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CHAPTER 4: RESULTS

Table 1 depicts the socio-demographic characteristics of the subjects aged 60 years or more. The age variable was broken down to reflect subgroups separated by increments of 10 years until the age of 80 years or more. The majority of the subjects (51.1%) were between 60 and 69 years old. There were more females than males (52.5% versus 47.5%). The highest percentage of the subjects was Whites, followed by African Americans, then Hispanics and lastly those of other races. For marital status, the majority were married (54.0%).

Table 1. Socio-demographics Characteristics of Subjects

Variables	Number	Percent
Age Group
60-69 years	940	51.1
70-79 years	549	29.8
80 years or more	352	19.1
Total	1841	100.0
Gender
Male	874	47.5
Female	967	52.5
Total	1841	100.0
Race/Ethnicity
African American/Black	388	21.1
Hispanic	364	19.8
White	896	48.7
Other Race	193	10.5
Total	1841	100.0
Marital Status
Married	994	54.0
Widowed	391	21.2
Divorced	252	13.7
Separated	47	2.6
Never Married	111	6.0
Living with Partner	44	2.4
Not Reported	2	0.2
Total	1841	100.0

Table 2 depicts the educational and annual household income levels of the subjects. The largest percentage of subjects (26.9%) had educational levels belonging to the subgroup of Some College/AA degree. The annual household income level with the highest percentage (19.4%) was $20,000-34,999, while the lowest percentage (1.4%) was for less than $20,000.

Table 2. Socio-economic Characteristics of Subjects

Variables	Number	Percent
Education Level
Less than 9th Grade	230	12.5
9-11th Grade	261	14.2
High School Graduate/GED	440	23.9
Some College/AA Degree	496	26.9
College Graduate or Above	411	22.3
Not Reported	3	0.2
Total	1841	100.0
Annual Household Income
$9,999 or less	114	6.2
$10,000-19,999	320	17.4
$20,000-34,999	358	19.4
$35,000-54,999	324	17.6
$55,000-74,999	185	10.0
$20,000 or more	53	2.9
Less than $20,000	25	1.4
$75,000-99,999	132	7.2
$100,000 or more	242	13.1
Not Reported	88	4.7
Total	1841	100.0

Table 3 displays the percentages at which participants self-reported being told by a doctor or other health professional they had coronary heart disease (hereinafter referred to as diagnosis of CHD). Only ten percent of the participants reported being told they had coronary heart disease.

Table 3. Diagnosis of CHD

	Number	Percent
Yes	185	10.0
No	1642	89.2
Not Reported	14	0.8
Total	1841	100.0

Table 4 shows the sociodemographic and socioeconomic factors by diagnosis of CHD. As participants aged, the prevalence of diagnosis of CHD increased (from 7.6% to 19.4%). Additionally, males had a higher prevalence of CHD. Participants of White ethnicity had the highest prevalence of CHD (11.8%), while participants of Hispanic ethnicity had the lowest prevalence (4.7%). Participants with some level of college education had the lowest prevalence of CHD (8.8%). The prevalence of CHD decreased with annual household income, the highest prevalence (15.3%) being found in those with annual household incomes below $20,000.

Table 4. Socio-demographic and Socio-economic Characteristics by Diagnosis of CHD

Diagnosis of CHD
Age Group	Yes	No
60-69 years	7.6%	92.4%
70-79 years	11.8%	88.2%
80 years or more	19.4%	80.6%
Gender
Male	14.0%	86.0%
Female	8.1%	91.9%
Ethnicity
African American/Black	7.5%	92.6%
Hispanic	4.7%	95.4%
White	11.8%	88.2%
Other Race	9.8%	90.2%
Education Level
Less than 9th Grade	11.8%	88.2%
9-11th Grade	12.5%	87.5%
CONTINUED

TABLE 4. CONTINUED
High School Graduate/ GED	13.8%	86.2%
Some College/AA Degree	8.8%	91.2%
College Graduate or above	9.6%	90.4%
Annual Household Income
Less than $20,000	15.3%	84.7%
$20,000-54,999	11.9%	88.1%
$55,000-74,999	10.7%	89.3%
$20,000 or more	3.7%	96.3%
$75,000 or more	7.7%	92.3%

Table 5 displays significant relationships of age, gender, ethnicity, and annual household income to diagnosis of CHD. The variables which had a significant influence on the diagnosis of CHD were age group, gender, ethnicity and annual household income with an exception for educational level. Participants over the age of 70 years had the highest incidence of being diagnosed with CHD. Men were more likely than women to be diagnosed with CHD and Hispanics were the least likely of all the races to be diagnosed with CHD. As annual income level rose, the likelihood of being diagnosed with CHD decreased.

Table 5. Relationships of Socio-demographic and Socio-economic Factors to Diagnosis of Coronary Heart Disease

	Chi-Square Statistic	Probability Levela
Age Group	10.1	0.000 a
Gender	9.1	0.002 a
Ethnicity	6.5	0.000 a
Educational Level	1.1	0.362
Annual Household Income	3.1	0.015 a

aProbability levels below 0.05 indicate significant relationships between the two variables

Table 6 shows the four levels of food security experienced by the subjects. Of the four levels of food security, the overwhelming majority of subjects (79.3%) experienced

full food security, followed by those who were marginally food secure, then those who were low food secure and lastly those who experienced very low food security.

Table 6. Food Security Levels of Subjects

Variables	Number	Percent
Full Food Security	1459	79.3
Marginal Food Security	142	7.7
Low Food Security	136	7.4
Very Low Food Security	84	4.6
Not Reported	20	1.1
Total	1841	100.0

Table 7 displays the differing levels of food security by the diagnosis of CHD. The participants in the very low food security category had the highest prevalence of diagnosis of CHD (14.3%). No significant relationship was found between food security and diagnosis of CHD (chi-square statistic=1.24, p=0.292).

Table 7. Food Security by Diagnosis of Coronary Heart Disease

Ever told you had coronary heart disease
Food Security Category			Yes		No		Total Count
Full Food Security	%	10.2%		89.8%		100.0%
Count	147		130		1447
Marginal Food Security	%	8.5%		91.5%		100.0%
Count	12		130		142
Low Food Security	%	8.2%		91.8%		100.0%
Count	11		123		134
Very Low Food Security	%	14.3%		85.7%		100.0%
Count	182		1625		1807

Table 8 compares the dietary intakes of the nutrients associated with heart health of participants who self-reported as having been diagnosed with CHD, and those who did not self-report being diagnosed with the disease. Intakes of cholesterol, vitamin A, retinol, beta carotene and vitamin K were significantly lower in participants who were diagnosed with coronary heart disease.

Table 8. Dietary Intakes by Diagnosis of Coronary Heart Disease

Dietary Intake Variables	Ever told you had coronary heart disease	Number	Mean	Std. Error Mean
Energy (kcal)	Yes	158	1793.3a	72.9
No	1401	1843.0a	22.1
Dietary fiber (gm)	Yes	158	16.2a	0.9
No	1401	16.6a	0.3
Total fat (gm)	Yes	158	71.5a	4.1
No	1401	71.7a	1.1
Total saturated fatty acids (gm)	Yes	158	22.6a	1.3
No	1401	22.9a	0.4
Total monounsaturated fatty acids (gm)	Yes	158	25.2a	1.7
No	1401	25.2a	0.4
Total polyunsaturated fatty acids (gm)	Yes	158	17.5a	1.1
No	1401	17.0a	0.3
Cholesterol (mg)	Yes	158	256.6a	16.0
No	1401	270.4b	5.9
Vitamin A	Yes	158	559.1a	40.8
No	1401	653.5b	17.7
Retinol	Yes	158	382.9a	28.1
No	1401	417.2b	12.5
Vitamin E as alpha- tocopherol (mg)	Yes	158	8.7a	0.7
No	1401	8.1a	0.2
Beta-carotene (mcg)	Yes	158	1917.8a	289.4
No	1401	2560.6b	128.1
Lycopene (mcg)	Yes	158	5135.0a	704.2
No	1401	4512.9a	232.4
Total folate (mcg)	Yes	158	346.7a	18.4
No	1401	366.3a	5.9
Vitamin C (mg)	Yes	158	79.1a	6.8
No	1401	81.7a	2.2
CONTINUED

TABLE 8. CONTINUED
Vitamin K (mcg)	Yes	158	88.4a	7.9
No	1401	116.9b	4.1
Magnesium (mg)	Yes	158	275.6a	13.8
No	1401	279.4a	3.6
Sodium (mg)	Yes	158	3002.2a	119.2
No	1401	3089.2a	42.6
Potassium (mg)	Yes	158	2459.4a	97.8
No	1401	2494.3a	30.3
Selenium (mcg)	Yes	158	100.1a	4.7
No	1401	104.0a	1.7
PFA 18:3 (Octadecatrienoic) (gm)	Yes	158	1.6a	0.1
No	1401	1.6a	0.03
PFA 20:5 (Eicosapentaenoic) (gm)	Yes	158	0.03a	0.01
No	1401	0.03a	0.002
PFA 22:6 (Docosahexaenoic) (gm)	Yes	158	0.1a	0.01
No	1401	0.1a	0.01

a,bMeans with different letters as superscript are significantly different (p<0.05)

Table 9 displays BMI category by diagnosis of CHD. Participants who were obese had the highest prevalence of CHD diagnosis (12.0%). However, the BMI category was not significantly related to the diagnosis of CHD (chi-square statistic=1.206, p=0.306).

Table 9. BMI Category by Diagnosis of CHD

Ever told you had coronary heart disease
BMI Category			Yes		No		Total
Underweight1	%	3.6%		96.4%		100.0%
Count	1		30		31
Normal/Healthy Weight2	%	10.1%		89.9%		100.0%
Count	44		393		437
Overweight3	%	9.6%		90.4%		100.0%
Count	58		571		629
Obese4	%	12.0%		88.0%		100.0%
CONTINUED

	TABLE 9. CONTINUED
Count	69	572	629
Total	%	9.9%	90.1%	100.0%
Count	172	1566	1738

1BMI<18.5 kg/m2 2BMI 18.5-24.9 kg/m2 3BMI 25.0-29.9 kg/m2

4BMI≥30.0 kg/m2

Table 10 displays the use of any tobacco product in the last 5 days to the diagnosis of CHD. Participants who used any tobacco product in the last 5 days had a higher prevalence of being diagnosed with CHD (12.4%), compared with those who did not use any tobacco products (9.7%). Similar conclusions can be deducted from chi-square tests as the percent of persons diagnosed with CHD and had used any tobacco products in the last 5 days had a higher prevalence (18.2%) of diagnosis as compared to those with no tobacco use (9.6%). This variable had a significant relationship to the diagnosis of CHD (chi-square statistic=5.0, p= 0.02).

Table 10. Use of Any Tobacco Product by Diagnosis of CHD

Ever told you had coronary heart disease
Tobacco Use Variables			Yes		No		Total
Yes - Use of Tobacco Product Last 5 Days	%	12.4%		87.2%		100.0%
Count	31		218		249
No - Use of Tobacco Product Last 5 Days	%	9.7%		90.3%		100.0%
Count	138		1280		1418
Total	%	10.01%		89.9%		100.0%
Count	169		1498		1667

Table 11 displays participation in physical activities by diagnosis of CHD. Participants with the least prevalence of diagnosis of CHD (5.4%) were those who walked or used bicycles for transportation, while those with the highest prevalence (11.2%) belonged to the moderately intense work activity category. An increasing trend in diagnosis of CHD can be seen from the vigorous and moderate recreational activity variables (8.2% and 9.5%).

Table 11. Participation in Physical Activities by Diagnosis of CHD

Ever told you had coronary heart disease
Physical Activity Variable				Yes		No		Total
Vigorous intensity work activity	Yes	%	9.8%		90.2%		100.0%
Count	18		166		184
No	%	10.2%		89.8%		100.0%
Count	167		1476		1643
Moderate intensity work activity	Yes	%	11.2%		88.8%		100.0%
Count	50		398		448
No	%	9.7%		90.3%		100.0%
Count	1		1		2
Walk or bicycle for transportation	Yes	%	5.4%		94.6%		100.0%
	Count	19		332		351
No	%	11.2%		88.8%		100.0%
	Count	166		1310		1476
Vigorous recreational activity	Yes	%	8.2%		91.8%		100.0%
	Count	13		145		158
No	%	10.3%		89.7%		100.0%
	Count	172		1497		1669
Moderate recreational activity	Yes	%	9.5%		90.5%		100.0%
CONTINUED

	TABLE 11. CONTINUED
	Count	66	630	696
No	%	10.5%	89.5%	100.0%
	Count	185	1642	1827
Total		%	10.1%	89.9%	100.0%
	Count	185	1642	1827

Table 12 depicts the relationships of participation in physical activities to the diagnosis of CHD. A significant relationship was found between walking or bicycling for transportation and diagnosis of CHD (chi-square statistic=10.3, p=0.001). This is evident as the majority (94.6%) of participants who walked or used a bicycle for transportation were not diagnosed with CHD. According to chi-square tests, participants who performed vigorous work activity had a 10.5% prevalence of CHD, participants who performed moderate work activity had the highest (11.9%) prevalence of CHD, participants who walked or used a bicycle for transportation had the lowest (5.4%) prevalence of CHD, participants who performed vigorous recreational activities had a 9.3% prevalence of CHD, and participants who performed moderate recreational activities had a 10.2% prevalence of CHD,

Table 12. Relationships of Participation in Physical Activities to Diagnosis of CHD

Physical Activity Variables	Chi-Square Statistic	Probability Levela
Vigorous intensity work activity	0.008	0.930
Moderate work activity	0.580	0.444
Walk or bicycle for transportation	10.355	0.001 a
Vigorous recreational activity	0.249	0.618
Moderate recreational activity	0.262	0.609

aProbability levels below 0.05 indicate significant relationships between the two variables

Table 13 displays the significant relationships of walking and bicycling for transportation, vigorous and moderate recreational activities to CHD category. The mean values between variables shared no significant relationship to the diagnosis of CHD.

Table 13. Physical Activity Levels by Diagnosis of CHD

Physical Activity Variables	Ever told you had coronary heart disease	Number	Mean	Std. Error Mean
Number of days of vigorous intensity work in a typical week	Yes	18	3.7a	0.5
No	166	3.6a	0.1
Number of minutes of vigorous intensity work in a typical day	Yes	18	105.7a	23.4
No	166	147.2a	10.1
Number of days of moderate intensity work in a typical week	Yes	50	3.9a	0.3
No	398	4.5a	0.4
Number of minutes of moderate intensity work in a typical day	Yes	50	113.8 a	17.1
No	396	126.8 a	5.9
Number of days walk or bicycle for transportation in a typical week	Yes	19	4.6 a	0.5
No	332	4.9 a	0.1
Number of minutes walk or bicycle for transportation in a typical day	Yes	19	39.7 a	6.1
No	332	89.1 a	30.1
Number of days of vigorous recreational activities in a typical week	Yes	13	3.9 a	0.5
No	145	3.7 a	0.1
Number of minutes of vigorous recreational activities in a typical day	Yes	13	85.8 a	23.0
No	145	63.8 a	3.6
Number of days of moderate recreational activities in a typical week	Yes	66	5.3 a	1.5
No	630	3.9 a	0.1
Number of minutes of moderate recreational activities in a typical day	Yes	65	59.1 a	13.7
No	630	58.6 a	2.2

a,bMeans with different letters as superscript are significantly different (p<0.05)

Table 14 displays blood pressure category by diagnosis of CHD. Participants who were normotensive had the highest prevalence of CHD diagnosis (11.4%), while participants who were prehypertensive had the lowest prevalence (8.8%) of diagnosis of CHD. Blood pressure category was not significantly related to the diagnosis of CHD (chi- square statistic=1.206, p=0.306).

Table 14. Blood Pressure Categories by Diagnosis of CHD

Ever told you had coronary heart disease
Blood Pressure Category		Yes	No	Total
Hypertensive1	%	9.6%	90.4%	100.0%
Count	52	491	543
Prehypertensive2	%	8.8%	91.2%	100.0%
Count	62	641	703
Normotensive3	%	11.4%	88.6%	100.0%
Count	51	396	447
Total	%	9.7%	90.3%	100.0%
Count	165	1528	1693

1Systolic Blood Pressure ≥ 140 mm Hg and/or Diastolic Blood Pressure ≥ 90 mm Hg 2Systolic Blood Pressure 120-139 mm HG and/or Diastolic Blood Pressure 80-89 mm Hg 3Systolic Blood Pressure < 120 mm Hg and Diastolic Blood Pressure < 80 mm Hg

Table 15 displays blood pressure (BP) levels by diagnosis of CHD. There was a significant relationship shared between average diastolic BP (p = 0.000) level and diagnosis of CHD, but no link was discovered between average systolic BP and CHD diagnosis (p=0.208). The links amongst the hypertensive, prehypertensive, and normotensive groups were inconclusive as the differences in incidence of diagnosis ranged notably.

Table 15. Blood Pressure Levels by Diagnosis of CHD

Variables	Ever told you had coronary heart disease	Number	Mean	Std. Error Mean
Average Systolic BP (mm Hg)	Yes	167	131.7a	1.7
No	1533	133.0a	0.5
Average Diastolic BP (mm Hg)	Yes	165	63.5a	1.0
No	1524	67.9b	0.3

a,bMeans with different letters as superscript are significantly different (p<0.05)

Table 16 displays the blood lipid levels by diagnosis of CHD. The variable total cholesterol, triglyceride, HDL-cholesterol and LDL-cholesterol were all found to be linked to the diagnosis of CHD.

Table 16. Blood Lipid Levels by Diagnosis of CHD

Variables	Ever told you had coronary heart disease	Number	Mean	Std. Error Mean
Total Cholesterol (mg/dL)	Yes	172	167.8a	3.0
No	1515	190.3b	1.1
Triglyceride (mg/dL)	Yes	85	134.8a	7.8
No	745	115.9b	2.5
HDL-cholesterol (mg/dL)	Yes	172	49.5a	1.2
No	1515	55.0b	0.4
LDL-cholesterol (mg/dL)	Yes	85	85.6a	3.3
No	740	110.8b	1.3

a,bMeans with different letters as superscript are significantly different (p<0.05)

Table 17 displays the diagnosis of diabetes by the diagnosis of CHD. Participants who were diagnosed as diabetic had a higher prevalence (19.9%) of being diagnosed with CHD versus those belonging to the normal category had the lowest prevalence (7.3%) of being diagnosed with CHD. Participants who were prediabetic had an 11.2% prevalence of diagnosis of CHD. The decline in prevalence of CHD among the different categories supports that this variable had a significant relationship to the diagnosis of CHD (chi-square statistic = 4.0, p=0.018).

Table 17. Diagnosis of Diabetes by Diagnosis of CHD

Ever told you had coronary heart disease
Diabetes Category			Yes		No		Total
Diabetic1	%	19.9%		80.1%		100.0%
	Count	28		145		173
Prediabetic2	%	11.2%		88.8%		100.0%
	Count	32		337		369
Normal3	%	7.2%		92.7%		100.0%
	Count	25		273		298
Total	%	100.0%		100%		100.0%
	Count	85		755		840

1Fasting blood glucose ≥ 126 mg/dL 2Fasting blood glucose 100-125 mg/dL

3Fasting blood glucose < 100 mg/dL

Multiple logistic regression was conducted to determine variables predictive of CHD diagnosis. Only variables found to be significantly related to CHD diagnosis, based on bivariate tests, were included in the model. Appendix shows these variables and their p-values.

Table 18 shows the odds ratios as well as the upper and lower 95% confidence limits. Of the variables in the model; age group, gender, tobacco use, participants who did not walk or bicycle for transportation, diabetes, and average diastolic BP were found to be predictive of CHD diagnosis.

Table 18. Multiple Logistic Regression

Independent Variables and Effects	Odds Ratio	Lower 95% Confidence Limit	Upper 95% Confidence Limit
Intercept	0.62	0.01	27.21
Age Group
60-69 years	2.86	1.23	6.62
70-79 years	2.22	0.88	5.57
80 years or more	1.00	1.00	1.00
Gender
CONTINUED

TABLE 18. CONTINUED
Male	0.43	0.21	0.85
Female	1.00	1.00	1.00
Ethnicity
African American	1.70	0.4	7.23
Hispanics	2.56	0.55	11.87
Whites	0.58	0.16	2.03
Other Race	1.00	1.00	1.00
Annual household income
Under $20,000	0.59	0.01	9.72
$20,000-34,999	1.22	0.39	3.79
$35,000-54,999	0.97	0.32	2.95
$55,000-74,999	1.04	0.11	9.72
$75,000-99,999	1.82	0.34	9.65
$100,000 or more	1.00	1.00	1.00
Used any tobacco product last 5 days
Yes	0.29	0.12	0.71
No	1.00	1.00	1.00
Walk or bicycle
Yes	1.17	0.38	3.54
No	1.00	1.00	1.00
Diabetes Category
Diabetic	0.36	0.15	0.85
Prediabetic	0.69	0.31	1.53
Normal	1.00	1.00	1.00
Cholesterol (mg)	1.00	1.00	1.00
Vitamin A, RAE (mcg)	1.00	0.99	1.02
Retinol (mcg)	1.00	0.98	1.01
Beta-carotene	1.00	1.00	1.00
Vitamin K (mcg)	1.00	1.00	1.00
Total Cholesterol (mg/dL)	0.68	0.22	2.11
Triglyceride (mg/dL)	1.07	0.86	1.35
LDL-cholesterol (mg/dL)	1.51	0.48	4.74
Direct HDL-cholesterol (mg/dL)	1.46	0.47	4.56
Average Systolic BP	1.01	0.99	1.03
Average Diastolic BP	1.03	1.00	1.05

CHAPTER 5: DISCUSSION

Despite efforts during recent years to identify new risk factors or biomarkers that can predict cardiovascular diseases, no major breakthrough has been made in the clinical setting to conquer the traditional risk factors that have been known for decades: high

younger patients, including diabetes, hypertension, tobacco smoking, dyslipidemia, obesity, family history, and physical inactivity. However, this present research has disproven that theory as systolic hypertension and BMI were not predictive risk factors for the diagnosis of CHD in the elderly.

The prevalence of CHD was seen to increase with age in Table 4. This interpretation of epidemiologic data is also supported by Goldberg et al. (2014) findings on the high prevalence of CHD in the elderly. Normal aging leads to changes in the structure and function of the heart. In the arteries, an increase in the thickness of the interior walls results from a progressive accumulation of cholesterol. A gradual increase in the rigidity of the vessels, formation of aneurysms in areas of expanding arteriosclerotic plaque, and damage in larger arteries are structural changes considered to be normal in the aging process (Goldberg et al., 2014). Anatomic changes in older adults include decrease in heart size, decrease in the size of the cavity in the left ventricle, and increase in size of the left atrium. Heart valves become more rigid and thick as collagen deposits increase. Calcification in the aortic valves also occurs (Goldberg et al., 2014).

In this research, males had a higher prevalence (14.0%) compared to females (8.0%). This can be due in part to the proposition that most of the elderly are women, and women tend to lose some of their coronary disease incidence advantage over men as they undergo menopause. Framingham Study data indicate that undergoing menopause promptly escalates women's risk of coronary disease to three times that of women the same age who are still menstruating. However, the influence of an early menopause on the rate of development of coronary disease after attaining an advanced age is unclear (Drawber et al., 2015).

It becomes increasingly important to address the disparities that exist in CHD care from knowledge of the epidemiology to primary prevention and long-term disease management. In Hispanics older than 65, heart disease takes over as the leading cause and accounts for 26.3 % of deaths (Leigh et al., 2016), and CHD has a significant impact on the Hispanic population with rates similar to or lower than those for the non-Hispanic white population (Mozafarrian et al., 2015). Contrary to these reports, as seen in Table 4, Hispanics were found to have the lowest prevalence (4.7%) of CHD of all ethnic groups. To date, the majority of research involving heart disease in American Hispanics has focused on Mexican subjects (Rodriguez et al., 2014). While Mexican-Americans make up the majority (64.9 %) of the Hispanic population of the USA, the population is diverse with regard to region of origin. This heterogeneity makes it important to pursue additional studies and realize it may not be appropriate to generalize results from Mexicans to all Hispanics (Mozafarrian et. al, 2015). Among Blacks, CHD prevalence was lower than that in Whites, 7.5 versus 11.8 % (see Table 4). Despite the lower prevalence, death rates from CHD remain higher in blacks than whites (Mozafarrian et al., 2015).

There are many disparities among minority populations that are related to cardiovascular health, including having lower levels of education and higher rates of poverty (CDC, 2013). In Table 4, it can be seen that as the level of education rose, so did the prevalence of diagnosis of CHD. It is only after the participants achieved educational levels of some college or higher, the prevalence of being diagnosed with CHD decreased. Thus, after noting the significance of education level in Table 5 (p=0.365), it can be concluded that it has no relationship to the diagnosis of CHD. As annual household income increased, a decreasing trend in diagnosis of CHD can be observed, and its link to CHD is further supported by its significant relationship to CHD diagnosis (see Table 5). Disparities in CHD mortality by socioeconomic status are posited to result from multiple factors, including early life environment and material disadvantage social and behavioral risk factors access to care, and systematic underestimation of risk among persons with lower SES in clinical care as well as educational level (Jenkins K, et al., 2014).

Epidemiological studies have associated food insecurity with a large and growing number of health conditions, including coronary heart disease (Berkowitz et al., 2017). However, according to Table 7, persons with very low food security had the highest prevalence of CHD (14.3%). However, no significant relationship was found between the food security and CHD.

Adhering to a healthy diet is vital both for maintaining heart health and managing this disease once it occurs (Wilson et al., 2017). Table 8 depicted dietary intakes of participants who were diagnosed with CHD compared with those not diagnosed with the disease. The impact of dietary nutrients on the health of persons of all ages is complex and multifactorial, and chemosensory, involving biology, food antioxidants,

chronobiology, environment, culture, religion, eating habits, memory loss, intake of natural products and herbal remedies (such as phytoalexins, polyphenols, carotenoids, spices and aromatic herbs, alcoholic and non-alcoholic beverages), commercial and marketing hype, language, interventions (pharmacological and “non-drug”), special cuisines, nursing and domestic care (intravenous and tube feeding), primary nutrients like omega-3 fatty acids, (e.g., alpha-linolenic acid) carbohydrates (glucose-monosaccharide- energy), amino acids (tryptophan), vitamins (B12, B6, C) and trace elements (Wilson et al., 2017). Eighteen dietary intakes of nutrients were investigated to determine their association with heart health and the following nutrients were found to not be associated with heart health: dietary fiber, total fat, total saturated fatty acids, total monounsaturated fatty acids, total polyunsaturated fatty acids, vitamin E, lycopene, folate, vitamin C, magnesium, sodium, potassium, and selenium. However, cholesterol, vitamin A, retinol, beta carotene and vitamin K were found to be associated with heart health (See Table 8). The advent of cheap but non-nutritious foods has given rise to the existence of obesity, along with complications of obesity, such as diabetes and heart disease (Berkowitz, 2017). BMI is a useful measure of overweight and obesity, as it estimates body fat, and is a good gauge of risk for diseases that can occur with more body fat. According to a systematic evidence review from the obesity expert panel (2013), the higher the BMI, the higher the risk of certain diseases such as CHD. However, the findings in Table 9 indicate no significant relationship between BMI and CHD. This deviation, according to Batsis et al. (2015), is due to the gradual decrease in lean muscle mass with aging thereby affecting the validity and interpretability of BMI as a marker of adiposity

among older populations.

There is a real association between cigarette smoking and CHD as smoking is found to be causally related to some, one, or many precursors of myocardial infarction (Stallones, 2015). This statement is supported by the data in Table 10 which compared participants who used any tobacco product in the last 5 days to those who did not use any tobacco products. Those who used any tobacco product had a higher prevalence of being diagnosed with CHD (12.4% vs 9.7%). Smoking was found to be significantly related to the diagnosis of CHD.

According to the Rotterdam Study on physical activity types and coronary heart disease risk in middle-aged and elderly persons, there is little consistent evidence for a decreased risk of CHD in the physically active or highly fit (Koolhaas et al., 2016). This statement is upheld by results of Table 12 which indicate no significant relationship between the mean values and the diagnosis of CHD. Koolhaas et al. (2016) declares that it is not possible to elucidate the type, frequency, duration and intensity of physical activity necessary for optimal protection against CHD from the few published studies. It is noteworthy, however, that one study agreed with the findings in this research, reporting an inverse association between cycling and risk of CHD risk (Schnohr P, 2012). Table 13 depicts a significant relationship was found between walking or bicycling for transportation and diagnosis of CHD (chi-square statistic=10.3, p=0.001). The plausible biological mechanisms by which physical activity may reduce CHD risk include reducing blood pressure and body weight, increasing high density lipoprotein cholesterol, and maintaining normal glucose tolerance (Koolhaas et al., 2016).

Both the incidence and the prevalence of hypertension rise with age (Mozaffarian et al., 2016). However, according to Lionakis (2012), after risk factor adjustment, a

positive relation between hypertension and CHD declined significantly with age. Lionakis (2012) suggests that this is primarily due to a large increase in the risk of CHD in men aged 75 to 93 years without hypertension. Table 14 supports Lionakis’ (2012) study as the blood pressure category was found to be not significantly related to the diagnosis of CHD (chi-square statistic=1.206, p=0.306). This is also supported in Table 14 where the total number of participants who were prehypertensive and normotensive (1,150), outnumbered those with hypertension (543). Table 15 indicated that average diastolic BP level was significantly lower in subjects diagnosed with CHD. There was no significant link discovered between average systolic BP and CHD diagnosis. Franklin SS (2012), showed that this association of average diastolic BP level with CHD was not a simple epiphenomenon because of concomitant chronic illness, cardiac failure, or increased arterial stiffness but was associated with reduced peripheral resistance/pressure wave reflections and potentially aggressive blood pressure reduction, possibly jeopardizing coronary perfusion.

According to Hamiltion-Craig (2015) blood lipids measured after 65 years of age have not been as consistently found to be related to the rate of development of coronary disease as those measured earlier. This study does not support this claim as Table 16 indicated that total cholesterol, triglyceride, HDL-cholesterol and LDL-cholesterol were all found to be significantly lower in subjects diagnosed with CHD. This is also supported by a preponderance of investigations finding a significant relationship between dyslipidemia and development of coronary disease in the elderly (Phrommintikul, 2016).

All manifestations of CHD are at least twofold more common in patients with type

2 diabetes than in nondiabetic individuals (Laakso, 2010). Table 17 supports this

statement as this variable had a significant relationship to the diagnosis of CHD. The incidence of CHD and diabetes can be seen rising in this table as it ranges from normal to prediabetic to diabetic. It has been postulated that the association between type 2 diabetes and CHD might result from a shared antecedent, which could provide a fundamental link between type 2 diabetes and CHD via the metabolic (or insulin resistance) syndrome (Pradeepa et al., 2012). Evidence from Pradeep R. et al. (2012) also suggests that inflammatory markers are high among subjects with diabetes and CAD. Inflammation is considered to be a part of the insulin resistance syndrome and this, to some extent, explains the high risk for CAD among diabetic subjects (Pradeepa et al., 2012).

Discussion of Hypotheses

In hypothesis one, it was posited that food insecurity was a significant risk factor for coronary heart disease in adults aged 60 years and over. However, in this study, no significant relationship was found between food insecurity and the diagnosis of CHD in adults 60 years and over. Therefore, this hypothesis is not support.

In hypothesis two, the following ten other risk factors for CHD were disputed to have a significant influence on the diagnosis of CHD in adults aged 60 years and over: increasing age; Male gender; African-American or Hispanic ethnicity; Low socioeconomic status; Unhealthy diets (low intakes of nutrients associated with heart health - dietary fiber, total monounsaturated fatty acids, total polyunsaturated fatty acids, cholesterol, vitamin E, vitamin A, lycopene, folate, vitamin C, vitamin K, magnesium, sodium, potassium, and selenium; as well as high intakes of total fat, and total saturated fatty

acids); Obesity and overweight; Smoking; Physical inactivity; High blood lipid levels; High blood pressure levels; Diabetes.

In the first risk factor for CHD in adults aged 60 years and over, it was hypothesized that the prevalence of CHD diagnosis among these participants would increase as they became older. As revealed in the findings of this study there was a significant relationship between age group and diagnosis of CHD (Table 5). As the participants age increased, they were significantly more likely to be diagnosed with CHD (Table 4). Therefore, this risk factor supported the hypothesis.

The second risk factor hypothesized that men will be significantly more likely than women to be diagnosed with CHD in adults aged 60 years and over. In this study a significant relationship was found between gender and CHD diagnosis in adults aged 60 years and over (Table 5). It was revealed that in adults who were 60 years and over, men had a higher prevalence of being diagnosed with CHD than women (Table 4). Therefore, this risk factor supports the hypothesis.

The third risk factor posed the hypothesis that the diagnosis of CHD in African- Americans and Hispanics was more prevalent than in Whites who were 60 years and over. Table 5 proved that there was a significant relationship between ethnicity and the diagnosis of CHD, however, Table 4 indicates that Whites were more likely than African- American and Hispanic participants to have CHD. Therefore, this risk factor does not support the hypothesis.

The fourth risk factor posited that low socio-economic status particularly annual household income and educational level were significant risk factors for diagnosis of CHD in adults aged 60 years and over. The findings regarding educational level were

inconclusive. Table 5 indicated that there was no significant relationship between educational level and diagnosis of CHD and Table 4 displayed an inconsistent distribution of diagnoses among the differing levels of education. The findings between annual household income and the diagnosis of CHD were more definitive. Table 4 depicted a steady increase in the diagnosis of CHD as annual household income decreased. Therefore, the risk factor of annual household income supports the hypothesis, but educational level does not.

The fifth risk factor stated that low intakes if the 18 nutrients associated with heart health were significantly related to the diagnosis of CHD in adults 60 years and over. However, Table 8 indicated the most significant nutrients consumed by this population were cholesterol, vitamin A, retinol, beta carotene and vitamin K. Thus, the following nutrients: dietary fiber, total fat, total saturated fatty acids, total monounsaturated fatty acids, total polyunsaturated fatty acids, vitamin E, lycopene, folate, vitamin C, magnesium, sodium, potassium, and selenium; did not support the hypothesis as risk factors.

It was purported that overweight and obesity was the sixth significant risk factor for CHD in adults aged 60 years and over. Although overweight and obese participants had a higher prevalence of being diagnosed with CHD (Table 9), BMI was proven to have no significant relationship among participants. Therefore, this risk factor was not supported by the hypothesis.

The seventh risk factor posed the hypothesis that tobacco use was linked to the diagnosis of CHD in adults 60 years and over. The incidence of CHD was higher in

participants who used tobacco, as seen in Table 10. Therefore, this risk factor was supported.

The eighth risk factor stated that physical inactivity had a significant relationship with the diagnosis of CHD in adults 60 years and over. This variable had varying categories of physical activity that included vigorous intensity work activity, moderate work activity, walk or bicycle for transportation, and vigorous recreational activity. The diagnosis of CHD was unevenly distributed among the categories, with walking or bicycling for transportation having the least prevalence of being diagnosed with CHD and holding the only significant relationship with the diagnosis of CHD. Therefore, this risk factor was not supported by the hypothesis.

Lastly it was hypothesized that the prevalence of high blood lipid levels, high blood pressure and diabetes would be significantly higher in adults 60 years and over who were diagnosed with CHD. Table 16 and 17 showed that blood lipid levels and participants diagnosed with diabetes were respectively shared a significant relationship with the diagnosis of CHD. No significant relationships were found for high blood pressure. Therefore, the hypothesis was supported only for blood lipid level and diabetes as risk factors.

CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS Conclusions

In conclusion, this study demonstrated the following:

Participants who were food insecure were not more likely to be diagnosed with CHD (chi-square=1.24, p=0.292).
The prevalence of CHD significantly increased with age in adults aged 60 years and over. Increasing age was significantly linked to CHD;
The prevalence of CHD was significantly higher in males than in females aged 60 years and over. Gender was a significantly linked to CHD;
The prevalence of CHD was significantly lower in African Americans and Hispanics than in Whites aged 60 years and over. Ethnicity was significantly linked to CHD;
Participants with less than a 9th grade level of education, 9-11th grade level, high school graduates, and those with GEDs were significantly more likely to be diagnosed with CHD. College graduates and those with higher levels of education were significantly less likely to be diagnosed with CHD. The rates of CHD were highest among participants who were high school graduates, and those with GEDs (13.8%). Educational level was not significantly linked to the diagnosis of CHD in adults aged 60 years and over;
The prevalence of CHD was highest (15.3%) in those with an annual household income of less than $20,000. Annual household income was significantly linked to CHD;
Inadequate dietary intakes of cholesterol, vitamin A, retinol, beta carotene and vitamin K increased the prevalence of CHD in adults aged 60 years and over;
Overweight or obese participants were not more likely to be diagnosed with CHD (chi-square statistic=1.206, p=0.306);
Participants who used any tobacco product in the last 5 days had a higher prevalence of being diagnosed with CHD;
Adults aged 60 years and over who walked or used bicycles for transportation had the least prevalence of diagnosis of CHD;
Hypertensive adults aged 60 years and over were not more likely to be diagnosed with CHD (chi-square statistic=1.206, p=0.306);
The presence of abnormal lipid levels increased the prevalence of the diagnosis of CHD in adults aged 60 years and over;
Participants who were diagnosed with diabetes had a higher prevalence of being diagnosed with CHD;
The following variables were found to be predictive factors for CHD in adults aged 60 and over: Increasing age, Gender, Use of tobacco products, Diabetes diagnosis, Not walking or using a bicycle for transportation and, High diastolic BP.

Recommendations

Based on the results of this study, the following recommendations are made for future studies:

Given the complexity of food insecurity, future studies need to further investigate the relationships between both the risk factors for food insecurity and coronary heart disease in older adults aged 60 years and over;
Dietary patterns instead of single nutrients should be examined in more detail to identify what dietary changes promote the diagnosis of CHD in adults aged 60 years and older.

Limitations of the Study

As with most studies, this research comes with no exception with regard to limitations. The use of secondary data from NHANES can possibly skew the results as the data set used is inherently cross-sectional. This hinders the ability to make inferences about causality. The self-reporting of some data in this study may not have been accurate and could have also skewed the results. Additionally, the NHANES data set provided no data on family history and stress which are also a known risk factors for CHD but could not be extrapolated according to the target population.