Heart Disease in the United States Week 7 Lab Assignment

Table of Contents
Introduction 3
Background 3
Hypothesis 7
Methods 8
Results 9
Table 1 Western Governor Township Race by Family History of Heart Disease 10
Table 3 Analysis of Variance Difference in Household Income by Race 11
Conclusion 12
References 14
List of Tables
Table 1 Western Governor Township Race by Family History of Heart Disease 4
Table 2 Household Income and History of Disease 4
Table 3 Analysis of Variance Difference in Household Income by Race 5
Introduction
Heart disease has been an enormous challenge in the United States. The most common heart disease in the nation is Coronary Heart disease (CHD). There are various heart diseases apart from CHD. Heart disease approximately causes 1 in 4 deaths in the US. This number happens to be rather high, hence the need for an intervention. Experts have blamed Americans' lifestyle and ignorance on the sharp rise of heart disease deaths. Although heart disease can affect anyone at any age, some experts argue that it affects the aged severely – in comparison to the other age brackets.
In this assignment, the relationship between heart diseases and various other variables is going to be expounded. The first section of this assignment will expound on the background of heart diseases in the United States. Several studies will be explored in this assignment. After the background formulations of the hypothesis based on the literature review, the analysis will be based on the heart disease data. The SPSS software will be used to perform the statistical test, and the results will also be discussed. Finally, a conclusion will be made based on the study's output.
Background
Heart disease affects both the male gender and the female gender. This implies that both genders have an equal risk of being diagnosed with related heart disease (AHA 2019 Heart Disease and Stroke Statistics - American College of Cardiology, 2019). The percentage of males succumbing to illness resulting from heart disease is approximately 25%, whereas the approximate rate of women who succumbed to heart-related disease is 22% (CDC, 2020). The difference in the percentage is notable, but not significant.
Even though the gender male and female death rates are almost the same, heart disease symptoms among the two genders are different (AHA 2019 Heart Disease and Stroke Statistics - American College of Cardiology, 2019). Health practitioners tend to misdiagnose women as their heart disease symptoms are similar to those of other diseases. To a large extent, the commonly ignored symptoms amongst women are inclusive of cold sweats, chest discomfort, and nausea. This could explain the notable (but not significant) higher percentage of women succumbing to heart-disease-related issues, in comparison to that of men.
In as far as race and ethnicity are concerned, it would be prudent to note that as Virani et al. (2020) point out, “in the United States, certain racial and ethnic groups face a higher risk of dying from heart disease than others” (p. 27). As the authors further point out, according to data from the American Heart Association, blacks tend to have a higher risk of heart disease than other groups (Virani et al., 2020). They are closely followed (at second place) by non-Hispanic whites. It would, however, be important to note that those with the least risk of heart disease are Hispanics.
Even though heart disease is the most common cause of death in the country today, this was not the case several decades ago – i.e. in the '90s. The sudden increase in heart disease has been attributed to Americans' lifestyles. There are several risk factors that have been identified for heart disease. The said risk factors are inclusive of, but they are not limited to; obesity, physical inactivity, poorly controlled diabetes, smoking, etc. It is also important to note that autopsy reports have indicated that a rise in atherosclerosis leads to increased heart disease deaths (Virani et al., 2020). Increased intake of junk food by Americans and the buildup of too much bad cholesterol from the said dietary sources is a leading factor in as far as increased incidence of this particular disease is concerned. Education also happens to be a factor in as far as susceptibility to heart disease is concerned. Citizens who lack basic education are more susceptible to heart-related diseases owing to the fact that they have limited knowledge and access to information related to healthy living.
To a large extent, “government investment has facilitated remarkable advances in cardiovascular science and medicine” (Holtz, 2020, p. 113). Efforts in this case have been inclusive of encouraging healthy behaviors, i.e. in as far as healthy eating habits and embrace of physical activities is concerned. For instance, as Mastroianni, Kahn, and Kass (2019) observe, “government effort to remove trans fats from the food supply has been an efficient way to reduce health risk from partially hydrogenated oils” (p. 213). Also, in conjunction with other levels of government, the American government has in the past sought to increase awareness on the most viable approaches to prevent the disease – so as to reduce mortality rates related to the same (Virani et al., 2020). One of the said effort has been the placement of heart icon stickers at strategic places. The heart stickers and emojis are a constant reminder to the country’s citizens that heart-related diseases can be avoided and contained.
There are also a wide range of socio-economic factors that frustrate efforts to contain heart disease in the country. This is more so the case given that in some sections of the society, people lack sufficient funds to access health care services to detect early or impending heart disease. In addition, managing heart-related conditions is relatively expensive; hence low-income earners are at increased risk of succumbing to the same. Low-income Americans also have no access to quality foods (World Health Organization: WHO, 2019). Their limited food choices can result in the ingestion of foods that cause obesity or diabetes, or both. This consequently makes them susceptible to heart diseases.
The most prevalent condition leading to heart disease is high blood pressure or hypertension (World Health Organization: WHO, 2019). As the Centers for Disease Control and Prevention – CDC (2020) points out, “high blood pressure can damage your arteries by making them less elastic, which decreases the flow of blood and oxygen to your heart and leads to heart disease.” In the year 2017 alone, approximately 108 million Americans had hypertension. This number is expected to rise if haste intervention are not taken. However, Americans have started to adapt to a healthy lifestyle and fitness (World Health Organization: WHO, 2019). Some of the interventions that have been highlighted elsewhere in this text are likely to help reduce the growing rates of heart-related diseases in Americans.
In the country, February 5th is the month and day that have set aside to mark the National Wear Red Day. During this time, the citizens (particularly women) are usually encouraged to wear red to call attention to the condition – which incidentally affects a significant number of women across the nation (World Health Organization: WHO, 2019). Further, during this day, citizens raise awareness and donations to support those affected (or likely to be affected) by the condition. Some hospital also offers free checkups to the citizens that cannot be able to afford the same.





Hypothesis
To conduct a heart disease study, we will use the heart disease dataset on the western township governor. I will conduct two statistical tests in this study. The first test will be a chi-square test, and the second test will be an ANOVA test.
The null and alternative hypothesis of the chi-square test is;
Null Hypothesis (Ho): There is no significant relationship between race and history of heart disease; that is, the variables are independent.
Alternative Hypothesis (Ha): There is a meaningful relationship between race and history of heart disease; that is, the dependent variables.
The null and alternative hypothesis for the ANOVA test is:
Null Hypothesis (Ho): There is no significance in the average income between the four races, i.e., black race, Hispanic race, white race, and Asian race.
Alternative Hypothesis (Ha): At least one of the races' averages incomes are different. .. We will conduct ANOVA f test. The ANOVA test is used to determine whether there is a significant difference between two or more independent groups. In our scenario, the separate groups are the four races, whereas the dependent variable is the income. The races are in four groups, i.e., Black, Hispanic, White, and Asian races.
Methods
We will use both inferential and descriptive statistics to conduct the above hypothesis. The descriptive statistics will expound on the heart disease dataset's demographic nature (NEDARC - Hypothesis Testing, 2019). The sample size of the data is 100. The best method to test the null and alternative hypothesis:
Null Hypothesis (Ho): There is no significant relationship between race and history of heart disease; that is, the variables are independent.
Alternative Hypothesis (Ha): There is a significant relationship between race and history of heart disease; that is, the variables are dependent,
The chi-square test
The chi-square test is suitable since the variables race and heart disease history are categorical in nature (NEDARC - Hypothesis Testing, 2019). The Chi-square test is suitable when determining whether there is a relationship between two categorical variables, which is the case in the scenario above.
To test the null and alternative hypothesis:
Null Hypothesis (Ho): There is no significance in the average income between the four races, i.e., black race, Hispanic race, white race, and Asian race.
Alternative Hypothesis (Ha): At least one of the races' averages incomes are different. ..
The data analysis was conducted using SPSS.
Results
Descriptive statistics of the data were analyzed. The data analysis showed that 58% of the respondents had a heart disease history, whereas 42% of the data set do not account for heart disease. The bar graph below gives a visual impression of the data set.


In terms of race, the dataset comprised 43% of the white race, whereas the black race had 28%. The percentage of the Hispanic ethnicity and the Asian ethnicity was 17% and 12%, respectively. The results above show that the most dominant races were the whites and the black races. The number of bedrooms that the houses the respondents live in have was recorded. The descriptive statistics showed that 53% of the data set have 3-bedroom houses. The percentage of respondents with a two bedroomed house was 8%. 30% of the household lived in a four bedroomed house. The dataset that lives in a five-bedroomed and a six-bedroomed house is 8% and 1% respectively.
The cross-tabulation of race by the history of heart disease was conducted. Based on the output, 9 of the 28 blacks have no heart disease record, whereas 19 of 28 respondents have a heart disease history. The number of Hispanic races that have no history of heart disease is 6; of the 17 Hispanians. 11 of the 17 Hispanians have a history of heart disease. The number of whites of the 43 that do not account for heart disease is 22 and 21, respectively. Finally, the number of Asians with no history of heart disease is 5 of 12 respondents, whereas 7 of 12 respondents have a heart disease history. Table 1 below shows the summary of the cross-tabulation.
Table 1 Western Governor Township Race by Family History of Heart Disease

Race
Family History of Disease

Total


No
Yes


African-American
9
19
28

Hispanic
6
11
17

White
22
21
43

Asian
5
7
12

Total
42
58
100




Chi-square test statistics were conducted to determine whether there is a relationship between a history of heart disease and race. The output of the chi-square statistics is shown below:

Table 3
Chi-Square Tests


Value
df
Asymptotic Significance (2-sided)

Pearson Chi-Square
2.913a
3
.405

Likelihood Ratio
2.931
3
.402

Linear-by-Linear Association
1.716
1
.190

N of Valid Cases
100



a. 0 cells (0.0%) have an expected count less than 5. The minimum expected count is 5.04.



Based on the output above, the chi-square test static X^2(3, 0.05) =2.913, p value=0.405>0.05. Since the p-value is more significant than 0.05, there is not sufficient evidence to warrant the rejection of the claim that there is no significant relationship between race and history of heart disease; hence the variables are independent.
ANOVA analysis was conducted to determine whether the household income is significant by race. The ANOVA test was not significant since the p-value was greater than 0.05. The ANOVA results show that there was no significant effect on independent variable income on dependent variable race at the alpha level 0.05 [F (3,96) =1.522, p=2.14. The null hypothesis is thus not rejected (NEDARC - Hypothesis Testing, 2019).

ANOVA table results: The ANOVA test variable was Household Income and Race.

Table 4 Analysis of Variance Difference in Household Income by Race


Household Income



95% Confidence Interval for Mean







N
Standard
Deviation
Lower Bound
Upper Bound

Minimum

Maximum

Black
28
28.86
33.16
55.54
4
100

Hispanic
17
33.88
26.70
61.54
1
95

White
43
27.44
48.90
65.79
3
99

Asia
12
25.34
33.24
65.43
7
86

Total
100
50.5
44.74
56.26
1
100



ANOVA

df
Sum of Squares
Mean Square
F
p-value

Between Groups
3
3782373
1260.791
1.522
2.14

Within Groups
96
79542.627
828.569



Total
99
83325






Conclusion
Based on the data analysis above, we can conclude that the most dominant race in the western government township is the white and the black race. This is evident since the descriptive statistics of the white race comprised 43% of white and 28% of blacks. The above results agree with the literature review findings. The results also concluded that most people in the western government township have a history of heart disease. The results showed that 58% of the respondents have a heart disease history, while 42 % do not account for heart-related conditions. The results are alarming – effectively meaning that more drastic measures ought to be taken to reign in the situation.
The data analysis also shows that most Western government townships live in a 3-bedroom house. Thus, we could come to the conclusion that the population of the western government township largely comprises of people with families. The inferential statistics led to the conclusion that heart disease affects the four races in the western government township equally. The chi-square statistics show us that there is no relationship between race and heart disease history. This means that there is no race that is more prone to heart disease compared to the other races. This fact is in line with the literature review.
The ANOVA analysis leads to a conclusion that there is no significant difference in the household income between the four races. This means that the four races have an equal chance of earning good pay as they can have unequal pay. This contradicts the literature review interms of composition of income by race. Hence, we can conclude that more research needs to be done to better understand the different races and revenues earned.
In conclusion, the research and data analysis was a success. However, more and broad research needs to be done since the above analysis is based on only 100 respondents of western government township. The respondents' opinions might not represent the opinion of the general population.
References
? AHA 2019 Heart Disease and Stroke Statistics - American College of Cardiology (2019). AHA 2019 Heart Disease and Stroke Statistics. American College of Cardiology. https://www.acc.org/latest-in-cardiology/ten-points-to-remember/2019/02/15/14/39/aha-2019-heart-disease-and-stroke-statistics
Centers for Disease Control and Prevention - CDC (2020, September 8). Heart Disease Facts. https://www.cdc.gov/heartdisease/facts.htm
Centers for Disease Control and Prevention – CDC (2020, May 19). High Blood Pressure Symptoms and Causes. https://www.cdc.gov/bloodpressure/about.htm
Holtz, C. (2020). Global Healthcare: Issues and Policies. Jones & Bartlett Learning.
Mastroianni, A.C., Kahn, J.P. & Kass, N.E. (2019). The Oxford Handbook of Public Health Ethics. Oxford University Press.
NEDARC (2019). Hypothesis Testing. https://www.nedarc.org/statisticalHelp/advancedStatisticalTopics/hypothesisTesting.html
Virani, S. S., Alonso, A., Benjamin, E. J., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Chang, A. R., Cheng, S., Delling, F. N., Djousse, L., Elkind, M. S. V., Ferguson, J. F., Fornage, M., Khan, S. S., Kissela, B. M., Knutson, K. L., Kwan, T. W., Lackland, D. T. & Lewis, T. T. (2020). Heart Disease and Stroke Statistics—2020 Update: A Report from the American Heart Association. Circulation, 141(9). https://doi.org/10.1161/cir.0000000000000757
World Health Organization: WHO. (2019, June 11). Cardiovascular diseases. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1

?APPENDIX (SPSS OUTPUTS)

Statistics


Race
Annual Household Income
Home Price
No. Bedrooms
Household Size
History of Heart Disease

N
Valid
100
100
100
100
100
100


Missing
0
0
0
0
0
0

Mean
2.3900
131.3390
289.9910
3.4100
3.6300
.5800

Median
3.0000
131.3500
287.3000
3.0000
3.5000
1.0000

Mode
3.00
98.90a
301.60
3.00
3.00
1.00

Minimum
1.00
72.40
210.30
2.00
1.00
.00

Maximum
4.00
261.80
381.50
6.00
7.00
1.00

a. Multiple modes exist. The smallest value is shown



Race


Frequency
Percent
Valid Percent
Cumulative Percent

Valid
Black
28
28.0
28.0
28.0


Hispanic
17
17.0
17.0
45.0


White
43
43.0
43.0
88.0


Asia
12
12.0
12.0
100.0


Total
100
100.0
100.0




History of Heart Disease


Frequency
Percent
Valid Percent
Cumulative Percent

Valid
No
42
42.0
42.0
42.0


Yes
58
58.0
58.0
100.0


Total
100
100.0
100.0











Race * History of Heart Disease Crosstabulation

Count


History of Heart Disease
Total


No
Yes


Race
Black
9
19
28


Hispanic
6
11
17


White
22
21
43


Asia
5
7
12

Total
42
58
100



Chi-Square Tests


Value
df
Asymptotic Significance (2-sided)

Pearson Chi-Square
2.913a
3
.405

Likelihood Ratio
2.931
3
.402

Linear-by-Linear Association
1.716
1
.190

N of Valid Cases
100



a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 5.04.




T-Test

Case Processing Summary


Cases


Valid
Missing
Total


N
Percent
N
Percent
N
Percent

Household * History of Heart Disease
100
100.0%
0
0.0%
100
100.0%



ANOVA

Household


Sum of Squares
df
Mean Square
F
Sig.

Between Groups
3782.373
3
1260.791
1.522
.214

Within Groups
79542.627
96
828.569



Total
83325.000
99







Descriptives

Household


N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Minimum
Maximum






Lower Bound
Upper Bound



Black
28
44.3571
28.86394
5.45477
33.1649
55.5494
4.00
100.00

Hispanic
17
44.1176
33.88378
8.21802
26.6962
61.5391
1.00
95.00

White
43
57.3488
27.43727
4.18415
48.9049
65.7928
3.00
99.00

Asia
12
49.3333
25.33533
7.31368
33.2360
65.4306
7.00
86.00

Total
100
50.5000
29.01149
2.90115
44.7435
56.2565
1.00
100.00




ANOVA

Household


Sum of Squares
df
Mean Square
F
Sig.

Between Groups
3782.373
3
1260.791
1.522
.214

Within Groups
79542.627
96
828.569



Total
83325.000
99