Abstract
Smoking remains a leading cause of morbidity and mortality worldwide. Though age and gender differences in smoking in the United States population have been documented, data about these differences and smoking cessation among rural and urban residents is limited. The purpose of the research is to examine the relationship between health status, age, gender and smoking among samples of rural and urban adults. The paper also examined reasons why some respondents quit smoking and who encouraged them to quit. Data for this analysis were obtained by a telephone survey of two samples of adults 30 years of age and older: 150 residents from an urban county and 150 from a rural county in Pennsylvania. Data included information on health status, a number of health behaviors (including smoking cigarettes), age, gender, and smoking cessation. The results indicate that for respondents residing in the rural county, whether a person is a male or female is statistically related to smoking. Age is statistically related to smoking. There is no relationship between health status and smoking. Those who quitted smoking did for health reasons and were more likely to had support from family members (spouse and children). Smoking (smoke now, used to smoke and never smoked) did not relate consistently nor strongly to health status when residence and gender were controlled. Programs promoting smoking cessation might benefit by involving family or other household members.
Keywords: Smoking behavior, smoking cessation, Health Status, Rural and Urban residents
Introduction
According to the World Health Organization (2015), nearly 6 million people around the world die each year from tobacco-related causes. In the United States, the death rate among smokers is 2 to 3 times higher than that of nonsmokers (Carter et al., 2015). Cigarette smoking is responsible for about 480,000 deaths a year (Center for Disease Control and Prevention, 2016). Tobacco significantly increases the risk of respiratory disease, cataract, stroke, and low-birth-weight babies (Newman, 2019). More people die from smoking related causes than from HIV/AIDS, illegal drugs use, alcohol use, motor vehicle injuries, suicide, and murders combined (Newman, 2019). Some Healthy People 2020 national health objectives address smoking and smoking cessation.
Tobacco is a clearer health risk. Therefore, the purpose of this analysis is to examine the relationship between health status, age, gender and smoking among samples of rural and urban adults. Research in this area is of paramount importance in that it enables researchers and policy makers to identify groups or individuals who are particularly vulnerable to poor health behaviors, and who should, therefore, be the focus of special intervention. It also provides an insight into the factors that may influence health behaviors, so that these factors may be considered in policy formulation analysis at the state and federal levels. Series of questions were posed to sharpen the research objectives: How do people who smoke or do not smoke perceive their health status? Do rural and urban residents differ in the extent to which they smoke? Is gender a good predictor of smoking? Do men and women in the middle years differ in the extent to which they smoke? Addressing these questions will extend our knowledge of the relationships between some demographic variables and cigarette smoking. The adverse health effects of smoking and the benefits of quitting are well documented. Many smokers who try to quit cite a desire to improve their health as the main reason. In this study, I also examined smoking cessation. Identifying those who successfully quit smoking, why they did, and who supported them in quitting will help to target smoking cessation programs and interventions in the United States.
Literature Review
One health behavior as a risk factor needing greater control and attention worldwide is smoking. Smoking remains a leading cause of morbidity and mortality. Previous studies have shown that smoking is more common among men than among women (Newman, 2019; Waldron, 1983; 1986; Waldron et al., 1988). Furthermore, evidence indicates that concerns about health consequences of smoking have not had a greater influence on women’s smoking than on men’s smoking. Women who smoke have higher burden of smoking related diseases than men who smoke (Fisher et al., 1993; Hugh et al., 2013; Hymowitz, 1991; Venters et al., 1990). Furthermore, some studies have demonstrated the additive effects of smoking on bad health outcomes. (Hill & Gray, 1984; Waldron et al., 1988).
Though age is not related to health behavior in any consistent way, there is a strong evidence that some health-damaging habits (for example, smoking) begin in early adolescence (Newman, 2019; Norman, 1988; Hugh et al., 2013). No one factor has been found to provide a sufficient basis for predicting all health behaviors. Our health habits and practices are often daily actions which have been influenced by a host of demographic, social, cultural and biological factors (Newman, 2019). Understanding factors affecting smoking behavior of men and women may guide targeted gender-specific intervention programs and more effective policies, smoking cessation efforts and counselling programs.
Many studies have addressed factors associated with quitting attempt or with successful smoking cessation. Some examined such demographic variables as gender, age, marital status, income and education to determine whether they differed between smokers who tried to quit and those who did not, as well as between successful and unsuccessful quitters (Lee & Kahende 2007; Stueve & O’ Donnell, 2007; Venters et al., 1990; Hymowitz et al., 1991; Fisher et al., 1993; Newman, 2019).
In this study, I also examined why smoking cessation and type of cessation assistance used by individuals with successful smoking cessation. Such knowledge could help public health policy makers and medical care systems develop effective targeted interventions.
Methods and Procedures
The data for this analysis were obtained by a telephone survey of two samples of adults 30 years of age and older: 150 residents from one urban county and 150 from one rural county in Pennsylvania. Selection of the counties was based on the county classification scheme developed by Hines et al. (1975). One of the counties selected for this research had all of its population characterized as rural in 2010 and was not adjacent to any metropolitan county. The second county had 100 percent urban population in 2010 and was classified as a metro county.
The questionnaire used in the study was designed to obtain information on health status, a number of health behaviors (including smoking cigarettes), age, gender, and place of residence. Health status was derived from the question «How would you rate your health at the present time? Would you say it is» (1) very poor; (2) poor; (3) fair; (4) good; (5) very good. Responses were dichotomized to separate those with poor health status (responses 1 & 2) from those in good health status (3–5). Self-rated health has been shown to be a valid and reliable measure of general physical well-being ( Idler & Benyamini, 1997; Fosse & Haas, 2009) and is predictive of chronic and acute disease, physicians’ assessments, physical disability, and health behaviors (Idler & Benyamini, 1997).
Smoking data were derived from the following questions: “Have you smoked at least 10 packs of cigarettes in your entire life?” Responses categories were 1) yes 2) no. “Do you smoke cigarettes now?” Responses categories were 1) yes and 2) no. Respondents who had not smoked at least ten packs of cigarettes in their entire life were coded as never smoked. Those who smoked at least ten packs of cigarettes in their entire life but did not smoke at the time of the interview were coded as “used to smoke” while those who had smoked at least ten packs of cigarettes in their entire life and were smoking at time of interview were coded as “smoking now.” Gender data was obtained from the question: “Are you a male or female?” Place of residence was derived by classifying respondents according to the county of residence. Age data was obtained from the question: “How old are you?” Responses range from 30 to 90 years. This was coded into young (30–44), middle aged (45–64) and older adults (65–90).
To determine if any observed relationships were greater than chance or random occurrences, statistical significance was assessed using Chi-square analysis procedures. The .05 level was selected to determine statistical significance. Analyses were performed using Statistical Packages for the Social Sciences (SPSS) version 21.0 computer software program (SPSS Inc., IL: Chicago, USA).
Study procedures were reviewed and approved by the author’s University Office of Research Compliance prior to the collection of any data.
Results
The relationship between health status and smoking for both rural and urban residents is presented in Table 1. Whether a person is in poor or good health was not statistically related to smoking. Thus, there was no evidence or any statistical reason from the data to conclude that there was a meaningful relationship between health status and smoking in this sample.
Table 1.
The relationship between health status and smoking by residence.
| Residence | |||||
|---|---|---|---|---|---|
|
|
|||||
| Rural | Urban | ||||
|
|
|||||
| Health Status | Health Status | ||||
| Poor | Good | Poor | Good | ||
| ..................................Percent……………................ | |||||
| Smoke now | 40.5 | 25.9 | 27.9 | 37.6 | |
| Used to smoke | 24.3 | 29.6 | 31.1 | 26.9 | |
| Never smoke | 35.2 | 44.4 | 41.0 | 35.5 | |
| Total percent | 100 | 100 | 100 | 100 | |
| Number | 37 | 108 | 61 | 93 | |
|
| |||||
| X2 | 2.728 | 1.591 | |||
The extent to which the relationship between gender and smoking differs according to residence was also examined in Table 2. For respondents residing in the rural county, whether a person is a male or female is statistically related to smoking.
Table 2.
The relationship between gender and smoking by residence.
| Residence | ||||
|---|---|---|---|---|
| Rural | Urban | |||
|
| ||||
| Gender | Gender | |||
| Male | Female | Male | Female | |
| .........................................Percent.............................................. | ||||
| Smoke now | 31.0 | 28.0 | 38.2 | 29.5 |
| Used to smoke | 47.3 | 14.7 | 30.3 | 26.9 |
| Never smoke | 25.4 | 57.3 | 31.6 | 43.6 |
| Total percent | 100 | 100 | 100 | 100 |
| Number | 71 | 75 | 76 | 78 |
|
| ||||
| X2 | 20.389* | 2.492 | ||
Significant .001
Males were more likely than the females to smoke now or used to smoke. For the urban residents, the differences between males and females smoking were small and not statistically significant. However, for rural residents the relationship between gender and smoking behavior was statistically significant: males were much more likely than females to smoke “now” or to have smoked in the past.
Data showing the relationship of age to smoking by residence are presented in Table 3. For both rural and urban residents, age was statistically related to smoking. Those who have never smoked in their entire life or used to smoke were more likely to be older adults while those who smoked at the time of interview were most likely to be younger and middle aged. These patterns by age and smoking behavior are similar to what Norman (1988) found in his studies.
Table 3.
The relationship between age and smoking by residence.
| Residence | ||||||
|---|---|---|---|---|---|---|
|
| ||||||
| Rural | Urban | |||||
|
| ||||||
| Age | Age | |||||
| 30–44 | 45–64 | 65–90 | 30–44 | 45–64 | 65–90 | |
| ………..................................Percent.............................................. | ||||||
| Smoke now | 40.0 | 33.3 | 13.6 | 52.0 | 32.0 | 17.3 |
| Used to smoke | 22.0 | 29.4 | 34.1 | 18.0 | 30.0 | 38.5 |
| Never smoke | 38.0 | 37.3 | 52.3 | 30.0 | 38.0 | 44.2 |
| Total percent | 100 | 100 | 100 | 100 | 100 | 100 |
| Number | 50 | 51 | 44 | 50 | 50 | 52 |
|
| ||||||
| X2 | 9.399* | 14.641** | ||||
Significant .05
Significant .01
The relationships between age and smoking was were assessed for both males and females in Table 4. For both males and females, age is significantly associated with smoking. For males, those who used to smoke and those who have never smoked were most likely to be older while those who smoke at the time of interview were the younger ones. Similar patterns were found for the females.
Table 4.
The relationship between age and smoking by gender.
| Gender | ||||||
|---|---|---|---|---|---|---|
|
| ||||||
| Male | Female | |||||
| Age | Age | |||||
|
|
|
|||||
| 30–44 | 45–64 | 65–90 | 30–44 | 45–64 | 65–90 | |
| ……………........................Percent.......................................... | ||||||
| Smoke now | 49.0 | 38.8 | 17.0 | 43.1 | 26.9 | 14.3 |
| Used to smoke | 22.4 | 40.8 | 46.8 | 17.6 | 19.2 | 26.5 |
| Never smoke | 28.6 | 20.4 | 36.2 | 39.2 | 53.8 | 59.2 |
| Total percent | 100 | 100 | 100 | 100 | 100 | 100 |
| Number | 49 | 49 | 47 | 51 | 52 | 49 |
|
| ||||||
| X2 | 14.702** | 10.838* | ||||
Significant .05
Significant .01
The relationship between health status and smoking was assessed for both males and females, Table 5. Regardless of gender, there is no evidence from the data to conclude that there was a meaningful relationship between health status and smoking. Differences between men and women in regards to health status and smoking were not statistically significant.
Table 5.
The relationship between health and smoking controlling for gender.
| Gender | ||||
|---|---|---|---|---|
|
| ||||
| Male | Female | |||
|
| ||||
| Health Status | Health Status | |||
| Poor | Good | Poor | Good | |
| ……….............................. Percent.......................................................... | ||||
| Smoke now | 36.4 | 34.3 | 26.9 | 28.3 |
| Used to smoke | 38.6 | 35.3 | 20.4 | 21.2 |
| Never smoke | 25.0 | 30.4 | 50.0 | 50.5 |
| Total percent | 100 | 100 | 100 | 100 |
| Number | 44 | 102 | 54 | 99 |
|
| ||||
| X2 | .449 | .036 | ||
Finally, the data in Table 6 about smoking cessation indicate that family members and significant others (spouses) play a big part in helping a person become smoke free.
Table 6.
Respondents who quit smoking: reasons and who supported them to quit.
| Reasons Respondents Quit Smoking (N= 108) | |
| Pregnancy | 3.7 |
| Bad for others | 1.9 |
| Doctor’s advice | 10.2 |
| Health problems | 25.0 |
| Read | 4.6 |
| Decided not good | 35.0 |
| Family pressure | 7.4 |
| Cost | 2.8 |
| Others | 9.2 |
| Who Supported Quitting (N = 97) | |
| Spouse | 24.7 |
| Daughter | 21.6 |
| Son | 13.4 |
| Mother | 4.0 |
| Father | 3.0 |
| Friend | 17.5 |
| Neighbor | 3.0 |
| Co-worker | 3.0 |
| Others | 9.0 |
Summary and Conclusions
This analysis examined the relationships between residence, health status, age and smoking and smoking cessation among a sample of Pennsylvania. The relationship between health status and smoking was not statistically significant. Expected differences in health status between rural and urban residents were not found. Moreover, a number of other variables thought likely to relate to health status did not. Thus, smoking (smoke now, used to smoke and never smoked) did not relate consistently nor strongly to health status when residence and gender were controlled. Indeed, age was the only variable found to be significantly and consistently associated with smoking; the younger respondents were more likely to smoke now than were the older respondents. I did find that gender made a statistical difference in smoking. Rural males were much more likely to have previously smoked while rural females were much more likely to have never smoked. It may well be that in rural areas smoking is not accepted for the females.
While other research found a similar relationship between age and smoking, the overwhelming importance of this variable in the present analysis was not anticipated. All other explanatory variables paled in importance relative to the person’s age. Expected differences in smoking rates between males and females were not found. The decision of a male or female to smoke or not depends more on his or her age. Apparently, expanded socialization contacts that occur within age groups are likely to lead to smoking. There is a high and increasing rate of smoking among young adults.
Despite the large number of research studies that have dealt with consequences of smoking, there is still much that is not fully understood. The present study extends the existing log of smoking research in at least one way. Unlike most studies on smoking, this research has focused on rural-urban differences. It suggests that the relationship between perceived health status and smoking is not statistically significant for residents of either area. The need for further studies of differences in smoking is underscored. Findings from the research indicate that younger adults should be the target group for smoking cessation programs. These new data can be strategically used by health providers and public health officials to communicate the benefits of quitting, increasing motivation to quit, and engage young smokers in supportive services to help them quit and stay quit. The older adults in this sample changed their lifestyles to decrease risk for illness and disability and to enhance wellness. Health appears to motivate adults to quit smoking. Effective interventions such as motivational interviewing, brief cessation counseling and supplementary referral to the Quitline by health providers can help many people. Public health efforts should target all young people to refrain from smoking to reduce morbidity and mortality.
Finally, the study suggests that cessation program need to take a holistic approach including social support from spouse or partner, children and friends. The health concerns reported by participants as important motivators for quitting indicate that much more attention should be given to health care related interventions.
Limitation of the study
The findings in this manuscript are subject to two major limitations. The smoking data in this study relied on self-reporting. Participants might under-report smoking and over-report quitting smoking. Race was omitted in the analysis because of low percentage of some groups of interest (e.g., African American and Hispanics) do not live this rural part of Pennsylvania.
Footnotes
Author Note
The opinions expressed in this articles are solely those of the author and do not necessary reflect those of the institution where he is employed. The author wishes to express gratitude to the anonymous reviewers for their helpful comments.
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