Establishment of a Hypertension Predictive Model and Analysis of Its Influencing Factors Among Residents in Tibet Autonomous Region, China: A Health Ecological Model (HEM)-Based Study

Establishment of a Hypertension Predictive Model and Analysis of Its Influencing Factors Among Residents in Tibet Autonomous Region, China: A Health Ecological Model (HEM)-Based Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Establishment of a Hypertension Predictive Model and Analysis of Its Influencing Factors Among Residents in Tibet Autonomous Region, China: A Health Ecological Model (HEM)-Based Study ShiJie Zhang, Yongzhulacuo none, Yangzong none, Zhaxidawa none This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7812496/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : This study aimed to analyze the status and influencing factors of hypertension among residents in the high-altitude areas of Tibet based on the Health Ecological Model, to provide a reference for improving hypertension prevention and control strategies in the region. Methods : Data were obtained from the Seventh National Health Service Survey in Tibet (2023), including 9,480 participants aged ≥18 years. Based on self-reported hypertension status, they were categorized into hypertensive and non-hypertensive groups. Participants were randomly divided into a training set and a validation set at a 7:3 ratio using a random number table. The Chi-square test or Fisher’s exact test was used to compare categorical variables. Significant predictors were selected via LASSO regression, and a nomogram was developed using logistic regression. The model's predictive performance was evaluated. Results : Through univariate analysis, LASSO selection, and logistic regression, nine key variables were identified from the initial 30 for nomogram construction: age group, BMI classification, self-rated health, alcohol consumption, self-treatment for illness, prefecture-level city, drinking water type, annual household income, and family doctor service utilization. The model demonstrated an area under the curve (AUC) of 0.899 (95% CI: 0.889–0.909) in the training set and 0.873 (95% CI: 0.856–0.891) in the validation set. The calibration curve indicated good agreement between predicted and observed outcomes. Decision curve analysis (DCA) confirmed the clinical utility of the model. Conclusion : Integrating the Health Ecological Model, this study developed a risk prediction nomogram for hypertension. This tool can assist primary healthcare providers in identifying high-risk individuals, thereby facilitating early intervention and prevention. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Health Ecological Model hypertension LASSO nomogram Seventh National Health Service Survey Tibet Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction In recent years, with rapid socioeconomic development in China, living standards and average life expectancy have improved substantially. However, this progress has been accompanied by significant shifts in the spectrum of diseases and causes of death. Among these shifts, hypertension has emerged as a major risk factor for cardiovascular and cerebrovascular diseases, imposing a substantial health burden on the Chinese population. Despite its recognized importance, the prevention and control of hypertension in China remain inadequate 1 . Consequently, experts have called for the implementation of more comprehensive and proactive strategies to curb the rising prevalence of hypertension and pre-hypertension 2 . Substantial disparities in economic development and living standards persist across China, including between provinces and urban-rural areas. Although China has implemented a village- and community-centered family doctor service model to protect the health of primary-level residents, those in plateau regions inhabit a unique environment defined by high altitude, with consequent hypobaric hypoxia. Studies have continually explored hypertension risk factors in these regions 3 , and the prevalence among plateau residents is notably higher than in lowland populations 4 . However, hypertension control in plateau areas faces multiple challenges, including geographical barriers and limited healthcare accessibility. Given these environmental constraints, accurately identifying individuals with hypertension is crucial for enhancing the precision of prevention and control strategies. While some scholars have developed and validated risk assessment models for pulmonary hypertension among Tibetan residents 5 , research in this field remains limited. Therefore, utilizing data from the Seventh National Health Service Survey in Tibet, this study constructed a hypertension risk prediction nomogram using LASSO-logistic regression. This model aims to facilitate the early identification and management of hypertension in this population. 1. Subjects and Methods 1.1 Data Source Study Design and Participants This study utilized data from the Seventh Tibet National Health Service Survey (hereafter "Tibet 7th Survey"). The survey covered 161 villages (neighborhood committees) across seven prefectures in Tibet. A total of 11,104 individuals who completed the questionnaire comprised the initial sample. Inclusion criteria were: (1) age ≥18 years; and (2) provision of complete questionnaire data. Exclusion criteria were: (1) occupation listed as "student"; and (2) missing key data (e.g., gender, age, household income). Consequently, 9,480 participants were included in the final analysis. Ethical Considerations The study was approved by the Ethics Committee of the Tibet Autonomous Region Health Commission. Informed consent was obtained from all participants before their participation. 1.2 Flow Chart See Fig. 1 for the study flow chart. 1.3 Variable Selection 1.3.1 Dependent Variable The dependent variable was self-reported hypertension, assessed by the question, “Have you been diagnosed with hypertension?” in the 2023 Tibet 7th National Health Service Survey. A positive response was classified as hypertension if it met one of the following clinical criteria: (1) a physician's diagnosis within the six months prior to the survey; or (2) a historical diagnosis (more than six months prior) with evidence of recent disease activity, such as recurrent episodes, medication use, or active management to control the condition within the six months before the survey. 1.3.2 Independent Variables The independent variables (potential hypertension influencers) were selected based on the Health Ecological Model 6 and a review of the literature. They were categorized into the following five dimensions: Personal characteristics: Gender, age group, ethnicity, BMI classification. Behavioral characteristics: Self-rated health, insomnia, smoking, alcohol consumption, secondhand smoke exposure, physical exercise, comorbid chronic diseases, self-perceived disease severity, self-treatment, medical consultation. Interpersonal network: Marital status, living alone, household registration in the current county. Work and living conditions: Annual household income, urban/rural residence, prefecture, educational level, employment status, drinking water type. Policy environment: Regional medical services, insurance coverage, and economic development 7 . Based on professional knowledge and relevant literature, all independent variables were coded and assigned specific values (see Table 1). The framework of the Health Ecological Model applied in this study is presented in Fig. 2. Table 1. Coding and assignment of independent variables based on the Health Ecological Model Independent Variable Assignment Gender 1 = Male; 2 = Female Age Group (years) 1 = 18–24; 2 = 25–34; 3 = 35–44; 4 = 45–54; 5 = 55–64; 6 = ≥ 65 Ethnicity 1 = Tibetan; 2 = Other ethnic groups BMI Classification 1 = < 18.5; 2 = 18.5–23.9; 3 = 24.0–27.9; 4 = ≥ 28.0 Self-Rated Health(SRH) 1 = ≤ 33 points; 2 = 34–66 points; 3 = ≥ 67 points Insomnia Status 1 = With insomnia; 2 = Without insomnia Smoking Status 1 = Current smoker; 2 = Former smoker; 3 = Never smoked Weekly Secondhand Smoke Exposure(WSSE) 1 = No exposure; 2 = 1–3 days/week; 3 = 4–6 days/week; 4 = Almost every day Alcohol Drinking Status(ADS) 1 = Drinks alcohol; 2 = Does not drink alcohol Physical Activity Status(PAS) 1 = No exercise; 2 = 1–2 times/week; 3 = ≥ 3 times/week Chronic Disease Comorbidity(CDC) 1 = With comorbidities; 2 = Without comorbidities Routine Health Check-up Utilization(RHCU) 1 = Received check-up; 2 = No check-up Self-Treatment for Illness(STI) 1 = Yes; 2 = No Doctor Visit Within Two Weeks(DVTW) 1 = Yes; 2 = No Marital Status 1 = Unmarried; 2 = Married; 3 = Widowed; 4 = Divorced Living Alone 1 = Yes; 2 = No Household Registration at Current Residence(HRCR) 1 = Yes; 2 = No Household Annual Income(HAI;10,000 CNY) 1 = < 2.00; 2 = 2.00–4.99; 3 = 5.00–9.99; 4 = ≥ 10.00 Urban-Rural Residence(URR) 1 = Urban; 2 = Rural Prefecture-Level City(City) 1 = Lhasa; 2=Shigatse;3=Shannan; 4 = Nyingchi; 5 = Qamdo; 6 = Nagqu; 7 = Ngari Educational Attainment 1 = Never attended school; 2 = Primary school; 3 = Junior high school; 4 = Senior high school or above Employment Status 1 = Unemployed; 2 = Out of work; 3 = Employed (including retired) Drinking Water Type(DWT) 1 = Purified water; 2 = Protected water; 3 = Unprotected water Family Doctor Service Utilization(FDSU) 1 = Yes; 2 = No Medical Insurance Type(MIT) 1 = Employee medical insurance; 2 = Resident medical insurance; 3 = No medical insurance Endowment Insurance Type(EIT) 1 = Employee endowment insurance; 2 = Resident endowment insurance; 3 = No endowment insurance City-Level Per Capita (GDP;10,000 CNY) 1 = < 5.00; 2 = 5.00–7.99; 3 = ≥ 8.00 Nearest Medical and Health Institution(NMHI) 1 = Provincial/municipal hospital; 2 = County/district hospital; 3 = Township hospital; 4 = Health service station Distance to Medical and Health Institution(DMHI) 1 = < 2 km; 2 = 2.0–3.9 km; 3 = ≥ 4 km Travel time to Medical and Health Institution(TMHI) 1 = < 15 minutes; 2 = 15–29 minutes; 3 = ≥ 30 minutes 1.4 Training and Validation of the Nomogram The 9,480 participants were randomly divided into a training set (n = 6,636) and a validation set (n = 2,844) in a 7:3 ratio. In the training set, univariate analysis was performed to screen potential factors. Significant variables from this screening were then incorporated into a LASSO regression to identify the most relevant predictors for the construction of a nomogram 8 . The model's discriminatory ability was assessed using the receiver operating characteristic (ROC) curve, with the area under the curve (AUC) quantifying the prediction accuracy for hypertension. Calibration plots and the Hosmer-Lemeshow test were used to evaluate the calibration of the nomogram. Additionally, decision curve analysis (DCA) was conducted using R software (v4.5) to evaluate the clinical net benefit of the nomogram across various probability thresholds. 1.5 Statistical Methods All statistical analyses were performed using R software (version 4.5.0). Univariate analyses were first conducted to identify factors associated with hypertension. Continuous variables, presented as mean ± standard deviation, were compared using the independent samples t-test. Categorical variables, presented as frequencies and percentages [n (%)], were compared using the Chi-square test. Least Absolute Shrinkage and Selection Operator (LASSO) regression was then applied to select the most significant predictors from the univariate analysis for inclusion in the logistic regression model. The predictive performance of the resulting nomogram was evaluated by assessing its discrimination, calibration, and clinical utility. Discrimination was measured by the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC). Calibration was assessed using calibration plots and the Hosmer-Lemeshow test. Clinical utility was evaluated via Decision Curve Analysis (DCA). A significance level of α = 0.05 was used for all tests. 2 Results 2.1 Comparison of Baseline Characteristics The 9,480 participants were randomly divided into a training set (n = 6,636) and a validation set (n = 2,844) in a 7:3 ratio. Comparison of baseline characteristics demonstrated the general comparability of the training and validation sets. Most baseline variables did not differ significantly between the two sets (P > 0.05). Significant differences (P < 0.05) were observed for only four variables: living Alone, FDSU, NHMI, and TMHI. Overall, the random allocation was successful, confirming the appropriateness of the data division for model development and validation. Details are provided in Table 2. Table 2. Comparison of Baseline Characteristics Between the Training Set and Validation Set Variable Train（6636） Test（2844） P value Variable Train（6636） Test（2844） P value Gender 0.231 HRCR 0.700 1 3134 (47.23%) 1382 (48.59%) 1 6401 (96.46%) 2738 (96.27%) 2 3502 (52.77%) 1462 (51.41%) 2 235 ( 3.54%) 106 ( 3.73%) AgeGroup 0.597 HAI 0.825 1 304(4.58%) 122(4.29%) 1 1431(21.56%) 610(21.45%) 2 1085(16.35%) 457(16.07%) 2 2506(37.76%) 1080(37.97%) 3 1557(23.46%) 686(24.12%) 3 1687(25.42%) 703(24.72%) 4 1444(21.76%) 654(23.00%) 4 108(11.21%) 451(15.86%) 5 1285(19.36%) 541(19.02%) URR 0.976 6 961(14.48%) 384(13.50%) 1 1772 (26.70%) 761 (26.76%) Ethnicity 0.149 2 4864 (73.30%) 2083 (73.24%) 1 6556(98.79%) 2820(99.16%) City 0.765 2 80(1.21%) 24(0.84%) 1 1463 (22.05%) 636 (22.36%) BMIClassification 0.234 2 1599 (24.10%) 688 (24.19%) 1 423(6.37%) 190(6.68%) 3 689 (10.38%) 291 (10.23%) 2 3846(57.96%) 1605(56.43%) 4 428 ( 6.45%) 189 ( 6.65%) 3 1767(26.63%) 757(26.62%) 5 1205 (18.16%) 543 (19.09%) 4 600(9.04%) 292(10.27%) 6 911 (13.73%) 359 (12.62%) SRH 0.344 7 341 ( 5.14%) 138 ( 4.85%) 1 152(2.29%) 60(2.11%) EducationalAttainment 0.680 2 1390(20.95%) 562(19.76%) 1 3467(52.25%) 1492(52.46%) 3 5094(76.76%) 2222(78.13%) 2 1985(29.91%) 872(30.66%) InsomniaStatus 0.732 3 678(10.22%) 285(10.02%) 1 1257(18.94%) 548(19.27%) 4 234(3.53%) 86(3.02%) 2 5379(81.06%) 2296(80.73%) 5 272(4.10%) 109(3.83%) SmokingStatus 0.826 EmploymentStatus 0.751 1 783(11.80%) 346(12.17%) 1 1555(23.43%) 669(23.52%) 2 170(2.56%) 69(2.43%) 2 82(1.24%) 43(1.51%) 3 5683(85.64%) 2429(85.41%) 3 4999(75.33%) 2132(74.96%) WSSE 0.141 DWT 0.809 1 527(7.94%) 251(8.83%) 1 1349（20.33%） 583（20.50%） 2 538(8.11%) 257(9.04%) 2 5020（75.65%） 2139（75.21%） 3 1045(15.75%) 419(14.73 %) 3 267（4.02%） 122（4.29%） 4 4526(68.20%) 1917(67.41%) FDSU 0.017 ADS 0.586 1 3726(56.15%) 1673(58.83%) 1 1442(21.73%) 603(21.20%) 2 2910(43.85%) 1171(41.17%) 2 5194(78.27%) 2241(78.80 %) MIT 0.147 PAS 0.581 1 282(4.25%) 107(3.76%) 1 3914(58.98%) 1702(59.85%) 2 6308(95.06%) 2708(95.22%) 2 967(14.57%) 419(14.73%) 3 46(0.69%) 29(1.02%) 3 1755(26.45%) 723(25.42%) EIT 0.420 CDC 0.252 1 214(3.22%) 80(2.81%) 1 540（8.14%） 211（7.42%） 2 5677(85.55%) 2459(86.46%) 2 6096（91.86%） 2633（92.58%） 3 745(11.23%) 305(10.72%) STI 0.572 GDP 0.878 1 958（14.44%） 424（14.91%） 1 2116（31.89%） 902（31.72%） 2 5678（85.56%） 2420（85.09%） 2 2629（39.62%） 1117（39.28%） DVTW 1.000 3 1891（28.50%） 825（29.01%） 1 498（7.50%） 213（7.49%） NHMI 0.008 2 6138（92.50%） 2631（92.51%） 1 265（3.99%） 117（4.11%） RHCU 0.784 2 849（12.79%） 394（13.85%） 1 4209（63.43%） 1813（63.75%） 3 2984（44.97%） 1350（47.47%） 2 2427（36.57%） 1031（36.25%） 4 2538（38.25%） 983（34.56%） MaritalStatus 0.187 DMHI 0.125 1 684(10.31%) 285(10.02%) 1 3629（54.69%） 1493（52.50%） 2 5375(81.00%) 2347(82.52%) 2 1305（19.67%） 574（20.18%） 3 469(7.07%) 168(5.91%) 3 1702（25.65%） 777（27.32%） 4 108(1.63%) 44(1.55%) TMHI 0.027 LivingAlone 0.042 1 4366（65.79%） 1820（63.99%） 1 843（12.70%） 18（11.18%） 2 1395（21.02%） 668（23.49%） 2 5793（87.30%） 2526（88.82%） 3 875（13.19%） 356（12.52%） 2.2 Feature Selection and Regression Analysis In the training set, univariate analysis was performed, followed by an assessment of multicollinearity for the significant variables (Fig. 3). Subsequently, LASSO regression identified 18 predictors (Figs. 4 and 5) for inclusion in a multivariate logistic regression analysis to identify independent factors associated with hypertension (Table 3). The multivariate logistic regression identified several factors independently associated with hypertension (all P < 0.05). Increasing age, higher BMI, urban residence, self-treatment behavior, alcohol consumption, and utilization of family doctor services were significant risk factors. In contrast, higher annual household income, better self-rated health, and consumption of purified water emerged as significant protective factors. Table 3. Univariate and Multivariate Logistic Regression Analyses of Factors Influencing Hypertension Among Residents Based on the Health Ecological Model Variable OR (95%CI) P value OR (95%CI) P value Gender 1.06 (0.93-1.20) 0.410 AgeGroup 2.73 (2.55-2.92) ＜0.001 2.37（2.18-2.59） <0.001 Ethnicity 0.77 (0.41-1.46) 0.430 BMIClassification 1.63 (1.50-1.78) ＜0.001 1.60（1.43-1.79） <0.001 SRH 0.36 (0.32-0.40) ＜0.001 0.81（0.69-0.95） 0.009 InsomniaStatus 0.35 (0.31-0.41) ＜0.001 0.90（0.74-1.10） 0.313 SmokingStatus 1.14 (1.03-1.27) 0.013 0.91（0.80-1.05） 0.186 WSSE 0.97 (0.90-1.04) 0.376 ADS 1.24 (1.05-1.46) 0.009 0.73（0.59-0.92） 0.008 PAS 1.06 (0.98-1.14) 0.138 RHCU 0.83 (0.72-0.95) 0.007 1.05（0.87-1.26） 0.59 CDC 0.00 (0.00-∞) 0.941 DVTW 0.41 (0.33-0.50) ＜0.001 STI 0.05 (0.04-0.05) ＜0.001 0.07（0.05-0.08） ＜0.001 MaritalStatus 2.08 (1.84-2.36) ＜0.001 1.13（0.95-1.35） 0.173 LivingAlone 1.00 (0.82-1.21) 0.985 HRCR 0.49 (0.32-0.77) 0.002 0.58（0.32-1.02） 0.064 URR 0.99 (0.85-1.14) 0.841 City 1.07 (1.04-1.11) ＜0.001 1.10（1.02-1.17） 0.491 DWT 1.25 (1.09-1.44) 0.002 1.37（1.11-1.68） 0.003 HAI 0.83 (0.78-0.89) ＜0.001 0.88（0.80-0.96） 0.005 EducationalAttainment 0.60 (0.56-0.66) ＜0.001 EmploymentStatus 0.58 (0.54-0.62) ＜0.001 0.94（0.85-1.04） 0.213 GDP 0.85 (0.78-0.92) ＜0.001 0.97（0.82-1.14） 0.682 MIT 1.30 (0.96-1.77) 0.094 1.24（0.80-1.94） 0.348 EIT 1.14 (0.96-1.35) 0.126 FDSU 0.65 (0.57-0.74) <0.001 0.73（0.61-0.87） ＜0.001 NMHI 1.00 (0.92-1.08) 0.991 DMHI 0.99 (0.92-1.07) 0.839 TMHI 1.11 (1.02-1.21) 0.017 1.11（0.99-1.26） 0.082 2.3 Construction and Validation of the Nomogram A nomogram for predicting hypertension risk was developed using the significant variables from the logistic regression analysis (Fig. 6). The model demonstrated high discriminatory ability, with an area under the curve (AUC) of 0.899 (95% CI: 0.889–0.909) in the training set and 0.873 (95% CI: 0.856–0.891) in the validation set (Fig. 7). Calibration performance, assessed by calibration plots (Fig. 8) and the Hosmer-Lemeshow test (training set = 13.061, P = 0.110; validation set: =13.717, P = 0.089), indicated good consistency between predictions and observations. Decision curve analysis (DCA) further demonstrated the clinical utility of the nomogram, showing a positive net benefit across a wide range of threshold probabilities in both the training and validation sets (Fig. 9). 3. Discussion The high comorbidity burden of hypertension, dyslipidemia, and diabetes ("the three highs") among Tibetan residents 9 , coupled with the complex challenges of hypertension management in China 10 , underscores the need for enhanced early prevention in primary care. Nomograms have proven effective for intuitive disease risk prediction 11 , offering a potential solution to this public health issue. Grounded in the Health Ecological Model, this study developed and validated a nomogram to assess hypertension risk. The final model incorporated nine key factors: older age, higher BMI, urban residence, self-treatment, alcohol consumption, and utilization of family doctor services were identified as risk factors, while higher annual household income, better self-rated health, and consumption of purified water were protective factors. TheThe model's discriminatory power was evaluated using ROC analysis, yielding AUC values of 0.899 (95% CI: 0.889–0.909) in the training set and 0.873 (95% CI: 0.856–0.891) in the validation set, indicating strong performance. Furthermore, calibration curves demonstrated good agreement between predicted probabilities and observed outcomes. In summary, this study developed a risk assessment tool based on nine indicators derived from the Health Ecological Model. This nomogram serves as an effective and practical instrument for primary healthcare providers to identify individuals at high risk for hypertension. This study has several limitations. First, its cross-sectional design precludes causal inferences, as all variables were assessed concurrently. Future prospective and multi-center studies are needed to validate these findings and enhance clinical generalizability. Second, although the sample size was substantial, the model's performance was only internally validated. External validation in diverse populations is essential. Finally, incorporating additional predictors, such as detailed dietary patterns 12 , sedentary behavior 13 , and clinical biomarkers 14 , could further improve model accuracy and provide a more comprehensive application of the Health Ecological Model in hypertension prediction. 4. Conclusion Grounded in the Health Ecological Model, this study developed and validated a nomogram for predicting hypertension risk. This readily applicable tool can assist primary healthcare practitioners in identifying high-risk individuals and facilitating early intervention. Declarations Data Availability Statement The data used in this study are not publicly available due to confidentiality policies but can be obtained from the corresponding author upon reasonable request. Ethics Statement This study involving human participants was approved by the Ethics Committee of the Tibet Autonomous Region Health Commission. The study was conducted in accordance with local regulations and institutional requirements. Participants signed electronic informed consent forms to participate in this study. Funding The authors declare that the research, authorship, and/or publication of this article received financial support. This study was funded by the Expanded Project of the 7th Tibet Health Service Survey, with the grant number 18080278. Acknowledgments We would like to thank all participants who took part in this study. Conflicts of Interest The authors declare no commercial or financial relationships that could be construed as potential conflicts of interest during the study. References Hypertension Alliance of China, Committee on Chinese Expert Recommendations for High-Quality Blood Pressure Management in Hypertensive Patients (2024). Chinese expert recommendations for high-quality blood pressure management in hypertensive patients. Chin J Hypertens, 32(2): 104-111. Yu ZQ, Zhou X (2024). Improving hypertension control rate: standardizing the diagnosis and treatment of essential hypertension is the key. Chin J Hypertens, 32(11): 1006-1010. Yao YY, Zhang X, Zhao LM, et al (2024). Current status and prospects of plateau-related hypertension research. J Sichuan Univ (Med Sci), 55(6): 1454-1459. Zhang ZC, Qi HL (2024). Research progress on the pathogenesis of hypertension under the influence of plateau environment. Chin J Hypertens, 32(8): 727-736. Tang J, Yang R, Li H, et al (2024). Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China. Elife, 13: RP98169. Chang HJ, Lin CH, Huang JR, et al (2024). Influencing factors of hypertension among residents in Fujian Province based on the Health Ecological Model. Chin J Hypertens, 32(9): 859-869. Zhang Y, Jiang XT, Wang PY (2025). Analysis of depressive symptoms in Chinese elderly population based on the Health Ecological Model. Chin J Chronic Dis Prev Control, 33(1): 8-14. Luo L, Long X, Cheng C, et al (2024). Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne), 15: 1338167. - PubMed Yu Y, Jinmeiquzhen, Bai GX, et al (2025). Prevalence and influencing factors of hypertension, dyslipidemia, and diabetes comorbidity among Tibetan residents. Chin J Chronic Dis Prev Control, 33(6): 442-446. Guo JW, Zhou J, Guo ZH (2022). Role of national basic public health services and medical insurance payment reform in hypertension prevention and treatment. Chin J Hypertens, 30(10): 932-937. Xu XY, Li D, Song LR, et al (2022). Nomogram for predicting an individual prospective hemorrhage risk in untreated brainstem cavernous malformations. J Neurosurg, 138(4): 910-921. - PubMed Yao D, Yu DM, Sun JY, et al (2025). Prevalence of metabolic syndrome and its association with diet among elderly people aged 65 years and above in rural Beijing, 2023. J Hyg Res, 54(2): 244-251. Chen KY, Jiang QY, Wang JY, et al (2023). Construction and empirical analysis of a rapid identification method for populations at high risk of hypertension complications. Chin J Public Health, 39(9): 1108-1113. Liu Q, Gong M, Su Z, et al (2025). Development and validation of a nomogram for predicting the probability of postpartum chronic hypertension in women with hypertensive disorders of pregnancy: a multicenter, cross-sectional study. J Clin Hypertens (Greenwich), 27(7): e70094. - PubMed Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7812496","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593334394,"identity":"f314095e-1b57-4717-ad9f-497fd533415f","order_by":0,"name":"ShiJie Zhang","email":"","orcid":"","institution":"Medical College of XiZang University","correspondingAuthor":false,"prefix":"","firstName":"ShiJie","middleName":"","lastName":"Zhang","suffix":""},{"id":593334397,"identity":"bc1a3fa1-d1eb-4bfc-b59d-42d81247fc56","order_by":1,"name":"Yongzhulacuo none","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Yongzhulacuo","middleName":"","lastName":"none","suffix":""},{"id":593334400,"identity":"f500acae-c0c2-4b57-93eb-045ca0d7e757","order_by":2,"name":"Yangzong none","email":"","orcid":"","institution":"Medical College of XiZang University","correspondingAuthor":false,"prefix":"","firstName":"Yangzong","middleName":"","lastName":"none","suffix":""},{"id":593334401,"identity":"fd685443-d7cd-426f-8385-0d9e352a84d6","order_by":3,"name":"Zhaxidawa none","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDACCSBOMLCRY2NvIElLRZoxH88BUrQwnDmcOE8igUgd8rObn0k8bEtLbJN8vPEGQ41NNEEtjHOOGRskttkYt0mnFVswHEvLbSCkhVkiwfBBYluabJt0jpkEY8NhwlrYJNI/HEhsO8zYJnmGSC08EjmGDxLOHFZsk+AhUouERE6xASiQ2XiAfkkgxi/yM9K3Sf4ARqV8++GNNz7U2BDWggwMiI4aJC2k6hgFo2AUjIKRAQDfdDvXIRMVLAAAAABJRU5ErkJggg==","orcid":"","institution":"Medical College of XiZang University","correspondingAuthor":true,"prefix":"","firstName":"Zhaxidawa","middleName":"","lastName":"none","suffix":""}],"badges":[],"createdAt":"2025-10-09 03:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7812496/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7812496/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103178832,"identity":"1e368cc1-778e-4e01-87a7-417fef06223a","added_by":"auto","created_at":"2026-02-22 17:06:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127259,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of our study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/f58722ad1534befb0b248b89.png"},{"id":103178827,"identity":"04b07874-8777-4d49-bdf4-24edfcd6801c","added_by":"auto","created_at":"2026-02-22 17:06:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179991,"visible":true,"origin":"","legend":"\u003cp\u003eHealth ecological model of factors affecting hypertension in Tibetan residents\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/7a76af53c57860e4f5156741.png"},{"id":103505323,"identity":"d631e061-0261-47be-a90f-567d81e78143","added_by":"auto","created_at":"2026-02-26 13:29:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88401,"visible":true,"origin":"","legend":"\u003cp\u003eColinear diagnosis between variables\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/f9bf60465a6344461d01c4a3.png"},{"id":103178828,"identity":"5c4496a9-faad-4187-b126-55ab2611998c","added_by":"auto","created_at":"2026-02-22 17:06:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69528,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression coeffcient trajectory\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/7064e674926ce9ad5cc6f154.png"},{"id":103505792,"identity":"36af2e23-33c8-47ba-83a9-1a002007fdab","added_by":"auto","created_at":"2026-02-26 13:33:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66164,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO regression10-fold cross-validation\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/cce546486b2f88a2a17ffec4.png"},{"id":103505342,"identity":"70459859-98dc-4978-b8da-bdf8d749a15d","added_by":"auto","created_at":"2026-02-26 13:30:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":218465,"visible":true,"origin":"","legend":"\u003cp\u003eThe risk profile model of hypertension residents in Tibet\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/2a25e64519e5d39c610e7c54.png"},{"id":103178834,"identity":"fa0784dd-44da-4591-b6b0-e2cd6d56ed3a","added_by":"auto","created_at":"2026-02-22 17:06:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95876,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction performance of the model. Receiver operaing characteristic (ROC) curve plot in the training cohort (Left); ROC curve plot in the validation cohort (Right); AUC, the area under the ROC.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/1ab340d6ab958ccdecde8fb2.png"},{"id":103178835,"identity":"a173e0b0-81f9-4fb2-b82c-7a4ff5b9fd89","added_by":"auto","created_at":"2026-02-22 17:06:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":111192,"visible":true,"origin":"","legend":"\u003cp\u003eCalibratlon curve plot in each cohort. (Left ) the training cohort;(Right) \u0026nbsp;\u0026nbsp;the validation cohort.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/5efa17b989ade5790f0b8753.png"},{"id":104397328,"identity":"7b46c568-7878-41c2-b434-2f6f7ac7642a","added_by":"auto","created_at":"2026-03-11 11:46:39","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":99317,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of training group (Left ) and validation cohort (Right) for the risk of hypertension.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/cbc3701cd5742c8e8bbd8af1.png"},{"id":104409970,"identity":"57d71af5-6d63-4068-9503-c82e437b7e52","added_by":"auto","created_at":"2026-03-11 12:48:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1807613,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7812496/v1/31edfdd9-15b4-4687-8dd4-2ba7a11c6180.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishment of a Hypertension Predictive Model and Analysis of Its Influencing Factors Among Residents in Tibet Autonomous Region, China: A Health Ecological Model (HEM)-Based Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, with rapid socioeconomic development in China, living standards and average life expectancy have improved substantially. However, this progress has been accompanied by significant shifts in the spectrum of diseases and causes of death. Among these shifts, hypertension has emerged as a major risk factor for cardiovascular and cerebrovascular diseases, imposing a substantial health burden on the Chinese population. Despite its recognized importance, the prevention and control of hypertension in China remain inadequate \u003csup\u003e1\u003c/sup\u003e. Consequently, experts have called for the implementation of more comprehensive and proactive strategies to curb the rising prevalence of hypertension and pre-hypertension \u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSubstantial disparities in economic development and living standards persist across China, including between provinces and urban-rural areas. Although China has implemented a village- and community-centered family doctor service model to protect the health of primary-level residents, those in plateau regions inhabit a unique environment defined by high altitude, with consequent hypobaric hypoxia. Studies have continually explored hypertension risk factors in these regions \u003csup\u003e3\u003c/sup\u003e, and the prevalence among plateau residents is notably higher than in lowland populations \u003csup\u003e4\u003c/sup\u003e. However, hypertension control in plateau areas faces multiple challenges, including geographical barriers and limited healthcare accessibility. Given these environmental constraints, accurately identifying individuals with hypertension is crucial for enhancing the precision of prevention and control strategies.\u003c/p\u003e\n\u003cp\u003eWhile some scholars have developed and validated risk assessment models for pulmonary hypertension among Tibetan residents \u003csup\u003e5\u003c/sup\u003e, research in this field remains limited. Therefore, utilizing data from the Seventh National Health Service Survey in Tibet, this study constructed a hypertension risk prediction nomogram using LASSO-logistic regression. This model aims to facilitate the early identification and management of hypertension in this population.\u003c/p\u003e"},{"header":"1. Subjects and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Data Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy Design and Participants\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the Seventh Tibet National Health Service Survey (hereafter \u0026quot;Tibet 7th Survey\u0026quot;). The survey covered 161 villages (neighborhood committees) across seven prefectures in Tibet. A total of 11,104 individuals who completed the questionnaire comprised the initial sample. Inclusion criteria were: (1) age \u0026ge;18 years; and (2) provision of complete questionnaire data. Exclusion criteria were: (1) occupation listed as \u0026quot;student\u0026quot;; and (2) missing key data (e.g., gender, age, household income). Consequently, 9,480 participants were included in the final analysis.\u003c/p\u003e\n\u003cp\u003eEthical Considerations\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the Tibet Autonomous Region Health Commission. Informed consent was obtained from all participants before their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Flow Chart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSee Fig. 1 for the study flow chart.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Variable Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.3.1 Dependent Variable\u003c/p\u003e\n\u003cp\u003eThe dependent variable was self-reported hypertension, assessed by the question, \u0026ldquo;Have you been diagnosed with hypertension?\u0026rdquo; in the 2023 Tibet 7th National Health Service Survey. A positive response was classified as hypertension if it met one of the following clinical criteria: (1) a physician\u0026apos;s diagnosis within the six months prior to the survey; or (2) a historical diagnosis (more than six months prior) with evidence of recent disease activity, such as recurrent episodes, medication use, or active management to control the condition within the six months before the survey.\u003c/p\u003e\n\u003cp\u003e1.3.2 Independent Variables\u003c/p\u003e\n\u003cp\u003eThe independent variables (potential hypertension influencers) were selected based on the Health Ecological Model \u003csup\u003e6\u003c/sup\u003e and a review of the literature. They were categorized into the following five dimensions:\u003c/p\u003e\n\u003cp\u003ePersonal characteristics: Gender, age group, ethnicity, BMI classification.\u003c/p\u003e\n\u003cp\u003eBehavioral characteristics: Self-rated health, insomnia, smoking, alcohol consumption, secondhand smoke exposure, physical exercise, comorbid chronic diseases, self-perceived disease severity, self-treatment, medical consultation.\u003c/p\u003e\n\u003cp\u003eInterpersonal network: Marital status, living alone, household registration in the current county.\u003c/p\u003e\n\u003cp\u003eWork and living conditions: Annual household income, urban/rural residence, prefecture, educational level, employment status, drinking water type.\u003c/p\u003e\n\u003cp\u003ePolicy environment: Regional medical services, insurance coverage, and economic development \u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBased on professional knowledge and relevant literature, all independent variables were coded and assigned specific values (see Table 1). The framework of the Health Ecological Model applied in this study is presented in Fig. 2.\u003c/p\u003e\n\u003cp\u003eTable 1. Coding and assignment of independent variables based on the Health Ecological Model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003eAssignment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Male; 2 = Female\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eAge Group (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = 18\u0026ndash;24; 2 = 25\u0026ndash;34; 3 = 35\u0026ndash;44; 4 = 45\u0026ndash;54; 5 = 55\u0026ndash;64; 6 = \u0026ge; 65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Tibetan; 2 = Other ethnic groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eBMI Classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = \u0026lt; 18.5; 2 = 18.5\u0026ndash;23.9; 3 = 24.0\u0026ndash;27.9; 4 = \u0026ge; 28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSelf-Rated Health(SRH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = \u0026le; 33 points; 2 = 34\u0026ndash;66 points; 3 = \u0026ge; 67 points\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eInsomnia Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = With insomnia; 2 = Without insomnia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSmoking Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Current smoker; 2 = Former smoker; 3 = Never smoked\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eWeekly Secondhand Smoke Exposure(WSSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = No exposure; 2 = 1\u0026ndash;3 days/week; 3 = 4\u0026ndash;6 days/week; 4 = Almost every day\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eAlcohol Drinking Status(ADS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Drinks alcohol; 2 = Does not drink alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003ePhysical Activity Status(PAS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = No exercise; 2 = 1\u0026ndash;2 times/week; 3 = \u0026ge; 3 times/week\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eChronic Disease Comorbidity(CDC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = With comorbidities; 2 = Without comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eRoutine Health Check-up Utilization(RHCU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Received check-up; 2 = No check-up\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSelf-Treatment for Illness(STI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Yes; 2 = No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eDoctor Visit Within Two Weeks(DVTW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Yes; 2 = No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Unmarried; 2 = Married; 3 = Widowed; 4 = Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eLiving Alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Yes; 2 = No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eHousehold Registration at Current Residence(HRCR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Yes; 2 = No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eHousehold Annual Income(HAI;10,000 CNY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = \u0026lt; 2.00; 2 = 2.00\u0026ndash;4.99; 3 = 5.00\u0026ndash;9.99; 4 = \u0026ge; 10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eUrban-Rural Residence(URR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Urban; 2 = Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003ePrefecture-Level City(City)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Lhasa; 2=Shigatse;3=Shannan; 4 = Nyingchi; 5 = Qamdo; 6 = Nagqu; 7 = Ngari\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eEducational Attainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Never attended school; 2 = Primary school; 3 = Junior high school; 4 = Senior high school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eEmployment Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Unemployed; 2 = Out of work; 3 = Employed (including retired)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eDrinking Water Type(DWT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Purified water; 2 = Protected water; 3 = Unprotected water\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eFamily Doctor Service Utilization(FDSU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Yes; 2 = No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMedical Insurance Type(MIT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Employee medical insurance; 2 = Resident medical insurance; 3 = No medical insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eEndowment Insurance Type(EIT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Employee endowment insurance; 2 = Resident endowment insurance; 3 = No endowment insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eCity-Level Per Capita (GDP;10,000 CNY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = \u0026lt; 5.00; 2 = 5.00\u0026ndash;7.99; 3 = \u0026ge; 8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eNearest Medical and Health Institution(NMHI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = Provincial/municipal hospital; 2 = County/district hospital; 3 = Township hospital; 4 = Health service station\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eDistance to Medical and Health Institution(DMHI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = \u0026lt; 2 km; 2 = 2.0\u0026ndash;3.9 km; 3 = \u0026ge; 4 km\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eTravel time to Medical and Health Institution(TMHI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e1 = \u0026lt; 15 minutes; 2 = 15\u0026ndash;29 minutes; 3 = \u0026ge; 30 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Training and Validation of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 9,480 participants were randomly divided into a training set (n = 6,636) and a validation set (n = 2,844) in a 7:3 ratio. In the training set, univariate analysis was performed to screen potential factors. Significant variables from this screening were then incorporated into a LASSO regression to identify the most relevant predictors for the construction of a nomogram \u003csup\u003e8\u003c/sup\u003e. The model\u0026apos;s discriminatory ability was assessed using the receiver operating characteristic (ROC) curve, with the area under the curve (AUC) quantifying the prediction accuracy for hypertension. Calibration plots and the Hosmer-Lemeshow test were used to evaluate the calibration of the nomogram. Additionally, decision curve analysis (DCA) was conducted using R software (v4.5) to evaluate the clinical net benefit of the nomogram across various probability thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Statistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.5.0). Univariate analyses were first conducted to identify factors associated with hypertension. Continuous variables, presented as mean \u0026plusmn; standard deviation, were compared using the independent samples t-test. Categorical variables, presented as frequencies and percentages [n (%)], were compared using the Chi-square test. Least Absolute Shrinkage and Selection Operator (LASSO) regression was then applied to select the most significant predictors from the univariate analysis for inclusion in the logistic regression model. The predictive performance of the resulting nomogram was evaluated by assessing its discrimination, calibration, and clinical utility. Discrimination was measured by the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC). Calibration was assessed using calibration plots and the Hosmer-Lemeshow test. Clinical utility was evaluated via Decision Curve Analysis (DCA). A significance level of \u0026alpha; = 0.05 was used for all tests.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Comparison of Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 9,480 participants were randomly divided into a training set (n = 6,636) and a validation set (n = 2,844) in a 7:3 ratio. Comparison of baseline characteristics demonstrated the general comparability of the training and validation sets. Most baseline variables did not differ significantly between the two sets (P \u0026gt; 0.05). Significant differences (P \u0026lt; 0.05) were observed for only four variables: living Alone, FDSU, NHMI, and TMHI. Overall, the random allocation was successful, confirming the appropriateness of the data division for model development and validation. Details are provided in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2. Comparison of Baseline Characteristics Between the Training Set and Validation Set\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eTrain（6636）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eTest（2844）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eTrain（6636）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eTest（2844）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eHRCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e3134 (47.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1382 (48.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e6401 (96.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2738 (96.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e3502 (52.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1462 (51.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e235 ( 3.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e106 ( 3.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eAgeGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eHAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e304(4.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e122(4.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1431(21.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e610(21.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1085(16.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e457(16.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2506(37.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1080(37.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1557(23.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e686(24.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1687(25.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e703(24.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1444(21.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e654(23.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e108(11.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e451(15.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1285(19.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e541(19.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eURR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e961(14.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e384(13.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1772 (26.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e761 (26.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e4864 (73.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2083 (73.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e6556(98.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2820(99.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e80(1.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e24(0.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1463 (22.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e636 (22.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eBMIClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1599 (24.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e688 (24.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e423(6.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e190(6.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e689 (10.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e291 (10.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e3846(57.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1605(56.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e428 ( 6.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e189 ( 6.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1767(26.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e757(26.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1205 (18.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e543 (19.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e600(9.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e292(10.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e911 (13.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e359 (12.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e341 ( 5.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e138 ( 4.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e152(2.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e60(2.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eEducationalAttainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1390(20.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e562(19.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3467(52.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1492(52.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5094(76.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2222(78.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1985(29.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e872(30.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eInsomniaStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e678(10.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e285(10.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1257(18.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e548(19.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e234(3.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e86(3.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5379(81.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2296(80.73%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e272(4.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e109(3.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSmokingStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eEmploymentStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e783(11.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e346(12.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1555(23.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e669(23.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e170(2.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e69(2.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e82(1.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e43(1.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5683(85.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2429(85.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e4999(75.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2132(74.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eWSSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eDWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e527(7.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e251(8.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1349（20.33%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e583（20.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e538(8.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e257(9.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e5020（75.65%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2139（75.21%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1045(15.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e419(14.73 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e267（4.02%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e122（4.29%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e4526(68.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1917(67.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eFDSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eADS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3726(56.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1673(58.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1442(21.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e603(21.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2910(43.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1171(41.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5194(78.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2241(78.80 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003ePAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e282(4.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e107(3.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e3914(58.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1702(59.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e6308(95.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2708(95.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e967(14.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e419(14.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e46(0.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e29(1.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1755(26.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e723(25.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eEIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eCDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e214(3.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e80(2.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e540（8.14%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e211（7.42%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e5677(85.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2459(86.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e6096（91.86%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2633（92.58%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e745(11.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e305(10.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e958（14.44%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e424（14.91%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2116（31.89%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e902（31.72%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5678（85.56%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2420（85.09%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2629（39.62%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1117（39.28%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eDVTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1891（28.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e825（29.01%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e498（7.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e213（7.49%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNHMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e6138（92.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2631（92.51%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e265（3.99%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e117（4.11%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eRHCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e849（12.79%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e394（13.85%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e4209（63.43%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1813（63.75%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2984（44.97%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1350（47.47%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2427（36.57%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1031（36.25%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2538（38.25%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e983（34.56%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eMaritalStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eDMHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e684(10.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e285(10.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3629（54.69%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1493（52.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5375(81.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2347(82.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1305（19.67%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e574（20.18%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e469(7.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e168(5.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1702（25.65%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e777（27.32%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e108(1.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e44(1.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTMHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eLivingAlone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e4366（65.79%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1820（63.99%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e843（12.70%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e18（11.18%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1395（21.02%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e668（23.49%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5793（87.30%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2526（88.82%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e875（13.19%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e356（12.52%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Feature Selection and Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the training set, univariate analysis was performed, followed by an assessment of multicollinearity for the significant variables (Fig. 3). Subsequently, LASSO regression identified 18 predictors (Figs. 4 and 5) for inclusion in a multivariate logistic regression analysis to identify independent factors associated with hypertension (Table 3). The multivariate logistic regression identified several factors independently associated with hypertension (all P \u0026lt; 0.05). Increasing age, higher BMI, urban residence, self-treatment behavior, alcohol consumption, and utilization of family doctor services were significant risk factors. In contrast, higher annual household income, better self-rated health, and consumption of purified water emerged as significant protective factors.\u003c/p\u003e\n\u003cp\u003eTable 3. Univariate and Multivariate Logistic Regression Analyses of Factors Influencing Hypertension Among Residents Based on the Health Ecological Model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.06 (0.93-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eAgeGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.73 (2.55-2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.37（2.18-2.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.77 (0.41-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eBMIClassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.63 (1.50-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.60（1.43-1.79）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eSRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.36 (0.32-0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.81（0.69-0.95）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eInsomniaStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.35 (0.31-0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.90（0.74-1.10）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eSmokingStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.14 (1.03-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.91（0.80-1.05）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eWSSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.97 (0.90-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eADS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.24 (1.05-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.73（0.59-0.92）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003ePAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.06 (0.98-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eRHCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.83 (0.72-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.05（0.87-1.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eCDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.00 (0.00-\u0026infin;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eDVTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.41 (0.33-0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eSTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.05 (0.04-0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.07（0.05-0.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eMaritalStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.08 (1.84-2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.13（0.95-1.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eLivingAlone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.00 (0.82-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eHRCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.49 (0.32-0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.58（0.32-1.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eURR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.99 (0.85-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.07 (1.04-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.10（1.02-1.17）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eDWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.25 (1.09-1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.37（1.11-1.68）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eHAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.83 (0.78-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.88（0.80-0.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eEducationalAttainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.60 (0.56-0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eEmploymentStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.58 (0.54-0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.94（0.85-1.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.85 (0.78-0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.97（0.82-1.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eMIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.30 (0.96-1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.24（0.80-1.94）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eEIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.14 (0.96-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eFDSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.65 (0.57-0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.73（0.61-0.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eNMHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.00 (0.92-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eDMHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.99 (0.92-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 155px;\"\u003e\n \u003cp\u003eTMHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.11 (1.02-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.11（0.99-1.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Construction and Validation of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA nomogram for predicting hypertension risk was developed using the significant variables from the logistic regression analysis (Fig. 6). The model demonstrated high discriminatory ability, with an area under the curve (AUC) of 0.899 (95% CI: 0.889\u0026ndash;0.909) in the training set and 0.873 (95% CI: 0.856\u0026ndash;0.891) in the validation set (Fig. 7). Calibration performance, assessed by calibration plots (Fig. 8) and the Hosmer-Lemeshow test (training set \u003cimg width=\"24\" height=\"20\" src=\"data:image/wmf;base64,R0lGODlhJAAeAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAQABQAcABUAhQAAAAAAAB0AAAAAHRwcHAAAMwAdMh0dNAAcSAAzWh0zWh1GbDMAADIdADMeRzNGRjVbbjNbgEgcAEgdHVozAFozHVszM0gzM1tINV1/f1lubkhuf1luf2xGHW5dXX9uSH9/XX9uWWaIiIBbM4iIZgECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwZyQIBwSCwahYFA4shsKoSCRXM69Aw71GwRodVij4JkkigOFLoAScBQ/AgO6KHgTJxA4uTi5igVRpgdSlRldExmQnt4RQwDBH+KRRQBfZBEklyVRA9zmUVqj5WgAZ1FApiKiUOMqV0CT0UNAadZZaeBtVlBADs=\" alt=\"image\"\u003e= 13.061, P = 0.110; validation set: \u003cimg width=\"24\" height=\"20\" src=\"data:image/wmf;base64,R0lGODlhJAAeAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAQABQAcABUAhQAAAAAAAB0AAAAAHRwcHAAAMwAdMh0dNAAcSAAzWh0zWh1GbDMAADIdADMeRzNGRjVbbjNbgEgcAEgdHVozAFozHVszM0gzM1tINV1/f1lubkhuf1luf2xGHW5dXX9uSH9/XX9uWWaIiIBbM4iIZgECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwZyQIBwSCwahYFA4shsKoSCRXM69Aw71GwRodVij4JkkigOFLoAScBQ/AgO6KHgTJxA4uTi5igVRpgdSlRldExmQnt4RQwDBH+KRRQBfZBEklyVRA9zmUVqj5WgAZ1FApiKiUOMqV0CT0UNAadZZaeBtVlBADs=\" alt=\"image\"\u003e=13.717, P = 0.089), indicated good consistency between predictions and observations. Decision curve analysis (DCA) further demonstrated the clinical utility of the nomogram, showing a positive net benefit across a wide range of threshold probabilities in both the training and validation sets (Fig. 9).\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe high comorbidity burden of hypertension, dyslipidemia, and diabetes (\u0026quot;the three highs\u0026quot;) among Tibetan residents \u003csup\u003e9\u003c/sup\u003e, coupled with the complex challenges of hypertension management in China \u003csup\u003e10\u003c/sup\u003e, underscores the need for enhanced early prevention in primary care. Nomograms have proven effective for intuitive disease risk prediction \u003csup\u003e11\u003c/sup\u003e, offering a potential solution to this public health issue.\u003c/p\u003e\n\u003cp\u003eGrounded in the Health Ecological Model, this study developed and validated a nomogram to assess hypertension risk. The final model incorporated nine key factors: older age, higher BMI, urban residence, self-treatment, alcohol consumption, and utilization of family doctor services were identified as risk factors, while higher annual household income, better self-rated health, and consumption of purified water were protective factors.\u003c/p\u003e\n\u003cp\u003eTheThe model\u0026apos;s discriminatory power was evaluated using ROC analysis, yielding AUC values of 0.899 (95% CI: 0.889\u0026ndash;0.909) in the training set and 0.873 (95% CI: 0.856\u0026ndash;0.891) in the validation set, indicating strong performance. Furthermore, calibration curves demonstrated good agreement between predicted probabilities and observed outcomes.\u003c/p\u003e\n\u003cp\u003eIn summary, this study developed a risk assessment tool based on nine indicators derived from the Health Ecological Model. This nomogram serves as an effective and practical instrument for primary healthcare providers to identify individuals at high risk for hypertension.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, its cross-sectional design precludes causal inferences, as all variables were assessed concurrently. Future prospective and multi-center studies are needed to validate these findings and enhance clinical generalizability. Second, although the sample size was substantial, the model\u0026apos;s performance was only internally validated. External validation in diverse populations is essential. Finally, incorporating additional predictors, such as detailed dietary patterns \u003csup\u003e12\u003c/sup\u003e, sedentary behavior \u003csup\u003e13\u003c/sup\u003e, and clinical biomarkers \u003csup\u003e14\u003c/sup\u003e, could further improve model accuracy and provide a more comprehensive application of the Health Ecological Model in hypertension prediction.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eGrounded in the Health Ecological Model, this study developed and validated a nomogram for predicting hypertension risk. This readily applicable tool can assist primary healthcare practitioners in identifying high-risk individuals and facilitating early intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are not publicly available due to confidentiality policies but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving human participants was approved by the Ethics Committee of the Tibet Autonomous Region Health Commission. The study was conducted in accordance with local regulations and institutional requirements. Participants signed electronic informed consent forms to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research, authorship, and/or publication of this article received financial support. This study was funded by the Expanded Project of the 7th Tibet Health Service Survey, with the grant number 18080278.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all participants who took part in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no commercial or financial relationships that could be construed as potential conflicts of interest during the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHypertension Alliance of China, Committee on Chinese Expert Recommendations for High-Quality Blood Pressure Management in Hypertensive Patients (2024). Chinese expert recommendations for high-quality blood pressure management in hypertensive patients. Chin J Hypertens, 32(2): 104-111.\u003c/li\u003e\n\u003cli\u003eYu ZQ, Zhou X (2024). Improving hypertension control rate: standardizing the diagnosis and treatment of essential hypertension is the key. Chin J Hypertens, 32(11): 1006-1010.\u003c/li\u003e\n\u003cli\u003eYao YY, Zhang X, Zhao LM, et al (2024). Current status and prospects of plateau-related hypertension research. J Sichuan Univ (Med Sci), 55(6): 1454-1459.\u003c/li\u003e\n\u003cli\u003eZhang ZC, Qi HL (2024). Research progress on the pathogenesis of hypertension under the influence of plateau environment. Chin J Hypertens, 32(8): 727-736.\u003c/li\u003e\n\u003cli\u003eTang J, Yang R, Li H, et al (2024). Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China. Elife, 13: RP98169.\u003c/li\u003e\n\u003cli\u003eChang HJ, Lin CH, Huang JR, et al (2024). Influencing factors of hypertension among residents in Fujian Province based on the Health Ecological Model. Chin J Hypertens, 32(9): 859-869.\u003c/li\u003e\n\u003cli\u003eZhang Y, Jiang XT, Wang PY (2025). Analysis of depressive symptoms in Chinese elderly population based on the Health Ecological Model. Chin J Chronic Dis Prev Control, 33(1): 8-14.\u003c/li\u003e\n\u003cli\u003eLuo L, Long X, Cheng C, et al (2024). Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne), 15: 1338167. - PubMed\u003c/li\u003e\n\u003cli\u003eYu Y, Jinmeiquzhen, Bai GX, et al (2025). Prevalence and influencing factors of hypertension, dyslipidemia, and diabetes comorbidity among Tibetan residents. Chin J Chronic Dis Prev Control, 33(6): 442-446.\u003c/li\u003e\n\u003cli\u003eGuo JW, Zhou J, Guo ZH (2022). Role of national basic public health services and medical insurance payment reform in hypertension prevention and treatment. Chin J Hypertens, 30(10): 932-937.\u003c/li\u003e\n\u003cli\u003eXu XY, Li D, Song LR, et al (2022). Nomogram for predicting an individual prospective hemorrhage risk in untreated brainstem cavernous malformations. J Neurosurg, 138(4): 910-921. - PubMed\u003c/li\u003e\n\u003cli\u003eYao D, Yu DM, Sun JY, et al (2025). Prevalence of metabolic syndrome and its association with diet among elderly people aged 65 years and above in rural Beijing, 2023. J Hyg Res, 54(2): 244-251.\u003c/li\u003e\n\u003cli\u003eChen KY, Jiang QY, Wang JY, et al (2023). Construction and empirical analysis of a rapid identification method for populations at high risk of hypertension complications. Chin J Public Health, 39(9): 1108-1113.\u003c/li\u003e\n\u003cli\u003eLiu Q, Gong M, Su Z, et al (2025). Development and validation of a nomogram for predicting the probability of postpartum chronic hypertension in women with hypertensive disorders of pregnancy: a multicenter, cross-sectional study. J Clin Hypertens (Greenwich), 27(7): e70094. - PubMed\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Health Ecological Model, hypertension, LASSO, nomogram, Seventh National Health Service Survey, Tibet","lastPublishedDoi":"10.21203/rs.3.rs-7812496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7812496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: This study aimed to analyze the status and influencing factors of hypertension among residents in the high-altitude areas of Tibet based on the Health Ecological Model, to provide a reference for improving hypertension prevention and control strategies in the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data were obtained from the Seventh National Health Service Survey in Tibet (2023), including 9,480 participants aged ≥18 years. Based on self-reported hypertension status, they were categorized into hypertensive and non-hypertensive groups. Participants were randomly divided into a training set and a validation set at a 7:3 ratio using a random number table. The Chi-square test or Fisher’s exact test was used to compare categorical variables. Significant predictors were selected via LASSO regression, and a nomogram was developed using logistic regression. The model's predictive performance was evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Through univariate analysis, LASSO selection, and logistic regression, nine key variables were identified from the initial 30 for nomogram construction: age group, BMI classification, self-rated health, alcohol consumption, self-treatment for illness, prefecture-level city, drinking water type, annual household income, and family doctor service utilization. The model demonstrated an area under the curve (AUC) of 0.899 (95% CI: 0.889–0.909) in the training set and 0.873 (95% CI: 0.856–0.891) in the validation set. The calibration curve indicated good agreement between predicted and observed outcomes. Decision curve analysis (DCA) confirmed the clinical utility of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Integrating the Health Ecological Model, this study developed a risk prediction nomogram for hypertension. This tool can assist primary healthcare providers in identifying high-risk individuals, thereby facilitating early intervention and prevention.\u003c/p\u003e","manuscriptTitle":"Establishment of a Hypertension Predictive Model and Analysis of Its Influencing Factors Among Residents in Tibet Autonomous Region, China: A Health Ecological Model (HEM)-Based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 17:06:36","doi":"10.21203/rs.3.rs-7812496/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8e80595-ab24-486c-bcb4-46830d77b868","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63137974,"name":"Health sciences/Diseases"},{"id":63137975,"name":"Health sciences/Health care"},{"id":63137976,"name":"Health sciences/Medical research"},{"id":63137977,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-11T05:25:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 17:06:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7812496","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7812496","identity":"rs-7812496","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}