RESEARCH ARTICLE |
https://doi.org/10.5005/jp-journals-10045-00211 |
Awareness of Myocardial Infarction Risk Factors among Secondhand Smokers
1-4Faculty of Allied Health Science, Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India
5-7Cardiac Care Technology, Department of Allied Health Science, Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India
Corresponding Author: Thirumurugan E, Faculty of Allied Health Science, Dr. M.G.R. Educational and Research Institute, Chennai, Tamil Nadu, India, Phone: +91 7358581003, e-mail: thirumurugan.cct@drmgrdu.ac.in
Received: 17 July 2022; Accepted: 08 September 2022; Published on: 15 December 2022
ABSTRACT
Background: Coronary heart disease accounts for the leading cause of death and disability in the world, causing the deaths of 9.4 million people a year. About 1.9 million (or 21%) of these deaths are related to tobacco use and secondhand smoke (SHS) exposure. Secondhand smokers’ awareness of myocardial infarction (MI) is thought to impact the burden of disease, prevention, and time management. Hence, promoting more awareness of MI risk factors among secondhand smokers is incredibly important.
Objectives: The study aimed to assess the level of awareness regarding MI risk factors among secondhand smokers.
Methods: The study was conducted at Dr. M.G.R Educational and Research Institute, India, using a quantitative approach and descriptive research design. A sample of 508 subjects, using self-structured knowledge questionnaires, was selected for data collection and analysis. The study excluded patients with mental retardation, previous history of percutaneous transluminal coronary angioplasty (PTCA), and coronary artery bypass graft surgery (CABG). The study plan was submitted to the Institutional Ethics Committee (IEC), and ethical clearance was granted.
Results: A total of 508 respondents participated in this study, of whom 54.7% were females and the rest 45.3% were males. The majority, 55.5% of them, were in the age group of 20–29 years, followed by 11.6% in the age group of 30–39 years, 20.7% in the age group of 40–49 years, 8.9% in the age group of 50–59 years, and 3.3% in the age group of >60 years. Most participants, 272 (53.6%), had bachelor’s degrees as their highest educational level, followed by 36.6% from high school and 9.8% illiterate. We performed a multivariate logistic regression analysis to identify the demographic factors affecting the awareness of MI risk factors (secondhand smoking) and found that the age of 30–39 years [odds ratio (OR), 3.01; 95% confidence interval (CI), 1.13–8.01; p < 0.05], illiterate (OR, 2.33; 95% CI, 1.08–5.01; p < 0.05) were the factors affecting the recognition of acute MI risk factors (SHS).
Conclusion: In our study, we conclude that females, age group of 30–39 years and >60 years and illiterate were unaware of risk factors of MI among secondhand smokers. To enhance the awareness of MI risk factors among secondhand smokers in India, education and promotion must be conducted with consideration of gender, age, education level, and occupation. Moreover, active engagement by the government, educational institutions, and medical institutions are required for awareness of both modifiable and non-modifiable MI risk factors.
How to cite this article: Thirumurugan E, Gomathi K, Swathy P, et al. Awareness of Myocardial Infarction Risk Factors among Secondhand Smokers. J Med Sci 2021;7(3):38-42.
Source of support: Nil
Conflict of interest: None
Keywords: Awareness, Myocardial infarction, Myocardial infarction risk factors, Secondhand smokers
INTRODUCTION
Myocardial infarction, commonly known as “heart attack,” occurs when the blood flow to a portion of the heart is limited or ceases entirely. As a result of coronary artery occlusion, the myocardium is deprived of oxygen. An extended loss of oxygen can cause myocardial necrosis. According to a joint report produced by World Health Organization (WHO), the WHO Heart Federation, and Newcastle University, Australia, for World Heart Day, which was celebrated around the globe on 29th September, there is a well-established link between tobacco smoking and several coronary heart diseases.
Coronary heart disease accounts for the leading cause of death and disability in the world, causing the deaths of 9.4 million people a year. About 1.9 million (or 21%) of these deaths are related to tobacco use and SHS exposure. Smokers are more likely to experience an acute cardiovascular event at a younger age, and their risk of heart disease increases significantly even among occasional tobacco users or those who smoke only one cigarette every day in a series of tobacco knowledge summaries.1 Smoking-induced diseases are the most prevalent killer in modern society. More than 5 million deaths are caused by smoking each year. The harmful effects of smoking on health have been documented in the literature. As a general rule, smoking is associated with lung cancer, with people who smoke having a 25% greater risk than nonsmokers. Smoking is also linked to other cancers, such as liver cancer. The risk of diabetes is higher for smokers compared to nonsmokers. Smokers have negative health effects on their immune systems and their reproductive systems. Consequently, smokers are more prone to infectious diseases and birth defects compared to nonsmokers.
In today’s world, SHS is no longer an isolated problem. It is inhaled by people who smoke and exhaled by people who do not smoke. SHS has become an increasingly important public health issue. As a well-known contributor to morbidity and mortality, SHS is a serious problem worldwide. A report shows that since 1964, SHS has caused approximately 2.5 million deaths among nonsmokers in the United States. The effectiveness of SHS in the development of heart diseases may be increased by 25–30% in individuals who are frequently exposed to SHS. The government has instituted a policy in response to the hazards associated with SHS to limit the exposure of people to SHS. These policies include increasing taxes on cigarettes and banning smoking in all public places. As part of the government’s anti-smoking program, Tak Nak, a nationwide campaign, has been organized to raise public awareness regarding the negative effects of smoking. Nevertheless, the campaign has not been very successful. It is estimated that >40% of adults are exposed to SHS both at work and at home. Furthermore, about 70% of adults breathe in tobacco smoke while visiting restaurants or when using public transportation. These alarming statistics may be due to a lack of awareness of SHS’s deadly effects on the health of nonsmokers. Smokers may not realize that tobacco smoke can negatively impact nonsmokers too.2
Overall, 93% of people still live in countries without smoke-free regulations. As of now, smoking is banned in healthcare, government, and educational facilities, as well as in public transportation. Unfortunately, some places do not fully enforce their smoking bans, such as indoor offices and restaurants. Literature reviews concluded that SHS’s toxic elements cause significant cardiovascular morbidity and mortality. As a result, a nonsmoker policy is vital at a societal level to ensure zero SHS exposure. Furthermore, the American Heart Association (2019) recommended clinicians should counsel patients to avoid SHS exposure in their homes, vehicles, workplaces, and public places. According to a study conducted in Germany, the prevalence of ischemic heart disease in 2040 will drop by 50% if secondhand smoking is eradicated. Secondhand smokers’ awareness of MI is thought to impact the burden of disease, prevention, and time management. Hence, promoting more awareness of MI risk factors among secondhand smokers is incredibly important.3
METHODS
The study was conducted at the Dr. M.G.R. Educational and Research Institute, India, using a quantitative approach and descriptive research design. This study considered people who are exposed to smoking from other people as passive or secondhand smokers. A sample of 508 subjects, using self-structured knowledge questionnaires, was selected for data collection and analysis. The study excluded patients with mental retardation and previous history of PTCA and CABG. The study plan was submitted to the IEC, and ethical clearance was granted. During the data collection phase, the subjects were asked to sign an informed consent form. Confidentiality and anonymity of the responses were assured. Data were collected by interview method, using self-structured questions for each subject, including an examination of sociodemographic data and a question on risk factors of MI. A total of 25 questions were asked for each subject.
Awareness of MI Risk Factors
An individual’s awareness was assessed by their responses to the question, “Which of the following are risk factors for a heart attack?” (1) secondhand smoking; (2) obesity; (3) diabetes; (4) physical inactivity; (5) unhealthy diets; (6) stress; (7) high blood pressure; (8) alcohol; and (9) genetic.
Statistical Analysis
Statistical analysis was carried out using the Statistical Package for the Social Sciences (SPSS) software. Among the nominal variables, the number and percentage were presented. A multivariate logistic regression analysis was performed to identify the factors affecting the probability of belonging to the aware or unaware groups. A significance level of p < 0.05 was used.
RESULTS
A total of 508 respondents participated in this study, of whom 54.7% were females and the rest 45.3% were males. The majority, 55.5% of them, were in the age group of 20–29 years, followed by 11.6% in the age group of 30–39 years, 20.7% in the age group of 40–49 years, 8.9% in the age group of 50–59 years, and 3.3% in the age group of >60 years. Most participants, 272 (53.6%), had bachelor’s degrees as their highest educational level, followed by 36.6% from high school and 9.8% illiterate, as outlined in Figure 1.
Fig. 1: Educational status
Awareness of MI Risk factors among SHS
We performed a multivariate logistic regression analysis to identify the demographic factors affecting awareness of MI risk factors (secondhand smoking) and found that age of 30–39 years (OR, 3.01; 95% CI, 1.13–8.01; p < 0.05) and illiterate (OR, 2.33; 95% CI, 1.08–5.01; p < 0.05) were the factors affecting the recognition of acute MI risk factors (secondhand smoking) (Table 1).
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think secondhand smoking is a risk factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.99 (0.61–1.62) | 0.98 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 3.01 (1.13–8.01) | 0.02* | |
| 40–49 years | 1.43 (0.53–3.82) | 0.42 | |
| 50–59 years | 1.37 (0.43–4.30) | 0.58 | |
| 60 and above | 3.05 (0.81–11.42) | 0.09 | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 1.23 (0.49–3.05) | 0.64 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.54 (0.92–2.59) | 0.09 | |
| Illiterate | 2.33 (1.08–5.01) | 0.03* | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.84 (0.49–1.42) | 0.51 | |
*Significant; p-value <0.05 were considered as significant
We performed a multivariate logistic regression analysis to identify the demographic factors affecting the awareness of MI risk factors (obesity) and found that high school (OR, 1.85; 95% CI, 1.10–3.13; p < 0.05) and illiterate (OR, 3.70; 95% CI, 1.70–8.02; p < 0.05) were the factors affecting the recognition of acute MI risk factors (obesity) (Table 2).
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think obesity is a major risk factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.65 (0.40–1.06) | 0.09 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 1.41 (0.51–3.94) | 0.50 | |
| 40–49 years | 1.02 (0.37–2.80) | 0.95 | |
| 50–59 years | 0.99 (0.31–3.17) | 0.99 | |
| 60 and above | 3.28 (0.87–12.28) | 0.07 | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 1.23 (0.49–3.05) | 0.64 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.85 (1.10–3.13) | 0.02* | |
| Illiterate | 3.70 (1.70–8.02) | 0.01** | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.76 (0.45–1.30) | 0.32 | |
*Significant; **highly significant; p-value <0.05 were considered as significant
We performed a multivariate logistic regression analysis to identify the demographic factors affecting the awareness of MI risk factors (diabetic) and found that age of >60 years (OR, 4.11; 95% CI, 1.10–15.34; p < 0.05) was the factor affecting the recognition of acute MI risk factors (diabetic) (Table 3).
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think diabetes is a major risk factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.89 (0.63–1.31) | 0.58 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 1.35 (0.60–3.04) | 0.46 | |
| 40–49 years | 0.92 (0.43–2.00) | 0.85 | |
| 50–59 years | 0.82 (0.32–2.05) | 0.67 | |
| 60 and above | 4.11 (1.10–15.34) | 0.03* | |
| 3. | Marital Status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 1.08 (0.54–2.17) | 0.81 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.19 (0.79–1.81) | 0.39 | |
| Illiterate | 1.29 (0.65–2.56) | 0.45 | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.92 (0.60–1.39) | 0.70 | |
*Significant; p-value <0.05 were considered as significant
We performed a multivariate logistic regression analysis to identify the demographic factors affecting the awareness of MI risk factors (physical inactivity) and found that age of >60 years (OR, 8.82; 95% CI, 1.03–75.2; p < 0.05) and illiterate (OR, 2.60; 95% CI, 1.16 to 5.83; p < 0.05) were the factors affecting the recognition of acute MI risk factors (physical inactivity) (Table 4).
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think physical inactivity is a major factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | 1.00 (reference) | |
| Male | |||
| Female | 0.67 (0.45–0.99) | 0.04 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 1.30 (0.56–3.01) | 0.53 | |
| 40–49 years | 1.44 (0.65–3.17) | 0.35 | |
| 50–59 years | 1.16 (0.45–2.96) | 0.74 | |
| 60 and above | 8.82 (1.03–75.24) | 0.04* | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 1.54 (0.76–3.14) | 0.22 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.33 (0.86–2.05) | 0.18 | |
| Illiterate | 2.60 (1.16–5.83) | 0.02* | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.92 (0.60–1.41) | 0.71 | |
*Significant; p-value <0.05 were considered as significant
We performed a multivariate logistic regression analysis to identify the demographic factors affecting the awareness of MI risk factors (unhealthy diet) and found that illiterate (OR, 2.55; 95% CI, 1.21–5.39; p < 0.05) was the factor affecting the recognition of acute MI risk factors (unhealthy diet) (Table 5).
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think unhealthy diet is a major factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.82 (0.51–1.34) | 0.45 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 1.03 (0.37–2.82) | 0.95 | |
| 40–49 years | 1.10 (0.43–2.82) | 0.84 | |
| 50–59 years | 0.92 (0.30–2.28) | 0.89 | |
| 60 and above | 2.13 (0.58–7.88) | 0.25 | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 0.81 (0.33–1.94) | 0.64 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.26 (0.75–2.13) | 0.37 | |
| Illiterate | 2.55 (1.21–5.39) | 0.01** | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.98 (0.58–1.67) | 0.96 | |
**highly significant; p-value <0.05 were considered as significant
We performed a multivariate logistic regression analysis to identify the demographic factors affecting the awareness of MI risk factors (stress) and found that illiterate (OR, 2.88; 95% CI, 1.17–7.05; p < 0.05) was the factor affecting the recognition of acute MI risk factors (stress) (Table 6).
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think stress is a major factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.97 (0.52–1.78) | 0.92 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 0.41 (0.10–1.70) | 0.22 | |
| 40–49 years | 0.97 (0.30–3.08) | 0.96 | |
| 50–59 years | 0.80 (0.20–3.14) | 0.75 | |
| 60 and above | 1.52 (0.32–7.26) | 0.59 | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 0.81 (0.27–2.41) | 0.71 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.39 (0.72–2.69) | 0.31 | |
| Illiterate | 2.88 (1.17–7.05) | 0.02* | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.92 (0.48–1.78) | 0.82 | |
*Significant; p-value <0.05 were considered as significant
We performed a multivariate logistic regression analysis to identify the demographic factors affecting awareness of MI risk factors (high blood pressure, alcohol, and genetics) and found that all groups of demographic factors were sensitive to recognizing acute MI risk factors (high blood pressure, alcohol, and genetics) (Tables 7 to 9) (Fig. 1).
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think high blood pressure is a major factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.64 (0.34–1.20) | 0.17 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 0.50 (0.13–1.89) | 0.31 | |
| 40–49 years | 0.54 (0.16–1.77) | 0.31 | |
| 50–59 years | 0.33 (0.71–1.58) | 0.16 | |
| 60 and above | 0.93 (0.17–4.84) | 0.93 | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 0.59 (0.20–1.68) | 0.32 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.04 (0.52–2.08) | 0.89 | |
| Illiterate | 1.05 (0.34–3.23) | 0.93 | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.88 (0.44–1.77) | 0.73 | |
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think alcohol is a major factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.75 (0.49–1.16) | 0.20 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 1.83 (0.74–4.52) | 0.18 | |
| 40–49 years | 0.71 (0.28–1.78) | 0.47 | |
| 50–59 years | 0.99 (0.34–2.84) | 0.99 | |
| 60 and above | 1.48 (0.41–5.35) | 0.54 | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 1.46 (0.65–3.26) | 0.35 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 1.21 (0.76–1.93) | 0.42 | |
| Illiterate | 1.94 (0.91–4.14) | 0.08 | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.99 (0.62–1.59) | 0.99 | |
| Predictors of awareness of MI risk factors | |||
|---|---|---|---|
| Do you think genetic is a major factor of heart attack? | |||
| Sl. no. | Variables (classification of variable) | aOR (95% CI) | p-value |
| 1. | Gender | ||
| Male | 1.00 (reference) | ||
| Female | 0.83 (0.56–1.23) | 0.37 | |
| 2. | Age | ||
| 20–29 years | 1.00 (reference) | ||
| 30–39 years | 1.58 (0.67–3.72) | 0.28 | |
| 40–49 years | 0.96 (0.44–2.09) | 0.92 | |
| 50–59 years | 1.06 (0.42–2.70) | 0.89 | |
| 60 and above | 0.65 (0.19–2.14) | 0.48 | |
| 3. | Marital status | ||
| Married | 1.00 (reference) | ||
| Unmarried | 1.04 (0.51–2.12) | 0.90 | |
| 4. | Educational status | ||
| Graduate | 1.00 (reference) | ||
| High school | 0.87 (0.57–1.32) | 0.51 | |
| Illiterate | 1.97 (0.92–4.22) | 0.08 | |
| 5. | Occupational status | ||
| Employed | 1.00 (reference) | ||
| Unemployed | 0.86 (0.56–1.32) | 0.50 | |
DISCUSSION
Currently, this is the first study of high-risk Indian populations to examine heart disease risk factors among secondhand smokers. An assessment of risk factors, such as obesity, diabetes, stress, high blood pressure, unhealthy diet, genetics, smoking (SHS), and physical inactivity, was performed among secondhand smokers. Study results showed that subjects of the age group 30–39 years, >60 years, and illiterate had a lack of awareness of modifiable heart disease risk factors.
According to a survey conducted by Koziński et al., obesity, poor diet, lack of physical activity, and smoking were the most frequently mentioned risk factors.4 A similar study by Rząca et al. found that 52.7% of respondents believed that genetics predisposed to heart disease.5
Our survey revealed that high blood pressure, alcohol, and genetics were the most frequently mentioned risk factors among study participants.
CONCLUSION
In our study, we conclude that females, age group of 30–39 years and >60 years, and illiterate were unaware of risk factors of MI among secondhand smokers. To enhance the awareness of MI risk factors among the secondhand smokers in India, education and promotion must be conducted with consideration of gender, age, education level, and occupation. Moreover, active engagement by the government, educational institutions, and medical institutions are required for awareness of both modifiable and non-modifiable MI risk factors.
ACKNOWLEDGMENTS
Researchers thankfully acknowledge Dr. Kalpana Devi, Principal, and Dr. Kala Victor, Director of Allied Health Science of Dr. M.G.R. Educational and Research Institute for active guidance throughout the present study.
ORCID
Thirumurugan E https://orcid.org/0000-0003-4638-9050
Gomathi K https://orcid.org/0000-0002-9909-382X
Swathy P https://orcid.org/0000-0002-2658-0168
Syed Ali Afrin H https://orcid.org/0000-0002-2468-8298
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