Study setting and data collection
This study used a cross-sectional design to observe the prevalence of the five chronic diseases and the multimorbidity of the elderly population in Changchun city, which is the capital of Jilin Province in northeastern China. We used a multistage and stratified random sampling method to select subjects aged ≥60 in Changchun City. The sample size was calculated based on a 31.7% prevalence (P) of chronic diseases3 with a 3.0% uncertainty level (E) using the formula N = Z2PQ/E2 (where Z = 1.96 with 95% confidence intervals). We estimated that a total of 924 subjects would be needed.
These subjects underwent interviews, physical examinations and related laboratory tests. A total of 2184 elderly people participated in this study, excluding those with incomplete data, and 2171 subjects aged 60-88 were finally enrolled.
Information was collected through face-to-face interviews with the respondents or with a household member for those who were disabled for reading or answering.
The questionnaire assessed sociodemographic characteristics: sex, age, household monthly income, education level, occupation, health behavior, past illness history, etc. The age groups were categorized as 60–64, 65–69, 70-74 and ≥75 years old.
Anthropometric data included height, weight, waist circumference (WC) and blood pressure(BP). Standing height was recorded to the nearest 0.1 cm. Body weight was measured to the nearest 0.1 kg using an electronic scale. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2)14. Blood pressure was recorded three times at 1-minute intervals by a trained nurse using an upper arm electronic monitor (Omron). The mean values for each participant were then calculated15.
In addition, we performed laboratory tests on the subjects (after at least 8 hours of fasting), including blood routine, urine routine (urinary protein, urine glucose, urine sediment), fasting blood glucose (FBG), albumin (ALB), alanine aminotransferase (ALT), serum lipids (total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG)), serum creatinine (Scr) and blood urea nitrogen (BUN).
In this study, chronic diseases included hypertension, diabetes, dyslipidemia, CHD and CKD. Self-reported chronic disease status was obtained by asking the question, “Have you been diagnosed by a physician with the following conditions?”
Diabetes was defined as FPG≥7.0 mmol/L, previous diagnosis by a physician or the use of insulin or oral hypoglycemic agents 16.
Hypertension was defined if the subjects had a mean systolic blood pressure (SBP)≥140 mmHg or a mean diastolic blood pressure (DBP)≥90 mmHg15, were already taking antihypertensive medication, or had any self-reported history of hypertension.
Dyslipidemia was defined as a physician’s diagnosis and/or abnormal blood lipids (TC≥5.18 mmol/L, TG≥1.7 mmol/L, HDL-C<1.04 mmol/L or LDL-C≥3.37 mmol/L)17.
Coronary heart disease (CHD) was defined as a positive response by self-report8.
An eGFR (glomerular filtration rate) less than 60 (min×1.73 m2) or proteinuria was defined as CKD18. eGFR was calculated according to the Modification of Diet in Renal Disease (MDRD) Study19. We defined proteinuria as the presence of protein (>1+) in a spot urine dipstick analysis due to the lack of quantitative data20.
Continuous variables were tested using the Kolmogorov-Smirnov test for normality, and nonnormal data are presented as medians and quartiles. The Mann-Whitney U test and Wilcoxon signed ranks test were used to assess differences between the two groups. Categorical variables are presented as frequencies (percentages) and were analyzed using the chi-squared test. The prevalence of chronic diseases was standardized by age (ASR) using direct standardization based on the population composition of the Sixth National Population Census of China (2010). For standardization, we divided the participants into four age groups (60-64 years, 65-69 years, 70-74 years and ≥75 years). Data were collected and analyzed using SPSS 20 (SPSS, Inc., Chicago, IL, USA). P values < 0.05 were considered statistically significant.