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The mean age of the 178,558 eligible participants was 55.1 ± 13.8 years, and 102,591 (55.6%) were females. The crude prevalence of dyslipidemia was 35.6%. The characteristics of the sample are shown in Table 1.
Variables Number (% or CI) Overall 178,558 Age (years) 18–29 9,340 (5.2) 30–39 17,626 (9.9) 40–49 32,241 (18.1) 50–59 46,950 (26.3) 60–69 48,642 (27.2) 70+ 23,759 (13.3) Sex Female 99,690 (55.8) Male 78,868 (44.2) Region Rural 105,532 (59.1) Urban 73,026 (40.9) Dyslipidemia Overall 38.2 (27.1–39.2) High TC 8.2 (7.7–8.7) High LDL-C 8.0 (7.4–8.5) Low HDL-C 20.9 (19.9–21.9) High TG 18.4 (17.4–19.3) Awareness of dyslipidemia* 17.5 (16.4–18.6) Treatment of dyslipidemia* 10.1 (9.3–10.8) Control of dyslipidemia* 6.9 (6.3–7.4) Note. Standardized rate adjusted to the Sixth National Population Census (2010). *Participants with dyslipidemia. Table 1. Characteristics of the 178,558 study participants
Figure 2 illustrates the standardized prevalence of dyslipidemia by province. The prevalence of each province was divided into quintile intervals. The provinces with the highest prevalence of dyslipidemia quintile (43.4%–49.7%) were Guizhou (49.7%, 95% CI: 30.9%–68.6%), Jilin (47.5%, 95% CI: 42.9%–52.1%), Tianjin (47.4%, 95% CI: 41.5%–53.4%), Liaoning (46.1%, 95% CI: 41.1%–51.3%), Heilongjiang (46.2%, 95% CI: 41.0%–51.4%), Shanxi (45.9%, 95% CI: 42.0%–49.8%), and Hunan (43.4%, 95% CI: 38.8%–48.1%). Whereas provinces with the lowest prevalence of dyslipidemia quintile (27.8%–35.0%) included Tibet (27.8%, 95% CI: 17.9%–40.4%), Qinghai (30.8%, 95% CI: 24.4%–38.0%), Shanghai (32.5%, 95% CI: 27.7%–37.6%), Jiangxi (33.7%, 95% CI: 29.5%–38.2%), Sichuan (34.6%, 95% CI: 27.6%–42.2%), Chongqing (34.7%, 95% CI: 30.7%–38.9%), and Shaanxi (35.0%, 95% CI: 31.5%–38.7%). Generally, provinces that lie between north latitudes 30 to 35 degrees crossed by the Yangtze River had a relatively lower prevalence (27.8%–37.2%) of dyslipidemia.
The prevalence of high TC ranged from 2.5% to 16.7%, and high LDL-C prevalence ranged from 2.1% to 19.7% across provinces. Noticeably, the geographic distribution of these two dyslipidemia types was similar. The highest and lowest prevalence rates of high TC and LDL-C were found in Guangdong (high TC: 16.7%, 95% CI: 12.1%–22.7%; high LDL-C: 19.7%, 95% CI: 15.3%–24.9%), and Ningxia (high TC: 2.5%, 95% CI: 1.3%–4.7%; high LDL-C: 2.1%, 95% CI: 1.2%–3.6%), respectively. Provinces in both the highest prevalence of high TC (≥ 10.7% and ≤ 16.7%) and high LDL-C (≥ 9.9% and ≤ 19.7%) quintiles included Guangxi, Hainan, Fujian, and Beijing, while provinces with the lowest prevalence of high TC (≥ 2.5% and < 6.1%) and high LDL-C (≥ 2.1% and < 5.2%) quintile included Gansu, Xinjiang, Shaanxi, and Qinghai.
Provinces in the northwest and northeast of China generally had higher low HDL-C levels. The highest prevalence rate of low HDL-C quintile was 24.9%–31.4%, with the highest in Gansu (31.4%, 95% CI: 24.7%–38.9%). High TG was more common in the southwest and northeast of China, with the highest prevalence rate of high TG quintile of 20.4%–35.9%.
The standardized treatment rates and dyslipidemia control by province are illustrated in Figure 3. Provinces to the south of the Yangtze River have lower treatment rates of dyslipidemia, while provinces in the northern part of China have lower control rates. Guizhou and Guangdong were reported to have the lowest treatment rates (0.4%, 95% CI: 0.2%–0.7%) and dyslipidemia control (9.5, 95% CI: 8.5%–10.7%), respectively, whereas the highest rates were illustrated in Beijing (treatment: 6.6%, 95% CI: 5.4%–8.0%) and Heilongjiang (control: 27.9%, 95% CI: 21.9.1%–34.7%).
The prevalence rate of high LDL-C was correlated with SDI (r = 0.46, P = 0.01) and urbanization rate (r = 0.37, P = 0.04) (Figure 4). The treatment rate of dyslipidemia was correlated with SDI (r = 0.56, P < 0.001), urbanization rate (r = 0.45, P = 0.01), and ABTEM (r = 0.54, P < 0.001) (Figure 5). Supplementary Table S1, (available in www.besjournal.com).
Figure 4. Correlations between lipid abnormalities and SDI and urbanization rate. TC, high total cholesterol; LDL, low-density lipoprotein; TG, high triglycerides; HDL, high-density lipoprotein; SDI, socio-demographic index
Figure 5. Correlations between dyslipidemia treatment and economic level and accessibility to healthcare. ABTEM, affordable basic technologies and essential medicines; SDI, socio-demographic index; GPs, general practitioners
Regions Urbanization rate SDI Affordable basic technologies
and essential medicinesNumber of general practitioners
per 10,000 peopleBeijing 86.5 0.8354 84.66 3.96 Tianjin 83.15 0.8011 4.13 2.41 Hebei 56.43 0.7159 29.13 1.33 Shanxi 58.41 0.7063 18.02 1.72 Inner Mongolia 62.71 0.7218 0.08 1.58 Liaoning 68.1 0.7406 10.08 1.44 Jilin 56.98 0.7132 3.5 1.89 Heilongjiang 59.55 0.7045 0.17 1.19 Shanghai 88.13 0.8201 28.51 3.51 Jiangsu 69.61 0.7502 24.8 3.43 Zhejiang 68.9 0.7453 14.46 5.39 Anhui 54.69 0.6451 1.95 1.67 Fujian 65.82 0.7203 0.38 1.76 Jiangxi 56 0.6553 3.03 1.14 Shandong 61.18 0.7356 8.25 1.36 Henan 51.71 0.6994 11.34 1.63 Hubei 60.3 0.7002 6.93 1.52 Hunan 56.02 0.6905 1.93 1.03 Guangdong 70.7 0.7677 9.9 2.03 Guangxi 50.22 0.6861 11.24 1.28 Hainan 59.06 0.7048 0 1.22 Chongqing 65.5 0.6945 0.72 1.26 Sichuan 52.29 0.6702 12.92 1.37 Guizhou 47.52 0.5873 2.09 1.4 Yunnan 47.69 0.632 0.07 1.09 Tibet 31.14 0.5712 0.08 0.73 Shaanxi 58.13 0.69 2.35 0.93 Gansu 47.69 0.6216 0.72 1.46 Qinghai 54.47 0.6387 0.65 2.06 Ningxia 58.88 0.6578 7.84 1.36 Xinjiang 50.91 0.7074 7.55 1.81 Table S1. Urbanization rates, SDI, affordable basic technologies and essential medicines and number of general practitioners per 10,000 people by regions
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