Baylor University Medical Center Proceedings April 2014, Volume 27, Number 2 | Seite 22

A cohort analysis of the cardiovascular risk factors in the employees of a pediatric hospital from 2009 to 2012 Lilly Ramphal, MD, MPH, Jun Zhang, MS, and Sumihiro Suzuki, PhD A retrospective longitudinal cohort regression analysis was completed in 853 of the 3435 employees of Cook Children’s Hospital who participated all 4 years (2009 to 2012) in an employer wellness program. The presence of the metabolic syndrome (MS) was used as an outcome measure for the success of the wellness program. Data were stratified by weight, gender, and ethnicity. The odds ratios and regression analysis showed a significant decline in MS over the 4 years of the study (P = 0.008), as well as a significant association between MS and obesity and overweight status (P < 0.0001), male gender (P = 0.0018), and all ethnic categories (P < 0.05) except African American ethnicity and the multiple ethnicity category. Age was strongly associated with risk for MS. Overall, the study showed that the wellness program significantly decreased the incidence of MS (P < 0.05). he metabolic syndrome (MS) is a serious public health concern defined by interconnected factors that directly impact the risk of coronary heart disease. MS has been defined by the International Diabetes Federation (IDF) and the National Cholesterol Education Program (3). Both groups present similar criteria for MS, but for purposes of this study the IDF definition, which put more emphasis on waist circumference, was used. Waist circumference, independent of other parameters of MS, is the most significant predictor of cardiovascular risk (1–3). This study identified trends in the prevalence of MS in 853 employees who consistently participated in a hospital wellness program from 2009 to 2012. T METHODS The study was approved by the Cook Children’s Hospital institutional review board. A retrospective longitudinal cohort regression analysis was completed on 853 (24.8%) volunteers who had participated in all 4 years of a hospital wellness program from 2009 to 2012 and had submitted complete demographic records. A total of 2582 employees were excluded from the study because they participated inconsistently during the study period. Participants in the wellness program were incentivized with an annual monetary award to reduce their cardiovascular risk by reducing four or more of the measured criteria in 2 consecutive years. Each participant’s demographic information was obtained from survey health questionnaires collected by the 96 employer’s wellness coordinator. Measured parameters such as weight, waist circumference, height, and systolic and diastolic blood pressures were obtained annually by registered nurses. No formal instruction on how to reduce these parameters was given to participants. They were referred to their personal doctors for direction or sought out resources to improve their health. All data were analyzed by statisticians using SAS. Two models were used to analyze the variables affecting MS risk: a conventional logistic regression and a longitudinal generalized estimated equation (GEE) model. Model 1 for the output analysis is shown below: Logit[P(MS = 1)] = β0 + β1 ∗ √t + β3 ∗ WeightStatus + β4 ∗ Gender + β5 ∗ Ethnicity Odds ratio estimates were used to evaluate risk. Both analyses molded in risk factor variables including square root of time, gender, weight status (normal, overweight, and obese), ethnicities (American Indian, Asian, Black, Hispanic other, Hispanic White, Multiple, and White), and age groups (18–30, 30–40, 40–50, 50–65, and 65+). In both regression models, 2009 was the baseline. Normal weight was used as the baseline and compared with overweight and obese categories. The underweight were excluded due to the very small numbers in this category. Caucasian (White) ethnicity was set as the baseline and compared to Asian, African American (Black), Hispanic Other, Hispanic White, American Indian, and Pacific Islander ethnicities. For age, the 18–30 age group was used as the reference group, and all other age categories were compared to it. All analyses were performed using SAS (V9.2). R ESULTS Table 1 presents the demographic characteristics of the 853 participants who were eligible for longitudinal study. More From Cook Children’s Hospital, Fort Worth, Texas (Ramphal); and the Departments of Environmental Health (Ramphal) and Biostatistics (Zhang, Suzuki), the University of North Texas School of Public Health. Dr. Ramphal is now with Blue Cross Blue Shield. Corresponding author: Lilly Ramphal, MD, MPH, Department of Environmental Health, University of North Texas School of Public Health, 3500 Camp Bowie Boulevard, Fort Worth, TX 76107-2699 (e-mail: [email protected]). Proc (Bayl Univ Med Cent) 2014;27(2):96–99