Socioeconomic and lifestyle determinants of prevalence of hypertension among the elderly in rural southwest China: a structural equation modeling approach

Background: This study examines the association between socioeconomic and lifestyle factors and the prevalence of hypertension among the elderly in rural southwest China. Methods: A cross-sectional survey of 4,833 consenting adults aged ≥60 years in rural regions of Yunnan Province, China was conducted in 2017. Data on individual socioeconomic status, sleep quality, physical activity level, and family history of hypertension were collected with a standardized questionnaire. Blood pressure, fasting blood glucose, height, weight, and waist circumference were also measured. An individual socioeconomic position (SEP) index was constructed using principal component analysis. Structure equation modeling (SEM) was applied to analyze the association between socioeconomic and lifestyle factors and the prevalence of hypertension. Results: The overall prevalence of hypertension was 50.6% in the study population. The following associated factors had statistically significant effect on hypertension ： body composition, including measures of obesity and central obesity, had the greatest total effect on hypertension (0.21), followed by family history of hypertension (0.14), gender (0.08), sleep quality (-0.07), SEP (-0.06), physical inactivity (0.06), and diabetes (0.06). Body composition, SEP, and family history of hypertension had both direct and indirect effects on hypertension, whereas those of physical inactivity, diabetes, and sleep quality were directly associated with prevalence of hypertension. Gender was indirectly associated with prevalence of hypertension. Conclusion: Individual SEP, body composition, physical diabetes, and sleep quality of Future interventions to attention to individuals SEP should focus on controlling and increasing physical activity levels, and improving quality of sleep among older adults aged ≥ 60 China.

2001 and 2010 among urban residents aged ≥60 years [8] . As the population ages and prevalence grows, China is facing an increasingly serious challenge to manage the economic cost of hypertension and its complications.
Most investigations of disease risk factors focus mainly on identifying the direct effect of the studied factors on disease. It is well known that individual educational level, household income, socioeconomic position (SEP), obesity, central obesity, family history of hypertension, diabetes, and physical inactivity have strong associations with the development of hypertension [9,10] . However, hypertension is caused by the complex interplay of these various factors simultaneously, some with direct and some with indirect effects. Few studies have analyzed the indirect effect of all of these factors on hypertension and the interaction between the variables.
Epidemiologists are increasingly interested in and able to explore all these factors concurrently as a network of multiple pathways leading to disease [11] . Specifically, structure equation modeling (SEM) offers a tool to measure both direct and indirect effects of observational and latent characteristics of observational variables on risk of diseases [12] . Previous studies have employed it to identify risk factors of pre-diabetes and pre-hypertension [11,13] .
Yunnan Province, an economically disadvantaged region in southwestern China, is home to 47.4 million people, including 6.1 million adults aged ≥60 years.
Hypertension has become one of Yunnan's greatest public health challenges, imposing a considerable economic burden over the past several decades [14] . Large urban-rural gaps in hypertension prevalence existed and deserved more attention of researchers [15] . However, the literature focusing on SEP and lifestyle factors of hypertension in rural older adults aged ≥60 years in Yunnan is sparse, with prior epidemiological studies focusing on urban areas and overlooking rural communities.
The present study aims to fill this knowledge gap.
Namely, the purpose of the present study was to use an SEM approach to test a hypothesized model of socioeconomic factors (gender, age, ethnicity, and SEP) and lifestyle factors (sleep quality, physical inactivity, obesity, central obesity, diabetes, and family history of hypertension) in terms of direct and indirect effects of prevalence of hypertension among adults aged ≥60 years in rural Yunnan Province.

Study area and population
We conducted a community-based cross-sectional survey in Yunnan Province. To ensure a representative study sample, we used a multistage stratified random sampling method. First, all 129 Yunnan counties were classified into three categories, advantaged economic status, normal economic status, and economically disadvantaged, according to per capita gross domestic product (GDP) based on the 2016 Yunnan Statistical Yearbook [16] . One county was then randomly selected from each category. Second, townships in each selected county were divided into two categories according to GDP, and one township was selected from each category, for a total of six townships. Third, three villages from each township were selected using probability proportional to size (PPS) method, for a total of 18 villages. Finally, individuals aged ≥60 years were selected from each village using a simple random sampling method with random number tables. Older adults with various mental diseases, malignant tumors and acute and chronic infectious diseases were excluded.
A total of 5004 older adults aged ≥60 years were included in this study. Of these, after excluding 171 individuals with missing variables, 4833 participates were considered for the final analysis.

Data collection and measurement
Sixteen medical students from Kunming Medical University were selected as interviewers for data collection. All students participated in a training workshop before the commencement of the study to learn how to administer the questionnaire as well as how to measure height, weight, waist circumference, blood pressure (BP), and fasting blood glucose (FBG).
Each study participant who gave informed consent was interviewed by one of these trained interviewers using a pretested and structured questionnaire to collect information on demographic characteristics, socioeconomic status, sleep quality, physical activity, and family history of hypertension.
BP, FBG, height, weight, and waist circumference were measured according to standard protocols. Following American Heart Association recommendations, BP was recorded in the sitting position in the right arm supported at the level of the heart using a sphygmomanometer after five minutes of rest [17] . Three BP measurements were taken at 5 minute intervals. The final recorded measurement was the average of these three BP readings. Height was measured in centimeters with an accuracy of 0.1 cm using a standard height-measuring ruler. Weight was measured in kilograms with an accuracy of 0.1 kg with a digital scale. The participants were asked to wear light clothes, stand in right position, take feet 30 cm apart and put arms aside the body. Waist circumference was measured in centimeters with an accuracy of 0.1 cm using a measuring tape at the level of midpoint between the lower edge of the 12th costal arch and the anterior superior iliac crest.

Definitions
Hypertension was defined as systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg, use of anti-hypertension medication during the two weeks prior to the study, and/or self-reports of a diagnosis of hypertension by a healthcare professional.
Diabetes was defined as FBG ≥ 7.0mmol/l, when participants self-reported a diagnosis of diabetes by a healthcare professional, or when participants reported using anti-diabetes medication during the previous two weeks.
Body mass index (BMI) was calculated as weight (kg) divided by height squared (m 2 ).
Obesity was defined as BMI ≥ 28 kg/m 2 for both men and women. Central obesity was defined as a waist circumference of ≥ 90 cm for males and ≥ 80 cm for females, following World Health Organization (WHO) recommendations for Asian adults [18] .
Illiterate was defined as the inability to read or write a full sentence with understanding.  [19] . Each factor was scored on a 0-3 point scale for a total score of 0-21.
High scores refer to worse sleep quality, with poor sleep quality defined as a score of 6 or greater.
Physical inactivity was measured by sitting time in daily life and activity intensity during household work, in accordance with WHO guidelines [20] .Sitting time and activity intensity were used as continuous variables, and scored on a 1-3 point scale for a total score of 2-5. High scores refer to physical activity defined as a score of 3 or greater, with low scores refer to physical inactivity defined as a score of 2 or less.
Specifically, physical inactivity was classified into two levels: physical inactivity referred to sitting time of ≥4 hours in a day and light activity as a result of household work, whereas physical activity referred to sitting time of <4 hours in a day and engagement in moderate or vigorous activity during household work.
Household income, house made materials and toilet built were used as dichotomous variables, and scored on 1 or 2 points scale for a total scores of 3-6. High scores refer to good household assets defined as a scores of 5 or greater, while low scores refer to poor household assets defined as a scores of 4 or less. Poor household assets were defined as individuals with a annually household income of less than US$945 and a house made from adobe or stone and without a toilet. Good household assets were defined as individuals with a household income of more than US$945 and a house made from brick or concrete with a toilet. Good access to medical services was defined as living within a 30 minute walk to the nearest medical facility, while poor access to medical services was defined as living more than 30 minutes walking time to the nearest medical facility.
A positive family history of hypertension was defined as the presence of hypertension in at least 1 grandparent, parent, or sibling [21] .

Statistical analysis
A chi-squared test was used to compare categorical variables of socioeconomic factors(gender, age, ethnicity, education level, household assets and access to medical services) and lifestyle factors(sleep quality, physical inactivity, obesity, central obesity, In this study, SEM analyses were conducted in two stages. First, we constructed a hypothesized model which is based on the literature review of previous model proposed by Taherian et al [22] . We constructed two latent variables, including SEP and body composition. SEP incorporated educational level, household assets, and access to medical services, whereas body composition was composed of obesity and central obesity. Modification indices were used to evaluated and selected appropriated paths for the best fitted model. We calculated Goodness-of-Fit indices, including root mean square error of approximation (RMSEA), goodness-of-fit index(GFI), comparative fit index (CFI), Tacker-Lewis index (TLI), and weighted root mean square residual (WRMR), to evaluate the best fit model. Direct, indirect, and total effect were calculated and recorded. We have tested several paths and found a final model that fitted the hypothesis most appropriated. Non-significant paths were removed, specially age and ethnicity were eliminated because the paths were not significant(α=0.05) in this model.

Results
A total of 5000 individuals aged ≥60 years were involved in the survey. The response rate was 96.6% with 4833 consenting to participate. General characteristics of target participants are shown in Table 1

Discussion
To our knowledge, this is the first study to use SEM to examine the direct and indirect effects of socioeconomic and lifestyle determinants on hypertension in China. The findings indicate that body composition, SEP, and family history of hypertension have both direct and indirect effects on hypertension, while physical inactivity, diabetes, and sleep quality are directly associated with hypertension and gender is indirectly associated with hypertension.
Our study indicated noticeably higher prevalence rates of physical inactivity and poor sleep quality among older adults aged ≥60 years in rural southwest China. Prevalence of physical inactivity in our participant population was also higher than the 27.5% globally [20] . Further, prevalence of poor sleep quality was higher than that observed in previous studies in China [23] . These high prevalence rates may result from the demographic transition caused by economic development currently underway in China as well as lack of awareness of the impact of lifestyle choices on chronic disease among older adults aged ≥60 years in rural Yunnan Province. Correspondingly, as these central determinants of blood pressure lead to a higher prevalence of hypertension among the studied population, hypertension prevalence was similarly with the reported prevalence rate of 44.6%-60.1% in China Hypertension Survey [24] , and coincident with the worldwide prevalence of 33-59% among age ranged from 40-79years [25] .
SEM was used as a powerful tools to construct a complex theoretical model which presented approaching reality. The study indicated that body composition was the most important risk factor that directly affected development of hypertension in our study. Further, the high prevalence of obesity and central obesity directly contributed to the occurrence of hypertension, consistent with WHO report [3] . Body composition also indirectly affected hypertension via diabetes. This may be due to the alterations at hormonal, inflammatory, and endothelial levels which result from obesity and central obesity [26] . The findings suggest that, especially for older adults with diabetes, obesity and central obesity are key opportunity points for hypertension control.
The study established that high SEP was a protective factor which directly affected hypertension. This finding is inconsistent with studies conducted in Sudan [27] and South Africa [28] . This may result from the fact that older adults with better SEP had better health awareness and more opportunities to prevent, diagnosis, treat, and manage hypertension in Yunnan Province compared with Sudan and South Africa.
SEP also indirectly affected hypertension through body composition. Thus, the findings indicate that older adults with lower SEP need more effective prevention and intervention programs for hypertension prevention.
The present study also found that poor sleep quality had a direct effect on the prevalence of hypertension. The contribution of sleep quality to the development of hypertension has been established in previous research [29] . Poor sleep quality is common in older populations, and our results of high prevalence of poor sleep quality were similar to a cross-section survey conducted in Iran [30] . Given this finding, improving sleep quality among Yunnan residents could have a positive effect on prevention of hypertension.
Physical inactivity was positively and directly associated with prevalence of hypertension in our study. This finding aligns with previous studies in China [31] , Greece [32] , and the UK [33] . Our study suggested that increasing moderate and vigorous physical activity as well as reducing sitting time is important for the prevention of hypertension among rural older adults. Moreover, we found family history of hypertension also directly affected the prevalence of hypertension, a result consistent with previous research [34] . This study thus indicates that those with a family history of hypertension should have regular screenings for hypertension and undergo lifestyle interventions for hypertension prevention.
In our study, diabetes had a positive, direct effect on the prevalence of hypertension.
In the basic model of our study, diabetes was also affected by gender, age, ethnicity, and body composition. Finally, age, gender, and ethnicity were eliminated from the model because the paths were not significant. This is consistent with previous research that found the prevalence of diabetes and hypertension was associated with similar risk factors [35] . This finding also suggests that maintaining proper body composition promotes hypertension and diabetes prevention.
Our study also showed that the effect of gender on hypertension was mediated by body composition, physical inactivity, and sleep quality. This was contrary to a previous study in China [36] . The precise reason for this discrepancy requires further exploration. The

Ethical approval and consent to participate
This study was approved by the Ethics Committee of Kunming Medical University prior to the commencement of research.Written informed consent was obtained from the participants.

Consent for publication
Not applicable.

Competing interests
None declared.
Funding: This study was supported by grants from the National Natural Science

Availability of data and material
The datasets used and/or analysed during the current study is available from the corresponding author on reasonable request.

Contributors
CL was involved in the studies design. WGY, FLM, CWL, LYN and SJR collected the data. LX performed the statistical analysis. LX and ARG wrote the paper. All authors reviewed and approved the final version of the paper.