Abstract
Background: Cardiovascular diseases (CVDs) or diseases of the heart and blood vessels are one of the largest causes of death. More than 16 million Americans have a CVD. Approximately smoking causes one of every four deaths from CVD.
Objective: To predict the odds of developing heart disease in a lifetime among tobacco smokers compared to non-smokers using a simple binary logistic regression.
Methodology: Respondents (n = 55271), aged 12 and above, for the 2014 National Survey on Drug Use and Health (NSDUH) in the Inter-university Consortium for Political and Social Research (ICPSR) datasets were use. The NSDUH is a quarterly and annual nationwide survey that primarily measures the prevalence and correlates of drug use in the United States both on the national and state level. Respondents were selected from the civilian, non-institutionalized population residing in the United States. The 2014 NSDUH was transferred into the SPSS and a simple binary logistic regression analysis was performed to predict the odds of developing a CVD in a lifetime among cigarette smokers compared to non-smokers. The number of respondents is enough to provide the confidence that the false null hypothesis for the study analysis will be correctly rejected at least 80% of the time.
Results: The result of the regression indicates that a full model chi test for the variable eversmokecigrarette as the predictor variable was statistically significant χ²(1) = 192.494, p < .001. Wald’s statistical value for respondents who reported having smoked cigarettes was statistically significant, Wald χ² =176.136, p < .001. Thus, the odds of developing heart disease in a lifetime among respondents who reported ever smoking cigarettes and those who never reported smoking cigarettes differ significantly from 1.00. Further analysis indicates that the odds of developing heart disease for cigarette smokers were approximately 2.232 times higher than those of non-cigarette smokers with a 95% CI [1.983, 2.513].
Conclusion: Cigarette smoking significantly increases the odds of developing CVD in a lifetime. Therefore, cigarette smoking is harmful to hearth health. Cigarette smokers are more likely to develop heart disease in a lifetime compared to non-cigarette smokers.
Key words: Cardiovascular disease, Smoking, Cigarette, Heart disease, Coronary heart disease, Cerebrovascular disease, Stroke, Heart failure, Atherosclerosis, Cardiovascular mortality
Introduction
Studies have shown that cardiovascular diseases (CVD) or diseases of the heart and the blood vessels are one of the largest causes of death in the United States of America, and that more than 16 million Americans have a CVD. The annual mortality rate of Americans from coronary heart disease is about 993000 (CDC, n.d.). About 25500 die of cerebrovascular diseases (stroke) annually, and about 15500 die of other vascular diseases annually. Approximately, smoking causes one of every four deaths from CVDs (CDC, n.d.). Additionally, almost all deaths from abdominal aortic aneurysms are caused by smoking. Therefore, tobacco smoking is a primary cause of CVD (DʼAlessandro et al., 2012; Papathanasiou et al., 2014). Cardiovascular diseases include coronary artery constriction, heart attack, cerebrovascular diseases or strokes, peripheral vascular disease (PVD), peripheral artery disease (PAD), and abdominal aortic aneurysm. The risk of developing a CVD, including aortic aneurysms, due to tobacco smoking increases with the number of cigarettes smoked per day and when smoking continues for years (Burns, 2003; Pyrgakis, 2009).
Studies have also shown that tobacco smoke contains about 4000 chemical substances, including nicotine and carbon monoxide (CO), associated with CVDs, other serious health problems, and a high mortality rate (APA, 2013; Papathnasiou et al., 2014; West, 2017). For instance, tobacco smoking lowers high-density lipoproteins (HDL or good cholesterol), increases low-density lipoproteins (LDL or bad cholesterol), and causes a build-up of bad cholesterol, fats, and other substances (or plaque) in the aorta and coronary arteries (DʼAlessandro et al., 2012; Papathanasiou et al., 2014). This build-up of plaque leads to atherosclerosis, the hardening and thickening of the artery walls, leading to the arteries becoming narrower and slowing down the blood flow. Ultimately, several CVDs ensue, including arteriosclerosis (also known as coronary artery disease or coronary heart disease), a condition affecting the arteries that supply the heart with blood, and cerebrovascular diseases (stroke), loss of brain function when blood flow to the brain is interrupted. Atherosclerosis due to cigarette smoking is also associated with a myocardial hypercoagulable state, stable angina, heart attack, cardiac arrest, peripheral arterial disease, and peripheral vascular disease (Tonstad & Johnston, 2006). Peripheral arterial disease and peripheral vascular disease occur when the thickened and narrowed arteries reduce blood flow to the pelvis, arms, hands, legs, hands, and feet. Smoking also causes abdominal Aortic Aneurysm, damage to the abdominal aorta. A ruptured abdominal aortic aneurysm is life-threatening.
However, limited research has been conducted to gain insight into the odds of developing CVD by cigarette smokers compared to non-smokers. Therefore, the problem to be addressed in this study is the odds of developing CDVs by cigarette smokers compared to non-cigarette smokers. Specifically, I will address this problem by performing a simple logistic regression analysis, in which smoking a cigarette predicts developing a heart problem in a lifetime compared to non-smoking a cigarette. Since CDV is a significant health problem, underscoring the likelihood of smokers developing a heart problem compared to non-smokers would impact some motivational behavior in reducing these odds. This study will help health providers provide well-informed information on the odds of developing heart diseases to their clients who smoke cigarettes.
Study Purpose
The purpose of this descriptive quantitative study is to predict the odds of developing heart disease in a lifetime among cigarette smokers compared to non-cigarette smokers using respondents (n = 55271) for the 2014 National Survey on Drug Use and Health (NSDUH) in the Inter-university Consortium for Political and Social Research (ICPSR) datasets (U. S. Department of Health and Human Services, 2016). The independent (predictor) variable is the categorical variable eversmokecigrarette (CiGARETTEEVER) with two grouping levels: 0 (no) and 1 (yes). The dependent (outcome) variable for this study is the categorical variable, had heart disease in lifetime (HARTDLIF) with two grouping levels: 0 (no) and 1 (yes).
Research Question and Hypotheses
Question. How much higher are the odds of developing heart disease in a lifetime for cigarette smokers compared to non-cigarette smokers?
Null Hypothesis. The odds of developing heart disease in a lifetime for cigarette smokers and non-cigarette smokers are equal to 1.00, or the probability of developing heart disease in a lifetime for cigarette smokers and non-cigarette smokers is equal to .50 (or 50%).
Alternative hypothesis. The odds of developing heart disease in a lifetime among tobacco smokers and non-cigarette smokers differ significantly from 1.00, or the probability of developing heart disease for cigarette smokers and non-cigarette smokers varies significantly from .50 (or 50%).
Methodology
The NSDUH is a survey series in the Inter-university Consortium for Political and Social Research (ICPSR) datasets. The NSDUH is a quarterly and annual nationwide survey that primarily measures the prevalence and correlates of drug use in the United States, both at the national and state levels. Respondents were aged 12 and above and were selected from the civilian, non-institutionalized population residing in the United States. They provided information on the use of illicit drugs (e.g., heroin, cocaine, hallucinogens, marijuana), alcohol, tobacco, and non-medical use of prescription drugs (e.g., pain relievers, sedatives, and tranquilizers). Respondents also provided information about their physical and mental health, age when a substance was first used, lifetime substance use, usage for the past month and past year, illegal activities, arrest records, problems resulting from drugs and needle-sharing. The survey also included questions concerning health care access and coverage, treatment history for substance abuse and mental health-related disorders, perceived needs for treatment, and questions (e.g., dependence and abuse) based on the criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM).
For the 2014 NSDUH, nationwide 91, 640 respondents were selected. Only 56271 participants made the final sample. This final sample represented the United States’ general population. However, the valid number of respondents for this current study was 54163 (98%), while 1108 (2%) were missing. In addition to representing the general U. S. population, the number of respondents for this study is enough to provide the confidence that the false null hypothesis for the binary logistic regression will be correctly rejected at least 80% of the time. In other words, the binary logistic regression analysis for the current study has enough statistical power to reject the false null hypothesis correctly. For this 2014 survey, questions introduced in previous administrations were retained. Most survey questions were sensitive. Consequently, interviewers used audio computer-assisted self-interviewing (ACAS) to provide participants with a private and confidential mode to collect questions about illicit drugs and other sensitive behaviors. However, interviewers used personal computer-assisted personal interviewing (CAPI) to collect information from respondents on less sensitive items (U. S. Department of Health and Human Services, 2016).
Furthermore, I transferred the 2014 NSDUH from the ICPSR dataset into SPSS and performed a simple binary logistic regression analysis to predict the odds of developing CVD in a lifetime among cigarette smokers compared to non-smokers. In this logistic regression, the independent (predictor) variable was the dummy coded variable eversmokecigrarette (CiGARETTEEVER), which I created from the categorical variable ever smoked cigarettes (CIGEVER) with two grouping levels 1 (yes) and 2 (no) and recoded as 0 (no) and 1 (yes). The never-smoked cigarette group was the reference group to which the ever-smoked cigarette group was compared. Also, 46.8.% of respondents reported that they had never smoked cigarettes, while 53.2% reported that they had smoked cigarettes (see Table 5). The dependent (outcome) variable was the categorical variable, had heart disease in a lifetime (LIFHARTD) with two grouping levels: 0 (no) and 1 (yes). Additionally, based on the cross-tabulation from SPSS (see Table 5), I displayed the binary logistic regression raw score summary in Table 4.
Results
The results of the regression revealed that a full model chi test for the variable eversmokecigrarette (CiGARETTEEVER) as the predictor variable was statistically significant χ²(1) = 192.494, p < .001 (see Table 6). Similarly, Wald’s statistical value (-3.354 = -4.157 + .803) for the B1 coefficient or the logit L1 for the eversmokedcigaretter group (X = 1) was statistically significant, Wald χ² =176.136, p < .001 (see Tables 4 and 8). Like the coefficient B1, the Wald’s statistical value (-4.157) for coefficient B0 or the logit L0 for the never-smoked cigarette group (reference group; X = 0) was also statistically significant; Wald χ² =6651.27, p < .001 (see Tables 4 and 8). These results mean that the odds of developing heart disease in a lifetime among respondents who reported ever smoking cigarettes and those who never reported smoking cigarettes differed significantly from 1.00 (Warner, 2013). In other words, the risk of developing heart disease was different from 50% (Warner, 2013). Thus, developing heart disease in a lifetime does not have an equal chance of occurring among cigarette smokers and non-cigarette smokers. Therefore, the null hypothesis is rejected.
Further analyses indicated that the Exp(B) for an ever-smoked cigarette group was 2.232 with 95% CI [1.983, 2.513] (see tables 8). Put in another way, the odds ratio (conditional odds of cigarette smokers developing CVD divided by the conditional odds of non-cigarette smokers developing CVD) was approximately 2.232 (see table 4). This finding means that the odds ratio of developing heart disease for cigarette smokers were approximately 2.232 times higher than the non-cigarette smokers. In other words, cigarette smokers are 2.232 times more likely to develop heart disease in a lifetime compared to non-cigarette smokers. There was a 95% confidence that the odds of developing heart disease in a lifetime for cigarette smokers were between 1.983 and 2.513 higher times than non-cigarette smokers. We can conclude that cigarette smoking significantly increases the odds of developing CVD in a lifetime. However, the result indicates Cox and Snell’s R2 as .004 and Negelkerche’s R2 as .017 (see Table 7). The Cox and Snell’s R2 value means that the predictor variable, cigarette smoking, accounted for .4% of the variance in the odds of developing heart disease. This is a small effect (Warner, 2013). This means that several other factors (e.g., differences in age, high blood pressure, other substance use, etc. might account for the rest of the variance of the odds of developing heart disease.
Additionally, I displayed the binary logistic regression raw score based on the cross-tabulation from SPSS (see Tables 4 and 5) to examine how other functions, including the conditional probability and risk ratio, are related to the odds ratio of developing CVD for cigarette smokers. The logistic regression raw scores indicate that the conditional probability of cigarette smokers developing CVD was approximately .0338 (i.e., 973/2783), while the risk ratio of developing CVD for cigarette smokers compared to non-cigarette smokers was approximately 2.191(i. e., the conditional probability of cigarette smokers divided by the conditional probability of non-cigarette smokers = .0337788/.0154192). This result means that the risk of developing heart disease was approximately 2.191 times higher for respondents who smoked cigarettes than for non-smokers. This figure is closer to the odds ratio (2.232) of developing heart disease for respondents who smoked cigarettes compared to those who never smoked cigarettes.
Table 1
| Case Processing Summary | ||||||
| Cases | ||||||
| Valid | Missing | Total | ||||
| N | Percent | N | Percent | N | Percent | |
| EversmokedCigarette * HAD HEART DISEASE IN LIFETIME | 54163 | 98.0% | 1108 | 2.0% | 55271 | 100.0% |
Table 2
| Dependent Variable Encoding | |
| Original Value | Internal Value |
| No (LIFHARTD=6,99) | 0 |
| Yes (LIFHARTD=1) | 1 |
Table 3
| Categorical Variables Codings | |||
| Frequency | Parameter coding | ||
| (1) | |||
| EversmokedCigarette | .00 | 25358 | .000 |
| 1.00 | 28805 | 1.000 |
Table 4
| Ever smoked Cigarette | No LIFHARTD | Yes LIFHARTD | Total | Conditional Probability | Conditional Odds | Logit | Odd Ratio | Risk Ratio | Constant |
| 0 (no) | 24967 | 391 | 25358 | .0154192 | .0156606 | -4.15660 | .4479615 | .456475 | -.4.154 |
| 1 (yes) | 27832 | 973 | 28805 | .0337788 | .0349597 | -3.35355 | 2.232334 | 2.190697 | .803 |
Table 5
EversmokedCigarette * HAD HEART DISEASE IN LIFETIME Crosstabulation | |||||
| HAD HEART DISEASE IN LIFETIME | Total | ||||
| No (LIFHARTD=6,99) | Yes (LIFHARTD=1) | ||||
| EversmokedCigarette | .00 | Count | 24967 | 391 | 25358 |
| % within HAD HEART DISEASE IN LIFETIME | 47.3% | 28.7% | 46.8% | ||
| 1.00 | Count | 27832 | 973 | 28805 | |
| % within HAD HEART DISEASE IN LIFETIME | 52.7% | 71.3% | 53.2% | ||
| Total | Count | 52799 | 1364 | 54163 | |
| % within HAD HEART DISEASE IN LIFETIME | 100.0% | 100.0% | 100.0% |
Table 6
| Omnibus Tests of Model Coefficients | ||||
| Chi-square | df | Sig. | ||
| Step 1 | Step | 192.494 | 1 | .000 |
| Block | 192.494 | 1 | .000 | |
| Model | 192.494 | 1 | .000 |
Table 7
| Model Summary | |||
| Step | -2 Log likelihood | Cox & Snell R Square | Nagelkerke R Square |
| 1 | 12544.204a | .004 | .017 |
| a. Estimation terminated at iteration number 7 because parameter estimates changed by less than .001. |
Table 8
| Variables in the Equation | |||||||||
| B | S.E. | Wald | df | Sig. | Exp(B) | 95% C.I.for EXP(B) | |||
| Lower | Upper | ||||||||
| Step 1a | EversmokedCigarette(1) | .803 | .061 | 176.136 | 1 | .000 | 2.232 | 1.983 | 2.513 |
| Constant | -4.157 | .051 | 6651.279 | 1 | .000 | .016 | |||
| a. Variable(s) entered on step 1: EversmokedCigarette. |
Discussion
The odds of developing heart disease in a lifetime among respondents who reported having ever smoked cigarettes and those who never reported smoking cigarettes differed significantly. Cigarette smoking was associated with a higher tendency to develop heart disease in a lifetime compared to non-cigarette smoking. Thus, developing heart disease in a lifetime does not have an equal chance of occurring among cigarette smokers and non-cigarette smokers. Precisely, the results showed that the odds of developing heart disease for cigarette smokers were approximately 2.232 times higher than those of non-cigarette smokers. In other words, cigarette smokers were 2.232 times more likely to develop heart disease in a lifetime compared to non-cigarette smokers. There was a 95% confidence that the odds of developing heart disease in a lifetime for cigarette smokers were between 1.983 and 2.513 times higher than those of non-cigarette smokers. The odds ratio figure (2.232) was closer to the risk ratio figure (2.191) in the developing heart disease for respondents who smoked cigarettes compared to those who never smoked cigarettes. Therefore, we conclude that cigarette smoking significantly increases the odds of developing CVD in a lifetime. However, cigarette smoking accounted for .4% of the variance in the odds of developing heart disease, which is a small effect (Warner, 2013). In other words, several other factors (e.g., differences in age, high blood pressure, other substance use, etc. might account for the rest of the variance of the odds of developing a CVD.
Although several studies have associated cigarette smoking with several health problems, including CVD, limited studies have evaluated the odds of developing CVD for cigarette smokers compared to non-cigarette smokers in a general form. The few related studies indicating the tendency to develop some forms of CVD for cigarette smokers compared to non-cigarette smokers are consistent with the current study. For instance, Banks et al. (2019) conducted a study on the risk of developing 36 subtypes of CVD with 188,167 CVD- and cancer-free participants, aged 45 years and older, from the general Australian population who joined the 45 and Up Study from 2006 to 2009. These participants completed a poster questionnaire by the end of 2015 and were followed up through repeated data collection and data linkage. The study results indicate that of the 36 common specific CVD subtypes, there was a significant increase in event rates for 29 subtypes in current smokers. Adjusted risk ratios in current smokers compared to never smokers indicated the following: 1.63 with 95% CI [1.56, 1.71] for any major CVD; 2.45 with 95 CI [2.22, 2.70] for acute myocardial infarction; 2.16 with 95% CI [1.93, 2.42] for stroke; and 2.23 with 95% CI [1.96, 2.53) for heart failure. Others included 5.06 with 95% CI [4.47, 5.74] for PAD; 1.50 with 95% CI [1.24, 1.80] for paroxysmal tachycardia; 1.31 with 95% CI [1.20, 1.44] for atrial fibrillation/flutter; 1.41 with 95% CI [1.17, 1.70] for pulmonary embolism.
Shinton and Beevers (1989) conducted a meta-analysis of 32 studies on the relation between cigarette smoking and stroke. They found that the overall risk of stroke associated with cigarette smoking was 1.5 with 95% CI (1. 4, 1. 6). The result of their study also revealed that the relative risk of developing cerebral aneurysms and subarachnoid hemorrhage for cigarette smokers was 2.93 with 95% CI [2.48, 3.46], and the relative risk of developing cerebral infarction associated with cigarette smoking was 1.92 with 95% CI [1.71, 2.16]. Hacksaw et al. (2018) also conducted a meta-analysis of 55 publications containing 141 cohort studies on the risk of coronary heart disease and stroke based on low cigarette consumption. They found that the pooled relative risk for coronary heart disease among men was 1.48 for smoking one cigarette per day and 2.04 for 20 cigarettes per day (or 1.74 and 2.27, respectively, using relative risks adjusted for multiple factors). The study results also show that the pooled relative risks for coronary heart disease among women were 1.57 for smoking one cigarette per day and 2.84 for 20 cigarettes per day (or 2.19 and 3.95 using relative risks adjusted for multiple factors). For stroke, the pooled relative risks for men were 1.25 for smoking one cigarette per day and 1.64 for smoking 20 cigarettes per day (or 1.30 and 1.56, respectively, using relative risks adjusted for multiple factors). In women, the pooled relative risks were 1.31 and 2.16 for smoking one cigarette per day and 20 cigarettes per day (1.46 and 2.42, respectively, using relative risks adjusted for multiple factors).
Implications for Social Change and Limitation
In addition to supporting and increasing the number of few studies on the odds of developing CVD associated with cigarette smoking compared to non-cigarette smokers, this study has important consequences for cigarette smokers who believe that smoking does not harm the heart. This study will improve healthy living among Americans and the wide world population. Additionally, it will help health providers, educators, and policymakers to provide well-informed information on the odds of developing heart diseases regarding cigarette smoking. This evidence-based information will enhance behavioral motivation towards tobacco smoking cessation and promote positive social change at the individual, community, and social levels. Moreover, it will open the door to more specific research on the odds of developing heart diseases based on a specific number of cigarettes smoked per day, week, or month and the odds of developing each specific subtype of CVD for cigarette smokers.
Finally, this study has a few limitations. The primary limitation of this study is that I used a secondary data source. Using individuals currently engaging in tobacco smoking would yield more accurate findings than secondary data sources. Additionally, the study does not evaluate the odds of developing heart diseases based on a specific number of cigarettes smoked per day, week, or month or the odds of developing each specific subtype of CVD for cigarette smokers. The study only broadly evaluated the odds of developing heart diseases for cigarette smokers compared to non-cigarette smokers. Several other variables could also be considered in evaluating the odds of developing heart diseases. For instance, adding variables, such as age differences, high blood pressure, any other substance use, would increase the statistical significance of the full model chi test for the logistic regression, the Wald statistical significance for smoking cigarettes, and the odds of developing a CVD for cigarette smokers in comparison to non-cigarette smokers.
Acknowledgments
I thank God almighty for the health and wisdom to produce this work. I also thank the thousands of people who participated in the 2014 National Survey on Drug Use and Health (NSDUH). My profound gratitude goes to Dr. Monny Sklov, whose feedback on this work provided great insight fine tuning this work. Many thanks also go to all those who provided me with a conducive environment while producing this work
Data Availability and Ethical Terms
This study uses data from the Inter-university Consortium for Political and Social Research (ICPSR) datasets. The identity of individuals and establishments were not disclosed. All direct identifiers and any other characteristic that might lead to identification are omitted from the dataset. The data in this dataset were used for statistical reporting and analysis only. There was no attempt to learn the identity of any person or establishment included in these data. There was also no attempt to link this dataset with individually identifiable data from other datasets. Additionally, there was no attempt to engage in any efforts to assess disclosure methodologies applied to protect individuals and establishments or any research on methods of re-identification of individuals and establishments.
Contributors and Conflicts of Interest Statement
Enyinna Akanaefu is the only author who contributed to writing this article. This article does not involve any funding, and the author has no conflicts of interest at this time.
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Social Change 