Geographic and Individual Correlates of Subclinical Atherosclerosis in an Asymptomatic Rural Appalachian Population

Author’s Accepted Manuscript Geographic and individual correlates of subclinical atherosclerosis in asymptomatic rural Appalachian population Hadii M. Mamudu, Antwan Jones, Timir Paul, Pooja Subedi, Liang Wang, Arsham Alamian, Ali Alamin, Gerald Blackwell, Matthew Budoff www.elsevier.com

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S0002-9629(17)30465-2 http://dx.doi.org/10.1016/j.amjms.2017.08.011 AMJMS526

To appear in: The American Journal of the Medical Sciences Cite this article as: Hadii M. Mamudu, Antwan Jones, Timir Paul, Pooja Subedi, Liang Wang, Arsham Alamian, Ali Alamin, Gerald Blackwell and Matthew Budoff, Geographic and individual correlates of subclinical atherosclerosis in asymptomatic rural Appalachian population, The American Journal of the Medical Sciences, http://dx.doi.org/10.1016/j.amjms.2017.08.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

TITLE Geographic and individual correlates of subclinical atherosclerosis in asymptomatic rural Appalachian population

Short title Effects of geographic factors on subclinical atherosclerosis Author:

Hadii M. Mamudu, PhD, MPA Associate Professor of Public Health Department of Health Services Management and Policy College of Public Health East Tennessee State University P.O. Box 70264 ,Johnson City, TN, USA Tel: 423-439-4484 Fax: 423-439-6710 Email: [email protected]

Co-authors

Antwan Jones, PhD Associate Professor Department of Sociology George Washington University Washington, DC 20052 Emil: [email protected] Timir Paul, MD, PhD Assistant Professor of Medicine Division of Cardiology Director, Cardiac Rehabilitation and Prevention James. H. Quillen College of Medicine East Tennessee State University 329 N State of Franklin Rd Johnson City, TN 37604 Phone: 423-979-4106 Fax: 423-979-4134 Email: [email protected] Pooja Subedi, MPHc Department of Biostatistics and Epidemiology College of Public Health, East Tennessee State University Johnson City, TN 37614 Email: [email protected]

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Liang Wang, MD, DrPH Associate Professor Department of Biostatistics and Epidemiology College of Public Health East Tennessee State University Email: [email protected] Arsham Alamian, PhD, MSc, MACE Associate Professor Department of Biostatistics and Epidemiology College of Public Health East Tennessee State University Email: [email protected] Ali Alamin, MBBS, MPH Department of Health Services Management and Policy College of Public Health East Tennessee State University Johnson City, TN 37614 Email: [email protected] Gerald Blackwell, MD Wellmont CVA Heart Institute Kingsport, TN 37660 Email: [email protected] Matthew Budoff, MD Professor of Medicine Los Angeles Biomedical Research Institute 1124 W Carson Street Torrance, CA 90502 Phone: 310-222-4107 Fax: 310-782-9652 Email: [email protected] Corresponding author: Hadii M. Mamudu, PhD, MPA Conflict of interest statement The authors have no conflict of interest. Financial disclosure None to disclose

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ACKNOWLEDGEMENT The researchers would like to thank the Department of Health Services Management and Policy, College of Public Health, at East Tennessee State University (ETSU) for the logistical support. We would also like to thank the Wellmont CVA Heart Institute, Wellmont Health System, for the collaboration and for providing the researchers with access to data. Further, we would like to thank the Office of Diversity and Equity at East Tennessee State University for providing the funding that supported research assistants who painstakingly collected the data for this paper. Finally, we would like to say special thanks to Olivia Luzzi, MPH candidate in Department of Health Services Management and Policy at ETSU College of Public Health for diligently editing the final version of the manuscript. ABSTRACT Objective: To examine the association between subclinical atherosclerosis (ascertained as coronary artery calcium; CAC) in asymptomatic individuals in the Central Appalachian region of the United States and individual- and geographic-level factors. Methods: Data were obtained from participants in CAC screening during 2012 and 2016. CAC score was assessed as CAC=0 (no plaque), 1≤CAC≤99 (mild plaque), 100≤CAC≤399 (moderate plaque), and CAC≥400 (severe plaque). Additionally, data on demographics (age, sex, and race), medical conditions, lifestyle factors, and family history of coronary artery disease (CAD) were obtained. Further, zip codes of place of residence for participants were used to generate geographic-level data. Descriptive statistics were used to estimate the prevalence of CAC, and multinomial logistic regression models were used to delineate significant factors. Results: Of 1512 participants, 57.6% had CAC>0. The prevalence of mild, moderate, and severe plaques was 31.6%, 16.3%, and 9.7%, respectively. Demographic, medical conditions, lifestyle factors, and family history of CAD were associated with increased risk for subclinical atherosclerosis. Further, the proportion of minority residents significantly increased the risk for severe plaque [RRR=1.06; p-value=0.04] and the proportion of residents on government assistance significantly decreased the risk for mild plaque [RRR=0.93; p-value=0.03]. Conclusion: The results imply that the proportion of minority residents in a geographic area is associated with increased relative risk for subclinical atherosclerosis, while the proportion of residents on government assistance decreased such risk. However, future geographic or neighborhood-level studies with larger sample size are needed to delineate further the consistency of these results in the Central Appalachian population. Keywords: coronary artery calcium; geographic-level factors; risk factors; cardiovascular disease; Central Appalachia

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INTRODUCTION Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality in the United States (U.S.), accounting for one in three deaths and over $300 billion in healthcare costs [1–3]; coronary artery disease (CAD), the most common form of CVD, accounts for one in six deaths [4]. The individual risk factors of CVD, including genetic composition, high cholesterol, high blood pressure, diabetes, smoking, physical inactivity, and unhealthy diets, have been established [1]. Additionally, growing evidence indicates an association between CVD and geographic or neighborhood characteristics, including unemployment, poverty, income, education, psychosocial factors, and sociodemographic characteristics [2,3-10]. Coronary artery calcium (CAC) is an established subclinical marker of CAD that predicts the risk of future cardiovascular (CV) events among asymptomatic individuals with high prognostic significance [5–7]. However, little is known about the relationship between geographic or neighborhood factors [8–14], and CAC, especially in areas with high CVD prevalence such as Central Appalachia [15–17]. Few studies [8,18] have assessed the relationship between distinct geographic or neighborhood characteristics and subclinical atherosclerosis. The high prevalence of CVD and its risk factors in the Central Appalachian region implies that CVD outcomes are associated with social determinants [19] and geographic or neighborhood factors [14,20–28]. However, to our knowledge, no study to date has been undertaken on this topic in Central Appalachia, or Appalachia as a whole. The population and geographic disparities in CVD in the U.S. [17,29–33] suggest the critical need for geographic-level studies that delineate the factors making populations such as those in the Central Appalachian region predisposed and vulnerable to CVD, including subclinical atherosclerosis and related risk factors.

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This study aimed to examine the risk for subclinical atherosclerosis (i.e. CAC) among asymptomatic patients residing in Central Appalachia based on geographic–level factors, controlling for modifiable and non-modifiable individual CV risk factors. The Central Appalachian region consists of underserved, distressed, and at-risk population [34], with high level of poverty [35] unemployment [36], limited access to and utilization of health services [37–39], and higher healthcare costs [39,40]. Therefore, it was hypothesized that geographic-level factors such as low educational attainment, high poverty, unemployment, and low-income levels will likely increase the risk for subclinical atherosclerosis. The results of the study could inform primary and secondary prevention efforts to address the high prevalence of CVD and related risk factors in Central Appalachia.

METHODS The study population comprises 2,094 asymptomatic adults without prior history of CVD or medications usage for CV risk factors, from the Central Appalachian region of Northeast Tennessee, Western North Carolina, Southeast Kentucky, and Southwest Virginia, who participated in CAC screening at the largest tertiary heart institute in the region between August, 2012 and September, 2016. The detailed description of recruitment procedures has been published in earlier studies [41–44]. Briefly, these participants, both self- and physician-referred, were assessed for eligibility for the CAC screening using standard protocol [45]. The screening was conducted with 64 Multislice Computed Tomography (CT) to determine CAC score in accordance with Institutional Review Board (IRB) protocol and the Health Information Portability and Accountability Act (HIPAA) regulations; hence, informed consent was obtained before the screening. Following the standard procedure and protocol [45], participants in the CAC screening were required to remove all metal objects from the chest area and three stick-on electrode leads were placed on the chest. The person then lies still on the CT scanner as it advances through the donut-shaped scanner. The scanner passes over the person three times,

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with the person holding his/her breathe for ≤10 seconds for each time. After the procedure, CAC score based on the Agatston algorithm46–48 was calculated to quantify the volume of calcification or plaque burden, an indicator of the risk for CAD. All the participants completed a self-reported questionnaire on demographics, medical conditions [49], current health behaviors [50], and family history of CAD [51], which was administered before the CAC screening. Out of the 2,094 study participants, an analytic sample of 1,512 was used after excluding participants without zip codes corresponding to the Central Appalachian region or who have missing CAC scores. No statistically significant difference was observed between the excluded and analytic samples. The IRBs of the corresponding author’s university and the collaborating health systems approved the study. The outcome of the study is CAC score, which determines plaque burden (Figure 1). Based on the standard Agatston scale [46,52], CAC score was divided into four levels: no plaque (CAC=0), mild plaque (1 ≤ CAC ≤ 99), moderate plaque (100 ≤ CAC ≤ 399), and severe plaque (CAC ≥ 400). Other studies have used these categories to approximate the severity of risk for CAD [41–43,46,52]. The participants in the CAC screening were asked if they were diagnosed as being hypertensive, hypercholesteremic, or diabetic (yes/no) in order to account for the CVD risk factors [49]. Respondents were also asked for their current weight and height. Using U.S. Centers for Disease Control and Prevention (CDC) guidelines, respondents’ body mass index (BMI) was calculated and divided into four categories: underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). Smoking status was recorded as three different categories: nonsmoker, current smoker, and former smoker. If the respondent exercised less than 1 time per week, they were coded as being sedentary. This study also included whether the respondent had a family history of CAD. Demographic variables (age, race, and gender) were also included in the analysis.

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The zip code of each participant was linked to the 2010-2014 American Community Survey five-year data. The U.S. Bureau of the Census recommends using five-year estimates because they are more precise than using actual one-year estimates, and are ideal for analyzing small populations [53]. Using zip code measures allows for the control of the geographic context, which may play some role in the risk of CAD. Population was assessed as the total population in the zip code. It is logged in the analyses for normality purposes. The proportion of minority residents, female-headed households, residents below the federal poverty level, residents on any governmental assistance, and college graduates was assessed at the zip code level. Additionally, data on unemployment rate for ages 16 and higher and median housing values were included in the analysis at the zip code level. An index scale for these measures was created; however, it was not included because the internal consistency reliability estimates were low (Cronbach’s α = 0.49). Bivariate relationships were explored through either one-way ANOVA for continuous measures or chi-square tests for categorical measures. Because of the multi-categorical nature of the outcome variable and because variables are derived from both individuals and zip codes, multinomial logistic regression models were employed to estimate associations between individual and geographic measures and CAC risk. Relative risk ratios (RRR) were estimated in order to differentiate between two levels of CAC. A score test [54] was conducted to determine whether the model is more parsimonious if the outcome measure is treated with an ordered logit model. The asymptotic chi-square test was significant (p = 0.04), suggesting that multinomial logistic regression is more appropriate for modeling CAC stages. Cross-level interactions were tested but were not statistically significant (p > 0.05). The analyses were performed using STATA version 14.1 (College Station, Texas, USA).

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RESULTS Table 1 shows that of the total analytic sample of 1,512, around 42.4% had no plaque (CAC=0), 31.6% had mild plaque (1≤CAC≤99), 16.3% were classified as having moderate plaque (100≤CAC≤399), and 9.7% had severe plaque (CAC≥400). Among the participants with no plaque, 41% had hypertension (p<0.0001) compared to those with mild (52.9%), moderate (59.4%) and severe (60.5%) plaque (Figure 2). A similar pattern was found for respondents who were hypercholesteremic; under half (47.7%) of those with no plaque had high blood cholesterol compared to over half of those with mild (64.4%), moderate (60.6%), and severe (64.6%) plaque (p<0.00). Diabetes was most represented in the severe plaque group (27.2%), followed by the moderate plaque group (17.9%), and the mild plaque group (12.8%), with the no plaque group showing the least representation (10.9%; p<0.0001). Within each CAC group, over half of the respondents were either overweight or obese; however, there was no significant difference in BMI across the CAC levels. About 66.6% of the respondents with no plaque were nonsmokers, compared to 64.6% of the mild plaque group, 56.5% of the moderate plaque group, and 51% of the severe plaque group (p< 0.0001). Current smokers represented about 10% of each of the CAC groups. However, there was variation in the percentage of former smokers within each group. About 22.9%, of the no plaque group and 25.7% of the mild plaque group were former smokers, while 33.7% of the moderate plaque group and 39.46% of the severe plaque group were former smokers (p< 0.0001). Around 36% to 40% of respondents in each CAC group were sedentary, and approximately 19% to 24% of each group had a family history of CAD.

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The study sample was overwhelmingly white. However, the mean age across CAC levels diverged. The average age for individuals with no, mild, moderate, and severe plaque was 54.5, 58.5, 62.8, and 65.4 years, respectively. Females constituted the largest percentage of respondents in the no plaque group (68.9%), and men comprised the largest percentage of individuals with mild (52.7%), moderate (57.3%), and severe (68%) plaque. The average population in each of the 122 zip codes included in the study was around 19,500. The mean proportion of minority residents across zip codes was 5.1%. The average zip code proportion of female-headed households was 14.3%. Across zip codes, on average, 17.5% of residents were below the federal poverty level. A larger percentage of residents (20.2%) relied on government assistance in the zip codes used in the analyses. For this sample, the average unemployment rate was 9.1%, while the average percentage of college graduates was 7.8%. For the zip codes used in these analyses, the average median housing value is $124,280. There were no statistical differences in the zip code measures across CAC levels. Table 2 summarizes the results of the multilevel multinomial logistic regression. The expected risk of having mild (RRR=1.41, p=0.01), moderate (RRR=1.58, p =0.01), or severe plaque (RRR=1.66, p =0.02) was higher for individuals who were hypertensive, relative to the risk of having no plaque. A similar relationship was observed for respondents who were hypercholesteremic. Those who were diabetic had around 2.5 times (RRR=2.47, p<0.0001) the risk of having severe plaque in contrast to having no plaque and compared to those who were not diabetic. Similarly, respondents who were underweight had 5.7 times (RRR=5.70, p=0.03) the relative risk of having severe versus no plaque compared to those who were normal weight. Having a family history of CAD and being a current smoker (compared to never smokers) were associated with a higher relative risk of having mild, moderate, or severe plaque than having no plaque. Being a former smoker was also associated with higher relative risks of having moderate or severe plaque than having no plaque. Each additional year of age increased the relative risk of having mild, moderate, and severe plaque by 7% (RRR=1.07, p<0.0001), 15%

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(RRR=1.15, p<0.0001), and 20% (RRR=1.20, p<0.0001), respectively. The relative risks of having mild, moderate, or severe plaque compared to having no plaque was around 4 (RRR=4.01, p<0.0001), 6 (RRR=6.37, p<0.0001), and 13 times (RRR=12.86, p<0.0001) higher in men than in women, respectively. The geographic measures also significantly predicted CAC risk. A one-percentage increase in minority residents in a zip code was associated with a 6% (RRR=1.06, pvalue=0.04) increase in the relative risk in the respondent having severe plaque versus no plaque. Additionally, a one-percentage increase in residents who were on government assistance in the respondent’s zip code was associated with a 7% (RRR=0.93, p-value=0.03) decrease in mild plaque.

DISCUSSION National public health goals outlined in Healthy People 2020 [55] and the American Heart Association Impact Goals [56,57] include reducing the prevalence of CVD and its risk factors, improving the health status of patients living with CVD, and eliminating CVD disparities across population subgroups and geographic areas [15–17,58,59]. The Central Appalachian region of the U.S. is noted for the high prevalence of CVD and related risk factors [16,17,29– 33]. Thus, this study investigated the relationships between geographic-level factors ascertained through zip codes and CAC, controlling for individual factors in an asymptomatic population. It was found that about three in five (57.6%) of study participants had a positive CAC score (i.e. CAC score > 0), which suggests the prevalence of atherosclerosis and the risk for CAD. This finding is consistent with earlier studies [41–43] indicating the pervasiveness of CAC among asymptomatic individuals in the Central Appalachian region. In such a high at-risk population, it is incumbent on policymakers and healthcare providers to design programs and policies that allow clinicians to identify at-risk individuals for risk-stratification and for treatment, so as to achieve the population-level CVD health as espoused by organizations such as the Institute of

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Medicine (IOM) under Vital Signs [60]. Perhaps CAC screening can be incorporated in CV health care in such a high CVD prevalence environment, given that presence of calcification in coronary arteries is a quantifiable marker for coronary atherosclerosis and a measure of plaque burden [46,61–63], CAC is associated with the traditional CVD risk factors [64–71], and CAC improves CV risk prediction when added to traditional risk factors [6,72]. Moreover, the expert consensus is that CAC screening is appropriate for individuals at intermediate risk on traditional risk assessment tools [1,61,67,73,74] such as those in Central Appalachia where significant proportion of the population has more than one CVD risk factor [42]. With the high prevalence of subclinical atherosclerosis among asymptomatic individuals, the key issue was identifying predisposition factors. The results show that age and sex, significantly increased the risk for subclinical atherosclerosis, which is similar to previous studies [75–78,79]. However, sex was the strongest demographic factor in this population because being male increased the relative risk for CAC by 4.01, 6.37, and 12.86 times for mild, moderate, and severe plaque, respectively. The link between CVD and lifestyles/behaviors, such as smoking, physical inactivity/sedentary lifestyle, and unhealthy diet, has been established in previous studies [1,50,80]. Consistently, it was found that while being a current smoker significantly increased the risk for mild, moderate, and severe plaque, being a former smoker increased the risk for moderate and severe plaque [1,81,82]. Yet, the results of studies involving subclinical atherosclerosis have been mixed [75,78,79,83,84]. While large studies such as DanRisk have found positive association between smoking and the presence of CAC [83], others did not [42,43]. Nevertheless, the findings about the significantly increased risk for CAC by being a current or former smoker is consistent with findings in large population-based studies, including Multi-Ethnic Study of Atherosclerosis (MESA) in the U.S. and Heinz Nixdorf Recall study and DanRisk in Europe.

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Research suggests that medical conditions such as hypertension, hypercholesterolemia, and diabetes are associated with CAC [75–79,83]. In a comparative study involving five countries, including the U.S., it was found that these medical conditions were independently predictive of CAC in both males and females [75]. This study revealed that both hypertension and hypercholesterolemia increased the risk for CAC across all categories, while diabetes significantly increased the risk for only severe plaque. Thus, health promotion and education to improve the literacy about these medical conditions and for a healthy lifestyle in Central Appalachia are needed to curtail the onset or progression of CAC in this high-risk population. Also, there is the need for further investigation into medication adherence, because about half of the patients do not adhere to the medication regimen as prescribed by physicians or health care providers [85–88]. Research suggests that the family history of CAD is a predictor of CAC [41–43,78,83]. This is consistent with the results of this study that show a family history of CAD increased risk for mild, moderate, and severe plaque (by 45%, 47%, and 87%, respectively). However, it should be noted that other studies did not find predictive significance for family history of CAD in the presence and extent of CAC [79]. Nevertheless, the overall consistency of the study results reported here with those of larger studies, such as the MESA and the Heinz Nixdorf Recall study, suggests the importance of screening for family history of CAD in high-risk environments, such as Central Appalachia, to identify those at risk for subclinical atherosclerosis and CAD for primary prevention. Evidence indicates that social determinants, psychosocial, environmental, and geographic or neighborhood factors increase the risk for CVD [19,89–92]. In this respect, a study by Patel et al. found variation in county-level CVD mortality and the major contributors to CVD mortality were median income and a high percentage without a high school education [89]. Consistent with this literature, there is emerging research that suggests an association between the risk for subclinical atherosclerosis and neighborhood-level factors [8–14]. Using 2,974 adults from the Coronary Artery Risk Development in Young Adults (CARDIA) Study, Kim

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et al. found that neighborhood deprivation and low social cohesion significantly increased the odds for CAC by 2.49 and 1.87 times, respectively [8]. In line with this finding, this study examined the association between geographic-level factors measured at the zip code level and CAC, controlling for demographics, medical conditions, lifestyle factors, and family history of CAD. It was found that the proportion of minority residents increased the risk for severe plaque by 6%, while the proportion of residents on government assistance significantly decreased the risk for mild plaque by 7%. While the significantly positive association between CAC and the proportion of minority residents in the zip code may be attributed to the disproportionately higher burden of CVD and risk factors in minority populations [17,29,93,94], the significant negative association between CAC and the proportion of residents on government assistance may be attributed to many issues, including access to health services, reduced stress and other psychosocial factors. Thus, government assistance for underserved and high-risk populations has the potential to improve CVD health outcomes, which suggests that further studies on this topic is critically needed. The majority of the geographic-level variables were not significant, which is not consistent with previous studies [8,18,95–98] that found stronger associations between CAC and neighborhood factors. These results may be due to ascertaining such factors at the zip code level in tandem with the relatively small sample sizes of the three CAC subgroups, compared to the Census tract block-group-level by Kim et al. [8]. This suggests the need for further studies that utilizes different measures to ascertain geographic or neighborhood factors. The environment in Central Appalachia that predisposes the population to high prevalence of CVD and related risk factors has not been studied adequately, and this study provides the foundation for deeper exploration of how geographic- or neighborhood-level factors impact the risk for CVD in the region.

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The results of this study should be interpreted against the backdrop of certain limitations. First, except the CAC score, all the other variables were ascertained through self-reports, which are likely subject to recall and social desirability biases. Second, there is the possibility of selection bias as participants in the CAC screening were not selected randomly. Third, as mentioned earlier, zip codes were used to ascertain the geographic factors, which is larger than the ideal neighborhood level for this type of study (e.g., Census tract block-group-level data) that can facilitate analyses based on distances. Moreover, this study was constrained by the type of information available to the researchers because an ideal study would have included individual socioeconomic and psychosocial factors that have been shown to increase the risk for CVD. CONCLUSION This study showed that after controlling for the individual demographics, lifestyle factors, and family history of CAD, the proportion of minority residents in the geographic area is associated with increased relative risk for subclinical atherosclerosis, while the proportion of residents on government assistance decreased such risk. However, many of the geographiclevel factors were not significant, which suggests that future geographic- or neighborhood-level studies with larger sample size and smaller geographic or neighborhood units are needed to delineate further the consistency of these results in the Central Appalachian population.

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Figure 1: Coronary Artery Calcium (CAC) Deposit

26

Figure 2: Risk of Increased CAC Score by Medical/Health Conditions (%; N=1512) Figure 6: Risk of Increased CAC Score by Medical Condition (%) 70.0% 60.0%

Percent (%)

50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Hypertensive No Plaque

Mild Plaque

Hypercholesteremic Moderate Plaque

Diabetic Severe Plaque

27

Table 1: Descriptive analysis of the independent measures used in the study by CAC stages (N = 1,512) No Plaque (n=641) (%)

Mild Plaque (n=478) (%)

Moderate Plaque (n=246) (%)

Severe Plaque (n=147) (%)

N

P-value

Hypertensive (yes)

41.03

52.93

59.35

60.54

751

<0.0001

Hypercholesteremic (yes) Diabetic (yes)

47.74

64.44

60.57

64.63

858

<0.0001

10.92

12.76

17.89

27.21

215

<0.0001

Underweight

1.56

0.84

0.41

2.72

19

0.16

Normal Weight

24.02

17.99

21.14

19.05

320

0.09

Overweight

39.94

45.61

39.02

40.82

630

0.20

Obese

34.48

35.56

39.43

37.41

543

0.56

Nonsmoker

66.61

64.64

56.50

51.02

950

<0.0001

Current Smoker

10.45

9.62

9.76

9.52

151

0.97

Former Smoker

22.93

25.73

33.74

39.46

411

<0.0001

Sedentary (yes)

39.78

39.75

35.77

38.10

589

0.70

Family History of CAD (yes) Age (mean, SD)*

18.56

22.59

21.14

23.81

314

0.30

54.50

58.52

62.81

65.38

1,512

<0.0001

White

98.60

98.95

98.37

99.32

1,493

0.81

Male

31.05

52.72

57.32

68.03

692

<0.0001

19,548.56

19,585.50

18,764.31

20,251.23

1,512

0.70

Minority Residents

4.99

5.28

4.93

5.32

1,512

0.52

Female-Headed Households Residents in Poverty

14.31

14.30

14.51

14.41

1,512

0.94

17.49

17.31

17.90

17.15

1,512

0.54

Residents on Government Assistance Unemployment Rate (Ages >16) Proportion of College Graduates Housing Values (in dollars; Median, range)*

20.25

20.03

20.15

20.12

1,512

0.33

9.02

9.06

9.25

8.88

1,512

0.65

7.68

8.20

7.38

8.17

1,512

0.20

123,673.95

125,722.59

122,265.45

125,608.16

1,512

0.58

Individual Measures

Body Mass Index

Smoking Status

Zip-Code Measures Population (n)*

* The results are numbers, not percentages

28

Table 2: Regression analysis of the relative risk for subclinical atherosclerosis (coronary artery calcium; N=1512) Mild Plaque Moderate Plaque Severe Plaque RRR*

P-value

RRR*

P-value

RRR*

P-value

Hypertensive

1.41

0.01

1.58

0.01

1.66

0.02

Hypercholesteremic

2.14

0.00

1.84

0.00

2.27

<0.0001

Diabetic

0.96

0.83

1.33

0.23

2.47

<0.0001

Underweight

0.90

0.88

0.59

0.64

5.70

0.03

Overweight

1.26

0.21

0.95

0.82

1.12

0.70

Obese

1.21

0.34

1.29

0.30

1.43

0.26

-

-

-

-

-

-

Current Smoker

1.52

0.05

2.25

0.01

2.80

0.01

Former Smoker

Individual Measures

Body Mass Index

Normal Weight Smoking Status

1.10

0.52

1.58

0.02

1.81

0.01

Nonsmoker

-

-

-

-

-

-

Sedentary

1.04

0.80

0.85

0.35

0.97

0.89

Family History of CAD

1.45

0.02

1.47

0.05

1.87

0.01

Age (centered)

1.07

0.00

1.15

0.00

1.20

<0.0001

White

1.34

0.64

0.80

0.74

2.60

0.42

Male

4.01

0.00

6.37

0.00

12.86

<0.0001

Individual Level Constant

1.35

0.82

0.18

0.29

0.05

0.15

Population (logged)

0.92

0.34

0.94

0.57

0.96

0.80

Minority Residents

1.03

0.16

0.99

0.81

1.06

0.04

Female-Headed Households

1.02

0.44

1.01

0.73

1.02

0.61

Residents in Poverty

0.97

0.12

0.99

0.73

0.96

0.15

Residents on Government Assistance Unemployment Rate (Ages >16)

0.93

0.03

0.95

0.23

0.94

0.30

1.03

0.33

1.03

0.34

0.99

0.90

Proportion of College Graduates

1.01

0.31

0.99

0.56

1.01

0.60

Median Housing Values (in $1,000s) Zip Code Level Constant (St. Dev.)

1.00

0.91

1.00

0.93

1.00

0.46

0.00

1.00

0.00

1.00

0.00

1.00

Zip-Code Measures

*RRR refers to relative risk ratio

29