The Relationship between Regional, Race, Gender and the Number of

Deaths from Heart Disease

Boda Xie

Hong Kong Baptist University, Hong Kong, China

Keywords: Heart Disease, Race, Gender, Region.

Abstract: In the United States, heart disease continues to be the leading cause of death in the United States. The National

Heart, Lung and Blood Institute reports that heart disease and cancer account for about 50 percent of deaths

among people aged 45 to 64. This article used cardiac patient data from all 50 states in the United States in

2015. In this paper, the author conducted the research by analyzing the data via ANOVA and multiple linear

regression. The number of heart disease deaths (per 100,000 people) also varied significantly by gender, race

and region. Men were more likely to die from heart disease than women; Native Americans and blacks have

higher rates of death from heart disease than any other race; Regionally, people in four states — the District

of Columbia, Massachusetts, Minnesota and Rhode Island — were less likely to die from heart disease than

in the rest of the country.

1 INTRODUCTION

The 2019 report of the National Heart, Lung and

Blood Institute (Sally and Curtin 2019) shows that the

death rate of heart disease in the United States has a

downward trend. As can be seen from the graph, the

death rate of men is still higher than that of women,

and the death rate of blacks is higher than that of

whites.

Heart disease has a huge impact on the American

people. The study published by the American Heart

Association and the National Heart, Lung and Blood

Institute looked at trends and numbers in the number

of people with heart disease. The factors that

contribute to heart disease have not been thoroughly

studied. So, this is a big gap in the current research.

It is of great significance for the state government

to adjust the medical policy and the state people to

seek more effective health resorts.

Therefore, heart disease is a major cause of death

in the United States. This paper intends to explore

whether there is a significant difference in the number

of heart disease deaths among different genders, races

and regions. It also means that there are significant

differences in the chances of dying from heart disease

among people of different genders, races and regions.

2 THE BACKGROUND OR THE

DEFINITION OF THE HEART

DISEASE

Heart disease is the leading cause of death in both

men and women in the United States. In the United

States, someone dies of cardiovascular disease every

36 seconds (Centers for Disease Control and

Prevention 2020). Each year, about 655,000

Americans die of heart disease — 1 in 4 of them

(Virani, Alonso, Benjamin, Bittencourt, Callaway,

Carson, et al 2020).

Every 40 seconds someone in the United States

has a heart attack. Every year, about 805,000

Americans suffer a heart attack. Of these, 605,000

were first-time heart attacks and 200,000 occurred in

people who had already had a heart attack.

From 2014 to 2015, heart disease cost the U.S. an

estimated $219 billion annually. This includes health

care services, medicines and lost productivity due to

death (Fryar, Chen and Li 2019).

Coronary heart disease is the most common type

of heart disease, causing 365,914 deaths in 2017.

Approximately 18.2 million adults age 20 and older

have coronary heart disease (approximately 6.7%).

Approximately two in 10 coronary heart disease

deaths occur in adults under 65 years of age

(Benjamin, Muntner, Alonso, Bittencourt, Callaway,

456

Xie, B.

The Relationship between Regional, Race, Gender and the Number of Deaths from Heart Disease.

DOI: 10.5220/0011372100003438

In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 456-464

ISBN: 978-989-758-596-8

Carson, et al 2019). About a fifth of heart attacks are

silent — the damage is done but the patient doesn’t

realise it.

Heart disease is the leading cause of death among

minority groups in the United States, including

African-Americans, American Indians, Alaska

Natives, Hispanics and whites. For women from the

Pacific Islands, as well as Asian American, American

Indian, Alaska Native and Hispanic women, heart

disease is only second to cancer (Heron 2019).

Death rates from heart disease and diabetes rose

significantly in the United States in 2020, and experts

believe a big reason may be that many people with

dangerous symptoms made the fatal mistake of

staying out of the hospital for fear of contracting the

coronavirus. It’s been the deadliest year in U.S.

history, and that’s largely because of COVID-19. But

death rates from heart disease and diabetes increased

by the most in at least 20 years, the data showed. The

death rate from heart disease rose to 167 per 100,000

from 161.5 the previous year. This is only the second

time in 20 years that the rate has risen. That’s more

than 3 percent, up from less than 1 percent in 2015. In

the raw numbers, there were about 32,000 more

deaths from heart disease than in the previous year.

3 RESULTS OF THE STUDY

3.1 Data Frequency and Descriptive

Statistics

First, the author sorted out the frequency table of

variables as shown below.

Table 1: Frequency statistics of each variable.

The Relationship between Regional, Race, Gender and the Number of Deaths from Heart Disease

457

As can be seen from Table 1, there are 13,870

samples in total, including 7,135 males and 6,735

females, with a proportion of 51.44% and 48.56%

respectively. Among ethnic groups, the Indian sample

was the least, with 753 people, accounting for 5.43%;

The white sample was the largest, 6,353 people,

accounting for 45.80%; Except for Indians and

Asians, there are more than 1300 people of other

ethnic groups, and the samples of other ethnic groups

account for more than 10%. There were only 10

samples in the least area, accounting for 0.07%; The

largest region, Texas, had 1,331 samples, or 9.60%.

In order to display the frequency information of

data more clearly, the author made the pie chart of

gender distribution of each race as shown below.

Figure 1: Gender distribution of each ethnic group.

As can be seen from Figure 1, in our data, white

people have the largest sample size (6353 samples),

followed by black people (3430 samples), Hispanics

(2160 samples), Asians (1174 samples) and

American Indians (753 samples). The overall sample

size is large, and the sample ratio of male and female

of each race is close to 1:1, avoiding the deviation

caused by too little data.

Further, the author made a bar chart of the

frequency and proportion of data in each region of the

United States, as shown in the figure below.

Figure 2: Frequency and proportion of data by region.

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As can be seen from Figure 2, Texas has the

largest number of data samples, with 1,331 samples.

Far more than Georgia’s 731 samples.

As for the number of deaths from heart disease,

we classified the number of deaths from heart disease

by gender and race in different regions of the United

States in 2015, and then conducted descriptive

statistics on the number of deaths from heart disease

per 100,000 people to observe its distribution

characteristics, as shown in the figure below.

Figure 3: descriptive statistics of deaths from heart disease (per 100,000) in the United States in 2015As can be seen from

Figure 3, the average number of deaths from heart disease in the United States was 348.1 and the median was 331.5. Where

the minimum value is 0; 25% quantile was 233.1; the 75% quantile is 442.9, while the maximum value is 5161.7. It can be

seen that the data shows a right-biased distribution, with more data below the mean on the whole, and a few data are larger.

It can be seen that the number of deaths from heart disease in the United States as a whole is low and well controlled, and

only a few areas have poor outcomes and outliers.

3.2 One-way ANOVA

To analyze whether there are differences in the

number of heart disease deaths among different

genders, different races and different regions, one-

way ANOVA was used for each variable (Wilkinson

and Rogers 1973).

First, we conducted one-way ANOVA for gender

to verify whether there is a significant difference in

the number of heart disease deaths of different

genders. The original hypothesis of ANOVA is that

there is no significant difference in the number of

heart disease deaths of different genders.

Figure 4: One-way ANOVA for gender grouping.

As can be seen from Figure 4, the death rate of

heart disease in females is about 272±107, and that in

males is about 420±174. After one-way ANOVA, the

F statistic was 3619 and P value <0.05, indicating that

we rejected the null hypothesis at the significance

level of 5%. There are significant differences between

the sexes in the number of heart disease deaths.

The Relationship between Regional, Race, Gender and the Number of Deaths from Heart Disease

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Figure 5: Data distribution of different gendersIt can be further seen from Figure 5 that the average number of heart disease

deaths was 420.10 for males and 271.76 for females. The average number of male deaths is about 1.55 times that of female

deaths. The overall data distribution was significantly higher for men than for women

Second, the author conducted a one-way ANOVA

on race to verify whether there was significant

difference in the number of heart disease deaths

among different races. The original hypothesis of

ANOVA was that there was no significant difference

in the number of heart disease deaths among different

races.

Figure 6: Results of variance analysis of different races.

As can be seen from Figure 6, the death toll from

heart disease among American Indians is about

415.76±190.81, Asians 168.30±71.77, African

Americans 440.51±169.48 and Hispanics

215.06±118.20. The number of heart disease deaths

among white Americans was 368.58±122.13. The

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460

results of one-way ANOVA showed that the F

statistic was 1506 and the P value was <0.05,

indicating that we rejected the null hypothesis at the

significance level of 5%. There are significant

differences in heart disease deaths among ethnic

groups. The data distribution of specific ethnic groups

is shown in the figure below.

Figure 7: Distribution of heart disease deaths by race.

As can be seen from Figure 7, from the overall

distribution of data, asians have the lowest number of

heart disease deaths, while black people have the

largest number, and the maximum value of black

people is also the largest, reaching more than 5000.

Similarly, we conducted a one-way ANOVA for

regions to verify whether there is a significant

difference in the number of deaths from heart disease

in different regions. The original assumption of

ANOVA was that there was no significant difference

in the number of deaths from heart disease in different

regions. Because of the excessive grouping of

regions, we omit the results of mean and standard

deviation for regions.

Figure 8: Analysis of variance in different regions.

FIG. 8 shows that the one-way ANOVA shows

that the F statistic is 47.23 and the P value is <0.05,

indicating that the null hypothesis is rejected. The

number of deaths from heart disease varies

significantly by region. The data distribution in

different areas is shown in the figure below.

The Relationship between Regional, Race, Gender and the Number of Deaths from Heart Disease

461

Figure 9: Distribution of different regions.

As can be seen from Figure 9, only the District of

Columbia, Massachusetts, Minnesota and Rhode

Island have lower mean lines, indicating that the

average number of deaths from heart disease in these

four states is lower.

3.3 Multiple Linear Regression

In order to analyze the impact of each variable on the

number of deaths from heart disease, we adopted

multiple linear regression, adding gender, race and

region as independent variables into the model. The

model fitting results are as follows (the regression

coefficients of each variable are not shown one by one

due to the excessive classification of regions).

Figure 10: Results of multiple linear regression.

As shown in Figure 10, the R2 of multiple linear

regression was 0.601, and the adjusted R2 was 0.599,

indicating that gender, race, region and other

variables explained 60% of the changes in the number

of deaths from heart disease, and the overall model

had a good fitting degree. The F statistic is 371.5, and

the corresponding P value <0.05 indicates that the

regression coefficients of the model are not all 0.

Further, we conduct model diagnosis on the

multiple linear regression model, and the diagnosis

results are shown in the figure below.

Figure 11: Multiple linear regression analysis model

diagnosis.

It can be seen from the upper left of Figure 11 that

the relationship between the fitted value and the

residual is a horizontal line, indicating that there is no

systematic correlation between the residual and the

fitted value, and there is no problem with the linear

hypothesis of the model.

It can be seen from the above figure on the right

that some of the QQ charts of residuals deviate from

the dotted line, indicating that residuals violate the

normality hypothesis.

As you can see from the lower left figure, the

points around the horizontal line are randomly

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462

distributed, indicating that the homovariance

hypothesis is satisfied.

As can be seen from the figure below, there are

three outliers in the value of Cook’s D, which may

have a little influence on the fitting effect of the

model due to the extreme data. However, the data

sample size in this paper is large, so the influence can

be ignored theoretically.

On the whole, from the model diagnosis of

multiple linear regression analysis, there is no

problem in model setting and no missetting model.

Finally, ANOVA was used to verify whether each

variable of the above multiple linear regression model

could not be deleted. The results of the analysis of

variance are as follows.

Figure 12: ANOVA results of multiple linear regression.

It can be seen from the figure above that the P-

values of the F-test of different variables removed are

all less than 0.05, which indicates that these variables

have a significant impact on the model and there are

significant differences between the removed model

and the original model.

4 DISCUSSION

From the above analysis, there was a significant effect

on the number of heart disease deaths (per 100,000)

by sex, race and region. The average death toll for

men was nearly 1.55 times higher than for women,

probably because women were better at taking care of

themselves.

In terms of race, the number of heart disease

deaths of American Indians and blacks was higher

than that of white Americans, with blacks having the

highest number. Asians and Hispanics are below the

average for white Americans. This may be related to

the high cost of medical treatment in the United

States, which is unaffordable for ordinary people

without medical insurance. The income of American

Indians and blacks is generally lower than that of

white Americans, resulting in fewer people buying

health insurance and thus more deaths. Asians and

Hispanics who move to America tend to be wealthy

locals, have better health insurance, and die less. It

could also have something to do with the smaller

sample sizes of Asians and Hispanics.

In terms of regions, the distribution of most

regions is similar, and only the District of Columbia,

Massachusetts, Minnesota and Rhode Island have

lower mean values, which should be related to the

environment and income and expenditure of each

region.

In the multiple linear regression, we found that

gender, race and region were all independent

variables that could not be ignored. In ANOVA, no

matter which variable was deleted, it would have a

significant impact on the multiple linear regression

model.

Compared with the previous analysis, the

previous data were mainly used for trend analysis to

study the changing trend of the number of deaths from

heart disease. Or to study the related causes of death

from heart disease, to see which causes have a higher

number of deaths. This paper does not stop at the

description and summary of data. Further analysis of

variance, multiple linear regression and other

methods are used to reveal the relationship between

data from the perspective of statistics.

5 CONCLUSIONS

This study found that the number of heart disease

deaths (per 100,000 people) was significantly

different by gender, race and region. Men were more

likely to die from heart disease than women, and

women were better able to take care of themselves.

Indians and blacks have higher rates of death from

heart disease than other races, which is probably

related to the lower income of Indians and blacks.

Regionally, people in the District of Columbia,

Massachusetts, Minnesota and Rhode Island died less

frequently from heart disease than people in other

regions.

Due to the data, there are still many conjectures

that cannot be verified. For example, the reason for

the higher death rate of heart disease among Indians

and blacks, and the reason for the higher death rate of

men than women remains to be revealed.

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