Abstract
Men have higher rates of all cancers and are more likely to die from cancer than women; however, men are less likely to utilize disease prevention services. African American/Black men and Hispanic men have lower cancer survival rates and are less likely to utilize health care services than non-Hispanic White men. The present study examined demographic variables (age, household income, education, marital status, race/ethnicity, health insurance status), motivators to engage in healthy eating, and motivators to engage in physical activity as predictors of culturally diverse, medically underserved men’s likelihood of getting a cancer screening (a) at the present time, (b) if no cancer symptoms are present, and (c) if a doctor discovers some cancer symptoms. Analyses were conducted using data from 243 men (47.3% non-Hispanic Black, 29.5% Hispanic, 16.5% non-Hispanic White, and 6.8% “other”) recruited at the Men’s Health Forum in Tampa, Florida. Age, having a medical or health condition that benefits from eating healthy, and having a commitment to physical activity were significant positive predictors of the likelihood of receiving a cancer screening. Motivation to engage in physical activity because of a personal priority was a significant negative predictor of the likelihood of getting a cancer screening. The findings from this study suggest that interventions to increase cancer screenings among culturally diverse, medically underserved men should be informed at least in part by an assessment of participating men’s motivators for engaging in health promoting lifestyle behaviors such as physical activity and healthy eating.
Men are more likely than women to have cancer (Siegel, Naishadham, & Jemal, 2013) and die from cancer over the course of their lifetime (Hoyt & Rubin, 2012). Furthermore, African Americans and Hispanics have lower cancer survival rates than non-Hispanic Whites (Walcott, Dunn, DeShields, & Baquet, 2014).
It is also noteworthy that men are less likely than women to utilize preventive health care services (Vaidya, Partha, & Karmakar, 2012) such as cancer screenings (Peterson, Murff, Ness, & Dittus, 2007). Underutilization of cancer screening services by men remains a problem across most types of cancer, including colorectal cancer (Rogers, Goodson, & Foster, 2015).
Barriers to and motivators of participation in cancer screenings may help explain the underutilization of cancer screening services by men. Barriers to cancer screenings reported by men include fear (Jones, Devers, Kuzel, & Woolf, 2011), perceived discomfort (Guerra, Dominguez, & Shea, 2005), threatened masculinity (Winterich et al., 2011), and lack of a recommendation for screening by a physician (Kelly, Dickinson, DeGraffinreid, Tatum, & Paskett, 2007). The present researchers are unaware of literature on the motivators of participation in cancer screenings among men.
Given that engaging in cancer screenings can be conceptualized as an aspect of a health promoting lifestyle (Caperchione et al., 2012), the motivators for engaging in health promoting behaviors such as healthy eating and physical activity might also be motivators for participating in cancer screenings. Motivation has been identified as an important intrapersonal variable relative to engagement in health promoting behaviors (Seifert, Chapman, Hart, & Perez, 2012). Importantly, engaging in health promoting behaviors (e.g., healthy eating and physical activity) have been found to be significant predictors of participation in cancer screening (Meissner et al., 2009). The present investigators are not aware of any studies investigating whether motivation to engage in health promoting behaviors such as healthy eating and physical activity is associated with getting cancer screenings among medically underserved men—who may be less likely to obtain recommended cancer screenings due to barriers such as lack of adequate health insurance, financial resources, and/or access to medical care. Engagement in healthy eating and physical activity have been reported to be associated with demographic characteristics such as household income, age, education, marital status, race/ethnicity, and health insurance status (Bukman et al., 2014). Nevertheless, the present investigators are not aware of any studies that have investigated whether cancer screening likelihood among medically underserved men also occurs in association with these demographic variables. Such studies may provide important additional knowledge to the growing body of literature investigating health promotion practices among medically underserved men.
The purpose of the present study was to examine selected demographic variables (age, household income, education, marital status, race/ethnicity, health insurance status), motivators to engage in healthy eating, and motivators to engage in physical activity as predictors of the likelihood that culturally diverse, medically underserved men would (a) get a cancer screening at the present time, (b) get an annual cancer screening if no cancer symptoms are present, and (c) get a cancer screening if a doctor discovers some symptoms of cancer. To achieve the study objectives, a convenience sample was drawn from men who attended the Men’s Health Forum, which is a novel, annual, community-driven initiative for medically underserved men (i.e., those who are uninsured, underinsured, or do not have a regular health care provider) in Tampa, Florida (Grant et al., 2012). The Men’s Health Forum was created to increase medically underserved men’s understanding of the importance of early detection and prevention of chronic illnesses, particularly cancer, and to increase the awareness of preventive care resources. A wide variety of health screenings and tests, health education workshops and presentations, and exhibitor displays are available to men who attend the day-long event, which currently is held in a modern, multistory student union at a large public university in Tampa, Florida.
Method
Participants
Of the 539 men who attended the 2013 Men’s Health Forum, 254 adult men (18 years of age or older) completed the assessments for the current study. However, data from 11 of these participants were not included in the study analyses because they did not complete all of the assessments, thus resulting in a sample of 243 men whose data were analyzed. All men who attended the event and were able to communicate verbally in either English or Spanish were eligible to participate in the present study. Participants were recruited during the event via (a) flyers and verbal information offered to attendees by trained study staff and (b) periodic announcements made during the various health education sessions. Most men who were offered a flyer took one, and many of these men asked for more information about the study; additionally, the vast majority of men who agreed to receive a flyer actually participated.
Measures
Participants completed an assessment battery consisting of a Demographic Data Questionnaire (DDQ), the Motivators of and Barriers to Health-Smart Behaviors Inventory (MB-HSBI), and selected questions from the Cancer Screening Questionnaire (CSQ). All instruments were available in English and Spanish.
Demographic Data Questionnaire
The DDQ was constructed by the researchers to collect demographic information including age, race/ethnicity, health insurance status, highest level of education completed, marital status, and household income.
Motivators of and Barriers to Health-Smart Behaviors Inventory
The MB-HSBI (Tucker et al., 2011) assesses the degree to which variables may be motivators of or barriers to four health-smart (health promoting) behaviors: (a) eating a healthy breakfast, (b) drinking water and other healthy drinks, (c) eating healthy foods and snacks, and (d) engaging in physical activity. These four health-smart behaviors make up the four domains of the MB-HSBI. Each domain has a motivator scale and a barriers scale, resulting in eight total scales (four motivator scales and four barrier scales). Each motivators scale has various subscales (e.g., Routine, Availability, and Health Benefits) and each barriers scale has various subscales (e.g., Negative Attitude, Availability, and Self-Control). In total, there are 28 subscales on the MB-HSBI. Respondents’ strength of agreement that certain subscale items are motivators of or barriers to engaging in specific health promoting behaviors (e.g., domain behaviors such as eating a healthy breakfast and eating healthy foods and snacks) is measured using a 4-point Likert-type scale ranging from strongly disagree (rating = 1) to strongly agree (rating = 4). A sample item of the MB-HSBI is “I like the taste of most fruits and vegetables.” Content, construct, and criterion-related validity of the MB-HSBI have been previously established. All but 2 of the 36 coefficient alphas for the motivator subscales and barrier subscales were above .70.
The current study focused on the participants’ motivators to engage in healthy eating and motivators to engage in physical activity and the associations of these variables with participants’ likelihood to get a cancer screening. Therefore, the following two scales of the MB-HSBI were used in the current study: (a) Motivators to Eat Healthy Foods and Snacks and (b) Motivators to Engage in Physical Activity. These 2 scales have 10 corresponding subscales, which were used in the subsequent analysis. The 10 subscales are Routine to Eat Healthy, Availability of Healthy Foods, Health Benefit of Eating Healthy, Having a Health Condition that Benefits from Eating Healthy, Convenience of Healthy Foods, Commitment to be Physically Active, Making Physical Activity a Priority, Having a Physical Activity Goal, Having a Personal Preference to be Physically Active, and Having a Medical or Health Condition that Benefits from Physical Activity.
Cancer Screening Questionnaire
The CSQ was adapted from a previously validated survey, the Tuskegee Legacy Project Questionnaire (Katz et al., 2006; Katz et al., 2008). The authors (Katz et al., 2006) based items on item discriminant validity (95% confidence interval cutoff point), item internal consistency (.40 or more cutoff point), and internal consistency (Cronbach’s α ≥ .70). Three questions regarding the participants’ likelihood of having a cancer screening were selected from the CSQ for inclusion in the present study: (1) “How likely are you to agree to have a cancer screening exam at the present time?” (2) “How likely are you to go for a regular annual cancer screening exam given by your doctor, if you have NO symptoms?” and (3) “If your own doctor told you that you have some symptoms and need a cancer screening exam, how likely are you to go and have that cancer screening exam?” Respondents rated each question on a 5-point scale ranging from very likely to very unlikely.
Procedure
The current study was approved by the Institutional Review Board at each of the two universities where the study staff members are based. Trained study staff members were available in the study participation room to conduct the informed consent process and, if requested, read assessment battery questions to participants in Spanish or English. Participants were told not to write their name or any other identifying information on their assessment battery. After each participant completed the assessment battery, he was asked to seal it in an unmarked manila envelope and put the envelope into an opaque drop-box that was different from the box in which signed informed consent forms were placed. Then, the participant was given a $10 gift card and thanked for participating in the study.
Statistical Analysis
Three forward likelihood ratio bivariate logistic regressions using the data of 243 male participants with all needed data were conducted to assess whether the earlier-specified demographic variables, agreement ratings on five subscales that assess motivators to engage in healthy eating, and agreement ratings on five subscales that assess motivators to engage in physical activity predict if participants were likely or unlikely to get a cancer screening exam under each of the three specified conditions. Bivariate analyses were appropriate given the binary nature of the data for the three outcome variables. Additionally, because categorical (e.g., binary) outcome variables violate the linear relationship assumption of linear regression analysis (Berry, 1993), binary logistic regression was employed to account for this violation (Berry & Feldman, 1985). Furthermore, a forward likelihood ratio (i.e., stepwise selection) analysis method was used as a result of the large number of predictor variables; this method enabled identification of the most parsimonious final statistical model by eliminating unnecessary variables due to their redundancy with other variables or lack of explained variance.
Results
The mean age of the sample was 49.6 (SD = 11.55) years, the median age was 50.0 years, and participants ranged from 19 to 85 years of age. Almost half (47.6%, n = 109) of the participants reported an annual household income below $25,000, and 76.9% (n = 176) reported an annual household income below $50,000. Almost 60% (n = 142) of participants reported not having health insurance. As a group, participants were well-educated, with 71.5% (n = 168) reporting having completed at least some college or a technical education program. A slight majority (56.2%, n = 131) of participants reported being married or in a committed relationship. See Table 1 for additional demographic information regarding the sample.
Demographic Characteristics of the Sample.
Race/Ethnicity was not available for 6 participants. bLevel of education was not available for 8 participants. cAnnual income was not available for 14 participants. dMarriage status was not available for 10 participants. eHealth insurance status was not available for 6 participants.
Of the 243 men surveyed, 83.5% (n = 203) reported that they were likely to have a cancer screening exam at the present time, whereas 16.5% (n = 40) indicated that they were not likely to do so. A forward likelihood ratio bivariate logistic regression was conducted to determine whether the investigated demographic variables, agreement ratings on five subscales that assess motivators to engage in healthy eating, and agreement ratings on five subscales that assess motivators to engage in physical activity predict if participants were likely or unlikely to get a cancer screening at the present time.
Results indicate that age is the only significant predictor of whether the participating men would get screened for cancer at the present time, χ2(1, N = 182) = 10.44, p = .002. Pseudo R2 analyses (Cox and Snell = .06, Nagelkerke = .1) indicate a small effect size. For each 1-year increase in age, the odds of the participant reporting that he is likely to get a cancer screening at the present time increases (β = 0.06, p = .002, odds ratio [OR] = 1.06), see Table 2. When using age as a predictor, the model correctly classifies 84.1% of participants, which is approximately 11% better than chance. The C-statistic (i.e., a measure of model fit) was significant (C = .64, p = .007). None of the other variables tested in this logistic regression were significant predictors of whether participants would get screened for cancer at the present time.
Final Models for Forward Likelihood Ratio Bivariate Logistic Regression Analyses.
Note. OR = odds ratio; CI = confidence interval.
p < .05. **p < .01.
Of the 243 men surveyed, 73.3% (n = 178) reported they were likely to go for an annual cancer screening exam given by their doctor if they had no symptoms, whereas 26.7% (n = 65) reported that they were not likely to do so. A forward likelihood ratio bivariate logistic regression was conducted to determine whether the investigated demographic variables, agreement ratings on five subscales that assess motivators to engage in healthy eating, and agreement ratings on five subscales that assess motivators to engage in physical activity predict if participants who had no cancer symptoms were likely or unlikely to go for an annual cancer screening exam by their doctor.
The final model, which included age and the agreement ratings on the healthy eating motivator subscale titled “Having a Medical or Health Condition that Benefits from Healthy Eating” was statistically significant, χ2(2, N = 182) = 10.94, p = .004. Pseudo R2 analyses (Cox and Snell = .06, Nagelkerke = .09) indicate a small effect size. The model correctly classifies 75.3% of participants, which is approximately 14% better than chance; additionally, the C-statistic was significant (C = .620, p = .006).
In this final model, age significantly predicted the likelihood that participants would report that they would go for an annual cancer screening exam given by their doctor if they had no symptoms. For each 1-year increase in age, the odds that participants who had no cancer symptoms were likely to go for an annual cancer screening exam by their doctor increases (β = 0.03, p = .05, OR = 1.03), see Table 2.
Of the subscales regarding motivators to engage in healthy eating and motivators to engage in physical activity, only the subscale titled “Having a Medical or Health Condition that Benefits from Healthy Eating” significantly predicted the likelihood that participants who had no cancer symptoms would report that they would go for an annual cancer screening exam given by their doctor. Participants who reported higher agreement that having a medical or health condition that benefits from healthy eating was a motivator for eating healthy foods and snacks, as compared with participants who reported lower agreement on this variable, were more likely to report that they would get an annual cancer screening given by their doctor if they had no cancer symptoms (β = 0.679, p = .021, OR = 1.97), see Table 2.
Of the 243 men surveyed, 91.8% (n = 223) reported that they would get a cancer screening exam if their doctor told them they had cancer symptoms, whereas 8.2% (n = 20) indicated that they were unlikely to do so. A forward likelihood ratio bivariate logistic regression was conducted to determine whether the investigated demographic variables, agreement ratings on five subscales that assess motivators to engage in healthy eating, and agreement ratings on five subscales that assess motivators to engage in physical activity predict if participants who were told by their doctor that they have some cancer symptoms were likely or unlikely to go for a cancer screening.
The final model, which included level of education and the agreement ratings on the physical activity motivator subscale titled “Making Physical Activity a Priority” and the physical activity motivator subscale titled “Commitment to Physical Activity” was statistically significant, χ2(6, N = 182) = 28.681, p = .001. Pseudo R2 analyses (Cox and Snell = .145, Nagelkerke = .48) indicate a small effect size. The model correctly classifies 94.5% of participants, which is approximately 3% better than chance; additionally, the C-statistic was significant (C = .886, p < .001).
The physical activity motivator subscales titled “Making Physical Activity a Priority” and “Commitment to Physical Activity” were statistically significant predictors of participants’ reporting they were likely to get a cancer screening exam if their doctor told them that they had some cancer symptoms. Participants who reported higher agreement on the Making Physical Activity a Priority subscale, as compared with participants who reported lower agreement on this variable, were less likely to report that they would get a cancer screening exam if their doctor told them that they had some cancer symptoms (β = −1.395, p = .038, OR = 0.25), see Table 2. Conversely, participants who reported higher agreement on the Commitment to Physical Activity subscale, as compared with participants who reported lower agreement on this variable, were more likely to report that they would get a cancer screening if their doctor told them that they had some cancer symptoms (β = 2.330, p = .015, OR = 10.28), see Table 2. In this model, level of education was not significant (p = .171).
Discussion
This study investigated the predictors of the likelihood that a sample of culturally diverse, medically underserved men would participate in a cancer screening under three circumstances. Study participants were recruited from men who attended the Men’s Health Forum, which occurs annually in Tampa, Florida. Specifically, predictors of the following conditions were investigated: (a) getting a cancer screening at the present time, (b) getting a regular annual cancer screening exam if no cancer symptoms are apparent, and (c) getting a cancer screening if the participant’s doctor told him that he had some cancer symptoms. The examined predictors were study participants’ (a) age, household income, education, health insurance status, marital status, and race/ethnicity; (b) agreement ratings on subscales that assess motivators to engage in eating healthy foods and snacks; and (c) agreement ratings that assess motivators to engage in physical activity.
Results indicate that age is a significant predictor of receiving a cancer screening at the present time. As the age of the participants increased, the likelihood that they would get a cancer screening exam at the present time increased. This finding is expected given that cancer screening is more common among older adults (Cullati, Charvet-Bérard, & Perneger, 2009), possibly due to the increased medical attention warranted with age. Additionally, the mean age of the men was 49.6 years, and many cancer screening recommendations suggest that regular screening begin around age 50. Therefore, it is likely that many of the men in this sample were considering cancer screenings based on these recommendations. Although expected, this finding is noteworthy given that it is now supported among culturally diverse, medically underserved, mostly racial/ethnic minority men—a group that is typically underrepresented in research similar to the present study.
The likelihood of men receiving an annual cancer screening exam from their doctor if told by their doctor that they have no cancer symptoms was significantly predicted by age and having a medical condition that benefits from healthy eating. As the age of the participants increased, so did the likelihood that they would agree to an annual cancer screening exam even if they had no cancer symptoms. The same interpretations proposed above regarding the relationship between age and getting a cancer screening at the present time may apply here as well.
The agreement ratings on the “Having a Medical Condition that Benefits from Healthy Eating” subscale of the motivator scale in the MB-HSBI also predicted the likelihood of participants reporting they would get a cancer screening if they had no cancer symptoms. Specifically, the men in this study who more strongly agreed that having a medical or health condition that benefits from eating healthy foods and snacks motivates them to eat healthy foods and snacks were more likely to get a cancer screening even if they have no cancer symptoms than men in the study who less strongly agreed that having a medical or health condition that benefits from eating healthy foods and snacks motivates them to eat healthy foods and snacks. This finding is congruent with the findings of Meissner et al. (2009) that healthy eating is related to cancer screening, and it suggests that men who are motivated to make healthy lifestyle changes due to a medical condition are making other health promoting lifestyle choices as well.
The likelihood of getting a cancer screening if a doctor discovers some symptoms of cancer and recommends this screening was predicted by the agreement ratings on the “Making Physical Activity a Priority” subscale of the MB-HSBI. Men who were motivated to engage in physical activity due to making physical activity a personal priority reported being less likely to have a cancer screening if a doctor discovered some symptoms of cancer and recommended a cancer screening. One possible explanation for this surprising finding may be that men who engage in physical activity because it is a personal priority are aware that physical activity is known to help prevent cancer (Kushi et al., 2012), and therefore, they believe they do not need to be screened for cancer. However, a more plausible explanation may be that the association is unreliable. This is supported by the relatively high p value, confidence intervals that almost include 1.0, and the fact that the relationship is based on responses from less than 10% of the sample.
The likelihood of receiving a cancer screening if a doctor discovers some symptoms of cancer and recommends this screening was predicted by the agreement ratings on the “Commitment to be Physically Active” subscale of the motivator scale in the MB-HSBI. Men who were motivated to engage in physical activity due to a commitment to be physically active were more likely to have a cancer screening if their doctor discovered some cancer symptoms and recommended a cancer screening. This finding is expected given that cancer screening rates are higher among those who are committed to engage in other health promoting behaviors, such as physical activity (Low et al., 2014) and healthy eating (Meissner et al., 2009).
There are some noteworthy limitations of this study. One such limitation is the use of a convenience sample from a geographically limited population. Most of the men who attended the Men’s Health Forum were from Tampa, Florida and the surrounding areas. Additionally, almost three quarters of the men sampled in the present study indicated they had completed at least some college or technical school; however, a systematic review by Ouakrim et al. (2013) indicated that in more than half of the reviewed studies, education was not a significant predictor of cancer screening. Moreover, despite the relatively high percentage of well-educated men in the present study, almost half of the sample reported an annual income below $25,000 and over half reported not having medical insurance. Another limitation is the use of cancer screening in general as a dependent variable as opposed to specific types of cancer screening (e.g., colorectal, prostate, skin, testicular, and/or oral cancer screening). The use of specific types of cancer screening as dependent variables would address differences in types of cancer screenings and variations in cancer screening guidelines. However, given that the present study is one of the first of its kind using a culturally diverse and medically underserved sample, it is a necessary first step for understanding these relationships. Future research should focus on specific types of cancer screening.
Taken together, the findings in the present study suggest that interventions to increase the frequency of cancer screening exams among culturally diverse, medically underserved men should be informed at least in part by the specific motivators of participating in cancer screenings relevant to these men. Furthermore, such interventions should be customized for particular subgroups of culturally diverse, medically underserved men, as one intervention strategy likely will not work for all medically underserved men. Additionally, qualitative research, such as focus group studies, may be needed to better understand age differences in the likelihood that men would get cancer screenings even if they do not have cancer symptoms. Most importantly, the research findings in the present study suggest that increasing cancer screenings and reducing cancer and cancer screening disparities requires talking with culturally diverse men about their cancer screening decision making.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.