This study aimed at understanding the diabetic prevalence, awareness, treatment and control rates and their influencing factors among people aged ≥ 40 years in Shenyang, China. A face-to-face cross-sectional epidemiological survey was conducted on the respondents using the national unified questionnaire. A total of 3922 respondents were enrolled, including 609 cases of diabetes. The diabetic prevalence rate was 15.5%, and was higher in rural areas than that in urban areas (17.7% vs. 14.2%, p = 0.004), while no difference was observed between men and women (14.8% vs. 16.1%, p = 0.242). Advanced age, hypertension and dyslipidemia were the diabetes influencing factors. Among the 609 respondents with diabetes, the diabetic awareness and treatment rates, and the control rate of fasting plasma glucose were 82.3%, 36.6% and 17.1%, respectively. In different age groups, the diabetic awareness rate was higher in men than that in women, and the treatment rate was higher in women than that in men. The diabetic patients, who consumed fruit for ≥ 5 days a week, accounted for 16.3%, and their diabetic treatment (28.1%) and control rates (44.1%) were lower. Shenyang people aged ≥ 40 years have higher diabetic prevalence and awareness rates, and lower diabetic treatment and control rates. Finally, it is necessary to enhance awareness and education about diabetes, to improve its treatment and control rates.
These authors contributed equally: Xiaojiu Li and Muhui Lin.
Cerebrovascular disease has become a major human health threatening disease[
Shenyang is a high-prevalence area of cerebrovascular disease in the northeast of China, and there are currently no epidemiological surveys on adult diabetes in Shenyang. In this study, people aged ≥ 40 years, in Shenyang City communities and townships, Liaoning Province, were recruited as respondents, and diabetes prevalence, awareness, treatment, and blood glucose control rates (based on fasting plasma glucose, FPG) were investigated to provide a theoretical basis for diabetes prevention and treatment, and for the future development of stroke prevention strategies in Shenyang, Liaoning.
A total of 3949 people agreed to participate in the study, including 22 with incomplete data, and 5 without FPG test results. Finally, 3922 people were enrolled, including 2433 (62.0%) urban residents and 2195 (56.0%) women. The mean age was 58.47 ± 10.33 years, and 61.4% of them had senior high school and above education levels. A total of 941 (23.99%) had hypertension, 955 (24.35%) had dyslipidemia, 322 (8.21%) had a transient ischemic attack or stroke history, 29 (0.74%) had atrial fibrillation or valvular heart disease, and 81 (2.07%) had coronary heart disease. The general characteristics of participants are shown in Table 1.
Baseline characteristics of the population.
Items N (%) Diabetes prevalence Awareness Treatment Glycaemic control Total 3922 (100.0) 609 (15.5) 501 (82.3) 223 (44.5) 104 (46.6) Urban 2433 (62.0) 346 (14.2) 321 (92.8) 80 (24.9) 39 (48.8) Rural 1489 (38.0) 263 (17.7) 180 (68.4) 143 (79.4) 65 (45.5) χ2 8.342 60.645 138.808 0.224 0.004 < 0.001 < 0.001 0.636 Men 1727 (44.0) 255 (14.8) 218 (85.5) 80 (36.7) 42 (52.5) Women 2195 (56.0) 354 (16.1) 283 (79.9) 143 (50.5) 62 (43.4) χ2 1.367 3.126 9.54 1.723 0.242 0.077 0.002 0.189 40–49 915 (23.3) 49 (5.4) 29 (59.2) 16 (55.2) 8 (50.0) 50–59 1376 (35.1) 212 (15.4) 179 (84.4) 45 (25.1) 21 (46.7) 60–69 993 (25.3) 196 (19.7) 161 (82.1) 90 (55.9) 42 (46.7) 70–79 496 (12.6) 126 (25.4) 110 (87.3) 62 (56.4) 30 (48.4) ≥ 80y 142 (3.6) 26 (18.3) 22 (84.6) 10 (45.5) 3 (30.0) χ2 123.339 20.868 43.251 1.261 < 0.001 < 0.001 < 0.001 0.868 Primary school and below 421 (10.7) 87 (20.7) 62 (71.3) 46 (74.2) 17 (37.0) Junior school 1132 (28.9) 202 (17.8) 145 (71.8) 108 (74.5) 54 (50.0) Senior school 1174 (29.9) 124 (10.6) 112 (90.3) 30 (26.8) 17 (56.7) College and above 1195 (30.5) 196 (16.4) 182 (92.9) 39 (21.4) 16 (41.0) χ2 35.868 43.023 128.362 3.929 < 0.001 < 0.001 < 0.001 0.269 < 5000 515 (13.1) 101 (19.6) 72(71.3) 54 (75.0) 22 (40.7) 5000–10,000 439 (11.2) 76 (17.3) 51 (67.1) 43 (84.3) 25 (58.1) 10,000–19,999 418 (10.7) 68 (16.3) 55 (80.9) 39 (70.9) 18 (46.2) ≥ 20,000 2550 (65.0) 364 (14.3) 323 (88.7) 87 (26.9) 39 (44.8) χ2 10.842 30.853 115.728 3.159 0.013 < 0.001 < 0.001 0.368 Normal 2981 (76.0) 415 (13.9) 353 (85.1) 107 (30.3) 51 (47.7) Stage I 513 (13.1) 93 (18.1) 70 (75.3) 53 (75.7) 24 (45.3) Stage II 285 (7.3) 61 (21.4) 47 (77.0) 37 (78.7) 17 (45.9) Stage III 143 (3.7) 40 (28.0) 31 (77.5) 26 (83.9) 12 (46.2) χ2 32.894 7.103 98.129 0.094 < 0.001 0.069 < 0.001 0.993 Yes 955 (24.3) 264 (27.6) 216 (81.8) 127 (58.8) 60 (47.2) No 2967 (75.7) 345 (11.6) 285 (82.6) 96 (33.7) 44 (45.8) χ2 141.287 0.064 31.373 0.044 < 0.001 0.800 < 0.001 0.834 Normal 1992 (50.8) 280 (14.1) 235 (83.9) 91 (38.7) 47 (51.6) Overweight 1616 (41.2) 268 (16.6) 217 (81.0) 102 (47.0) 46 (45.1) Obesity 314 (8.0) 61 (19.4) 49 (80.3) 30 (61.2) 11 (36.7) χ2 8.303 0.996 9.275 2.214 0.016 0.608 0.01 0.331 Yes 452 (11.5) 81 (17.9) 64 (79.0) 42 (65.6) 21 (50.0) No 3470 (88.5) 528 (15.2) 437 (82.8) 181 (41.4) 83 (45.9) χ2 2.23 0.678 13.244 0.235 0.135 0.410 < 0.001 0.628 Yes 309 (7.9) 52 (16.8) 34 (65.4) 22 (64.7) 10 (45.5) No 3613 (92.1) 557 (15.4) 467 (83.8) 201 (43.0) 94 (46.8) χ2 0.433 11.106 6.023 0.014 0.511 0.001 0.014 0.907 Regular 2905 (74.1) 357 (12.3) 284 (79.6) 155 (54.6) 73 (47.1) Inactivity 1017 (25.9) 252 (24.8) 217 (86.1) 68 (31.3) 31 (45.6) χ2 89.585 4.357 26.902 0.043 < 0.001 0.037 < 0.001 0.835 Less 132 (3.4) 32 (24.2) 28 (87.5) 23 (82.1) 13 (56.5) Moderate 2270 (57.9) 205 (9.0) 144 (70.2) 87 (60.4) 41 (47.1) More 1520 (38.8) 372 (24.5) 329 (88.4) 113 (34.3) 50 (44.2) χ2 173.433 30.632 44.567 1.171 < 0.001 < 0.001 < 0.001 0.557 Less 170 (4.3) 32 (18.8) 27 (84.4) 22 (81.5) 16 (72.7) Moderate 3548 (90.5) 536 (15.1) 448 (83.6) 183 (40.8) 79 (43.2) More 204 (5.2) 41 (20.1) 26 (63.4) 18 (69.2) 9 (50.0) χ2 5.135 10.721 23.808 6.983 0.077 0.005 < 0.001 0.030 < 5d/w 928 (23.7) 112 (12.1) 83 (74.1) 68 (81.9) 35 (51.5) ≥ 5d/w 2994 (76.3) 497 (16.6) 418 (84.1) 155 (37.1) 69 (44.5) χ2 11.088 6.262 56.389 0.919 0.001 0.012 < 0.001 0.338 ≤ 2d/w 380 (9.7) 78 (20.5) 66 (84.6) 49 (74.2) 27 (55.1) 3–4d/w 1138 (29.0) 139 (12.2) 104 (74.8) 81 (77.9) 36 (44.4) ≥ 5d/w 2404 (61.3) 392 (16.3) 331 (84.4) 93 (28.1) 41 (44.1) χ2 17.874 6.846 106.628 1.811 < 0.001 0.033 < 0.001 0.404
Awareness rate: the proportion of people who were aware of diabetes among those with diabetes. Treatment rate: the proportion of people who received treatment among those who were aware of diabetes. Blood glucose control rate: the proportion of people whose blood glucose control reached the target level among those who received treatment. BMI body mass index.
Among the 3922 respondents, there were 609 (15.5%) cases of diabetes. The diabetic prevalence rate in rural areas was higher than that in urban areas (17.7% vs. 14.2%, p = 0.004), while it did not significantly differ between men and women (14.8% in men vs. 16.1% in women, p = 0.242). Differences in the diabetic prevalence rate, associated with the participants' age, educational levels, annual income, blood pressure, blood lipid levels, body mass index (BMI), exercise and dietary habits (taste, vegetables and fruit intake) (p < 0.05), were found. Among the respondents aged 40–79 years, the diabetic prevalence rate gradually rose with age [5.4% (40–49 years old) vs. 15.4% (50–59 years old) vs. 19.7% (60–69 years old) vs. 25.4% (70–79 years old), p < 0.001], blood pressure grade [13.9% (normal blood pressure) vs. 18.1% (grade I hypertension) vs. 21.4% (grade II hypertension) vs. 28.0% (grade III hypertension), p < 0.001] and BMI [14.1% (normal) vs. 16.6% (overweight) vs. 19.4% (obesity), p = 0.016]. The respondents with dyslipidemia had a significantly higher diabetic prevalence rate than those with a normal blood lipid level [27.6% (dyslipidemia) vs. 11.6% (normal blood lipid level), p < 0.001]. The diabetic prevalence rate was significantly higher in respondents who lacked exercise and preferred light or heavy taste than that in those who regularly exercised [24.8% (lack of exercise) vs. 12.3% (regular exercise), p < 0.001] and had moderate taste [24.2% (light taste) vs. 24.5% (heavy taste) vs. 9.0% (moderate taste), p < 0.001]. Besides, the diabetic prevalence rate negatively correlated with the increase in annual income [19.6% (< 5000 yuan) vs. 17.3% (5000–10,000 yuan) vs. 16.3% (10,000–19,999 yuan) vs. 14.3% (≥ 20,000 yuan), p = 0.013] (Table 1).
Among the 609 diabetic respondents, 501 (82.3%) cases were aware of diabetes. The diabetic awareness rate was higher in urban residents compared to that in rural residents (92.8% vs. 68.4%, p < 0.001), while it did not differ between men and women (85.5% in men vs. 79.9% in women, p = 0.077). Differences in the diabetic awareness rate, associates with the participants' age, educational levels, annual income, drinking status, exercise, and dietary habits (taste, lean meat intake, vegetables, and fruit intake) (p < 0.05) (Table 1).
Among the 501 respondents who were aware of diabetes, 223 (44.5%) cases were treated with medication. The treatment rate was different between that in rural and urban areas, and between men and women (p < 0.05). The diabetic treatment rate was also different depending on age, educational level, annual income, blood pressure grade, blood lipid level, BMI and living habits (smoking, drinking, exercise and dietary habits) (p < 0.05) (Table 1).
Among the 223 diabetic respondents who were treated with medication, FPG was controlled in 101 (46.6%) cases (FPG < 7.0 mmol/L). The FPG control level was different between the respondents and depended on the different levels of meat intake (p = 0.03) (Table 1).
As shown in Fig. 1, the diabetic prevalence rate in men and women rose with age among the respondents aged 40–79 years. The diabetic prevalence rate in respondents under the age of 60 years was slightly higher in men than that in women, while it was significantly higher in women over the age of 60 years. The diabetic awareness rate of respondents over the age of 40 years was significantly higher in men than that in women, and the diabetic treatment rate of respondents over the age of 40 years was higher in women than that in men. Moreover, the diabetic control rate was slightly higher in women than that in men among the respondents aged 50–59 and 70–79 years, while it was obviously higher in men than that in women among the respondents aged 40–49, 60–69 and ≥ 80 years.
Graph: Figure 1 Gender differences in diabetic prevalence, awareness, treatment, and control rates in different age groups.
After correction of other factors, we found that age, hypertension, dyslipidemia, living habits (exercise habits) and dietary habits (taste), are related to the diabetic prevalence rate. The risk of developing diabetes among the respondents aged 50–59, 60–69, 70–79 and ≥ 80 years was respectively 2.55 times [odds ratio (OR) = 2.55, 95% confidence interval (95% CI) 1.82–3.56, p < 0.001], 2.90 times (OR = 2.90, 95% CI 2.06–4.10, p < 0.001), 3.31 times (OR = 3.31, 95% CI 2.28–4.82, p < 0.001) and 2.41 times (OR = 2.41, 95% CI 1.40–4.17, p = 0.002) that among those aged 40–49 years. The respondents with a higher hypertension grade also had an increased risk of developing diabetes (p = 0.022). The risk of developing diabetes among the respondents with dyslipidemia was 2.13 times that among those with a normal blood lipid level (OR = 2.13, 95% CI 1.75–2.60, p < 0.001). Regular exercise and moderate salt intake were protective factors against diabetes (p < 0.05) (Table 2).
Multiple logistic regression analysis of influencing factors for diabetic prevalence, awareness, treatment, and control rates.
Items Prevalence (n = 3922) Awareness (n = 609) Treatment (n = 609) Glycaemic control (n = 609) OR, 95% CI OR, 95% CI OR, 95% CI OR, 95% CI Urban 1.00 1.00 1.00 1.00 Rural 0.73 (0.46, 1.15) 0.173 0.09 (0.03, 0.27) < 0.001 0.84 (0.34, 2.08) 0.708 0.54 (0.18, 1.61) 0.270 40–49 1.00 1.00 1.00 1.00 50–59 2.54 (1.82, 3.56) < 0.001 3.64 (1.61, 8.25) 0.002 0.91 (0.42, 2.00) 0.818 0.90 (0.34, 2.38) 0.832 60–69 2.90 (2.06, 4.10) < 0.001 4.14 (1.84, 9.31) 0.001 2.26 (1.06, 4.81) 0.035 1.68 (0.67, 4.21) 0.272 70–79 3.31 (2.28, 4.82) < 0.001 5.34 (2.09, 13.69) < 0.001 2.79 (1.24, 6.28) 0.013 2.00 (0.75, 5.35) 0.166 ≥ 80 2.41 (1.40, 4.17) 0.002 2.40 (0.61, 9.46) 0.213 2.48 (0.81, 7.66) 0.113 1.16 (0.25, 5.33) 0.846 Primary school and below 1.00 1.00 1.00 1.00 Junior school 1.08 (0.77, 1.50) 0.675 1.65 (0.77, 3.52) 0.198 1.56 (0.78, 3.09) 0.208 2.11 (0.92, 4.83) 0.077 Senior school 0.50 (0.31, 0.81) 0.005 2.03 (0.60, 6.79) 0.253 1.00 (0.38, 2.63) 0.999 1.71 (0.52, 5.57) 0.377 College and above 0.84 (0.52, 1.36) 0.474 2.39 (0.69, 8.31) 0.172 0.80 (0.30, 2.10) 0.650 1.01 (0.30, 3.39) 0.991 < 5000 1.00 1.00 1.00 1.00 5000–10,000 1.08 (0.75, 1.54) 0.676 0.84 (0.39, 1.84) 0.670 1.36 (0.67, 2.79) 0.397 1.77 (0.80, 3.92) 0.159 10,000–19,999 1.24 (0.83, 1.85) 0.290 1.04 (0.41, 2.62) 0.934 1.50 (0.69, 3.28) 0.310 1.05 (0.44, 2.51) 0.922 ≥ 20,000 1.33 (0.91, 1.96) 0.146 0.97 (0.41, 2.30) 0.945 0.97 (0.45, 2.06) 0.933 0.74 (0.29, 1.89) 0.533 Normal 1.00 1.00 1.00 1.00 Stage I 1.13 (0.82, 1.57) 0.455 3.08 (1.48, 6.40) 0.003 1.71 (0.90, 3.26) 0.101 1.35 (0.65, 2.85) 0.423 Stage II 1.43 (0.97, 2.10) 0.069 3.43 (1.51, 7.79) 0.003 2.02 (0.95, 4.29) 0.067 1.52 (0.65, 3.54) 0.338 Stage III 1.96 (1.24, 3.09) 0.004 3.07 (1.19, 7.91) 0.020 2.39 (1.01, 5.63) 0.047 1.91 (0.74, 4.92) 0.181 No 1.00 – – 1.00 1.00 Yes 2.13 (1.75, 2.60) < 0.001 – – 1.90 (1.28, 2.83) 0.001 1.65 (1.03, 2.65) 0.036 Normal 1.00 – – 1.00 – – Overweight 1.15 (0.94, 1.40) 0.172 – – 1.06 (0.70, 1.61) 0.785 – – Obesity 1.16 (0.83, 1.62) 0.384 – – 1.31 (0.66, 2.61) 0.440 – – Regular 1.00 1.00 1.00 1.00 Inactivity 0.50 (0.41, 0.61) < 0.001 1.08 (0.62, 1.87) 0.797 1.17 (0.76, 1.80) 0.476 1.17 (0.69, 1.98) 0.563 Less 1.00 1.00 1.00 1.00 Moderate 0.35 (0.21, 0.56) < 0.001 0.22 (0.06, 0.76) 0.017 0.54 (0.20, 1.43) 0.214 0.74 (0.29, 1.90) 0.535 Higher 0.81 (0.50, 1.31) 0.381 0.47 (0.13, 1.65) 0.239 0.54 (0.20, 1.41) 0.206 0.64 (0.25, 1.62) 0.345 < 5d/w 1.00 1.00 1.00 1.00 ≥ 5d/w 1.18 (0.84, 1.67) 0.342 1.85 (0.79, 4.30) 0.155 1.25 (0.61, 2.53) 0.543 1.09 (0.50, 2.39) 0.831 ≤ 2d/w 1.00 1.00 1.00 1.00 3–4d/w 0.95 (0.66, 1.35) 0.795 0.58 (0.23, 1.43) 0.233 0.86 (0.43, 1.72) 0.667 0.55 (0.26, 1.16) 0.116 ≥ 5d/w 0.99 (0.70, 1.40) 0.941 0.22 (0.09, 0.56) 0.001 0.31 (0.16, 0.60) 0.001 0.26 (0.12, 0.57) 0.001
After correction of other factors, we found that the region, age, hypertension, and dietary habits (fruit and salt intake), are related to the diabetic awareness rate. The higher age and blood pressure grade corresponded to a higher diabetic awareness rate (p < 0.05). The diabetic awareness rate was lower among the respondents who lived in rural areas, ate more fruit on a weekly basis, and moderately consumed salt (p < 0.05) (Table 2).
After correction of other factors, we found that age, dyslipidemia, and dietary habits (fruit intake), were related to the diabetic treatment rate. Among the diabetic respondents who were older and had dyslipidemia, the diabetes treatment rare was higher (p < 0.05). The rate was lower among the respondents who ate more fruit on a weekly basis (p < 0.05) (Table 2).
After correction of other factors, we found that dyslipidemia and dietary habits (fruit intake), were related to the FPG control rate. This rate was higher among the respondents with dyslipidemia than that among those with a normal blood lipid level (OR = 1.65, 95% CI 1.03–2.65, p = 0.036). Weekly consumption of fruit led to a lower FPG control rate (p = 0.003) (Table 2).
The proportion of hypertension [81.0% (rural areas) vs. 41.0% (urban areas), p < 0.001], dyslipidemia [47.9% (rural areas) vs. 39.9% (urban areas), p = 0.048], and overweight or obesity [normal weight: 34.2% (rural areas) vs. 54.9% (urban areas), p < 0.001] among rural areas' diabetic respondents were far higher than those in urban areas (Table 3).
Differences in gender, hypertension, dyslipidemia. and BMI among the diabetic respondents in urban and rural areas.
Items Urban Rural Total χ2 346 (100.0) 263 (100.0) 609 (100.0) 13.458 < 0.001 Men 167 (48.3) 88 (33.5) 255 (41.9) Women 179 (51.7) 175 (66.5) 354 (58.1) 98.080 < 0.001 Yes 142 (41.0) 213 (81.0) 355 (58.3) No 204 (59.0) 50 (19.0) 254 (41.7) 3.918 0.048 Yes 138 (39.9) 126 (47.9) 264 (43.3) No 208 (60.1) 137 (52.1) 345 (56.7) 35.574 < 0.001 Normal 190 (54.9) 90 (34.2) 280 (46.0) Overweight 138 (39.9) 130 (49.4) 268 (44.0) Obesity 18 (5.2) 43 (16.3) 61 (10.0)
In this study, the diabetic prevalence, awareness, treatment, control rates, and their influencing factors, were explored in 2019 and for the first time, among adults aged 40 years and above in Shenyang, China. A total of 3922 participants were enrolled, and the diabetic overall prevalence rate was 15.5%. According to a national survey in China in 2010, the diabetic prevalence, awareness, treatment and control rates in Chinese adults, were 11.6%, 30.1%, 25.8% and 39.7%, respectively[
According to the survey results, the diabetic prevalence rate was higher in rural areas compared to that in urban areas (17.7% vs. 14.2%, p = 0.004), while the diabetic awareness rate was lower in rural areas compared to that in urban areas (68.4% vs. 92.8%, p < 0.001). The above findings suggest that it is necessary to strengthen adults' diabetic screening in low economic level rural areas.
In this study, the diabetic prevalence rate was 82.3%, which is far higher than that in other Chinese regions and foreign countries (Shanghai: 28.06%[
In the present study, the diabetic treatment rate in rural areas, was higher than that in urban areas (79.4% vs. 24.9%, p < 0.001). There are studies showing that the better the individual's self-reported health conditions are, the less likely the diabetic medication would be is administered, and that such influence is greater in urban areas[
This study also revealed that the diabetic control rate in Shenyang was 17.1% higher than that in Shanghai (12.42%)[
There are few studies on gender differences in diabetic prevalence, awareness, treatment and control rates. In this study, the diabetic awareness rate was significantly higher in men than that in women (Fig. 1). A study has demonstrated that the women diabetic awareness rate is higher than in men in rural areas[
Advanced age, a low educational level, lack of exercise, hypertension, and dyslipidemia, are risk factors for diabetes[
The relation between drinking and diabetes remains controversial. According to a meta-analysis, light to moderate alcohol consumption leads to a lower risk of diabetes, while heavy alcohol consumption raises the risk of diabetes in men[
Several studies have indicated that an unhealthy diet raises the risk of diabetes[
There were imitations in this study. First, the study population was recruited from Shenyang, China, and they were aged ≥ 40 years, therefore, the research results failed to represent the epidemiology of diabetes in the Chinese population. Second, the cross-sectional design may have led to selection bias. Third, only FPG was used as a diabetes index for all respondents, the glucose tolerance test lacked, and the level of glycated hemoglobin was not detected, thus, the diabetic prevalence rate may be underestimated.
In this study, the diabetic prevalence, awareness, treatment, control rates and their related risk factors were reported in Shenyang, China in 2019. It was confirmed that the diabetic prevalence was higher, while the treatment and control rates were lower in Shenyang. Therefore, it is necessary to reduce the impact of related risk factors to lower the diabetic burden.
Based on the National Health Commission's public welfare project "Screening and Intervention of High-Risk Stroke Population in 2018", this survey was conducted on permanent residents (lived locally for 6 months or more) aged ≥ 40 years at the screening points of communities and townships through multi-stage cluster random sampling from April to May 2019. In the first stage, 2 survey sites were selected from the geographic area: Fengle Subdistrict, Dongling District, northeastern Shenyang (a middle-economic-level area), and Linshengbao Town, Sujiatun District, southwestern Shenyang (a middle-low-economic-level area). In the second stage, 8 communities and 8 village committees were randomly selected from the Fengle Subdistrict and Linshengbao Town, respectively. In the third stage, the respondents were selected from all adults aged ≥ 40 years in each community/village committee through simple random sampling. Pregnant women and people with mental disorders were excluded. If someone refused to participate in the research, or the data could not be collected for various reasons, other adults aged ≥ 40 years, in the nearest community or village, would have been selected as substitutes, thus ensuring sufficient samples.
In this study, it was planned to enroll 4000 adults aged ≥ 40 years old in Shenyang, Liaoning, China, including 2400 in urban areas (Fengle Subdistrict) and 1600 participants in rural areas (Linshengbao Town). However, a total of 3949 people agreed to participate, including 22 with incomplete data and 5 without FPG test results. Finally, a total of 3922 people were enrolled, including 2433 (62.0%) urban residents and 2195 (56.0%) women (Fig. 2).
Graph: Figure 2 The study sampling process.
Upon approval by the Stroke Prevention Project, National Health Commission, this study was conducted according to the protocol of "Screening and Intervention of High-Risk Stroke Population in 2018". The health-related information, physical examination and laboratory examination collected during the screening of this project did not pose any risk of injury to the investigated subjects. Venous blood sampling may cause temporary mild pain and subcutaneous stasis, which has been orally informed. The relevant information exposed in the study was implemented in accordance with the ethical norms proposed by the Brain prevention Commission of the National Health and Family Planning Commission (see "Technical plan of 2018 stroke High-risk Population Screening and Intervention Project 4. Ethical issue"). Before the survey, the statutory guardians signed the informed consent on behalf of the illiterate participants, and all other participants personally signed the informed consent. The number of people, who were screened in all communities and townships, reached more than 85% of the screening subjects at each screening point, and the used household registration information for the sampling came from the government departments.
Face-to-face questionnaire surveys, physical and laboratory examinations were performed by well-trained investigators using the standard technical solution. The survey questionnaire covered basic demographic information, such as the name, gender, date of birth, educational level, annual income and living habits. The respondents were divided into 5 age groups: 40–49 years old, 50–59 years old, 60–69 years old, 70–79 years old and ≥ 80 years old. For the educational level, the respondents were divided into 4 groups, according to the years of formal education: illiteracy[
Physical examination items included blood pressure (BP, including systolic and diastolic blood pressures) at rest (rest for at least 20 min), height and weight without shoes. The body mass index (BMI) was calculated as follows: BMI = body weight (kg)/height (m
Hypertension[
Diabetes[
The accuracy of all case information was reviewed and controlled by the quality control group members, and a diagnosis was made by the group experts. The questionnaire data were sorted, proofread, and entered using the EpiData3.1 software. SPSS25.0 was used for analysis, and p < 0.05 of two-sided test was considered statistically significant. Continuous variables were expressed as mean ± standard deviation (χ ± s), and t test was performed for the comparison between two groups. Besides, categorical variables were expressed as rate (%), and chi-square test (chi-square test for four-fold table data and R × C table data) was performed for the comparison between two groups and among three groups. Multivariate logistic regression analysis was adopted for the diabetic risk factors (p < 0.1 in univariate analysis), and the results were expressed as odds ratio (OR) with 95% confidence interval (CI).
We are very grateful to all the participants of this study. We would also like to thank the staff who participated in data collection, especially Fenglan Wei.
C.L. and X.L. performed and conducted the field works, data collection and analysis. M.L. and L.Z. designed the study and directed its implementation. C.L. and X.C. performed the statistical analysis and manuscript writing. All authors discussed the findings and reviewed the manuscript.
The authors declare no competing interests.
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By Cong Liu; Xiaojiu Li; Muhui Lin; Limin Zheng and Xiaohong Chen
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