Subjects
This study was conducted in Shandong Province, China. Shandong is the second most populated province in China (nearly 100 million in 2016). In 2008, the prevalence of diabetes mellitus in people living in Sandong Province who were aged ≥ 20 years was 9.9%, which was significantly higher than the average level in the whole country. For our study, we used a multi-stage stratified cluster sampling method to select participants, as described in detail in a previous study [27]. A total of 2481 diabetes patients registered in the NCDs management system in the sampling communities or villages were recruited to the survey, and 2183 diabetic patients for whom a complete dataset were available were included in the analysis, with a response rate of 87.99%. The inclusion criteria were: (1) diagnosis of diabetes based on WHO criteria for > 1 year [28, 29]; (2) age of ≥ 18 years; and (3) ability to understand the questions on the questionnaire.
A cross-sectional study was conducted from August to October 2016. All subjects were interviewed face-to-face using a structured questionnaire by trained Master students from Shandong University School of Public Health. To ensure quality, all of the completed questionnaires were carefully checked by quality supervisors after the interview each day. The questionnaire included: (1) the patient’s basic information, such as gender, age, marital status, education, employment status, family size, residence, exercise, household income; (2) patient’s health status, including self-reported health, body mass index (BMI), diabetes and its comorbidity, and duration of diagnosis; and (3) health management and health service use, including blood glucose monitoring by professionals, insulin injection, and outpatient and inpatient services.
Variables and Measures
Dependent Variable
The dependent variable was the frequency of blood glucose monitoring of participants, and the question was “How often did you monitor your blood glucose by professionals after being diagnosed with diabetes?” The answers were classified into five categories: 1—more than once a month, 2—once every 1–6 months, 3—once every 7–12 months, 4—less than once a year, 5—never. We combined five categories into two categories, namely, monthly and below and more than monthly, in order to conduct binary logistic regression, according to the Guideline for Blood Glucose Monitoring in China [7]. In the present study, “monitoring by professionals” refers to blood glucose being measured during visits to a healthcare professional.
Independent Variable
Independent variables included socio-demographic characteristics, such as gender (male vs. female), age (≤ 60, 61–70, > 70 years), marital status (single or married, with single encompassing the not-married, divorced, and widowed states, respectively), education level (illiterate, primary school, junior school, high school and higher), employment status (unemployed vs. employed), family size (≤ 3 vs. > 3), residence (rural vs. urban), exercise (no vs. yes), and household income (Q1, Q2, Q3, and Q4 quintiles, whereby quintile 1(Q1) was the lowest income and quintile 4 (Q4) was the highest income. Also included as independent variables were self-reported health (good, normal, poor), physical examination (no vs. yes, whereby physical examination means regular physical examination [excluding check-ups for the treatment of diseases]), BMI (< 18.5, 18.5–23.99, 24–27.99, ≥ 28 kg/m2), duration of diagnosis (≤ 5, 6–10, > 10 years), anti-diabetic drug or insulin therapy in the past 2 weeks (no vs. yes), diabetes-related education (no vs. yes), outpatient service in the past 2 weeks (no vs. yes), inpatient service in the past year (no vs. yes), and comorbidity (no vs. yes). In this study, comorbidity means the presence of any other disease(s) or condition(s) (diabetic nephropathy, diabetic eye complications, diabetic foot, diabetic cardiovascular complications, diabetic cerebrovascular disease, and diabetic neuropathy). The measurement details and the code for each variable are presented in Electronic Supplementary Material (ESM) 1.
Statistical Analysis
Data was entered into a computer using the Statistical Package for Social Sciences (SPSS) software version 24.0 (IBM Corp., Armonk, NY, USA). Data was described using frequencies and proportions wherever appropriate. Univariate logistic regression analysis was used to analyze the effect of each potential factor on a patient’s blood glucose monitoring compliance. Multiple binary logistic regressions were performed to determine variables for which a P value of < 0.05 in univariate logistic regression were associated with blood glucose monitoring adherence. A P value of < 0.05 was considered to be statistically significant. Survey procedures were used to analyze survey data by taking into account the sample design.
Compliance with Ethics Guidelines
The study protocol for this study involving human participants was approved by the Ethical Committee of Shandong University School of Public Health and was in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The investigation was conducted after the informed consent of all participants was obtained.