity to receive a single blood pressure measurement and non-fasting blood sample was offered. For the second part of the study, the EP, subjects without residence in the study area, with a history of cardiovascular events (myocardial infarction, stroke), vascular intervention, diabetes mellitus, and self-reported body mass index > 35 kg/m2 were excluded. The EP was conducted in the clinical trial examination center at the university hospital and included multiple standardized blood pressure measurements and a non-fasting blood sample.
Selection of participants
The flow of participants has been described in more detail elsewhere [10, 11]. Although individuals aged 40 to 75 years were eligible for the study, the recruited job center clients were between 40 and 65 years old, because the maximum age to register at a job center is 65. Therefore, only participants between 40 and 65 years were considered. A total of 2614 individuals were eligible, and 930 (35.6%) participated in the computer-assisted assessment, which was the first part of the SP. A total of 568 individuals were eligible to participate in the EP, of whom 460 (80.9%) participated. Among them, all individuals with complete data regarding TC and BP at the SP and in the EP were included (N = 307).
Measures
Self-reported data on sex, age and current smoking status were collected within the SP. TC and BP were assessed during both, the SP and the EP. TC was determined from blood samples using standard methodology at the Institute of Clinical Chemistry at the University Medicine Greifswald. A single blood pressure reading of the right arm in the seated position was taken during the SP. During the EP, blood pressure measurement was performed according to a standardized protocol by a certified nurse [12]. The first reading was taken after a 5-min rest. In total, three readings of the right arm and one reading of the left arm were taken at 3-min intervals. For all blood pressure measurements, an Omron 705IT blood pressure monitor (Omron Corporation, Tokyo, Japan) was used.
Variables used for the SCORE, in addition to TC and BP included age, sex, and smoking status. Since the standard of blood pressure measurement might be a key component for the SCORE, three variables were built. The first variable (SCORESP) included the result of TC and a single BP as the basis of the SP. The second variable and the third variable included TC and the first reading of BP (SCOREEP/BP-first) or the mean of the second and third readings of BP (SCOREEP/BP-mean) as the basis of the EP. The calculation of the SCORE was based on the equation scheme suggested by Conroy and colleagues [13].
Statistical analyses
Descriptive statistics were used to characterize the sample. Differences in TC and BP between participants in the SP and the EP were analyzed using linear mixed effect models with the individual as the random factor. All models were adjusted for sex, age, setting of recruitment and period between SP and EP (Mean = 24.1 days, SD = 24.8 days). Given that TC and BP measurements were viewed as the targets, two-way random effects models were used to estimate the Intraclass Correlation Coefficient (ICC, absolute agreement, average measures) with a 95% confidence interval across the two time points. We used the convention in which ICC values between 0.40 and 0.75 indicate fair to good correlation and values of 0.75 or greater indicate excellent correlation [14]. Estimates of the SCORE [1, 15] were calculated according to the three blood pressure measurement protocols. Pearson’s chi-square test was used to analyze if differences between SCORESP and SCOREEP/ BP-first and between SCORESP and SCOREEP/ BP-mean were statistically significant. Further, we performed multi-variable linear regression analyses, which estimated the average effect of age and sex on differences in SCORE (Outcome between SCORESP and SCOREEP/BP-first (Model 1) and between SCORESP and SCOREEP/ BP-mean (Model 2). Subsequently, differences in SCORE and covariates (age, sex) were also used in quantile regression for a more in-depth evaluation of effect size [16]. Compared with linear regression based on the conditional mean, quantile regression is more suitable in cases where the effect of covariates differs at different levels of the dependent variable. Thus, we tested whether potential associations of sex and age vary across the 2.5th to the 97.5th percentiles of individual SCORE differences. All regression models were adjusted for the setting of recruitment, duration between the SP and EP, and the SCORE value at SP. A p value < .05 was considered statistically significant. All analyses were carried out using Stata 14.1.