The National Health and Family Planning Commission (NHFPC, previously the Ministry of Health) of China and the Ethics Committee of the Chinese Center for Disease Control and Prevention approved the program. An informed consent form was completed by all participants.
Interviewers in this study were staff from local CDC and healthcare institutes. Passing a test after training was mandatory before staff conducted the survey. The test aimed to examine each interviewer’s familiarity with the questionnaires, operation standards, and measurement of height, weight, blood pressure, and precautions for blood draw. Anthropometric measurements were standardized before the survey. A checking system that comprised checking in the field by interviewers themselves, officials from the local CDC, and supervisors from the workgroup was applied to control the quality of this study. Personnel from local healthcare institutes and health offices helped organize the field investigation and explained the study procedures to participants. Participants with cognitive problems, language problems, mental disorders, or severe diseases were excluded. Recruited participants were interviewed twice if logical inconsistencies and missing values were detected.
Questionnaires were used to obtain information about participants’ demographic characteristics, dietary habits, lifestyle behaviors, disease history, and health status through face-to-face interviews conducted by trained local CDC staff. The questionnaire that was used in the survey was drafted by the China CDC and its reliability and validity were verified. Participants’ height was measured without shoes in meters to the nearest millimeter (Wuxi Weigher Factory Co., Ltd., Model TZG, precision 1 mm). Weight was measured by an electronic scale (TANITA Corporation, HD-390, precision 100 g) when participants were barefoot and bareheaded with only light clothes. Blood pressure, fasting, and postprandial blood samples were also tested. Details of this monitoring have been described elsewhere .
BMI (kg/m2) was calculated as weight in kilograms divided by the height in meters squared. Covariates were as follows. (1) Demographic characteristics including three areas in Shaanxi province, age of participants and education years (total years of schooling); (2) Dietary habits including consumption of red meat, fresh vegetables and fruits, oil, and salt. To assess the intake of red meat, fresh vegetables, and fruits, participants were first asked whether they had eaten the food or not in the last 12 months. If the answer was yes, then the frequency (year, month, week, or day) and amount (g) of each serving was collected. The daily consumption of the above foods was estimated by multiplying the frequency and amount of each time. The daily intake of oil and salt was estimated by the amount of each item a household consumed per day divided by the number of people in the family. (3) Lifestyle factors included cigarette smoking and alcohol intake. Cigarette smoking was defined as a participant who smoked every day during the monitoring period, and alcohol intake as consuming alcohol from any alcoholic beverages (beers or wines) every day, which was collected in the same way as dietary habits. (4) Physical activity including activity at work, during commuting, and in leisure time was divided into three categories (low, moderate, high) according to the Global Physical Activity Questionnaire Analysis Guide.
Initially, BMI and baseline data were reported as mean ± standard deviation and median (25th ~ 75th percentile) for normally and non-normally distributed continuous variables, respectively. They were also tested by t-tests (normally distributed data between two groups), analysis of variance (normally distributed data between three or more groups), or Kruskal–Wallis tests (not-normally distributed data among groups). Categorical variables were described by counts and proportions and examined by χ2 tests among groups. The skewness, kurtosis, and normality of BMI were also tested. To determine risk factors potentially associated with BMI distribution, we used QR. Linear regression based on the ordinary least squares (OLS) was used to explain the effects of risk factors on the mean value of BMI. QR could provide a detailed description of the effects on the BMI at any point along its distribution. The choice of BMI percentiles mainly depends on the research objectives and the distribution of the outcome. Nine BMI quantiles of were selected (from 10th to the 90th), step by 10th based on the lowest to the highest BMI value. Coefficients of regression for each quantile were computed and their significance was tested. Multivariate QR models, including demographic characteristics (area, age, sex, and education), dietary habits (red meat, fresh vegetables, and fruits), lifestyle factors (cigarette smoking and alcohol intake), and physical activities, were conducted for each BMI quantile. Coefficients of OLS were also estimated as comparisons. All statistical analyses were performed using R 3.3.1. P-values < 0.05 were considered statistically significant in this study.