High prevalence of hypertension and its association with psychological distress in urban slums in Pune, India

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Factors related to stress may be important contributors to hypertension in these populations in addition to traditional factors such as age and obesity. We examined stress-related and traditional hypertension risk factors among urban poor in Pune, India. We used cluster area random sampling to enroll adults aged ≥18 years from urban slum communities, collecting demographics, psychological distress (Kessler-6), tobacco use, anthropometrics, and blood pressure. Psychological distress was defined as Kessler-6 ≥11. Hypertension was defined as systolic blood pressure ≥140, diastolic blood pressure ≥90 or taking hypertension medications. We conducted multivariable logistic regression on hypertension, adjusting for age, BMI, and tobacco use. Among 502 adults, median age was 44 years (IQR 31-53), 75.5% (n=379) were female, and 72.2% (N=365) had overweight/obese BMI. Hypertension prevalence (age-standardized) was 50.4% (n=257). Psychological distress was independently associated with hypertension (aOR 2.14, 95% CI 1.23, 3.73). Age >50 years (aOR 2.75, 95% CI 1.42, 5.31), obese BMI (aOR 2.36, 95% CI 1.41, 3.94), and tobacco use at hazardous level (World Health Organization ASSIST score 4-26) (aOR 2.88, 95% CI 1.38, 6.02) were also significantly associated with hypertension. Upon sex-stratification, similar results were seen among women. We found a high prevalence of hypertension in a predominately female urban slum population in India. Hypertension was associated with stress. Public health programs for hypertension prevention in low-income populations should screen for and address stress in addition to traditional risk factors. hypertension psychological distress tobacco use low and middle income countries Figures Figure 1 Figure 2 Figure 5 1. Introduction Eighty percent of people with hypertension, the world’s leading cause of mortality, live in low and middle-income countries (LMICs). 1 In low-and middle income populations, including in India, risk factors for hypertension may include previously understudied factors such as stress in addition to traditional risk factors such as obesity or smoking. 2 Across LMICs, obesity accounts for only 38% of hypertension cases while 60–80% of hypertension is explained by obesity in the US-based Framingham study. 3 The factors that contribute to the remainder of the risk are poorly understood. Better understanding of contributors to hypertension in these populations will allow more targeted and effective interventions to prevent hypertension. In LMICs, psychosocial risk factors may be much more prevalent than in high-income countries. 4 , 5 In India, 87% of women report feeling stressed most of the time – the highest rate in the world. 6 In comparison, 55% of the US population reports feeling stressed most of the time. 7 Substance use disorders, which are often precipitated by chronic stress, are also increasingly common in low-income settings. 8 India ranks second highest in world prevalence of tobacco use. 9 Alcohol consumption is also growing at nearly 3 times the rate of high-income countries. 10 All of these risk factors are even more common among urban poor populations worldwide. More research on stress is urgently needed to understand how best to curb the worsening health threat of hypertension. In this analysis, we evaluate the prevalence of hypertension and both stress-related and traditional risk factors among a sample of adults living in Pune's slum communities. We hypothesize that psychological distress is an independent risk factor for hypertension. 2. Methods 2.1 Setting In 2019, Deep Griha Society conducted a health and demographic survey of slum communities in Pune, India. Deep Griha Society is a nongovernmental organization that has provided health and vocational services to slum communities in Pune, India for over 50 years. The slum communities included in this study were Tadiwala Road, Ramtekdi, and Bibwewadi (total population estimated: 50,000 adults). 2.2 Study design This was a cross-sectional study for which participants were recruited using cluster area random sampling. The three slums were divided by Deep Griha Society into a total of 50 clusters based on estimated population size (~ 1000 adults) for systematic random sampling. Clusters were defined based on Each fieldworker was randomly assigned 5 clusters. The center of each cluster was determined using Google maps. Each fieldworker visited their first assigned cluster and, starting from the center point of the cluster, spun a pen to indicate the direction in which to walk. 11 The first house in that direction was selected for surveying. Inclusion/exclusion criteria All adults over 18 years old in the household, including pregnant adults, were approached for the survey. Participant selection was not age or sex stratified. After the first household, each fifth household was surveyed in the same manner. Fieldworkers moved to the next cluster after a target of 10 participants were recruited in one cluster. 2.3 Data collection Individual data was collected in a de-identified manner on an encrypted tablet using the platform Open Data Kit Collect (Seattle, USA). The survey included questions about sociodemographic information, medical history, access to healthcare, substance use disorder (using the World Health Organization (WHO) Alcohol, Smoking and Substance Involvement Screening Test), 12 medication adherence, and psychological distress (Kessler-6 scale). Anthropometrics (height and weight without shoes, waist circumference and blood pressure) were measured per WHO STEPS guidelines. 13 Using the Omron® series 5 (Kyoto, Japan) blood pressure monitor, blood pressure was measured once on the upper arm after 5 minutes of rest. 14 Blood pressure monitors were bought new and did not require calibration during the study period. All other study instruments were calibrated weekly. All surveys were conducted in either Marathi or Hindi, based on the participant’s preference. All data collection was done on weekdays during the day. 2.4 Hypertension definition Hypertension was defined as systolic blood pressure of at least 140 mmHg or diastolic blood pressure of at least 90 mmHg per 2020 International Society of Hypertension Global Hypertension Practice Guidelines or on medication intended to reduce blood pressure. 15 Pre-hypertension was defined as systolic blood pressure 120–139 mmHg or diastolic blood pressure 80–89 mmHg. 15 Data on specific names of medication used at the time of the survey were not available. The prevalence of hypertension was age-standardized using direct standardization with the 2000–2025 WHO standard population data to account for bias in age groups during sampling. 16 Hypertension awareness was defined as having a diagnosis of hypertension prior to the study, and treatment was defined as currently taking any medication intended to reduce blood pressure. 2.5 Risk factor definitions BMI was categorized as underweight ( 25 kg/m 2 ) based on Asian cutoffs. 17 High waist circumference was defined as > 90cm for men and > 80cm for women. 13 Psychological distress was assessed for the preceding 30-day period using the Kessler-6 instrument. Moderate to severe psychological distress was defined as Kessler-6 score ≥ 11 and mild psychological distress was defined as ≥ 6 and < 11. 18 ASSIST substance-specific scores were calculated for smoking, alcohol use, and drug use and stratified into (a) no abuse, (b) hazardous or harmful use, or (c) dependence per ASSIST scoring criteria. Intravenous (IV) drug use was defined as any history of use of non-medicinal IV substances. 2.6 Statistical analysis Descriptive analysis was used to describe study participants’ basic demographics, risk factors, and prevalence of hypertension. Pre-estimated power for determining hypertension prevalence in this sample was > 80%. Power was also estimated to be > 80% among females but not males in the sex-stratified analysis. Clinically relevant variables (i.e. age, BMI, tobacco use) were included in a multivariate model. High waist circumference was excluded from the multivariate model as it was collinear with BMI. Data were not weighted. All analyses were conducted using Stata version 14.0 (College Station, TX). Participants with missing data in any of the independent or dependent variables were excluded (n = 9). 2.7 Ethics This study was approved by institutional review boards of both institutions. Written informed consent including consent to publish in Hindi, Marathi, or English was obtained from participants after discussion of study goals, content, risks, and voluntary nature of participation. For those who were illiterate, the consent form was read out loud to the participant by the fieldworker and a witnessed thumbprint was obtained. Participants with hypertension were given information about hypertension and referred to free and low-cost medical care. Participants with severe hypertension (systolic blood pressure > 180 and/or diastolic blood pressure > 110) were referred to the nearby government tertiary hospital. 3. Results A total of 511 participants were surveyed from all three sites (54.2% from Tadiwala Road, 30.5% from Ramtekdi, and 15.3% from Bibwewadi in accordance with their relative population sizes). No individuals refused participation in the survey. Nine participants were excluded due to missing data, and a total of 502 participants were included in the final analysis. (Fig. 1 ) 3.1 Baseline characteristics (Table 1 ) Table 1 Baseline characteristics of the cohort Total (%) N = 502 Women (%) N = 379 Men (%) N = 123 P value Basic demographics Median Age, years 44 (31–53) 41 (30–52) 47 (33–55) 0.12 < 25 years 54 (10.8%) 40 (10.6%) 14 (11.4%) 0.14 25–49 years 266 (53.0%) 210 (55.4%) 56 (45.5%) ≥ 50 years 182 (36.3%) 129 (34.0%) 53 (43.1%) < 4th grade education 137 (27.3%) 123 (32.5%) 14 (11.4%) < 0.01 Marital status Single 33 (6.6%) 17 (4.5%) 16 (13.0%) < 0.01 Married 399 (79.5%) 296 (78.1%) 103 (83.7%) Widowed/Divorced 69 (13.8%) 65 (17.2%) 4 (3.3%) Occupation Unemployed 299 (59.6%) 267 (70.5%) 32 (26.0%) < 0.01 Employed 203 (40.4%) 112 (29.6%) 91 (74.0%) Anthropometrics High waist circumference a 407 (81.1%) 315 (83.1%) 92 (74.8%) 0.04 Median BMI, kg/m 2 25.6 (22.5, 28.4) 25.7 (22.7, 28.7) 24.7 (22.1, 28.1) 0.13 BMI b Underweight 27 (5.4%) 18 (4.8%) 9 (7.3%) 0.41 Normal weight 113 (22.5%) 82 (21.6%) 31 (25.2%) Overweight 190 (37.9%) 143 (37.7%) 47 (38.2%) Obese 175 (34.3%) 136 (35.9%) 36 (29.3%) Median systolic BP, mmHg 125 (115–140) 125 (114–138) 125 (115–143) 0.37 Median diastolic BP, mmHg 80 (75–90) 81 (76–90) 80 (71–88) 0.01 % on Hypertension therapy 119 (23.7%) 92 (24.3%) 27 (22.0%) 0.28 Other risk factors Kessler-6 score c 0 38 (7.6%) 26 (6.9%) 12 (9.8%) 0.68 1–5 170 (33.9%) 132 (34.8%) 38 (30.9%) 6–10 211 (42.0%) 158 (41.7%) 53 (43.1%) 11–24 83 (16.5%) 63 (16.6%) 20 (16.3%) Tobacco use d None 427 (85.1%) 322 (85.0%) 105 (85.4%) 0.97 Hazardous or harmful use 44 (8.8%) 33 (8.7%) 11 (8.9%) Dependence 31 (6.2%) 24 (6.3%) 7 (5.7%) Alcohol use d None 492 (97.6%) 374 (98.2%) 118 (95.9%) 0.06 Hazardous or harmful use 9 (1.8%) 4 (1.1%) 5 (4.1%) Dependence 3 (0.6%) 3 (0.8%) 0 IV drug use e 32 (6.4%) 25 (6.6%) 7 (5.7%) 0.72 IQR: interquartile range; a Defined as > 90cm for males and > 80cm for females; BMI: body-mass index; b underweight is defined as 25 kg/m 2 ; c Kessler-6 of 11–24 is considered moderate to severe psychosocial distress 18 ; d subgroups per WHO ASSIST scoring criteria; e defined as ever use in lifetime The median age was 44 yrs (IQR 31–53), 137 participants (27.3%) had less than 4th grade (primary school) education, and 379 (75.5%) were female. BMI was overweight in 37.9% of people (n = 190) and obese in 34.3% (n = 172). Upon stratification by sex, female participants had a greater prevalence of less than 4th grade education (34.7 vs 11.3%, p < 0.01) and unemployment (69.7 vs 26.8%, p < 0.01), but had similar age and BMI as male participants. 3.2 Hypertension prevalence, awareness and care continuum (Fig. 2 ) The overall prevalence of hypertension was 51.2% (n = 257). With age-standardization to the WHO standard population, prevalence of hypertension was 50.4%. Of the 257 participants with hypertension, 129/257 (50.2%) were aware of their diagnosis of hypertension prior to the study, 119/257 (46.3%) said they were on treatment, and 70/257 (27.2%) had blood pressure control (< 140/90 mmHg). 3.3 Prevalence of risk factors (Table 2 ) Table 2 Univariate and Multivariable logistic regression of the association of risk factors with hypertension Characteristics No hypertension (%) N = 245 Hypertension (%) N = 257 OR (95% CI) P-value aOR (95% CI) P-value Basic demographics Median Age 37 (29–48) 48 (35–56) 1.04 (1.02, 1.05) < 0.01 < 25 years 30 (12.2%) 24 (9.3%) -ref- -ref- 25–49 years 155 (63.3%) 111 (43.2%) 0.90 (0.50, 1.61) 0.71 0.92 (0.49, 1.73) 0.79 ≥ 50 years 60 (24.5%) 122 (47.5%) 2.54 (1.37, 4.72) < 0.01 2.75 (1.42, 5.31) < 0.01 Sex Female 184 (75.1%) 195 (75.9%) 1.04 (0.69, 1.57) 0.84 Male 61 (24.9%) 62 (24.1%) Anthropometrics High waist circumference a+ 189 (77.1%) 218 *84.8%) 1.66 (1.05, 2.60) 0.03 Median BMI b 24.7 (21.4–27.3) 26.1 (23.4–29.3) 1.08 (1.04, 1.13) < 0.01 Underweight 20 (8.2%) 7 (2.7%) 0.44 (0.17, 1.13) 0.09 0.43 (0.16, 1.17) 0.10 Normal weight 63 (25.7%) 50 (19.5%) -ref- -ref- Overweight 97 (39.6%) 93 (36.2%) 1.21 (0.76, 1.93) 0.43 1.16 (0.71, 1.90) 0.56 Obese 65 (26.5%) 107 (41.6%) 2.07 (1.28, 3.36) < 0.01 2.36 (1.41, 3.94) < 0.01 Median systolic BP 119 (110–125) 140 (122–150) Median diastolic BP 78 (70–81) 89 (80–96) Stress-related risk factors Kessler-6 score c 0–5 114 (46.5%) 94 (36.6%) -ref- -ref- 6–10 99 (40.4%) 112 (43.6%) 1.37 (0.93, 2.01) 0.10 1.42 (0.94, 2.14) 0.10 11–24 32 (13.1%) 51 (19.8%) 1.93 (1.15, 3.25) 0.01 2.14 (1.23, 3.73) < 0.01 Tobacco use d None 220 (89.8%) 207 (80.5%) -ref- -ref- Hazardous or harmful use 11 (4.5%) 33 (12.8%) 3.19 (1.57, 6.47) < 0.01 2.88 (1.38, 6.02) < 0.01 Dependence 14 (5.7%) 17 (6.6%) 1.29 (0.62, 2.68) 0.50 1.09 (0.49, 2.45) 0.83 Alcohol use d None 241 (98.4%) 249 (96.9%) -ref- Hazardous or harmful use 4 (1.6%) 5 (2.0%) 1.21 (0.32, 4.56) 0.78 Dependence 0 3 (1.2%) n/a IV drug use Ever use 10 (4.1%) 22 (8.6%) 2.2 (1.20, 4.75) 0.04 2.14 (0.94, 4.88) 0.07 Never use 235 (95.9%) 235 (91.4%) -ref- -ref- IQR: interquartile range; a Defined as > 90cm for males and > 80cm for females; BMI: body-mass index; b underweight is defined as 25 kg/m 2 ; c Kessler-6 of 11–24 is considered moderate to severe psychosocial distress 18 ; d subgroups per WHO ASSIST scoring criteria; + not included in the multivariate model as it is collinear with BMI Supplemental Table 1. Multivariate logistic regression analysis of the odds of hypertension stratified by sex There were no significant differences between sex, marital status, and having less than a 4th grade education between people with and without hypertension. Participants with hypertension had higher BMI than participants without hypertension (26.1 vs 24.7 kg/m 2 p < 0.01) as well as a higher prevalence of high waist circumference (84.8% vs 77.1%, p = 0.03). Participants with hypertension also had a greater prevalence of moderate to severe psychological distress (19.8% vs 13.1%, p = 0.03), tobacco use at hazardous level per ASSIST criteria (12.8% vs 4.5%, p < 0.01) and having ever tried intravenous drugs (8.6% vs 4.1%, p = 0.04) compared to participants without hypertension. 3.4 Independent risk factors for hypertension (Table 2 ) On multivariate analysis, risk factors independently associated with hypertension included: age > 50 years (aOR 2.75, 95% CI 1.42, 531), obese BMI (aOR 2.36, 95% CI 1.41, 3.94), tobacco use at hazardous level (ASSIST score 4–26) (aOR 2.88, 95% CI 1.38, 6.02), and psychological distress (OR 2.14, 95% CI 1.23, 3.73). After stratification by sex, only psychological distress remained a significant, independent risk factor for hypertension in women (aOR = 2.60 (1.36, 4.98) but not in men (Supplemental Table). 4. Discussion Our study demonstrated a high prevalence of hypertension and pre-hypertension in a low-income, predominately female population in India, on par with prevalence estimates from US adults. 19 Awareness and control of hypertension were low. Hypertension was associated with traditional risk factors such as age, BMI and tobacco use, but also independently associated with psychological distress. Our findings call for more attention to stress as a hypertension risk factor in public health programs which aim to prevent and manage hypertension, particularly for low-income women. Hypertension prevalence is increasing rapidly in low- and middle-income countries (LMICs), with > 40% of adults now affected in many countries in sub-Saharan Africa and central and South Asia. 20 Although men are known to have a higher risk of hypertension than women, in LMICs, there is lesser difference in hypertension prevalence between men and women than in high-income countries. 20 In our sub-population of predominately female, low-income adults in urban slums in India, hypertension prevalence was similar to the most severely affected LMICs, underscoring the vulnerability of the poor – and poor women – to non-communicable diseases. Half of participants were unaware of their hypertension. Hypertension, particularly undiagnosed hypertension, is exceptionally dangerous for the poor because of financial insecurity that limits access to care for adverse outcomes or medications. 21 Poorer adults are increasingly exposed to multiple stressors that are associated with hypertension but are not widely recognized as risk factors. Finding modifiable risk factors to prevent hypertension in these communities is of utmost importance. One potential reason for the high prevalence of hypertension in our population is the high prevalence of psychological distress. Psychological distress doubled the odds of hypertension. The association between psychological distress and hypertension has been noted in prior research, though with lower strength of association. In one study conducted in the USA, severe psychological distress was associated with 1.5 times the odds of hypertension (95% CI 1.3–1.8) compared to no distress, with a slightly stronger association noted in males. 4 Studies from Belarus, Kazakhstan, and Russia have reported similar results. Data from Asia using other markers of stress (such as the perceived stress scale) are of similar magnitude to ours, providing additional support to the hypothesis that stress and psychological distress may be important independent contributors to hypertension prevalence in Asians. 22 Psychological distress, which activates the sympathetic nervous system and adversely affects sleep, is common among Asian women in particular. 23 Public health programs should determine the drivers of psychological distress, which include food insecurity and gender inequality, 24 in these populations to broaden targets for addressing hypertension-related morbidity and mortality. As expected, obese BMI, older age, and tobacco use were also independently associated with hypertension. While higher BMI is a known risk factor for hypertension, it was surprising that more than 70% of this low-income cohort met Asian BMI criteria for overweight or obesity. Similar alarming prevalences of overweight and obesity have been seen in other studies from low-income populations in India and indicate the growing obesity problem in India is not just limited to middle and upper-income populations. 25 Deficiency of micronutrients is causally related to insulin resistance and weight gain, as is the increasing low-cost carbohydrate consumption which is being seen in India. 26 Physical activity is also difficult in crowded urban slum environments. 27 Women in our cohort had an even higher prevalence of high BMI than men, though we were unable to measure dietary factors and physical activity factors contributing to this relationship. Obesity and malnutrition in women of reproductive age have significant long-term impacts on population health because they result in greater adiposity in their children, perpetuating the risk of non-communicable diseases intergenerationally. 28 Tobacco use in the ‘hazardous’ range per WHO ASSIST criteria was associated with almost 3-fold greater odds of hypertension compared to no tobacco use. Nicotine in tobacco acutely increases blood pressure by stimulating catecholamine release. Women in India predominately used smokeless tobacco. 29 Smokeless tobacco (e.g. chewing tobacco) releases nicotine over a longer duration than cigarettes, and therefore may cause a more sustained elevation in blood pressure. Tobacco use also may be a result of chronic stress. 30 Strategies to reduce smokeless tobacco use may also prevent hypertension, particularly if targeted toward urban low-income populations. This study has several strengths and limitations. This was a large community study of an under-researched and high-risk population. To our knowledge, this was the first comprehensive health and demographic survey in urban Indian slum communities. It was powered for comparison of psychological distress in people with and without hypertension. We used validated questionnaires to measure multiple risk factors including psychological distress and tobacco use. 12 We collected anthropometric data per World Health Organization STEPs protocol using trained research staff. However, blood pressure was only measured once, which may have led to some overdiagnosis in hypertension prevalence. Sampling was done on participants who were at home during normal working hours, likely indicating that this study was conducted in a more sedentary population. This also limited power to detect hypertension prevalence and risk factors in males because fewer males were at home during working hours. However, this did allow us to identify risk factors in women, who are often under-represented in hypertension research. Urban low-income populations have unique considerations in hypertension prevention, especially in LMICs, where the risk of non-communicable diseases is rapidly increasing. Future studies should focus on understanding the burden and causes of stress in low-income communities to design novel interventions to address hypertension at a community level. 5. Conclusion Hypertension was present in 51% of adults residing in urban slums in Pune. More than 70% of adults were overweight or obese. Odds of hypertension were higher with psychological distress as well as with older age, obesity, and tobacco use. There was no difference in prevalence between men and women. In addition to obesity reduction, interventions targeted at stress reduction should be developed and implemented in public health programs for hypertension prevention. References (NCD-RisC) NRFC. 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BMC Public Health Nov. 2021;09(1):2047. 10.1186/s12889-021-12089-6 . Sinha R. Chronic stress, drug use, and vulnerability to addiction. Ann N Y Acad Sci Oct. 2008;1141:105–30. 10.1196/annals.1441.030 . Supplementary Files SupplementalTable1.docx 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-7779486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527278804,"identity":"4d43ea38-b87a-4a80-b560-ebb25e861476","order_by":0,"name":"Puja Chebrolu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHACxgcJBv/kgAwDCJ+duYGBgQ2vFmaDDxUHjBFamBkJamGTnHHmQGID0VrMJZKfSfO23Ulf2968geFHxb08fpCWD2WHcWqxnJFmbM3b9ix325ljBYw9Z4qLJZsZGxhnnMOtxeBGDuNt3jbm3G03cgyYGdsSEjccZmxg5m3Dq4UB6DDmdLP7byBa9oO0/MWvhQno/cMJZjd4oLYA/QJk4PFLzzNjYCCnGW47k1ZwsOdMQuIMoC0He86l49Rizp78EBiVNvJmxw9vfPCjIiGxv7354IMfZda4HYbMOYDBIKhlFIyCUTAKRgFWAABz4lxp6KmTDAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5422-2966","institution":"Weill Cornell Medicine","correspondingAuthor":true,"prefix":"","firstName":"Puja","middleName":"","lastName":"Chebrolu","suffix":""},{"id":527278805,"identity":"7057ddb2-800d-46e9-8318-08dacbee0321","order_by":1,"name":"Lily D Yan","email":"","orcid":"","institution":"Weill Cornell 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08:51:25","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112454,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7779486/v1/f179a45dc2085446cc6c7ccb.html"},{"id":94178712,"identity":"20cba8d0-fd7b-413f-b901-b977cac20285","added_by":"auto","created_at":"2025-10-23 08:59:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4522,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flowchart\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7779486/v1/3b90bbe350ccd896f839ab6d.png"},{"id":94177861,"identity":"aad8168e-112f-4834-bdd5-1b76f9fed276","added_by":"auto","created_at":"2025-10-23 08:51:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31110,"visible":true,"origin":"","legend":"\u003cp\u003eHypertension prevalence and care cascade\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7779486/v1/2e61c7ba9c5d940750d3f809.png"},{"id":94178713,"identity":"d1635fb7-99a1-41d9-8c27-96e612a8118a","added_by":"auto","created_at":"2025-10-23 08:59:25","extension":"eps","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":200376,"visible":true,"origin":"","legend":"Hypertension prevalence and care cascade","description":"","filename":"drawingimage1.eps","url":"https://assets-eu.researchsquare.com/files/rs-7779486/v1/188b9108f2481e54c157c588.eps"},{"id":94612407,"identity":"0b92e724-f16b-4b95-96e4-032e5b165006","added_by":"auto","created_at":"2025-10-29 02:10:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":946928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7779486/v1/e89376fc-8e59-43e4-864c-3cb0e6837239.pdf"},{"id":94177860,"identity":"a7df2e63-b8e7-4655-848a-940511b173f1","added_by":"auto","created_at":"2025-10-23 08:51:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17575,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7779486/v1/82cf5a7882e0dd824101ecae.docx"}],"financialInterests":"","formattedTitle":"High prevalence of hypertension and its association with psychological distress in urban slums in Pune, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEighty percent of people with hypertension, the world\u0026rsquo;s leading cause of mortality, live in low and middle-income countries (LMICs).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In low-and middle income populations, including in India, risk factors for hypertension may include previously understudied factors such as stress in addition to traditional risk factors such as obesity or smoking.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Across LMICs, obesity accounts for only 38% of hypertension cases while 60\u0026ndash;80% of hypertension is explained by obesity in the US-based Framingham study.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The factors that contribute to the remainder of the risk are poorly understood. Better understanding of contributors to hypertension in these populations will allow more targeted and effective interventions to prevent hypertension.\u003c/p\u003e\u003cp\u003eIn LMICs, psychosocial risk factors may be much more prevalent than in high-income countries.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In India, 87% of women report feeling stressed most of the time \u0026ndash; the highest rate in the world.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In comparison, 55% of the US population reports feeling stressed most of the time.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Substance use disorders, which are often precipitated by chronic stress, are also increasingly common in low-income settings.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e India ranks second highest in world prevalence of tobacco use.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Alcohol consumption is also growing at nearly 3 times the rate of high-income countries.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e All of these risk factors are even more common among urban poor populations worldwide.\u003c/p\u003e\u003cp\u003eMore research on stress is urgently needed to understand how best to curb the worsening health threat of hypertension. In this analysis, we evaluate the prevalence of hypertension and both stress-related and traditional risk factors among a sample of adults living in Pune's slum communities. We hypothesize that psychological distress is an independent risk factor for hypertension.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Setting\u003c/h2\u003e\u003cp\u003eIn 2019, Deep Griha Society conducted a health and demographic survey of slum communities in Pune, India. Deep Griha Society is a nongovernmental organization that has provided health and vocational services to slum communities in Pune, India for over 50 years. The slum communities included in this study were Tadiwala Road, Ramtekdi, and Bibwewadi (total population estimated: 50,000 adults).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study design\u003c/h2\u003e\u003cp\u003eThis was a cross-sectional study for which participants were recruited using cluster area random sampling. The three slums were divided by Deep Griha Society into a total of 50 clusters based on estimated population size (~\u0026thinsp;1000 adults) for systematic random sampling. Clusters were defined based on Each fieldworker was randomly assigned 5 clusters. The center of each cluster was determined using Google maps. Each fieldworker visited their first assigned cluster and, starting from the center point of the cluster, spun a pen to indicate the direction in which to walk.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e The first house in that direction was selected for surveying.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion/exclusion criteria\u003c/strong\u003e\u003cp\u003eAll adults over 18 years old in the household, including pregnant adults, were approached for the survey. Participant selection was not age or sex stratified. After the first household, each fifth household was surveyed in the same manner. Fieldworkers moved to the next cluster after a target of 10 participants were recruited in one cluster.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data collection\u003c/h2\u003e\u003cp\u003eIndividual data was collected in a de-identified manner on an encrypted tablet using the platform Open Data Kit Collect (Seattle, USA). The survey included questions about sociodemographic information, medical history, access to healthcare, substance use disorder (using the World Health Organization (WHO) Alcohol, Smoking and Substance Involvement Screening Test),\u003csup\u003e12\u003c/sup\u003e medication adherence, and psychological distress (Kessler-6 scale). Anthropometrics (height and weight without shoes, waist circumference and blood pressure) were measured per WHO STEPS guidelines.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Using the Omron\u0026reg; series 5 (Kyoto, Japan) blood pressure monitor, blood pressure was measured once on the upper arm after 5 minutes of rest.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Blood pressure monitors were bought new and did not require calibration during the study period. All other study instruments were calibrated weekly. All surveys were conducted in either Marathi or Hindi, based on the participant\u0026rsquo;s preference. All data collection was done on weekdays during the day.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Hypertension definition\u003c/h2\u003e\u003cp\u003eHypertension was defined as systolic blood pressure of at least 140 mmHg or diastolic blood pressure of at least 90 mmHg per 2020 International Society of Hypertension Global Hypertension Practice Guidelines or on medication intended to reduce blood pressure.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Pre-hypertension was defined as systolic blood pressure 120\u0026ndash;139 mmHg or diastolic blood pressure 80\u0026ndash;89 mmHg.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Data on specific names of medication used at the time of the survey were not available. The prevalence of hypertension was age-standardized using direct standardization with the 2000\u0026ndash;2025 WHO standard population data to account for bias in age groups during sampling.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Hypertension awareness was defined as having a diagnosis of hypertension prior to the study, and treatment was defined as currently taking any medication intended to reduce blood pressure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Risk factor definitions\u003c/h2\u003e\u003cp\u003eBMI was categorized as underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (18.5\u0026ndash;22.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (23-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) based on Asian cutoffs.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e High waist circumference was defined as \u0026gt;\u0026thinsp;90cm for men and \u0026gt;\u0026thinsp;80cm for women.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePsychological distress was assessed for the preceding 30-day period using the Kessler-6 instrument. Moderate to severe psychological distress was defined as Kessler-6 score\u0026thinsp;\u0026ge;\u0026thinsp;11 and mild psychological distress was defined as \u0026ge;\u0026thinsp;6 and \u0026lt;\u0026thinsp;11.\u003csup\u003e18\u003c/sup\u003e ASSIST substance-specific scores were calculated for smoking, alcohol use, and drug use and stratified into (a) no abuse, (b) hazardous or harmful use, or (c) dependence per ASSIST scoring criteria. Intravenous (IV) drug use was defined as any history of use of non-medicinal IV substances.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive analysis was used to describe study participants\u0026rsquo; basic demographics, risk factors, and prevalence of hypertension. Pre-estimated power for determining hypertension prevalence in this sample was \u0026gt;\u0026thinsp;80%. Power was also estimated to be \u0026gt;\u0026thinsp;80% among females but not males in the sex-stratified analysis. Clinically relevant variables (i.e. age, BMI, tobacco use) were included in a multivariate model. High waist circumference was excluded from the multivariate model as it was collinear with BMI. Data were not weighted. All analyses were conducted using Stata version 14.0 (College Station, TX). Participants with missing data in any of the independent or dependent variables were excluded (n\u0026thinsp;=\u0026thinsp;9).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Ethics\u003c/h2\u003e\u003cp\u003e This study was approved by institutional review boards of both institutions. Written informed consent including consent to publish in Hindi, Marathi, or English was obtained from participants after discussion of study goals, content, risks, and voluntary nature of participation. For those who were illiterate, the consent form was read out loud to the participant by the fieldworker and a witnessed thumbprint was obtained.\u003c/p\u003e\u003cp\u003eParticipants with hypertension were given information about hypertension and referred to free and low-cost medical care. Participants with severe hypertension (systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;180 and/or diastolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;110) were referred to the nearby government tertiary hospital.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 511 participants were surveyed from all three sites (54.2% from Tadiwala Road, 30.5% from Ramtekdi, and 15.3% from Bibwewadi in accordance with their relative population sizes). No individuals refused participation in the survey. Nine participants were excluded due to missing data, and a total of 502 participants were included in the final analysis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (%)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;502\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen (%)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMen (%)\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;123\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic demographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian Age, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (31\u0026ndash;53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (30\u0026ndash;52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (33\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e266 (53.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e210 (55.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;50 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182 (36.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (43.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;4th grade education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e399 (79.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e296 (78.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (83.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed/Divorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e299 (59.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e267 (70.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (26.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (74.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAnthropometrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh waist circumference\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e407 (81.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315 (83.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92 (74.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian BMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.6 (22.5, 28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.7 (22.7, 28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.7 (22.1, 28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143 (37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47 (38.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 (35.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (29.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian systolic BP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (115\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (114\u0026ndash;138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (115\u0026ndash;143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian diastolic BP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (75\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (76\u0026ndash;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (71\u0026ndash;88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% on Hypertension therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOther risk factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKessler-6 score\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (7.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170 (33.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211 (42.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158 (41.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (43.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTobacco use\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e427 (85.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e322 (85.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105 (85.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardous or harmful use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e492 (97.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e374 (98.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (95.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardous or harmful use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV drug use\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eIQR: interquartile range; \u003csup\u003ea\u003c/sup\u003eDefined as \u0026gt;\u0026thinsp;90cm for males and \u0026gt;\u0026thinsp;80cm for females; BMI: body-mass index; \u003csup\u003eb\u003c/sup\u003eunderweight is defined as \u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, normal weight as 18-22.9 kg/m\u003csup\u003e2\u003c/sup\u003e, overweight as 23-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e, and obese as \u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e; \u003csup\u003ec\u003c/sup\u003eKessler-6 of 11\u0026ndash;24 is considered moderate to severe psychosocial distress\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003ed\u003c/sup\u003esubgroups per WHO ASSIST scoring criteria; \u003csup\u003ee\u003c/sup\u003edefined as ever use in lifetime\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe median age was 44 yrs (IQR 31\u0026ndash;53), 137 participants (27.3%) had less than 4th grade (primary school) education, and 379 (75.5%) were female. BMI was overweight in 37.9% of people (n\u0026thinsp;=\u0026thinsp;190) and obese in 34.3% (n\u0026thinsp;=\u0026thinsp;172). Upon stratification by sex, female participants had a greater prevalence of less than 4th grade education (34.7 vs 11.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and unemployment (69.7 vs 26.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but had similar age and BMI as male participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Hypertension prevalence, awareness and care continuum (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe overall prevalence of hypertension was 51.2% (n\u0026thinsp;=\u0026thinsp;257). With age-standardization to the WHO standard population, prevalence of hypertension was 50.4%. Of the 257 participants with hypertension, 129/257 (50.2%) were aware of their diagnosis of hypertension prior to the study, 119/257 (46.3%) said they were on treatment, and 70/257 (27.2%) had blood pressure control (\u0026lt;\u0026thinsp;140/90 mmHg).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Prevalence of risk factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and Multivariable logistic regression of the association of risk factors with hypertension\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo hypertension (%) N\u0026thinsp;=\u0026thinsp;245\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypertension (%) N\u0026thinsp;=\u0026thinsp;257\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eaOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic demographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (29\u0026ndash;48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (35\u0026ndash;56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04 (1.02, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (63.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111 (43.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.50, 1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92 (0.49, 1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;50 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (47.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.54 (1.37, 4.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.75 (1.42, 5.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184 (75.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (75.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04 (0.69, 1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (24.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnthropometrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh waist circumference\u003csup\u003ea+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189 (77.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e218 *84.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.66 (1.05, 2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian BMI\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.7 (21.4\u0026ndash;27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.1 (23.4\u0026ndash;29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08 (1.04, 1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44 (0.17, 1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43 (0.16, 1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (19.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.76, 1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.16 (0.71, 1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (41.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07 (1.28, 3.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.36 (1.41, 3.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian systolic BP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (110\u0026ndash;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (122\u0026ndash;150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian diastolic BP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (70\u0026ndash;81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (80\u0026ndash;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStress-related risk factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKessler-6 score\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (46.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 (43.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37 (0.93, 2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.42 (0.94, 2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93 (1.15, 3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14 (1.23, 3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTobacco use\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220 (89.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207 (80.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardous or harmful use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (12.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.19 (1.57, 6.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.88 (1.38, 6.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29 (0.62, 2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09 (0.49, 2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e241 (98.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e249 (96.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardous or harmful use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.32, 4.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV drug use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEver use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2 (1.20, 4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14 (0.94, 4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235 (95.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235 (91.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-ref-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eIQR: interquartile range; \u003csup\u003ea\u003c/sup\u003eDefined as \u0026gt;\u0026thinsp;90cm for males and \u0026gt;\u0026thinsp;80cm for females; BMI: body-mass index; \u003csup\u003eb\u003c/sup\u003eunderweight is defined as \u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, normal weight as 18-22.9 kg/m\u003csup\u003e2\u003c/sup\u003e, overweight as 23-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e, and obese as \u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e; \u003csup\u003ec\u003c/sup\u003eKessler-6 of 11\u0026ndash;24 is considered moderate to severe psychosocial distress\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003ed\u003c/sup\u003esubgroups per WHO ASSIST scoring criteria;\u003csup\u003e+\u003c/sup\u003enot included in the multivariate model as it is collinear with BMI\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSupplemental Table\u0026nbsp;1. Multivariate logistic regression analysis of the odds of hypertension stratified by sex\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThere were no significant differences between sex, marital status, and having less than a 4th grade education between people with and without hypertension. Participants with hypertension had higher BMI than participants without hypertension (26.1 vs 24.7 kg/m\u003csup\u003e2\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) as well as a higher prevalence of high waist circumference (84.8% vs 77.1%, p\u0026thinsp;=\u0026thinsp;0.03). Participants with hypertension also had a greater prevalence of moderate to severe psychological distress (19.8% vs 13.1%, p\u0026thinsp;=\u0026thinsp;0.03), tobacco use at hazardous level per ASSIST criteria (12.8% vs 4.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and having ever tried intravenous drugs (8.6% vs 4.1%, p\u0026thinsp;=\u0026thinsp;0.04) compared to participants without hypertension.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Independent risk factors for hypertension (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e\u003cp\u003eOn multivariate analysis, risk factors independently associated with hypertension included: age\u0026thinsp;\u0026gt;\u0026thinsp;50 years (aOR 2.75, 95% CI 1.42, 531), obese BMI (aOR 2.36, 95% CI 1.41, 3.94), tobacco use at hazardous level (ASSIST score 4\u0026ndash;26) (aOR 2.88, 95% CI 1.38, 6.02), and psychological distress (OR 2.14, 95% CI 1.23, 3.73). After stratification by sex, only psychological distress remained a significant, independent risk factor for hypertension in women (aOR\u0026thinsp;=\u0026thinsp;2.60 (1.36, 4.98) but not in men (Supplemental Table).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study demonstrated a high prevalence of hypertension and pre-hypertension in a low-income, predominately female population in India, on par with prevalence estimates from US adults.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Awareness and control of hypertension were low. Hypertension was associated with traditional risk factors such as age, BMI and tobacco use, but also independently associated with psychological distress. Our findings call for more attention to stress as a hypertension risk factor in public health programs which aim to prevent and manage hypertension, particularly for low-income women.\u003c/p\u003e\u003cp\u003eHypertension prevalence is increasing rapidly in low- and middle-income countries (LMICs), with \u0026gt;\u0026thinsp;40% of adults now affected in many countries in sub-Saharan Africa and central and South Asia.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Although men are known to have a higher risk of hypertension than women, in LMICs, there is lesser difference in hypertension prevalence between men and women than in high-income countries.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e In our sub-population of predominately female, low-income adults in urban slums in India, hypertension prevalence was similar to the most severely affected LMICs, underscoring the vulnerability of the poor \u0026ndash; and poor women \u0026ndash; to non-communicable diseases. Half of participants were unaware of their hypertension. Hypertension, particularly undiagnosed hypertension, is exceptionally dangerous for the poor because of financial insecurity that limits access to care for adverse outcomes or medications.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Poorer adults are increasingly exposed to multiple stressors that are associated with hypertension but are not widely recognized as risk factors. Finding modifiable risk factors to prevent hypertension in these communities is of utmost importance.\u003c/p\u003e\u003cp\u003eOne potential reason for the high prevalence of hypertension in our population is the high prevalence of psychological distress. Psychological distress doubled the odds of hypertension. The association between psychological distress and hypertension has been noted in prior research, though with lower strength of association. In one study conducted in the USA, severe psychological distress was associated with 1.5 times the odds of hypertension (95% CI 1.3\u0026ndash;1.8) compared to no distress, with a slightly stronger association noted in males.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Studies from Belarus, Kazakhstan, and Russia have reported similar results. Data from Asia using other markers of stress (such as the perceived stress scale) are of similar magnitude to ours, providing additional support to the hypothesis that stress and psychological distress may be important independent contributors to hypertension prevalence in Asians.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Psychological distress, which activates the sympathetic nervous system and adversely affects sleep, is common among Asian women in particular.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Public health programs should determine the drivers of psychological distress, which include food insecurity and gender inequality,\u003csup\u003e24\u003c/sup\u003e in these populations to broaden targets for addressing hypertension-related morbidity and mortality.\u003c/p\u003e\u003cp\u003eAs expected, obese BMI, older age, and tobacco use were also independently associated with hypertension. While higher BMI is a known risk factor for hypertension, it was surprising that more than 70% of this low-income cohort met Asian BMI criteria for overweight or obesity. Similar alarming prevalences of overweight and obesity have been seen in other studies from low-income populations in India and indicate the growing obesity problem in India is not just limited to middle and upper-income populations.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Deficiency of micronutrients is causally related to insulin resistance and weight gain, as is the increasing low-cost carbohydrate consumption which is being seen in India.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Physical activity is also difficult in crowded urban slum environments.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Women in our cohort had an even higher prevalence of high BMI than men, though we were unable to measure dietary factors and physical activity factors contributing to this relationship. Obesity and malnutrition in women of reproductive age have significant long-term impacts on population health because they result in greater adiposity in their children, perpetuating the risk of non-communicable diseases intergenerationally.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTobacco use in the \u0026lsquo;hazardous\u0026rsquo; range per WHO ASSIST criteria was associated with almost 3-fold greater odds of hypertension compared to no tobacco use. Nicotine in tobacco acutely increases blood pressure by stimulating catecholamine release. Women in India predominately used smokeless tobacco.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Smokeless tobacco (e.g. chewing tobacco) releases nicotine over a longer duration than cigarettes, and therefore may cause a more sustained elevation in blood pressure. Tobacco use also may be a result of chronic stress.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Strategies to reduce smokeless tobacco use may also prevent hypertension, particularly if targeted toward urban low-income populations.\u003c/p\u003e\u003cp\u003eThis study has several strengths and limitations. This was a large community study of an under-researched and high-risk population. To our knowledge, this was the first comprehensive health and demographic survey in urban Indian slum communities. It was powered for comparison of psychological distress in people with and without hypertension. We used validated questionnaires to measure multiple risk factors including psychological distress and tobacco use.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e We collected anthropometric data per World Health Organization STEPs protocol using trained research staff. However, blood pressure was only measured once, which may have led to some overdiagnosis in hypertension prevalence. Sampling was done on participants who were at home during normal working hours, likely indicating that this study was conducted in a more sedentary population. This also limited power to detect hypertension prevalence and risk factors in males because fewer males were at home during working hours. However, this did allow us to identify risk factors in women, who are often under-represented in hypertension research.\u003c/p\u003e\u003cp\u003eUrban low-income populations have unique considerations in hypertension prevention, especially in LMICs, where the risk of non-communicable diseases is rapidly increasing. Future studies should focus on understanding the burden and causes of stress in low-income communities to design novel interventions to address hypertension at a community level.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eHypertension was present in 51% of adults residing in urban slums in Pune. More than 70% of adults were overweight or obese. Odds of hypertension were higher with psychological distress as well as with older age, obesity, and tobacco use. There was no difference in prevalence between men and women. In addition to obesity reduction, interventions targeted at stress reduction should be developed and implemented in public health programs for hypertension prevention.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e(NCD-RisC) NRFC. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. 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Ann N Y Acad Sci Oct. 2008;1141:105\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1196/annals.1441.030\u003c/span\u003e\u003cspan address=\"10.1196/annals.1441.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"hypertension, psychological distress, tobacco use, low and middle income countries","lastPublishedDoi":"10.21203/rs.3.rs-7779486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7779486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Hypertension prevalence in low-income populations globally is increasing rapidly and may have unique poverty-related determinants. Factors related to stress may be important contributors to hypertension in these populations in addition to traditional factors such as age and obesity. We examined stress-related and traditional hypertension risk factors among urban poor in Pune, India. We used cluster area random sampling to enroll adults aged ≥18 years from urban slum communities, collecting demographics, psychological distress (Kessler-6), tobacco use, anthropometrics, and blood pressure. Psychological distress was defined as Kessler-6 ≥11. Hypertension was defined as systolic blood pressure ≥140, diastolic blood pressure ≥90 or taking hypertension medications. We conducted multivariable logistic regression on hypertension, adjusting for age, BMI, and tobacco use. Among 502 adults, median age was 44 years (IQR 31-53), 75.5% (n=379) were female, and 72.2% (N=365) had overweight/obese BMI. Hypertension prevalence (age-standardized) was 50.4% (n=257). Psychological distress was independently associated with hypertension (aOR 2.14, 95% CI 1.23, 3.73). Age \u0026gt;50 years (aOR 2.75, 95% CI 1.42, 5.31), obese BMI (aOR 2.36, 95% CI 1.41, 3.94), and tobacco use at hazardous level (World Health Organization ASSIST score 4-26) (aOR 2.88, 95% CI 1.38, 6.02) were also significantly associated with hypertension. Upon sex-stratification, similar results were seen among women. We found a high prevalence of hypertension in a predominately female urban slum population in India. Hypertension was associated with stress. Public health programs for hypertension prevention in low-income populations should screen for and address stress in addition to traditional risk factors.","manuscriptTitle":"High prevalence of hypertension and its association with psychological distress in urban slums in Pune, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 08:51:21","doi":"10.21203/rs.3.rs-7779486/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":"cbe7842c-e022-401c-80be-e3f289e21540","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-28T23:43:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 08:51:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7779486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7779486","identity":"rs-7779486","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}