Abstract
South Asians have a high burden of cardiovascular disease compared to other racial/ethnic groups in the United States. Little has been done to evaluate how neighborhood environments may influence cardiovascular risk factors including hypertension and type 2 diabetes in this immigrant population. We evaluated the association of perceived neighborhood social cohesion with hypertension and type 2 diabetes among 906 South Asian adults who participated in the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study. Multivariable logistic regression adjusted for demographic, socioeconomic, psychosocial, and physiologic covariates. Subgroup analyses examined whether associations differed by gender. South Asian women living in neighborhoods with high social cohesion had 46% reduced odds of having hypertension than those living in neighborhoods with low social cohesion (OR=0.54, 95% CI 0.30–0.99). Future research should determine if leveraging neighborhood social cohesion prevents hypertension in South Asian women.
Keywords: social cohesion, hypertension, type 2 diabetes, South Asians, neighborhood environment
INTRODUCTION
Individuals of South Asian origin, (from India, Pakistan, Bangladesh, Nepal, and Sri Lanka) represent nearly a quarter of the world’s population and are one of the fastest growing ethnic groups in the United States [1 2]. The increased risk of cardiovascular disease (CVD) among South Asians is well established [3]. In addition, US South Asian immigrants have a considerably higher prevalence of type 2 diabetes (23%) compared to 6% in Caucasians, 18% in African Americans, 17% in Latinos, and 13% in Chinese Americans, and this risk persists after adjusting for socio-demographic characteristics [4]. There is a growing body of evidence on the clinical, behavioral, and genetic factors that influence the development of CVD risk factors among South Asian populations [4–6]. However, there is a paucity of studies examining how neighborhood environment influences U.S. South Asians’ high prevalence of CVD risk factors [7].
One important feature of the neighborhood is social cohesion, a concept that captures a key aspect of the neighborhood’s social environment. In prior studies, neighborhood social cohesion has been defined by constructs such as perceived connectedness, solidarity, and shared resources that allow people to act together[8]. Social cohesion is hypothesized to affect health by promoting social integration, buffering stress effects, and enforcing social norms and control facilitating positive behavioral changes. Perceived social cohesion has largely been studied in the context of mental health, and numerous studies have shown that social cohesion is protective against depression [9]. More recently, research has also shown that high social cohesion is associated with improved physical health and an overall lower prevalence of chronic illnesses including hypertension, myocardial infarction, and stroke [9–14].
Sex may moderate the effects of social cohesion on health outcomes. Conceptually, neighborhood social cohesion may impact women more than men because women traditionally have spent more time at home than men [15] and women may be more influenced by social-relational contexts than men [16]. One study found that the perceptions about the physical environment were important to men, whereas in women, perceptions about the social quality of the local environment were more important for overall health [17]. Stafford et al. have shown that residential stressors, such as sociopolitical and neighborhood environment (e.g. access to food shops, transportation, and overall quality of neighborhood) had a stronger association with self-rated health for women while economic activity was more strongly related to self-rated health for men [18].
For South Asians, there is evidence that gender roles may influence health, and a recent review showed that men were more likely to make executive decisions about dietary decisions in South Asian households while women often did not engage in physical activity due to competing interests of taking care of the family [19]. However, no studies have looked at the interaction of sex and social environment in this population.
Accordingly, our objective was to determine the association between perceived neighborhood social cohesion and prevalence of both hypertension and type 2 diabetes in U.S. South Asian immigrants. We hypothesized that after adjusting for demographic, socioeconomic, psychosocial, and physiologic risk factors at the individual level, higher neighborhood social cohesion would be associated with lower prevalence of hypertension and type 2 diabetes and that this association would be moderated by sex.
METHODS
Study Population
The Mediators of Atherosclerosis in South Asians Living in America (MASALA) study is a community-based cohort of South Asian adults without known CVD, which was modeled on the Multi-Ethnic study of Atherosclerosis (MESA). Study participants were recruited from two clinical sites – the San Francisco Bay Area through the University of California, San Francisco (UCSF) and the greater Chicago area through Northwestern University (NWU). A total of 906 subjects were recruited between October 2010 and March 2013. Detailed study methods have been previously published [20].
To be eligible for MASALA, participants had to be of South Asian ancestry and have at least three grandparents born in one of the following countries: India, Pakistan, Bangladesh, Nepal, or Sri Lanka, be between the ages of 40–84 years, and be able to speak and/or read English, Hindi or Urdu [20]. Exclusion criteria included a physician diagnosed heart attack, stroke or transient ischemic attack, heart failure, angina, use of nitroglycerin, a history of cardiovascular procedures, current atrial fibrillation, active treatment for cancer, life expectancy < 5 years due to a serious medical illness, impaired cognitive ability, plans to move out of the study region in the next 5 years, living in or being on a waiting list for a nursing home, and weight > 300 lbs [20].
Institutional Review Boards at UCSF and NWU approved of the MASALA Study protocol. The present analysis used de-identified data and was deemed exempt by the Institutional Review Board at the University of Michigan.
Outcome variables
Our primary outcome was a diagnosis of hypertension or type 2 diabetes at the time of baseline data collection. Seated resting blood pressure was measured three times using an automated blood pressure monitor (V100 Vital Signs Monitor; GE Healthcare, Fairfield, CT) with the average of the last 2 readings being used for analysis [20]. Hypertension was defined using the Joint National Committee criteria [21]as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg or use of anti-hypertensive medication. Type 2 diabetes mellitus was defined using the American Diabetes Association definition for type 2 diabetes [22]as a fasting plasma glucose ≥126 mg/dL2- hour post-load plasma glucose ≥ 200 mg/dL, or use of anti-hyperglycemic mediations. All blood samples were obtained after a 12-hour fast.
Principal Independent Variable
Our principal independent variable was perceived neighborhood social cohesion, which was measured using a well-validated five-item Likert scale [23 24]. Respondents were asked to report their levels of agreement with the following statements: ‘People around here are willing to help their neighbors,’ ‘People in this neighborhood generally don’t get along with each other,’ ‘People in this neighborhood can be trusted,’ ‘People in this neighborhood do not share the same values,’ and ‘Most people in this neighborhood know each other’[23 24]. For each scale, a score was created by taking the average across all items within the scale (Cronbach’s α=0.65). Scores were then divided into tertiles and defined as low, medium, and high neighborhood social cohesion.
Covariates
We included demographic and socioeconomic variables and observed psychosocial and physiologic risk factors in all multivariable models. Demographic covariates included age, sex, and site of recruitment (UCSF or NWU), and socioeconomic covariates included income, and education. Psychosocial risk factors included chronic burden, depression, social support, and marital status. Chronic burden was measured over the past six months using the Chronic Stress Burden Scale, which is a validated five item scale measuring work and other personal stressors [25]. A higher score reflected greater chronic burden. Depression was measured using the CES-D index where a score of 16 or greater indicates a risk of depression [26]. Social support was measured using the validated Emotional Social Support Index (ESSI, range 6–30) [27]. ESSI was divided into three categories of social support: low (score<12), moderate (score 12–24), and high (score>25) as had been done in prior research on social support and health in later life [28]. The scores were further dichotomized into a low/moderate score <24 and high score of >25 because <2% of the population (n=17) had a low score in our sample.
All participants completed a detailed questionnaire to ascertain socio-demographic information and behaviors (including tobacco and alcohol use) [20]. Physiologic risk factors included were tobacco and alcohol use, obesity, and physical activity. Smoking was defined as currently smoking, and alcohol use was defined as >1drink per week. Central obesity was directly measured as waist circumference in centimeters and used in place of BMI as it has been shown to be more predictive of obesity-related illnesses [29]. Specifically, within South Asians, central obesity has been shown to be highly correlated with insulin resistance after controlling for general obesity[30]. Physical activity was assessed using the Typical Week’s Physical Activity Questionnaire [31] categorized into three groups: less than 500 MET/min/wk, 500–1000 MET/min/wk, or greater than 1000MET/min/w based on recommendation of the American College of Sports Medicine and the American Heart Association [32].
Statistical Analysis
Using bivariate and multivariable logistic regressions of 2010–2013 data, we first estimated the associations between higher perceived neighborhood social cohesion and the odds of having hypertension and the odds of having type II type 2 diabetes using Stata 13 software (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP). We analyzed the association with four models: 1) unadjusted; 2) including demographic/socioeconomic covariates; 3) including both demographic/socioeconomic and psychosocial covariates; and 4) including demographic/socioeconomic level covariates, psychosocial covariates, and physiologic covariates.
Interaction analysis was performed to assess if the association between social cohesion and primary outcomes differed by sex. We also stratified the analysis by sex in the fully adjusted model to examine within-group differences in hypertension and type 2 diabetes across the three tertiles of social cohesion. In all estimations, the lowest level of neighborhood social cohesion was used as the reference category. We obtained predicted probabilities using margins of having hypertension and type 2 diabetes at each tertile of neighborhood social cohesion, and present the data as percentage chance of having hypertension or type 2 diabetes [33].
In this analysis, 26 out of 906 (3%) participants had missing information for the variable for income. We addressed the missing data using multiple imputation. Five imputed datasets that assumed a multivariable normal distribution with a Markov chain Monte Carlo approach were created [34 35]. We performed sensitivity analysis using the imputed data to see if there was a significant difference between the analysis with complete observations and the analysis with the imputed data for income.
RESULTS
Detailed socio-demographic characteristics of the MASALA cohort have been published previously [20]. Overall 98% of the study participants were immigrants who had lived in the U.S. for an average of 27±11 years[20]. The mean age of the population was 55 years; 46% were women; 60% had obtained higher than a bachelor’s degree in education, and overall the cohort had high socioeconomic status (Table 1). The population as a whole had a low prevalence of smoking and low alcohol use. One-third of the population did not meet recommended exercise goals. Women on average had central obesity, defined as a waist circumference >88cm in women. In comparison, men had a mean waist circumference of 89cm which is lower than the threshold of central obesity in men, defined as >102cm.
Table 1.
Characteristics of the MASALA sample at baseline (2010–2013) (n=906)
| Total | Men | Women | |
|---|---|---|---|
| Sample Size | 906 (100% | 486 (54%) | 420 (46%) |
| Mean age (SD) | 55 (9.4) | 56 (9.9) | 54 (8.6) |
| Marital Status | 829 (92%) | 469 (96.5%) | 360 (85.7%) |
| Education | |||
| Below bachelor’s degree | 110 (12.1%) | 48 (9.9%) | 62 (14.8%) |
| Bachelor’s degree | 261 (28.8%) | 123 (25.31%) | 138 (32.9%) |
| Higher than Bachelor’s degree | 535 (59.1%) | 315 (64.8%) | 220 (52.4%) |
| Incomea | |||
| <=74,999 | 235 (26.7%) | 130 (27.4%) | 105 (25.9%) |
| 75,000–199,000 | 389 (44.2%) | 210 (44.3%) | 179 (44.1%) |
| >=200,000 | 256 (29.1%) | 134 (28.3%) | 122 (30.1%) |
| Smoking | |||
| Yes | 31(3.4%) | 26 (5.4%) | 5 (1.2%) |
| No | 875 (96.6%) | 460 (94.7%) | 415 (98.8%) |
| Exercise | |||
| <500 MET/min/wk | 295 (32.6%) | 146 (30.0%) | 149 (35.5%) |
| 500–1000 MET/min/wk | 180 (19.9%) | 97 (20.0%) | 83 (19.8%) |
| >1000 MET/min/wk | 431 (47.6%) | 243 (50.0%) | 188 (44.8%) |
| Alcohol >1 drink per week | |||
| Yes | 299 (33.0%) | 220 (45.3%) | 79 (18.8%) |
| No | 607(67.0%) | 266 (54.7%) | 341 (81.2%) |
| Waist Circumference (SD) | |||
| Men | 92.8 (10.32) | 96.0 (9.5) | 89.1 (10.0) |
| Women | |||
|
Mean CES-D score (SD) (range 0–52) |
7.72 (7.1) | 7.2 (7.1) | 8.3 (7.6) |
| Chronic Burden in last 6 months | |||
| Score=0 | 484 (53.4%) | 277 (57.0%) | 207 (49.3%) |
| Score=1 | 261 (28.8%) | 129 (26.5%) | 132 (31.4%) |
| Score=2 | 106 (11.7%) | 58 (11.9%) | 48 (11.4%) |
| Score=3–5 | 55 (6.1%) | 22 (4.5%) | 33 (7.9%) |
| Emotional Social Support | |||
| Low/Moderate Social Support (Score 6–24) | 364 (40.1%) | 186 (38.3%) | 178 (42.4%) |
| High Social Support (25–30) | 542 (59.8%) | 300 (61.7%) | 242 (57.6% |
| Employment | |||
| Men | 634 (70.0%) | 387 (61.0%) | 247 (39.0%) |
| Women | |||
| Sitec | |||
| NWU | 410 (45.3%) | 238 (49%) | 172 (41%) |
| UCSF | 496 (54.8%) | 248 (51%) | 248 (59.1%) |
Income measured for 880/906 participants
Waist circumference measured in centimeters
NWU=Northwestern University, UCSF= University of California in San Francisco
Hypertension and type 2 diabetes were significantly associated with high tertile neighborhood social cohesion in unadjusted models with the full sample (Hypertension OR=0.62, 95% CI 0.43–0.91, type 2 diabetes OR=0.57, 95% CI 0.35–0.93) (Table 2). In the adjusted models, there was no statistically significant association between perceived neighborhood social cohesion and diagnosis of hypertension or type 2 diabetes. However, prevalence of hypertension and type 2 diabetes tended to decrease as social cohesion increased. The lowest tertile of social cohesion had a 42% (prevalence of hypertension compared to only 35% (OR=0.70, 0.44–1.09) of participants having hypertension in the highest tertile of social cohesion. Similarly in the lowest tertile of social cohesion, 22% of participants had type 2 diabetes compared to those in the highest cohesion group with 16% (OR=0.68 95% CI 0.40–1.16) prevalence of type 2 diabetes. Sensitivity analysis showed that when imputed incomes were included in the data, the results did not change.
Table 2.
Association of hypertension (OR and predicted prevalence) and perceived neighborhood social cohesion
| Model | Covariates | Hypertension | Diabetes | ||
|---|---|---|---|---|---|
| OR (CI) | (%) | OR (CI) | (%) | ||
| 1 | |||||
| Lowest Tertile | Unadjusted | Ref | 46% | Ref | 24% |
| Medium Tertile | 0.77 (0.57–1.04) | 40% | 0.91(0.64–1.29) | 22% | |
| Highest Tertile | 0.62 (0.43–0.91)* | 35% | 0.57(0.35–0.93)* | 15% | |
| p-for-trend:0.01 | p-for-trend:0.04 | ||||
| 2 | Socioeconomica | ||||
| Lowest Tertile | Ref | 43% | Ref | 22% | |
| Medium Tertile | 0.90 (0.75–1.25) | 40% | 0.99(0.68–1.42) | 22% | |
| Highest Tertile | 0.69 (0.45–1.06) | 35% | 0.65(0.39–1.10) | 16% | |
| p-for trend:0.10 | p-for-trend:0.16 | ||||
| 3 | Socioeconomic | ||||
| Lowest Tertile | +Psychosocialb | Ref | 42% | Ref | 22% |
| Medium Tertile | 0.93 (0.66–1.29) | 41% | 1.02(0.70–1.49) | 22% | |
| Highest Tertile | 0.71 (0.46–1.10) | 35% | 0.67(0.39–1.13) | 16% | |
| p-for trend:0.15 | p-for-trend:0.21 | ||||
| 4 | |||||
| Lowest Tertile | Socioeconomic | Ref | 42% | Ref | 22% |
| Medium Tertile | +Psychosocialb | 0.96 (0.68–1.35) | 41% | 1.06(0.72–1.55) | 23% |
| Highest Tertile | + Physiologicc | 0.70 (0.44–1.09) | 35% | 0.68(0.40–1.16) | 16% |
| p-for trend:0.14 | p-for-trend:0.26 | ||||
Socioeconomic Factors: age, gender, education, income, site
Psychosocial Factors: depression, chronic burden, social support, marital status
Physiologic Factors: smoking, alcohol, central obesity, physical exercise
P<0.05 when compared to thereference group
Interaction analysis showed no difference in the association of hypertension and type 2 diabetes with social cohesion between men and women. When stratified for sex, women in the highest tertile of social cohesion had a 27% prevalence (OR=0.54 95% CI 0.30–0.99) of developing hypertension compared to 39% prevalence among women in the lowest tertile of social cohesion (p=0.03). (Table 3). We a priori selected waist circumference to meaure central obesity to include in our model, since previous research suggests that it is more predictive of type 2 diabetes risk than BMI in South Asians [30]. However, we did perform an alternative analysis that included BMI with WHO suggested cut-offs for Asian populations[36] (<23=normal, 23–27.5=overweight, and >27.5=obese) which did not alter our reported associations.
Table 3.
Association of hypertension (OR and predicted prevalence) and perceived neighborhood social cohesion by sex
| Neighborhood Social Cohesiona |
Hypertension | Diabetes | ||||||
|---|---|---|---|---|---|---|---|---|
| Male | Female | Male | Female | |||||
| OR (CI) | (%) | OR (CI) | (%) | OR (CI) | (%) | OR (CI) | (%) | |
| Lowest Tertile | Ref | 45% | Ref | 39% | Ref | 28% | Ref | 14% |
| Medium Tertile | 1.03 (0.63–1.69) | 45% | 0.92 (0.53–11.57) | 37% | 0.83(0.50–11.37) | 25% | 1.53(0.76–13.1) | 19% |
| Highest Tertile | 0.91(0.52–11.59) | 44% | 0.57 (0.31–11.04)* | 28% | 0.74(0.41–1.33) | 23% | 0.90(0.40–2.02) | 13% |
| p-for trend:0.75 | p-for trend:0.03 | p-for trend:0.31 | p-for trend:0.95 | |||||
adjusted model included demographics, psychosocial, and physiologic covariates
p<0.05 compared to lowest tertile
DISCUSSION
To the best of our knowledge, this is the first paper to examine how the neighborhood environment influences objectively measured CVD risk factors in US South Asian immigrants, the 2nd fastest growing minority group in the US and one with a very high risk of CVD. In the overall sample, we did not find an association between social cohesion and prevalence of hypertension or type 2 diabetes. In South Asian women, however, higher perceived neighborhood social cohesion was significantly associated with decreased prevalence of hypertension. While there has been a plethora of studies looking at individual-level risk factors for CVD in this population, this is the first study to suggest that neighborhood social environment may exert an effect on South Asian women’s health.
In the MESA study, Mujahid et al. found that prior to controlling for race, residents in neighborhoods with more social cohesion were less likely to have hypertension [10]. Another study further found that increased neighborhood social cohesion was associated with better hypertension management [11]. Similarly, there have been studies showing that greater neighborhood resources for physical activity and food are inversely related to insulin resistance and an overall lower incidence of type 2 type 2 diabetes [37 38]. However, no study has examined the link between neighborhood social cohesion and objectively measured type 2 diabetes outcomes. The difference in our results between hypertension and type 2 diabetes may be driven by the pathways through which neighborhood social environments affect health. The neighborhood social environment is hypothesized to affect health by buffering or reducing stress via improving social connections and enforcing norms [9]. Clinically, it may take longer to see the effects of reducing stress on patients with type 2 diabetes outcomes compared to patients with hypertension and this will need to be studied further.
In addition, this study advances the literature by showing that the association of perceived social cohesion with hypertension may be modified by sex. Prior work has found that the effects of neighborhood social environments on health can be moderated by sex [17 18 39]. It has been suggested that women be more responsive to neighborhood environmental influences because they on average have lower levels of labor market participation, and they tend to spend more time in areas close to home because of their heavier involvement in family care as opposed to men who may be spending more time away from the neighborhood working [19 40]. In our sample, 61% of men were currently employed compared to 39% of women. However, we were unable to make inferences about this association due to a lack of power. Future studies will need to study the evolution of this relationship as a larger percentage of women are spending more time in work environments.
Our study had several limitations. First, the MASALA sample is largely comprised of Asian Indian immigrants living in the San Francisco Bay and Chicago areas. These populations may not be representative of all South Asians in the U.S and did on average have a higher level of education and income compared to the general U.S. population. However, it has been shown previously that this population is similar to the U.S. Census 2010 South Asian data [20]. Second, any participants with known cardiovascular disease or symptoms were excluded from the sample, so this sample likely represents an overall underestimation of type 2 diabetes and hypertension prevalence among South Asian Americans. Third, this is a cross-sectional study which limits our ability to make causal inferences. Longitudinal follow-up of the MASALA cohort will allow for further investigations about how these neighborhood effects may vary with time. Fourth, our data did not allow us to adjust for how long participants were living in their respective neighborhoods. Finally, although our study uses a well validated neighborhood social cohesion scale in general U.S. populations, the psychometric properties of this scale have not been evaluated in South Asian populations.
This study adds to the literature by presenting evidence on the association between neighborhood social cohesion and prevalence rates of hypertension and type 2 diabetes in high risk South Asian immigrants. Furthermore, it offers insight into how gender roles may moderate this association. Future research should determine if leveraging neighborhood social cohesion can help prevent hypertension in South Asian women.
Acknowledgments
The project described was supported by Grant Number P30DK092926 (MCDTR) from the National Institute of Type 2 diabetes and Digestive and Kidney Diseases, the Robert Wood Johnson Foundation Clinical Scholars Program, and by Grant Number R01HL093009 from the National Institutes of Health, National Heart, Lung, And Blood Institute. Data collection at UCSF was also supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 RR024131.
REFERENCES
- 1.Bank TW. Data. Secondary Data. 2015 http://data.worldbank.org/region/SAS. [Google Scholar]
- 2.Elizabeth Hoeffel SR, Myoung Ouk Kim, Hasan Shahid. The Asian Population: 2010. 2012 www.census.gov.
- 3.Palaniappan L, Wang Y, Fortmann SP. Coronary heart disease mortality for six ethnic groups in California, 1990–2000. Annals of epidemiology. 2004;14(7):499–506. doi: 10.1016/j.annepidem.2003.12.001. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 4.Kanaya AM, Herrington D, Vittinghoff E, et al. Understanding the high prevalence of diabetes in U.S. south Asians compared with four racial/ethnic groups: the MASALA and MESA studies. Diabetes care. 2014;37(6):1621–1628. doi: 10.2337/dc13-2656. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Gadgil MD, Anderson CA, Kandula NR, et al. Dietary Patterns Are Associated with Metabolic Risk Factors in South Asians Living in the United States. The Journal of nutrition. 2015 doi: 10.3945/jn.114.207753. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Radha V, Mohan V. Genetic predisposition to type 2 diabetes among Asian Indians. The Indian journal of medical research. 2007;125(3):259–274. [PubMed] [Google Scholar]
- 7.Zaman MJ, Bhopal RS. New answers to three questions on the epidemic of coronary mortality in south Asians: incidence or case fatality? Biology or environment? Will the next generation be affected? Heart (British Cardiac Society) 2013;99(3):154–158. doi: 10.1136/heartjnl-2012-302364. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 8.McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Social science & medicine. 2006;63(4):1011–1022. doi: 10.1016/j.socscimed.2006.03.012. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 9.Diez Roux AV, Mair C. Neighborhoods and health. Annals of the New York Academy of Sciences. 2010;1186:125–145. doi: 10.1111/j.1749-6632.2009.05333.x. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 10.Mujahid MS, Diez Roux AV, Morenoff JD, et al. Neighborhood characteristics and hypertension. Epidemiology. 2008;19(4):590–598. doi: 10.1097/EDE.0b013e3181772cb2. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 11.Schmitz MF, Giunta N, Parikh NS, et al. The association between neighbourhood social cohesion and hypertension management strategies in older adults. Age and ageing. 2012;41(3):388–392. doi: 10.1093/ageing/afr163. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 12.Thorpe CT, Oddone EZ, Bosworth HB. Patient and social environment factors associated with self blood pressure monitoring by male veterans with hypertension. Journal of clinical hypertension (Greenwich, Conn) 2008;10(9):692–699. doi: 10.1111/j.1751-7176.2008.00005.x. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kim ES, Hawes AM, Smith J. Perceived neighbourhood social cohesion and myocardial infarction. Journal of epidemiology and community health. 2014;68(11):1020–1026. doi: 10.1136/jech-2014-204009. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kim ES, Park N, Peterson C. Perceived neighborhood social cohesion and stroke. Social science & medicine. 2013;97:49–55. doi: 10.1016/j.socscimed.2013.08.001. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 15.SA R. Socioeconomic position and health: the independent contribution of community socioeconomic context. Ann Rev Sociol. 1999;25:489–516. [Google Scholar]
- 16.Fuhrer R, Stansfeld SA. How gender affects patterns of social relations and their impact on health: a comparison of one or multiple sources of support from "close persons". Social science & medicine. 2002;54(5):811–825. doi: 10.1016/s0277-9536(01)00111-3. [DOI] [PubMed] [Google Scholar]
- 17.Molinari C, Ahern M, Hendryx M. The relationship of community quality to the health of women and men. Social science & medicine. 1998;47(8):1113–1120. doi: 10.1016/s0277-9536(98)00114-2. [DOI] [PubMed] [Google Scholar]
- 18.Stafford M, Cummins S, Macintyre S, et al. Gender differences in the associations between health and neighbourhood environment. Social science & medicine. 2005;60(8):1681–1692. doi: 10.1016/j.socscimed.2004.08.028. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 19.Patel M, Phillips-Caesar E, Boutin-Foster C. Barriers to lifestyle behavioral change in migrant South Asian populations. Journal of immigrant and minority health / Center for Minority Public Health. 2012;14(5):774–785. doi: 10.1007/s10903-011-9550-x. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kanaya AM, Kandula N, Herrington D, et al. Mediators of Atherosclerosis in South Asians Living in America (MASALA) study: objectives, methods, and cohort description. Clinical cardiology. 2013;36(12):713–720. doi: 10.1002/clc.22219. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8) Jama. 2014;311(5):507–520. doi: 10.1001/jama.2013.284427. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 22.(2) Classification and diagnosis of diabetes. Diabetes care. 2015;38(Suppl):S8–s16. doi: 10.2337/dc15-S005. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 23.Mujahid MS, Diez Roux AV, Morenoff JD, et al. Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. American journal of epidemiology. 2007;165(8):858–867. doi: 10.1093/aje/kwm040. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 24.Mair C, Diez Roux AV, Shen M, et al. Cross-sectional and longitudinal associations of neighborhood cohesion and stressors with depressive symptoms in the multiethnic study of atherosclerosis. Annals of epidemiology. 2009;19(1):49–57. doi: 10.1016/j.annepidem.2008.10.002. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Bromberger JT, Matthews KA. A longitudinal study of the effects of pessimism, trait anxiety, and life stress on depressive symptoms in middle-aged women. Psychology and aging. 1996;11(2):207–213. doi: 10.1037//0882-7974.11.2.207. [DOI] [PubMed] [Google Scholar]
- 26.Lewinsohn PM, Zinbarg R, Seeley JR, et al. Lifetime comorbidity among anxiety disorders and between anxiety disorders and other mental disorders in adolescents. Journal of anxiety disorders. 1997;11(4):377–394. doi: 10.1016/s0887-6185(97)00017-0. [DOI] [PubMed] [Google Scholar]
- 27.Vaglio J, Jr, Conard M, Poston WS, et al. Testing the performance of the ENRICHD Social Support Instrument in cardiac patients. Health and quality of life outcomes. 2004;2:24. doi: 10.1186/1477-7525-2-24. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Mezuk B, Diez Roux AV, Seeman T. Evaluating the buffering vs. direct effects hypotheses of emotional social support on inflammatory markers: the multi-ethnic study of atherosclerosis. Brain, behavior, and immunity. 2010;24(8):1294–1300. doi: 10.1016/j.bbi.2010.06.006. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Janssen I. BMI, waist circumference and fat composition are not correlated with mortality risk in an older Korean population, but higher lean mass and lean mass index are predictors of reduced mortality risk. Evidence-based medicine. 2010;15(4):125–126. doi: 10.1136/ebm1088. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 30.McKeigue PM, Shah B, Marmot MG. Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. Lancet. 1991;337(8738):382–386. doi: 10.1016/0140-6736(91)91164-p. [DOI] [PubMed] [Google Scholar]
- 31.Ainsworth BE, Irwin ML, Addy CL, et al. Moderate physical activity patterns of minority women: the Cross-Cultural Activity Participation Study. Journal of women's health & gender-based medicine. 1999;8(6):805–813. doi: 10.1089/152460999319129. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 32.Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116(9):1081–1093. doi: 10.1161/CIRCULATIONAHA.107.185649. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 33.Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press; 2010. [Google Scholar]
- 34.Raghunathan TE, Lepkowski JM, Van Hoewky J, Solenberger P. A multivariate technique for muliply imputing missing values using a sequence of regression models. Survey Methodology. 2001;27:85–95. [Google Scholar]
- 35.van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research. 2007;16:219–242. doi: 10.1177/0962280206074463. [DOI] [PubMed] [Google Scholar]
- 36.Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157–163. doi: 10.1016/S0140-6736(03)15268-3. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 37.Auchincloss AH, Diez Roux AV, Brown DG, et al. Neighborhood resources for physical activity and healthy foods and their association with insulin resistance. Epidemiology. 2008;19(1):146–157. doi: 10.1097/EDE.0b013e31815c480. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]
- 38.Auchincloss AH, Diez Roux AV, Mujahid MS, et al. Neighborhood resources for physical activity and healthy foods and incidence of type 2 diabetes mellitus: the Multi-Ethnic study of Atherosclerosis. Archives of internal medicine. 2009;169(18):1698–1704. doi: 10.1001/archinternmed.2009.302. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Kavanagh AM, Bentley R, Turrell G, et al. Does gender modify associations between self rated health and the social and economic characteristics of local environments? Journal of epidemiology and community health. 2006;60(6):490–495. doi: 10.1136/jech.2005.043562. [published Online First: Epub Date]|. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Wen M, Zhang X. Contextual effects of built and social environments of urban neighborhoods on exercise: a multilevel study in Chicago. American journal of health promotion : AJHP. 2009;23(4):247–254. doi: 10.4278/ajhp.07052448. [published Online First: Epub Date]|. [DOI] [PubMed] [Google Scholar]