Abstract
Introduction
This study aimed to assess the prevalence of hypertension and to explore the disparities of its risk factors among urban and rural elderly.
Method
Data of hypertensive patients were collected from the China Health and Retirement Longitudinal Study (CHARLS) 2015. Stratified sample households were selected from 450 villages or communities of 150 counties from 28 provinces. Multivariable logistic regression was performed to analyze the factors correlated with hypertension.
Results
Prevalence of HBP was 47.6% (95% CI [45.2%–50.1%]) in total and it was close between urban and rural population (48.6% vs 47.2%). Factors associated with HBP were different between urban and rural areas. In urban areas, hypertension was significantly associated with literacy and diabetes in both genders, high BMI level and smoke quitters in males, and physical activity and dyslipidemia in females. In rural areas, hypertension was significantly associated with older age, higher BMI level in both males and females, and dyslipidemia in males.
Conclusions
The prevalence are about the same among urban and rural residents, but their risk factors vary from each other. Disparity in the risk factors between urban and rural population should be taken into consideration for further intervention.
Keywords: Prevalence, Hypertension, Urban, Risk factors, Rural, Chinese
Introduction
Hypertension is common in China and its prevalence is rising as the population grows older. More than 1/4 of Chinese were suffering from hypertension since 2012 according to Nutrition and Health Monitoring of Chinese Residents. Most of them were elderly. Overall, 27.8% of Chinese adults were hypertensive, with the prevalence higher in men than in women and increasing steeply with rising age (Li et al., 2017b). In 2013, 31.1% of the world’s adults had hypertension (Mills et al., 2016).
Hypertension is associated with higher risks for cardiovascular disease (CVD) mortality and accounts for about one third of deaths (Lewington et al., 2016). As the main target of intervention to reduce CVD risk event (Bi et al., 2015; Li, Zhao & Hu, 2016; Yan et al., 2014). Individual characteristics, social economic status, behavioral health, and comorbidities were common risk factors for hypertension in urban and rural elderly. (Yin et al., 2016; Zhang, Wang & Joo, 2017). For example, risk of hypertension increases with age and BMI level (Xu, Lai & Fang, 2016). Hypertension is associated with healthy behaviors including smoking, drinking and physical activity. Also, those who had chronic diseases are more likely to have HBP. The prevalence of diabetes and hyperlipidemia among hypertension were 22.32% and 9.4% among hypertensive patients (Wang et al., 2017), and the rate was even higher among the elderly (Huang et al., 2017).
The prevalence of hypertension has significantly increased with rapid economic development, urbanization, acceleration of population aging, and changes in traditional lifestyle in China. Although many studies have focused on hypertension in China (Hu et al., 2017; Li et al., 2017a; Li et al., 2017b; Lu et al., 2017), still marked ethical and regional differences exist in its prevalence. The rapid and disproportional economic growth in urban and rural China has brought unexpected changes from a public health perspective. A study of hypertension rates and its factors in northeast China presented a higher prevalence in urban areas and a urban-rural disparity in their risk factors (Wang et al., 2018a). However, this investigation cannot be generalized to the entire country. Elderly are the major population who suffer from hypertension the most. Despite China’s economic growth, social progress, improved living standards and accelerated urbanization, there were few cross-sectional studies comparing risk factors of elderly hypertension from regional perspective. The difference in their demographic composition and healthy lifestyles as well as comorbidity would finally contribute to their health status. Also, the previous study mostly focused on difference between urban and rural population without analysing characteristics and behaviours by different genders. To make further implementations of hypertension intervention among male and female elderly from regional perspective, this study aimed to assess regional hypertension prevalence and to explore disparities of its risk factors among urban and rural elderly.
Materials & Methods
Data and sample
Data for this study were collected from the China Health and Retirement Longitudinal Study (CHARLS) 2015. A four-stage, stratified, cluster sampling of 25,504 individuals in 28 provinces, 150 counties and 450 villages or communities was drawn by Charles-GIS software. In the first stage, 150 county-level units from 28 provinces were selected to give a mix of urban and rural settings and a wide variation in the level of economic development. All of the dwellings in each selected primary sampling unit were outlined on Google Earth maps using the CHARLS-GIS software package that was specifically designed for the survey. Finally, for the present investigation, 24 of the mapped households in each primary sampling unit were randomly selected (Zhao, John & Yang, 2013). In total 21,097 respondents were interviewed in 2015.
Inclusion criteria for this study included that: (i) subjects who were older than 60 years; (ii) subjects who had provided Blood Pressure (BP) measures. Exclusion criteria included subjects containing missing values for individual characteristics, waist circumference, Body-mass index (BMI), health behaviors and diagnosed chronic diseases. 1,776 participants were selected for analysis of this study.
Respondents were asked to relax and remain seated when BP was measured 3 times at 5-minute intervals using an Omron HEM-7200 Monitor during the day time. Respondents were asked whether they were taking anti-hypertensive medicine. A sample of 6,949 respondents above 60 years old had provided complete data for all study variables related to hypertension risk factors. The original CHARLS was approved by the Ethical Review Committee (IRB) at Peking University (IRB00001052–11015). A “Letter to the Residents” leaflet was sent to the selected households and the arrangements for the survey was permitted by all residents participated. The datasets analysed during the current study are publicly available on http://charls.pku.edu.cn/en/page/data/2015-charls-wave4.
Measurement
Hypertension was defined as (i) average systolic blood pressure (SBP) ≥ 140 mm Hg or average diastolic blood pressure (DBP) ≥ 90 mm Hg or higher, or (ii) the respondent was currently taking antihypertensive medications. ‘Sociodemographic characteristics included age, sex, educational attainment (no education, primary school, middle school and above), cohabitation status, household income, insurance, central obesity, BMI, health behaviors and diagnosed chronic diseases. The rural or urban administrative registration status is based on the National Bureau of Statistics definition where a PSU is defined as urban if it is located in a city, suburb of a city, a town, suburb of a town, or other special areas where nonfarm employment constitutes at least 70% of the workforce, such as a special econom-tailed). ic zone, state-owned farm enterprise. Living with a partner was defined as living with a spouse or a cohabitant. Household income was categorized into 4 groups based on the 25th, 50th, and 75th percentiles. BMI was defined as weight (kg) divided by height2 (m2) (<18.5 as low, 18.5–23.9 as normal, 24–27.9 as overweight and ≥ 28 as obesity) (Xu, Lai & Fang, 2016). Central obesity was defined as over 85 cm for males and over 80 cm for females (Chen & Lu, 2004). Health behaviors included physical activity, alcohol intake and smoking. Physical activity was assessed by whether subjects took any light, moderate or vigorous exercise recently. Alcohol intake was assessed by the question “Did you ever drink?” and “do you currently drinking?” Smokers were identified by the question “Did you ever smoke?” and “Do you currently smoking?” Diagnosed chronic diseases included diabetes, dyslipidemia, chronic lung diseases, kidney diseases, digestive diseases, psychiatric problems.
Statistical analysis
The prevalence was estimated using 95% CIs to show their ranges. The results were expressed in frequencies and percentages. Complex sample plan was created according to stratification, cluster and individual weight without non-response adjusted in this survey for chi-square test and multivariable logistic regression for cluster effect adjust. Basic sociodemographic variables were described by descriptive statistics. Chi-square bivariate tests were performed to examine the group differences (hypertension or not) for all the demographic variables. The association between hypertension and the independent variables was measured by logistic regression. Four separate regression models were run for each gender of the urbanity variable. Results of the regression analysis were presented as adjusted ORs with corresponding 95% CIs. The statistical significance test level was set at 0.05 (two-tailed). All statistical analysis was implemented by using SPSS 13.0 (SPSS Inc., Chicago, IL, USA).
Results
Population characteristics
Table 1 shows that the average age of the population was 68 years old. About one third of the population were from urban (34.0%) areas. The male ratio was 50.1%. The majority of the population was living with a spouse or a cohabitant. The rate of illiteracy was 31.9% and 47.2% finished primary school. There were more subjects educated in urban than that in rural areas. Most subjects were insured. The majority of the population were in the normal BMI level (48.9%) and the least belonged to the low level (7.3%). A BMI disparity was found between urban and rural population as the urban population had higher proportion of residents in the high BMI level (40.9% vs 26.2%). Most subjects did light to moderate level physical activity. Subjects in rural did more vigorous activity than urban ones. Most of the population never drank or smoke. The prevalence of drinking was 33.5% and smoking was 28.7%. The urban population also had higher rates of diabetes (16.4% vs 10.0%) and dyslipidemia (30.1% vs 18.1%). The prevalence of chronic lung diseases, psychiatrics, kidney diseases and digestive diseases were respectively 19.0%, 2.6%, 12.2% and 33.2%.
Table 1. Sample characteristics and prevalence of hypertension of the participants.
| Total | Urban | Rural | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Catergory | N (%) | Prevalence (95% CI) | P | N (%) | Prevalence (95% CI) | P | N (%) | Prevalence (95% CI) | P |
| Region | Urban | 604 (34.99) | 48.57 (44.49–52.64) | |||||||
| Rural | 1172 (65.99) | 47.16 (44.00–50.32) | ||||||||
| 0.59 | ||||||||||
| Sex | male | 890 (5.112) | 47.50 (43.88–51.11) | 282 (46.68) | 51.75 (45.60–57.90) | 608 (51.87) | 45.48 (45.60–57.90) | |||
| female | 886 (49.88) | 47.79 (44.53–51.05) | 322 (53.31) | 45.66 (39.74–51.58) | 564 (48.12) | 49.01 (39.74–51.58) | ||||
| 0.87 | 0.15 | 0.14 | ||||||||
| Age | 60-69 | 1233 (69.42) | 45.02 (42.13–47.90) | 392 (64.96) | 46.22 (41.27–51.17) | 841 (71.75) | 44.43 (41.27–51.17) | |||
| 70-79 | 460 (25.99) | 51.86 (46.98–56.74) | 178 (29.47) | 52.58 (45.10–60.06) | 282 (24.61) | 51.43 (45.10–60.06) | ||||
| 80- | 83 (4.673) | 60.65 (49.64–71.67) | 34 (5.63) | 54.96 (37.97–71.95) | 49 (4.19) | 64.24 (37.97–71.95) | ||||
| <0.01 | 0.24 | 0.01 | ||||||||
| Living with a partner | yes | 1382 (77.81) | 46.47 (43.61–49.34) | 454 (75.16) | 48.15 (43.46–52.84) | 928 (79.18) | 45.64 (43.46–52.84) | |||
| no | 394 (22.18) | 51.60 (46.14–57.06) | 150 (24.83) | 49.79 (40.69–58.89) | 244 (2.82) | 52.72 (40.69–58.89) | ||||
| 0.12 | 0.74 | 0.07 | ||||||||
| Literacy | Uneducated | 567 (31.92) | 48.53 (44.41–52.66) | 132 (21.85) | 46.39 (37.33–55.45) | 435 (37.11) | 49.15 (37.33–55.45) | |||
| Primary school | 838 (47.18) | 48.21 (44.58–51.84) | 281 (46.52) | 54.01 (47.85–60.18) | 557 (47.52) | 45.27 (47.85–60.18) | ||||
| Middle school and above | 371 (2.889) | 45.03 (38.70–51.37) | 191 (31.62) | 42.01 (33.19–50.83) | 180 (15.35) | 48.45 (33.19–50.83) | ||||
| 0.56 | 0.06 | 0.44 | ||||||||
| *Houshold income | poor | 446 (25.11) | 48.01 (42.80–53.22) | 102 (16.88) | 49.94 (39.21–60.67) | 344 (29.35) | 47.48 (39.21–60.67) | |||
| near poor | 477 (26.85) | 47.10 (41.98–52.22) | 94 (15.56) | 46.49 (35.63–57.35) | 383 (32.67) | 47.25 (35.63–57.35) | ||||
| middle income | 443 (24.94) | 48.16 (43.25–53.07) | 149 (24.66) | 51.11 (42.67-59.56) | 294 (25.85) | 46.60 (42.67–59.56) | ||||
| rich | 410 (23.85) | 47.35 (41.88–52.83) | 259 (42.88) | 47.43 (40.54–54.31) | 151 (12.88) | 47.23 (40.54–54.31) | ||||
| 0.99 | 0.83 | 1.00 | ||||||||
| Insured | yes | 1632 (91.89) | 47.33 (44.82–49.85) | 549 (9.89) | 47.24 (43.08–51.40) | 1083 (92.46) | 47.38 (43.08–51.40) | |||
| no | 144 (8.181) | 50.95 (41.21–60.69) | 55 (9.16) | 61.12 (43.82–78.43) | 89 (7.593) | 44.54 (43.82–78.43) | ||||
| 0.47 | 0.11 | 0.59 | ||||||||
| BMI | low | 129 (7.263) | 32.34 (23.49–41.18) | 38 (6.29) | 35.59 (16.20–54.99) | 91 (7.76) | 30.90 (16.20–54.99) | |||
| normal | 869 (48.93) | 38.54 (34.93–42.16) | 244 (4.40) | 38.84 (31.83–45.85) | 625 (53.32) | 38.43 (31.83–45.85) | ||||
| high | 554 (31.19) | 56.74 (52.03–61.46) | 247 (4.89) | 54.24 (46.92–61.57) | 307 (26.19) | 58.94 (46.92–61.57) | ||||
| obesity | 224 (12.61) | 70.88 (65.24–76.51) | 75 (12.41) | 68.99 (59.59–78.40) | 149 (12.71) | 71.76 (59.59–78.40) | ||||
| <0.01 | <0.01 | <0.01 | ||||||||
| Central obesity | yes | 699 (39.35) | 35.61 (31.72–39.49) | 188 (31.12) | 35.74 (27.57–43.91) | 511 (43.66) | 35.56 (27.57–43.91) | |||
| no | 1077 (6.641) | 55.59 (52.40–58.79) | 416 (68.87) | 54.40 (49.60–59.20) | 661 (56.39) | 56.36 (49.60–59.20) | ||||
| <0.01 | <0.01 | <0.01 | ||||||||
| Physical activity | none | 225 (12.66) | 56.20 (49.00–63.39) | 70 (11.58) | 62.31 (50.63–74.00) | 155 (13.22) | 53.70 (50.63–74.00) | |||
| light | 479 (26.97) | 52.23 (47.50–56.95) | 190 (31.45) | 49.68 (42.67–56.70) | 289 (24.65) | 53.96 (42.67–56.70) | ||||
| moderate | 511 (28.77) | 47.72 (43.05–52.38) | 225 (37.25) | 47.65 (40.23–55.06) | 286 (24.42) | 47.77 (40.23–55.06) | ||||
| vigrous | 561 (31.58) | 40.14 (36.03–44.25) | 119 (19.71) | 41.07 (32.65–49.48) | 442 (37.71) | 39.89 (32.65–49.48) | ||||
| <0.01 | 0.02 | <0.01 | ||||||||
| Drink | never | 944 (53.15) | 48.61 (45.28–51.93) | 336 (55.62) | 48.25 (42.70–53.80) | 608 (51.87) | 48.80 (42.70–53.80) | |||
| quit | 237 (13.34) | 49.03 (42.30–55.77) | 83 (13.74) | 52.55 (40.25–64.85) | 154 (13.13) | 47.11 (40.25–64.85) | ||||
| yes | 595 (33.52) | 45.55 (41.20–49.89) | 185 (3.63) | 47.34 (39.27–55.42) | 410 (34.98) | 44.72 (39.27–55.42) | ||||
| 0.49 | 0.75 | 0.41 | ||||||||
| Smoke | never | 923 (51.97) | 47.32 (44.11–50.52) | 331 (54.81) | 45.05 (39.71–50.38) | 592 (5.51) | 48.56 (39.71–50.38) | |||
| quit | 343 (19.31) | 59.22 (53.48–64.97) | 105 (17.38) | 65.68 (56.27–75.08) | 238 (2.37) | 56.14 (56.27–75.08) | ||||
| yes | 510 (28.71) | 40.18 (35.49–44.87) | 168 (27.81) | 43.78 (35.57–51.98) | 342 (29.18) | 38.35 (35.57–51.98) | ||||
| <0.01 | <0.01 | <0.01 | ||||||||
| Diabetes | yes | 216 (12.16) | 64.07 (57.10–71.05) | 99 (16.39) | 71.74 (62.55–80.92) | 117 (9.98) | 57.68 (62.55–80.92) | |||
| no | 1560 (87.83) | 45.50 (42.81–48.19) | 505 (83.69) | 44.37 (39.80–48.95) | 1055 (9.18) | 46.05 (39.80–48.95) | ||||
| <0.01 | <0.01 | 0.02 | ||||||||
| Dyslipidemia | yes | 394 (22.18) | 62.92 (57.98–67.87) | 182 (3.13) | 59.96 (53.26–66.66) | 212 (18.88) | 65.70 (53.26–66.66) | |||
| no | 1382 (77.81) | 43.34 (40.61–46.07) | 422 (69.86) | 43.46 (38.26–48.66) | 960 (81.91) | 43.29 (38.26–48.66) | ||||
| <0.01 | <0.01 | <0.01 | ||||||||
| Lung diseases | yes | 337 (18.97) | 48.74 (43.25–54.23) | 107 (17.71) | 52.68 (43.56–61.80) | 230 (19.62) | 46.90 (43.56–61.80) | |||
| no | 1439 (81.24) | 47.39 (44.69–50.09) | 497 (82.28) | 47.70 (42.93–52.47) | 942 (8.375) | 47.22 (42.93–52.47) | ||||
| 0.65 | 0.31 | 0.92 | ||||||||
| Psychiatric problem | yes | 47 (2.646) | 56.88 (40.77–72.99) | 18 (2.98) | 54.47 (28.30–80.64) | 29 (2.47) | 58.54 (28.30–80.64) | |||
| no | 1729 (97.35) | 47.39 (44.82–49.95) | 586 (97.19) | 48.37 (44.01–52.73) | 1143 (97.52) | 46.88 (44.01–52.73) | ||||
| 0.26 | 0.63 | 0.25 | ||||||||
| Kidney diseases | yes | 216 (12.16) | 42.98 (35.90–50.07) | 68 (11.25) | 47.56 (35.33–59.78) | 148 (12.62) | 40.96 (35.33–59.78) | |||
| no | 1560 (87.83) | 48.29 (45.64–50.95) | 536 (88.74) | 48.69 (44.28–53.10) | 1024 (87.37) | 48.08 (44.28–53.10) | ||||
| 0.15 | 0.85 | 0.08 | ||||||||
| Digestive diseases | yes | 589 (33.16) | 43.98 (39.76–48.19) | 186 (3.79) | 45.39 (37.80–52.98) | 403 (34.38) | 43.30 (37.80–52.98) | |||
| no | 1187 (66.83) | 49.47 (46.43–52.50) | 418 (69.25) | 50.01 (44.74–55.28) | 769 (65.61) | 49.17 (44.74–55.28) | ||||
| 0.04 | 0.31 | 0.05 | ||||||||
Notes.
Household income were categorized into 4 groups based on the 25th, 50th, and 75th percentiles-poor (0-1,575 RMB), near poor (1,575-4,504 RMB), middle income (4,504-24,820 RMB), and rich (above 24,820 RMB).
The urban-rural disparity of prevalence in hypertension
Prevalence of HBP was 47.6% (95% CI [45.2%–50.1%]) in total and it was close between urban and rural population (48.6% vs 47.2%). The prevalence was higher among those who were overweight, overall and central obese, with lower physical activity, smoke quitters, with diabetes and dyslipidemia and among older rural subjects. Table 2 shows the prevalence of HBP stratified by sex in both urban and rural areas. Results of cross-tabulation shows that HBP was more prevalent among both males and females in rural and urban areas who had higher BMI levels or central obesity; among urban males who quit smoke or had diabetes; among urban females who were under middle school education, with low physical activity, quit smoke, with diabetes, dyslipidemia, or psychiatric problems; among rural males who were older, smoke quitters, with diabetes or dyslipidemia; among rural females who had low physical activity, with dyslipidemia or without digestive diseases.
Table 2. Prevalence of hypertension across the explanatory variables.
| Urban | Rural | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Male | Female | Male | Female | ||||||||||
| Variable | Catergory | N(%) | Prevalence (95% CI) | P | N(%) | Prevalence (95% CI) | P | N(%) | Prevalence (95% CI) | P | N(%) | Prevalence (95% CI) | P |
| Age | 60-69 | 173 (61.34) | 50.27 (42.74–57.81) | 219 (68.12) | 42.78 (35.50–50.05) | 434 (71.38) | 43.12 (38.50–47.75) | 407 (72.16) | 45.85 (41.15-50.55) | ||||
| 70-79 | 93 (32.97) | 52.91 (43.02–62.79) | 85 (26.39) | 52.24 (41.12–63.35) | 148 (24.34) | 46.98 (39.41–54.55) | 134 (23.75) | 56.42 (47.90–64.94) | |||||
| 80- | 16 (5.67) | 61.12 (34.42–87.82) | 18 (5.60) | 48.93 (28.81–69.06) | 26 (4.276) | 70.53 (53.48–87.58) | 23 (4.78) | 56.76 (36.30–77.22) | |||||
| 0.56 | 0.23 | 0.01 | 0.05 | ||||||||||
| Living with a partner | yes | 238 (84.39) | 50.71 (44.40–57.02) | 216 (67.87) | 45.15 (37.65–52.64) | 510 (83.88) | 44.84 (40.32–49.37) | 418 (74.11) | 46.62 (41.81-51.43) | ||||
| no | 44 (15.62) | 57.35 (41.79–72.91) | 106 (32.91) | 46.64 (35.77–57.50) | 98 (16.11) | 48.50 (36.53–60.47) | 146 (25.88) | 55.82 (47.84–63.80) | |||||
| 0.34 | 0.81 | 0.57 | 0.05 | ||||||||||
| Literacy | uneducated | 25 (8.87) | 40.51 (20.75–60.27) | 107 (33.22) | 47.76 (36.71–58.82) | 106 (17.43) | 44.59 (34.73–54.45) | 329 (58.33) | 50.55 (45.27–55.83) | ||||
| primary school | 148 (52.48) | 56.75 (48.41–65.09) | 133 (41.34) | 50.99 (42.25–59.73) | 351 (57.73) | 43.50 (37.64–49.36) | 206 (36.52) | 48.41 (40.94–55.88) | |||||
| middle school and above | 109 (38.65) | 47.48 (36.44–58.52) | 82 (25.46) | 34.32 (23.45–45.19) | 151 (24.83) | 50.90 (41.94–59.86) | 29 (5.141) | 35.22 (16.34–54.10) | |||||
| 0.15 | 0.03 | 0.35 | 0.33 | ||||||||||
| Houshold income | poor | 45 (15.95) | 53.96 (39.37–68.55) | 57 (17.71) | 46.75 (30.92–62.58) | 169 (27.79) | 45.16 (37.35–52.97) | 175 (31.28) | 49.82 (41.80–57.84) | ||||
| near poor | 42 (14.89) | 44.43 (32.51–56.36) | 52 (16.14) | 48.17 (32.35–63.98) | 206 (33.88) | 46.48 (38.60–54.36) | 177 (31.38) | 48.14 (40.44–55.85) | |||||
| middle income | 67 (23.75) | 56.58 (44.33–68.84) | 82 (25.46) | 46.57 (35.00–58.14) | 153 (25.16) | 43.76 (36.21–51.31) | 141 (25.00) | 49.75 (41.59–57.91) | |||||
| rich | 128 (45.39) | 50.96 (40.46–61.45) | 131 (4.68) | 43.73 (35.14–52.32) | 80 (13.15) | 46.60 (34.98–58.23) | 71 (12.58) | 47.94 (36.86–59.03) | |||||
| 0.46 | 0.90 | 0.96 | 0.98 | ||||||||||
| Insured | yes | 267 (94.68) | 49.45 (42.91–55.98) | 282 (87.57) | 45.16 (39.43–50.90) | 570 (93.75) | 45.15 (40.82–49.48) | 513 (9.957) | 49.91 (45.90–53.91) | ||||
| no | 15 (5.319) | 77.36 (53.07–101.6) | 40 (12.42) | 49.76 (31.24–68.29) | 38 (6.25) | 50.03 (35.01–65.06) | 51 (9.43) | 40.21 (24.23–56.19) | |||||
| 0.04 | 0.56 | 0.50 | 0.19 | ||||||||||
| BMI | low | 20 (7.92) | 27.07 (7.151–47.00) | 18 (5.60) | 42.41 (14.52–70.30) | 40 (6.58) | 31.24 (15.55–46.92) | 51 (9.43) | 30.65 (19.01–42.30) | ||||
| normal | 137 (48.58) | 43.98 (34.64–53.32) | 107 (33.22) | 32.17 (23.05–41.28) | 368 (6.526) | 37.52 (32.18–42.86) | 257 (45.56) | 39.78 (33.48–46.07) | |||||
| high | 103 (36.52) | 60.06 (48.57–71.54) | 144 (44.72) | 49.60 (40.78–58.42) | 140 (23.26) | 61.63 (52.89–70.38) | 167 (29.69) | 56.66 (48.85–64.46) | |||||
| obesity | 22 (7.81) | 76.79 (61.41–92.16) | 53 (16.45) | 65.41 (52.33–78.48) | 60 (9.87) | 68.28 (57.38–79.19) | 89 (15.78) | 74.17 (64.34–83.99) | |||||
| <0.01 | <0.01 | <0.01 | <0.01 | ||||||||||
| Central obesity | yes | 119 (42.19) | 38.92 (28.97–48.88) | 69 (21.42) | 30.49 (17.82–43.15) | 341 (56.85) | 35.82 (30.26–41.38) | 170 (3.141) | 35.02 (28.14–41.90) | ||||
| no | 163 (57.81) | 60.59 (52.62–68.56) | 253 (78.57) | 50.08 (43.43–56.73) | 267 (43.91) | 58.08 (51.19–64.97) | 394 (69.85) | 55.18 (50.28–60.08) | |||||
| <0.01 | <0.01 | <0.01 | <0.01 | ||||||||||
| Physical activity | none | 36 (12.76) | 50.99 (32.16–69.83) | 34 (1.559) | 75.52 (59.09–91.94) | 79 (12.99) | 50.83 (37.34–64.33) | 76 (13.47) | 57.04 (44.73–69.35) | ||||
| light | 89 (31.56) | 47.60 (37.09–58.11) | 101 (31.36) | 51.50 (41.92–61.09) | 150 (24.67) | 53.07 (44.33–61.81) | 139 (24.64) | 54.91 (45.52–64.30) | |||||
| moderate | 86 (3.50) | 56.42 (43.31–69.54) | 139 (43.16) | 41.84 (32.58–51.10) | 122 (2.657) | 45.29 (34.81–55.77) | 164 (29.78) | 49.65 (41.97–57.33) | |||||
| vigrous | 71 (25.17) | 51.47 (40.75–62.19) | 48 (14.96) | 24.25 (14.08–34.42) | 257 (42.26) | 39.29 (33.24–45.34) | 185 (32.81) | 40.72 (33.52–47.93) | |||||
| 0.64 | <0.01 | 0.08 | 0.04 | ||||||||||
| Drink | never | 72 (25.53) | 55.27 (42.83–67.70) | 264 (81.98) | 46.12 (39.49–52.74) | 181 (29.76) | 44.99 (36.88–53.10) | 427 (75.79) | 50.47 (45.88–55.05) | ||||
| quit | 59 (2.92) | 53.41 (38.68–68.14) | 24 (7.45) | 50.55 (29.60–71.49) | 106 (17.43) | 45.15 (35.18–55.12) | 48 (8.52) | 51.19 (36.99–65.40) | |||||
| yes | 151 (53.54) | 49.29 (40.37–58.22) | 34 (1.56) | 38.45 (22.20–54.71) | 321 (52.79) | 45.86 (40.23–51.49) | 89 (15.78) | 40.34 (28.45–52.23) | |||||
| 0.69 | 0.53 | 0.97 | 0.23 | ||||||||||
| Smoke | never | 52 (18.43) | 44.86 (32.08–57.65) | 279 (86.64) | 45.08 (39.09–51.07) | 93 (15.29) | 44.83 (33.10–56.56) | 499 (88.47) | 49.30 (44.97–53.63) | ||||
| quit | 88 (31.25) | 65.01 (54.85–75.17) | 17 (5.28) | 69.18 (43.14–95.21) | 212 (34.86) | 58.02 (50.77–65.26) | 26 (4.70) | 40.67 (21.21–60.14) | |||||
| yes | 142 (5.35) | 45.31 (35.86–54.77) | 26 (8.745) | 35.92 (19.14–52.71) | 303 (49.83) | 36.72 (30.67–42.76) | 39 (6.91) | 50.86 (36.17–65.55) | |||||
| <0.01 | 0.03 | <0.01 | 0.73 | ||||||||||
| Diabetes | yes | 37 (13.12) | 69.79 (54.72–84.86) | 62 (19.25) | 72.98 (61.14–84.82) | 49 (8.592) | 63.44 (48.80–78.09) | 68 (12.56) | 53.50 (41.72–65.28) | ||||
| no | 245 (86.87) | 49.17 (42.57–55.77) | 260 (8.75) | 39.70 (33.15–46.25) | 559 (91.94) | 43.98 (39.40–48.56) | 496 (87.94) | 48.42 (44.18–52.66) | |||||
| 0.01 | <0.01 | 0.02 | 0.35 | ||||||||||
| Dyslipidemia | yes | 78 (27.65) | 58.34 (47.51–69.18) | 104 (32.29) | 61.31 (50.26–72.36) | 108 (17.76) | 66.43 (57.23–75.63) | 104 (18.43) | 64.90 (54.90–74.90) | ||||
| no | 204 (72.34) | 48.99 (41.66–56.32) | 218 (67.71) | 38.20 (31.33–45.07) | 500 (82.23) | 41.13 (36.34–45.93) | 460 (81.56) | 45.67 (41.40–49.94) | |||||
| 0.06 | <0.01 | <0.01 | <0.01 | ||||||||||
| Lung diseases | yes | 62 (21.98) | 53.73 (41.02–66.44) | 45 (13.97) | 51.08 (34.83–67.33) | 134 (22.39) | 46.70 (37.51–55.88) | 96 (17.21) | 47.20 (37.78–56.62) | ||||
| no | 220 (78.14) | 51.19 (43.90–58.48) | 277 (86.24) | 44.84 (38.55–51.13) | 474 (77.96) | 45.13 (40.49–49.77) | 468 (82.97) | 49.36 (45.10–53.62) | |||||
| 0.69 | 0.43 | 0.73 | 0.63 | ||||||||||
| Psychiatric problem | yes | 8 (2.84) | 30.37 (-1.75-62.51) | 10 (3.16) | 72.86 (46.70–99.02) | 10 (1.64) | 65.38 (33.74–97.02) | 19 (3.37) | 53.59 (30.20–76.98) | ||||
| no | 274 (97.16) | 52.39 (45.96–58.81) | 312 (96.89) | 44.68 (38.52–50.84) | 598 (98.35) | 45.08 (40.77–49.39) | 545 (96.63) | 48.87 (44.87–52.86) | |||||
| 0.22 | <0.01 | 0.21 | 0.70 | ||||||||||
| Kidney diseases | yes | 36 (12.76) | 45.80 (30.56–61.05) | 32 (9.94) | 49.44 (30.19–68.70) | 91 (14.96) | 41.65 (29.88–53.42) | 57 (1.16) | 39.84 (26.05–53.62) | ||||
| no | 246 (87.23) | 52.55 (46.11–58.99) | 290 (9.62) | 45.23 (39.02–51.45) | 517 (85.32) | 46.17 (41.50–50.84) | 507 (89.89) | 50.06 (45.89–54.23) | |||||
| 0.31 | 0.65 | 0.38 | 0.13 | ||||||||||
| Digestive diseases | yes | 78 (27.65) | 47.09 (35.64–58.54) | 108 (33.54) | 44.17 (33.13–55.21) | 175 (28.78) | 44.77 (36.96–52.57) | 228 (4.425) | 42.13 (35.74–48.52) | ||||
| no | 204 (72.34) | 53.51 (45.43–61.59) | 214 (66.45) | 46.46 (39.93–52.99) | 433 (71.21) | 45.77 (40.79–50.75) | 336 (59.57) | 53.59 (48.64–58.54) | |||||
| 0.34 | 0.68 | 0.78 | 0.01 | ||||||||||
The urban-rural disparity of risk factors associated with hypertension
Table 3 shows results of regression analysis of impact factors associated with HBP prevalence. Age, literacy, BMI level, physical activity, smoking, and having comorbidities of diabetes and dyslipidemia were associated with HBP.
Table 3. Associated risk factors of hypertension in rural and urban areas.
| Urban | Rual | ||||
|---|---|---|---|---|---|
| Male | Female | Male | Female | ||
| Variable | Category | OR(95% CI) | OR(95% CI) | OR(95% CI) | OR(95% CI) |
| Age | 70-79 | 1.124 (0.680–1.857) | 1.074 (0.665–1.734) | 1.343 (0.941–1.916) | **1.934 (1.282–2.917) |
| 80- | 1.551 (0.388–6.204) | 0.900 (0.365–2.220) | **3.695 (1.688–8.091) | 2.175 (0.825–5.734) | |
| 60-69 | 1 | 1 | 1 | 1 | |
| Living with a partner | no | 1.994 (0.898–4.431) | 0.945 (0.460–1.944) | 1.255 (0.789–1.997) | 1.499 (0.930–2.414) |
| yes | 1 | 1 | 1 | 1 | |
| Literacy | uneducated | 0.605 (0.230–1.591) | 0.875 (0.494–1.549) | 1.054 (0.670–1.658) | 1.141 (0.758–1.719) |
| middle school and above | *0.475 (0.275–0.819) | *0.326 (0.144–0.738) | 1.254 (0.784–2.006) | 0.558 (0.216–1.438) | |
| primary school | 1 | 1 | 1 | 1 | |
| Household income | poor | 0.696 (0.324–1.497) | 1.246 (0.571–2.720) | 1.169 (0.739–1.849) | 1.002 (0.606–1.656) |
| near poor | 0.671 (0.341–1.319) | 1.692 (0.722–3.964) | 1.114 (0.720–1.723) | 0.949 (0.565–1.595) | |
| rich | 0.602 (0.338–1.074) | 1.217 (0.710–2.085) | 1.031 (0.586–1.814) | 0.890 (0.451–1.755) | |
| middle income | 1 | 1 | 1 | 1 | |
| Insured | no | 2.460 (0.661–9.153) | 0.856 (0.416–1.761) | 1.378 (0.748–2.536) | 0.543 (0.259–1.140) |
| yes | 1 | 1 | 1 | 1 | |
| BMI | low | 0.400 (0.166–0.961) | 2.115 (0.462–9.685) | 0.736 (0.308–1.760) | 0.607 (0.336–1.098) |
| high | 1.572 (0.763–3.236) | 1.626 (0.898–2.946) | **2.135 (1.272–3.582) | **1.831 (1.187–2.825) | |
| obesity | *3.319 (1.154–9.546) | 2.197 (1.088–4.439) | **2.398 (1.374–4.184) | **3.707 (1.933–7.109) | |
| normal | 1 | 1 | 1 | 1 | |
| Central obesity | no | 1.556 (0.753–3.216) | 1.831 (0.742–4.515) | 1.425 (0.874–2.324) | 1.409 (0.917–2.165) |
| yes | 1 | 1 | 1 | 1 | |
| Physical activity | light | 0.758 (0.319–1.802) | **0.226 (0.105–0.486) | 0.978 (0.521–1.836) | 1.138 (0.614–2.107) |
| moderate | 1.219 (0.496–2.996) | **0.185 (0.076–0.451) | 0.901 (0.465–1.743) | 0.854 (0.450–1.620) | |
| vigorous | 1.044 (0.451–2.414) | **0.078 (0.034–0.180) | 0.668 (0.363–1.230) | 0.733 (0.387–1.387) | |
| none | 1 | 1 | 1 | 1 | |
| Drink | quit | 0.978 (0.381–2.512) | 1.012 (0.430–2.378) | 0.785 (0.444–1.389) | 0.860 (0.423–1.748) |
| yes | 0.871 (0.459–1.652) | 0.842 (0.439–1.615) | 1.160 (0.745–1.805) | 0.640 (0.376–1.087) | |
| never | 1 | 1 | 1 | 1 | |
| Smoke | quit | *2.432 (1.194–4.950) | 2.314 (1.112–4.813) | 1.133 (0.582–2.208) | 0.736 (0.316-1.712) |
| yes | 1.420 (0.800–2.520) | 0.776 (0.360–1.673) | 0.760 (0.423–1.364) | 1.571 (0.820–3.009) | |
| never | 1 | 1 | 1 | 1 | |
| Diabetes | yes | *2.836 (1.072–7.501) | **2.960 (1.560–5.619) | 1.266 (0.654–2.451) | 0.721 (0.396–1.313) |
| no | 1 | 1 | 1 | 1 | |
| Dyslipidemia | yes | 1.216 (0.656–2.254) | *2.148 (1.168–3.949) | **2.158 (1.305–3.568) | 1.685 (0.922-3.078) |
| no | 1 | 1 | 1 | 1 | |
| Lung diseases | yes | 1.139 (0.655–1.982) | 0.950 (0.459–1.967) | 1.103 (0.711–1.711) | 1.058 (0.676–1.655) |
| no | 1 | 1 | 1 | 1 | |
| Kidney diseases | yes | 0.524 (0.250–1.099) | 1.036 (0.463–2.317) | 0.686 (0.422–1.113) | 0.718 (0.393–1.310) |
| no | 1 | 1 | 1 | 1 | |
| Digestive diseases | yes | 0.695 (0.382–1.265) | 0.684 (0.383–1.222) | 1.150 (0.783–1.688) | 0.676 (0.465–0.981) |
| no | 1 | 1 | 1 | 1 | |
| Psychiatric problem | yes | 0.241 (0.043–1.347) | 1.973 (0.341–11.41) | 1.470 (0.384–5.626) | 1.486 (0.618–3.573) |
| no | 1 | 1 | 1 | 1 | |
Notes.
- OR
- Odds Ratio
- 95% CI
- 95% Confidence interval for odds ratio
P < 0.05.
P < 0.01.
In urban areas, HBP prevalence was associated with the education of males and females. Males and females who had middle school education and above had 62.4% (OR = 0.376, 95% CI [0.275–0.819]) and 67.4% (OR = 0.326, 95% CI [0.144–0.738]) lower odds of HBP. Urban males who were obese had 2.319 times (OR = 3.319, 95% CI [1.154–9.546]) higher odds of HBP, those who quit smoking had 1.432 times higher odds (OR = 2.432, 95% CI [1.194–4.950]) of HBP, and those who had diabetes had 1.836 times higher odds (OR = 2.836, 95% CI [1.072–7.501]) of HBP. Urban females had light, moderate, and vigorous physical activity had respectively 87.4% (OR = 0.226 95% CI [0.105–0.486]), 91.5% (OR = 0.185, 95% CI [0.076–0.451]), and 92.2% (OR = 0.078 95% CI [0.034–0.180]) lower odds of HBP than those who never exercise. Urban females who had diabetes and dyslipidemia had respectively 1.96 times (OR = 2.960, 95% CI [1.560–5.619]) and 1.148 times (OR = 2.148, 95% CI [1.168–3.949]) higher odd of HBP.
In rural areas, males who were above 80 years old had 2.695 times higher odds (OR = 3.695, 95% CI [1.688–8.091]) of HBP than those who were 60 to 69. Rural females who were 70 to 79 had 93.4% times higher odds (OR = 1.934, 95% CI [1.282–2.917]) of HBP than those who were 60 to 69. Among rural males and females, those who were overweight had respectively 1.135 times (OR = 2.135 95% CI [1.272–3.582]) and 83.1% times (OR = 1.831, 95% CI [1.187–2.825]) higher odds of HBP, and those who were obese had respectively 1.398 times (OR = 2.398, 95% CI [1.374–4.184]) and 1.707 times (OR = 3.707 95% CI [1.933–7.109]) higher odds of HBP than those had normal weight. Also among rural males, those who had dyslipidemia had 1.158 times higher odds (OR = 2.158, 95% CI [1.305–3.568]) of HBP than others.
Discussion
Based on the nationally representative data from CHARLS, this study explored the disparity of prevalence and risk factors of hypertension among the elderly in China. Our results showed that the prevalence of hypertension was close in urban and rural, which indicated a slightly higher prevalence of HBP in urban areas without statistical significance. Factors associated with HBP were different between urban and rural areas. In urban areas, hypertension was significantly associated with literacy and diabetes in both genders, high BMI level and smoke quitters in males, and physical activity and dyslipidemia in females. In rural areas, hypertension was significantly associated with older age, higher BMI level in both males and females, and dyslipidemia in males.
Results indicated that HBP is highly prevalent in Chinese elderly. Prevalence among the elderly was comparable to a national study in 60–79 years old (48.5% vs 50.1%) (Lu et al., 2017) and other investigations in Southern China (Xu, Lai & Fang, 2016; Zhou et al., 2018). However, it was lower than that resulted in a national survey (58.9% in 2012) as well as studies in the Northern areas (Yang et al., 2016; Zhang et al., 2017). The prevalence of hypertension of elderly reported in our study was less than some developed-countries such as Korea (49.7%–64.5%) (Kang et al., 2019) and Japan (57.3%–79.9%) in Asia, as well as other high-income countries such as United States(61.7%) (Fang et al., 2018) and German (56.0%–62.6%) (Labeit et al., 2012). However, it exceeded other middle-income-countries such as Malaysia (Abdul-Razak et al., 2016) and Thailand (Aekplakorn et al., 2012).
The prevalence of hypertension was close in urban and rural areas among the elderly. The prevalence gap between urban and rural areas has gradually narrowed. In 2010, a study of CHARLS found prevalence of urban and rural areas were, respectively, 44.3% and 55.7% (Yin et al., 2016). This finding was consistent with studies reported that seldom disparity (Wang et al., 2018b; Zhou et al., 2018) comparing urban and rural areas. However the higher prevalence in rural was found in a large population screening study in overall (Lu et al., 2017) as well as northeastern part of China (Wang et al., 2018a), while HBP was more prevalent in urban areas in the national survey and national study of prevalence in hypertension (Yang et al., 2017). The results were also found in research from Central (Chen & Yuan, 2018), and Southern China (Hu et al., 2017). It may be caused by sample selection of a certain area. The result is consistent with international urban-rural disparity in hypertension prevalence. The urban-rural disparity of prevalence in hypertension was smaller in upper-middle-income countries (Argentina, Brazil, Malaysia, et al.) (48.5% vs. 50.9%) and lower-middle-income countries (China, Colombia, Iran) (39.9% vs. 39.8%) than high-income (Canada, Sweden, United Arab Emirates) (39.2% vs. 44.6%) and low income countries (Bangladesh, India, Pakistan, Zimbabwe) (38.7% vs. 26.4%) (Chow et al., 2013).
Common risk factors for hypertension included individual characteristics, social economic status, behavioral health, and comorbidities, etc. Due to the significant differences in culture, social background and economy, the hypertension prevalence and its risk factors in urban and rural areas should be different. We designed this study based on national data in China. We also compared differences of risk factors by sex because different health behavior may occur between males and females. Our study showed that in urban areas, obesity and smoking were risk factors for males. Rapid increases in the rates of obesity and overweight are widely documented from urban and rural areas in the poorest countries of sub-Saharan Africa and South Asia to populations in countries with higher income levels (Popkin, Adair & Ng, 2012). Mean BMI in Chinese adults increased from 22.7 kg/m2 in 2004 to 23.7 kg/m2 (23.6–23.9) in 2010 (Jiang et al., 2015). A study indicated that faster BMI growth rates in the high-social economic status males than females in China (Wang et al., 2015). Urban females who usually have a higher level of education or higher family income are the low risk group for obesity (Wang et al., 2012). A study showed that in contrast to males and rural females, living standard improvement had a negative effect on weight for urban females. It means that urban females pay more attention on physical activity and weights than others (Zhao & Zheng, 2019). There are usually much more males who smoke than females (Li et al., 2016b). A study showed Chinese men smoke more than a third of the world’s cigarettes, following a large increase first in urban then rural usage (Chen et al., 2015). Those who come about health problems are more likely to tend to quit smoke. Above middle school education level was a protective factor of HBP for both urban males and females. Lower levels of education may lead to unhealthy lifestyles and lack of sufficient knowledge to prevent hypertension. A similar finding was shown in a study in Jilin (Wang et al., 2018a). As there is more access to better health service, more people who have high level of education are attracted to urban areas. Having comorbidity of diabetes was risk factor of HBP for both urban males and females, because living in urban area was positively associated with metabolic syndrome risk (Li et al., 2016a; Zuo et al., 2009). The blood lipid profile was less favorable in the urban area. These pronounced urban-rural differences may be related to urbanization, the associated nutrition transition and to access to health care (Pan et al., 2016).
In rural areas, older age and high BMI level were common risk factors for both males and females. Older age was positively associated with rural patients, possibly because the allocation of health resources is uneven between urban and rural areas and the provision of healthcare service is inadequate in rural areas in China (Dai, 2015). Hypertensive patients in urban areas have more access to health service in higher level hospitals which may weaken the relationship of HBP with aging. Research of recent years indicated rural Chinese not only had higher initial BMI status, but also their BMI increased more than urban Chinese. With the rapid development of economy and urbanization in rural areas, rural regions have remarkable changes in lifestyle compared with urban regions (Huang et al., 2018).
Some limitations of this study should be considered. First, our study was a cross-sectional study, which restricted the interpretation of observed associations in terms of cause and effect. Longitude research is required for further investigation of these findings. Second, hypertension diagnosis was not done by 24-hour ambulatory blood pressure monitoring, (O’Brien et al., 2000) which could be the best diagnosis way but would take longer time and not be cost-inefficient in a national survey. In addition, hypertension was associated with multiple risk factors. Most of the risk factors but not all of them were included in this study, such as salt intake which was not investigated in this survey.
Conclusions
Our findings indicated that hypertension is highly prevalent in the elderly population in China. The prevalence are about the same among urban and rural residents, but their risk factors vary from each other. In urban, hypertension was significantly associated with literacy and diabetes in both genders, high BMI level and smoke quitters in males, and physical activity and dyslipidemia in females. In rural areas, hypertension was significantly associated with older age, higher BMI level in both males and females, and dyslipidemia in males. These findings will provide the baseline information to develop more effective approaches to enhance current prevention and control management of hypertension. In this regard, we suggest taking the disparity in fields into consideration when implementing elderly hypertension intervention.
Supplemental Information
Funding Statement
This study was funded by the National Natural Science Foundation of China (71473097). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Additional Information and Declarations
Competing Interests
The authors declare there are no competing interests.
Author Contributions
Hongxun Song conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Da Feng, Ruoxi Wang and Zhanchun Feng conceived and designed the experiments, authored or reviewed drafts of the paper, approved the final draft.
Jian Yang, Yuanqing Li, Junliang Gao and Zi Wang performed the experiments, approved the final draft, language revision.
Ziqi Yan, Chengxu Long and Jiawei Zhou analyzed the data, approved the final draft, language revision.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers): The original CHARLS was approved by the Ethical Review Committee (IRB) at Peking University (IRB00001052–11015).
Data Availability
The following information was supplied regarding data availability: The raw data is in the Supplemental File. The datasets analyzed in this study are publicly available at the China Health and Retirement Longitudinal Study (CHARLS) site: http://charls.pku.edu.cn/en/page/data/2015-charls-wave4.
References
- Abdul-Razak et al. (2016).Abdul-Razak S, Daher AM, Ramli AS, Ariffin F, Mazapuspavina MY, Ambigga KS, Miskan M, Abdul-Hamid H, Mat-Nasir N, Nor-Ashikin MNK, Ng KK, Nawawi H, Yusoff K, Investigators R. Prevalence, awareness, treatment, control and socio demographic determinants of hypertension in Malaysian adults. BMC Public Health. 2016;16:351. doi: 10.1186/S12889-016-3008-Y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Aekplakorn et al. (2012).Aekplakorn W, Sangthong R, Kessomboon P, Putwatana P, Inthawong R, Taneepanichskul S, Sritara P, Sangwatanaroj S, Chariyalertsak S, IV NHESN Changes in prevalence, awareness, treatment and control of hypertension in Thai population, 2004-2009: Thai National Health Examination Survey III-IV. Journal of Hypertension. 2012;30:1734–1742. doi: 10.1097/HJH.0b013e3283568158. [DOI] [PubMed] [Google Scholar]
- Bi et al. (2015).Bi YF, Jiang Y, He J, Xu Y, Wang LM, Xu M, Zhang M, Li YC, Wang TG, Dai M, Lu JL, Li M, Chen CS, Lai SH, Wang WQ, Wang LH, Ning G. Status of cardiovascular health in Chinese adults. Journal of the American College of Cardiology. 2015;65:1013–1025. doi: 10.1016/j.jacc.2014.12.044. [DOI] [PubMed] [Google Scholar]
- Chen & Lu (2004).Chen C, Lu FC. Department of Disease Control Ministry of Health PRC The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomedical and Environmental Sciences. 2004;17:1–36. [PubMed] [Google Scholar]
- Chen & Yuan (2018).Chen CY, Yuan ZP. Prevalence and risk factors for prehypertension and hypertension among adults in Central China from 2000-2011. Clinical and Experimental Hypertension. 2018;40:734–743. doi: 10.1080/10641963.2018.1431252. [DOI] [PubMed] [Google Scholar]
- Chen et al. (2015).Chen ZM, Peto R, Zhou MG, Iona A, Smith M, Yang L, Guo Y, Chen YP, Bian Z, Lancaster G, Sherliker P, Pang ST, Wang H, Su H, Wu M, Wu XP, Chen JS, Collins R, Li LM, Collabo CKBC. Contrasting male and female trends in tobacco-attributed mortality in China: evidence from successive nationwide prospective cohort studies. Lancet. 2015;386:1447–1456. doi: 10.1016/S0140-6736(15)00340-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chow et al. (2013).Chow CK, Teo KK, Rangarajan S, Islam S, Gupta R, Avezum A, Bahonar A, Chifamba J, Dagenais G, Diaz R, Kazmi K, Lanas F, Wei L, Lopez-Jaramillo P, Lu FH, Ismail NH, Puoane T, Rosengren A, Szuba A, Temizhan A, Wielgosz A, Yusuf R, Yusufali A, Mckee M, Liu LS, Mony P, Yusuf S, Rural PPU. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. Jama-Journal of the American Medical Association. 2013;310:959–968. doi: 10.1001/jama.2013.184182. [DOI] [PubMed] [Google Scholar]
- Dai (2015).Dai B. The old age health security in rural China: where to go? International Journal for Equity in Health. 2015;14:119. doi: 10.1186/s12939-015-0224-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fang et al. (2018).Fang J, Gillespie C, Ayala C, Loustalot F. Prevalence of self-reported hypertension and antihypertensive medication use among adults aged >= 18 Years—United States, 2011-2015. Morbidity and Mortality Weekly Report. 2018;67:219–224. doi: 10.15585/mmwr.mm6707a4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hu et al. (2017).Hu LH, Huang X, You CJ, Li JX, Hong K, Li P, Wu YQ, Wu QH, Bao HH, Cheng XS. Prevalence and risk factors of prehypertension and hypertension in Southern China. PLOS ONE. 2017;12:e0170238. doi: 10.1371/journal.pone.0170238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang et al. (2018).Huang FF, Zhang MQ, Li Y, Li Z, Fang JY, Guo KY. The longitudinal trajectory of body mass index in the Chinese population: a latent growth curve analysis. PLOS ONE. 2018;13:e0207845. doi: 10.1371/journal.pone.0207845. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang et al. (2017).Huang XB, Tang WW, Liu Y, Hu R, Ouyang LY, Liu JX, Li XJ, Yi YJ, Wang TD, Zhao SP. Prevalence of diabetes and unrecognized diabetes in hypertensive patients aged 40 to 79 years in southwest China. PLOS ONE. 2017;12:e0170250. doi: 10.1371/journal.pone.0170250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jiang et al. (2015).Jiang Y, Xu Y, Bi YF, Wang LM, Zhang M, Zhou MG, Li YC, Xu M, Lu JL, Wang TE, Dai M, Zhang D, Wang WQ, Ning G, Wang LH, Zhao WH. Prevalence and trends in overweight and obesity among Chinese adults in 2004-10: data from three nationwide surveys in China. Lancet. 2015;386:77–77. [Google Scholar]
- Kang et al. (2019).Kang SH, Kim SH, Cho JH, Yoon CH, Hwang SS, Lee HY, Youn TJ, Chae IH, Kim CH. Prevalence, awareness, treatment, and control of hypertension in Korea. Scientific Reports. 2019;9:10970. doi: 10.1038/S41598-019-46965-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Labeit et al. (2012).Labeit AM, Klotsche J, Pieper L, Pittrow D, Einsle F, Stalla GK, Lehnert H, Silber S, Zeiher AM, Marz W, Wehling M, Wittchen HU. Changes in the prevalence, treatment and control of hypertension in Germany? A clinical-epidemiological study of 50,000 primary care patients. PLOS ONE. 2012;7:e52229. doi: 10.1371/journal.pone.0052229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lewington et al. (2016).Lewington S, Lacey B, Clarke R, Guo Y, Kong XL, Yang L, Chen YP, Bian Z, Chen JS, Meng JH, Xiong YP, He TY, Pang ZC, Zhang S, Collins R, Peto R, Li LM, Chen ZM. China Kadoorie Biobank C The burden of hypertension and associated risk for cardiovascular mortality in China. Jama Internal Medicine. 2016;176:524–532. doi: 10.1001/jamainternmed.2016.0190. [DOI] [PubMed] [Google Scholar]
- Li et al. (2017a).Li JJ, Shi LY, Li SX, Xu LZ, Qin W, Wang H. Urban-rural disparities in hypertension prevalence, detection, and medication use among Chinese adults from 1993 to 2011. International Journal for Equity in Health. 2017a;16:50. doi: 10.1186/s12939-017-0545-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li et al. (2016a).Li R, Li WC, Lun ZJ, Zhang HP, Sun Z, Kanu JS, Qiu S, Cheng Y, Liu YW. Prevalence of metabolic syndrome in mainland china: a meta-analysis of published studies. BMC Public Health. 2016a;16:296. doi: 10.1186/S12889-016-2870-Y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li et al. (2016b).Li SS, Meng LH, Chiolero A, Ma CW, Xi B. Trends in smoking prevalence and attributable mortality in China, 1991-2011. Preventive Medicine. 2016b;93:82–87. doi: 10.1016/j.ypmed.2016.09.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li et al. (2017b).Li YC, Yang L, Wang LM, Zhang M, Huang ZJ, Deng Q, Zhou MG, Chen ZM, Wang LH. Burden of hypertension in China: a nationally representative survey of 174,621 adults. International Journal of Cardiology. 2017b;227:516–523. doi: 10.1016/j.ijcard.2016.10.110. [DOI] [PubMed] [Google Scholar]
- Li, Zhao & Hu (2016).Li Z, Zhao Y, Hu X. The association between multimorbidity and medication non-adherence in elderly with hypertension in Western China. Hu li za zhi The Journal of Nursing. 2016;63:65–75. doi: 10.6224/jn.63.5.65. [DOI] [PubMed] [Google Scholar]
- Lu et al. (2017).Lu JP, Lu Y, Wang XC, Li XY, Linderman GC, Wu CQ, Cheng XY, Mu L, Zhang HB, Liu JM, Su M, Zhao HY, Spatz ES, Spertus JA, Masoudi FA, Krumholz HM, Jiang LX. Prevalence, awareness, treatment, and control of hypertension in China: data from 1.7 million adults in a population-based screening study (China PEACE Million Persons Project) Lancet. 2017;390:2549–2558. doi: 10.1016/S0140-6736(17)32478-9. [DOI] [PubMed] [Google Scholar]
- Mills et al. (2016).Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, Chen J, He J. Global disparities of hypertension prevalence and control a systematic analysis of population-based studies from 90 countries. Circulation. 2016;134:441–+. doi: 10.1161/Circulationaha.115.018912. [DOI] [PMC free article] [PubMed] [Google Scholar]
- O’Brien et al. (2000).O’Brien E, Coats A, Owens P, Petrie J, Padfield PL, Littler WA, De Swiet M, Mee F. Use and interpretation of ambulatory blood pressure monitoring: recommendations of the British Hypertension Society. British Medical Journal. 2000;320:1128–1134. doi: 10.1136/bmj.320.7242.1128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pan et al. (2016).Pan L, Yang Z, Wu Y, Yin RX, Liao Y, Wang J, Gao B, Zhang L, China National Survey of Chronic Kidney Disease Working G The prevalence, awareness, treatment and control of dyslipidemia among adults in China. Atherosclerosis. 2016;248:2–9. doi: 10.1016/j.atherosclerosis.2016.02.006. [DOI] [PubMed] [Google Scholar]
- Popkin, Adair & Ng (2012).Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews. 2012;70:3–21. doi: 10.1111/j.1753-4887.2011.00456.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang et al. (2012).Wang H, Wang J, Liu MM, Wang D, Liu YQ, Zhao Y, Huang MM, Liu Y, Sun J, Dong GH. Epidemiology of general obesity, abdominal obesity and related risk factors in urban adults from 33 communities of northeast china: the CHPSNE study. BMC Public Health. 2012;12:967. doi: 10.1186/1471-2458-12-967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang et al. (2017).Wang JJ, Ma JJ, Liu JQ, Zeng DDJ, Song C, Cao ZD. Prevalence and risk factors of comorbidities among hypertensive patients in China. International Journal of Medical Sciences. 2017;14:201–212. doi: 10.7150/ijms.16974. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang et al. (2018a).Wang JN, Sun W, Wells GA, Li ZB, Li TY, Wu JD, Zhang YY, Liu YY, Li LB, Yu YP, Liu YH, Qi C, Lu Y, Liu N, Yan YY, Liu LL, Hui G, Liu B. Differences in prevalence of hypertension and associated risk factors in urban and rural residents of the northeastern region of the People’s Republic of China: a cross-sectional study. PLOS ONE. 2018a;13:e0195340. doi: 10.1371/journal.pone.0195340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wang et al. (2015).Wang Y, Guo J, Lu WL, Li YL, Wang JH, Wan Z. Association of modifiable lifestyle behaviors with hypertension by various body mass index categories in tianjin province of china: a cross-sectional study. Asia-Pacific Journal of Public Health. 2015;27:Np266–Np277. doi: 10.1177/1010539512439227. [DOI] [PubMed] [Google Scholar]
- Wang et al. (2018b).Wang ZW, Chen Z, Zhang LF, Wang X, Hao G, Zhang ZG, Shao L, Tian Y, Dong Y, Zheng CY, Wang JL, Zhu ML, Weintraub WS, Gao RL. Investig CHS Status of hypertension in China results from the china hypertension survey, 2012-2015. Circulation. 2018b;137:2344–2356. doi: 10.1161/Circulationaha.117.032380. [DOI] [PubMed] [Google Scholar]
- Xu, Lai & Fang (2016).Xu L, Lai DJ, Fang Y. Spatial analysis of gender variation in the prevalence of hypertension among the middle-aged and elderly population in Zhejiang Province, China. BMC Public Health. 2016;16:447. doi: 10.1186/s12889-016-3121-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yan et al. (2014).Yan LL, Fang W, Delong E, Neal B, Peterson ED, Huang Y, Sun N, Yao C, Li X, MacMahon S, Wu Y. Population impact of a high cardiovascular risk management program delivered by village doctors in rural China: design and rationale of a large, cluster-randomized controlled trial. BMC Public Health. 2014;14:345. doi: 10.1186/1471-2458-14-345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang et al. (2017).Yang F, Qian DF, Liu XY, Univ NM. Socioeconomic disparities in prevalence, awareness, treatment, and control of hypertension over the life course in China. International Journal for Equity in Health. 2017;16:100. doi: 10.1186/s12939-017-0597-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang et al. (2016).Yang G, Ma Y, Wang SB, Su YY, Rao WW, Fu YL, Yu YQ, Kou CG. Prevalence and correlates of prehypertension and hypertension among adults in Northeastern China: a cross-sectional study. International Journal of Environmental Research and Public Health. 2016;13:82. doi: 10.3390/ijerph13010082. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yin et al. (2016).Yin MH, Augustin B, Fu Z, Yan MM, Fu A, Yin P. Geographic distributions in hypertension diagnosis, measurement, prevalence, awareness, treatment and control rates among middle-aged and older adults in China. Scientific Reports. 2016;6:37020. doi: 10.1038/srep37020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang, Wang & Joo (2017).Zhang D, Wang G, Joo H. A systematic review of economic evidence on community hypertension interventions. American Journal of Preventive Medicine. 2017;53:S121–S130. doi: 10.1016/j.amepre.2017.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang et al. (2017).Zhang FL, Guo ZN, Xing YQ, Wu YH, Liu HY, Yang Y. Hypertension prevalence, awareness, treatment, and control in northeast China: a population-based cross-sectional survey. Journal of Human Hypertension. 2017;32:54–65. doi: 10.1038/s41371-017-0003-4. [DOI] [PubMed] [Google Scholar]
- Zhao, John & Yang (2013).Zhao Y, John S, Yang G. China health and retirement longitudinal study–2011-2012 national baseline users’ guide. Peking University; Beijing: 2013. [Google Scholar]
- Zhao & Zheng (2019).Zhao YY, Zheng ZH. Do improvements of living standards lead to growth of obesity? Evidence from Chinese adults. Journal of Integrative Agriculture. 2019;18:1740–1752. doi: 10.1016/S2095-3119(19)62690-0. [DOI] [Google Scholar]
- Zhou et al. (2018).Zhou H, Wang K, Zhou XJ, Ruan SY, Gan SH, Cheng SY, Lu YA. Prevalence and gender-specific influencing factors of hypertension among Chinese Adults: A Cross-Sectional Survey Study in Nanchang, China. International Journal of Environmental Research and Public Health. 2018;15:382. doi: 10.3390/ijerph15020382. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zuo et al. (2009).Zuo H, Shi ZM, Hu XS, Wu M, Guo ZR, Hussain A. Prevalence of metabolic syndrome and factors associated with its components in Chinese adults. Metabolism-Clinical and Experimental. 2009;58:1102–1108. doi: 10.1016/j.metabol.2009.04.008. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The following information was supplied regarding data availability: The raw data is in the Supplemental File. The datasets analyzed in this study are publicly available at the China Health and Retirement Longitudinal Study (CHARLS) site: http://charls.pku.edu.cn/en/page/data/2015-charls-wave4.