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Keywords:

  • aging;
  • colorectal carcinoma risk;
  • adenomatous colon polyp;
  • colon adenoma;
  • cancer control

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

BACKGROUND

This report addresses the interface between cancer and aging in the context of colorectal carcinoma (CRC), the second leading cause of cancer death in the U.S. overall and the first leading cause among individuals age ≥ 75 years. Because polyp risk increases with age, interventions to prevent recurrent polyps among older adults likely would reduce CRC morbidity and mortality.

METHODS

Data for this study derive from Project Prevent, a multisite, randomized controlled trial designed to reduce behavioral risk factors for CRC among 1247 adults who underwent the removal of ≥ 1 adenomatous colon polyps. Middle-aged and older patients were compared on key cognitive-behavioral mechanisms associated with CRC risk and established age-related factors associated with adverse health outcomes. Relations between cognitive-behavioral mechanisms and age-related vulnerability factors identified subgroups of older polyp patients that may have an enhanced risk for CRC.

RESULTS

Compared with middle-aged patients, older patients were less concerned about developing CRC, less motivated to reduce their risk, and less confident that their behavior change efforts would succeed. As expected, they also reported more age-related physical, social, and economic vulnerabilities, as expected. Evidence for enhanced CRC risk was found for older patients with multiple comorbid conditions, low social support for change, and perceptions of income inadequacy.

CONCLUSIONS

The presence of age-related vulnerability factors may enhance the risk of CRC among older cancer patients by creating barriers to behavioral change. Efforts to reduce the cancer burden in older populations will require attention beyond early detection and surveillance to interventions that account for the unique physical and psychosocial characteristics of older adults. Cancer 2004;100:1085–94. © 2004 American Cancer Society.

Advancing age increases the risk of cancer.1 The incidence of cancer among individuals age ≥ 65 years is expected to double from 1.3 million to 2.6 million between 2000 and 2050.2 This means that health care providers are beginning to manage what will become unprecedented numbers of older cancer patients with excess morbidity, social support needs, and economic disadvantage. The comprehensive geriatric model3 suggests that the cancer experience in older patients is affected adversely by age-related physical and psychosocial vulnerabilities.4 Therefore, efforts to reduce the cancer burden in the older population will require consideration of the unique physical and psychosocial characteristics of older adults.

Colorectal carcinoma (CRC) is the second leading cause of cancer death in the U.S. overall and the first leading cause among individuals age ≥ 75 years.2 CRC arises from neoplastic adenomatous polyps,5 the prevalence of which increases from 20% to 25% at age 50 years and to 50% percent by ages 75–80 years.6 Research demonstrates that, although most CRC can be prevented by early endoscopic resection of colon adenomas,5 patients who have polyps removed have a 30% likelihood of developing recurrent polyps,7, 8 and many do not undergo additional screening.9 Because polyp risk increases with age, interventions among older adults to prevent recurrent polyps likely would reduce the absolute number of CRC diagnoses among those at highest risk for the disease.

Evidence-based recommendations to reduce the risk of adenomatous polyps and CRC include a diet that is low in red meat and alcohol10–17 and avoidance of smoking.18 Research also suggests that normal body weight should be maintained through regular exercise.19 In addition, micronutrients in fruits and vegetables may lower risk, and it also has been demonstrated that the folate in multivitamins also protects from CRC.20, 21 Although further studies are needed to identify major risk factors of CRC, older patients postpolypectomy at least should be informed that the National Cancer Institute recommends improving diet quality, increasing physical activity, and avoiding tobacco to lower overall cancer risk. Promotion of these behaviors among elderly populations has received little attention.22

Several theories, including the Health Belief Model23 and the Precaution Adoption Model,24 suggest that heightened vulnerability and personal acceptance of risk help promote successful behavioral change. In the context of CRC prevention, this means that interventions that build on the heightened risk perceptions and related concerns of patients with polyps may increase inclinations to adopt risk-reducing behaviors.25 Research also suggests that interventions should be tailored to the patient's level of readiness to adopt changes.26 Patients' expectations of benefits that result from behavior change also are important considerations when developing interventions.27

However, among older patients, age-related physical and psychosocial factors may influence responses to interventions. Multiple comorbid conditions, inadequate social support, or low financial resources may set up barriers to positive behavioral change. They also may affect motivation to reduce CRC risk and the perception of benefits potentially gained from behavior change. For example, fatigue or functional limitations secondary to comorbid conditions may prevent an older polyp patient from increasing levels of physical activity. Older patients who lack social support may eat poorly because they frequently are alone and are not inclined to prepare nutritious meals. Others may believe they cannot afford to meet dietary recommendations on fixed incomes. Awareness of vulnerable subgroups would inform the development of CRC prevention initiatives for the burgeoning population of older Americans.

In this report, we examine how well established physical, emotional, social, and financial age-related vulnerabilities28 affect CRC risk perceptions and behaviors among older adults who have had one or more adenomatous colon polyps removed. Given the evidence that perceptions of risk motivate efforts to reduce risk and that age-related vulnerability factors may affect older patients' responses to risk-reducing recommendations, we hypothesize that age will modulate the perception of CRC risk and, thus, the likelihood of adopting behavior to reduce CRC risk.

Four specific questions were asked. First, among a broad age range of patients with colon polyps (ages 40–75 years), we asked about the extent to which older persons (ages 60–75 years) differed from middle-aged individuals (ages 40–59 years) on cognitive-behavioral factors, including actual risk, perceived risk, and worry about colon cancer. Second, we asked about the extent to which older patients differed from middle-aged patients in levels of readiness to reduce CRC risk through behavioral change and in their expectations that such changes would be beneficial. Third, compared with middle-aged patients, we asked about the extent to which older patients provided evidence of age-related vulnerability factors that may pose additional challenges to the adoption of risk-reducing behavior. Finally, among the older patients, we asked about the extent to which age-related vulnerability factors related to CRC risk and cognitive-behavioral mechanisms that underpin successful behavior change.

Data to address these questions derive from Project Prevent, a multisite, randomized intervention trial to reduce behavioral risk factors for CRC among patients diagnosed with adenomatous polyps (National Cancer Institute Project RO1 CA74000-02). In this trial, eligible patients were randomized to receive either usual care or a multiple risk factor intervention consisting of 1) a health care provider recommendation letter concerning the importance of health behavior change, 2) tailored self-help materials, 3) a motivational and goal-setting telephone session delivered by a health educator, and 4) four follow-up telephone counseling calls and progress reports. The intervention aimed to reduce CRC behavioral risk factors related to dietary intake, multivitamin intake, physical activity, smoking, and alcohol use.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Sample

The study sample included 1247 patients, ages 40–75 years, who participated in Project Prevent, an intervention trial intended to 1) increase the use of daily multivitamins, 2) increase fruit and vegetable intake, 3) reduce red meat consumption, 4) increase physical activity, 5) decrease alcohol use, and 6) increase smoking cessation. Patients were eligible if they had adenomatous colon polyps removed within 4 weeks of study recruitment, no history of CRC, capacity for informed consent, the ability to read and speak English, telephone access for the baseline survey, and physician approval to participate in moderate physical activity. Participants completed the interviewer-administered baseline telephone survey and were assigned randomly to either a usual care group or to receive a multiple risk factor intervention. The intervention involved telephone counseling and tailored self-help materials that were designed to help participants reduce the targeted CRC risk behaviors. The analyses for this report utilized the baseline sample of 594 middle-aged patients (ages 40–59 years) and 653 older patients (ages 60–75 years).

Measures

Demographics.

Standard demographic measures included age, gender, education, marital status, race/ethnicity, height, weight, medical history, and characteristics of the household.

CRC risk factors

Servings of fruits/vegetables and red meat were assessed using an abbreviated form of the Food Frequency Questionnaire.29 A single item assessed average weekly multivitamin use (9 response options ranged from never to 7 days per week). Alcohol consumption was measured using the Quantity Frequency Index.30 A modified version of the Community Healthy Activities Model Program for Seniors (CHAMPS) Activities Questionnaire for Older Adults31, 32 indexed physical activity. Patients' smoking status was measured using standardized questions regarding lifetime and current smoking, intensity, quit attempts, and nicotine dependence.33 Based on these measures, “risk” status was conferred for each risk factor based on < 5 servings of fruits and vegetables per day, consumption of > 3 red meat servings per week, taking a multivitamin < 7 days per week, consuming > 1 (women) or > 2 (men) servings of alcohol per day, status as a current smoker, or participating < 150 minutes per week in moderate exercise (1, presence of risk factor; 0, absence of risk factor). Individual risk factor scores were summed to yield a categoric multiple risk factor score ranging from 0 (no risk factors) to 6 (all risk factors).34

Cognitive-behavioral mechanisms

Perceived risk was measured by asking participants how likely they were to get CRC in their lifetime (on a 5-point Likert scale that was reduced to 3 categories: unlikely, 50:50 or not sure, or likely). Patients also were asked about their level of worry/concern about developing CRC in their lifetime (0–10 scale). Readiness to change was based on an individual's readiness to change all of their risk factors in the coming 6 months. Participants with no risk factors were classified in the “maintenance stage.” Those who were unaware that they had risk factors were not classified as “ready to change,” regardless of their response to the question. For those who indicated that they had habits to change, outcome expectancies were indicated by agreement with the statement “changing my health habits will reduce my risk of colon cancer.” Finally, self-efficacy was indexed by asking patients who had identified risk factors to rate their confidence in changing all problem behaviors within the next 6 months (on a 5-point Likert scale, from not at all confident to extremely confident). This variable is treated as a continuous variable in the tables.

Age-related vulnerability factors

Physical vulnerability factors included higher levels comorbid illness and lower levels of perceived health. Comorbid illness was measured with a revised version of the Older American Resources and Services (OARS) questionnaire,35 on which respondents indicated the presence or absence of major chronic medical conditions (on a 0–10 scale; e.g., stroke, cancer, diabetes, heart disease, or lung disease). Patients with two or more comorbid illnesses were considered vulnerable. Self-rated health was indexed on a 4-point scale (anchors: excellent, good, fair, poor).36 It has been found that this measure is a significant predictor of mortality37; study participants with ratings of “fair” or ”poor“ were considered vulnerable.

Emotional vulnerability factors included negative affect and lower levels of life quality. Negative affect was measured by patient reports of the frequency of feeling downhearted and blue during the past month (4-point Likert scale with anchors ranging from rarely, to no time, to most/all of the time). Patients were considered vulnerable with ratings of “some of the time” or more often. Quality of life was measured by asking patients to rate the overall quality of their life on a 4-point scale (with anchors ranging from excellent to poor). Patients were considered vulnerable with ratings of “fair” or “poor.”

Social vulnerability factors included social isolation and low levels of social support. Social isolation was determined by asking patients to report whether they were married/cohabitating, living with others, or living alone. Those living alone were considered socially vulnerable. Social support was measured by asking participants to report the number of confidants (“of all the people you know, how many do you feel particularly close to?”). Patients who reported one confidant or none were considered vulnerable. Social support for change was captured by patients' reports of the extent to which friends and family would support their efforts to change their health habits (5-point Likert scale ranging from not at all to extremely). Those considered vulnerable reported that support for change would be “a little” or “not at all.”

Financial vulnerability factors included low levels of objective income and patients' perceptions that their income was inadequate for their needs. Annual household income was indexed by total yearly household income (categories ranging from < $15,000 to $45,001). Patients who reported incomes < $30,000 were considered vulnerable. Perceived income adequacy was measured by asking patients to consider their overall income and endorse one of the following: 1) have money for special things, 2) have money for bills but not extras, 3) must cut back to make bills, 4) have difficulty paying bills. Patients who endorsed 2, 3, or 4 were considered financially vulnerable.

Statistical Analyses

Analyses focused on age differences in cognitive-behavioral factors (perceived risk, worry about colon carcinoma, readiness and self-efficacy for change, and expectations that behavior change would be beneficial) and physical, emotional, social, and financial vulnerability factors. Among the older patients only, correlations between vulnerability factors and actual CRC risk and cognitive-behavioral mechanisms associated with CRC risk were examined. Analyses employed contingency table analysis with chi-square testing for discrete variables and Student t tests for continuous variables. All analyses were performed using SAS statistical software (release 8.02; SAS Inc., Cary, NC).

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

As shown in Table 1, the full sample was comprised of more men than women (58% vs. 42%, respectively). Participants' ages ranged from 40 years to 75 years, with an average age of 60 years (standard deviation, 8.4 years). Seventeen percent of patients were nonwhite. Most participants were married or lived with a partner, were well educated, and had annual household incomes ≥ $45,000. A minority of participants (9.3%) did not report income. Most participants had never been diagnosed with polyps.

Table 1. Full Sample Demographic Profile with a Comparison of the Middle-Aged and Older Patient Subsamples
DemographicsNo. of patients (%)P valuea
Entire sample (ages 40–75 yrs) (n = 1247)Middle-aged group (ages 40–59 yrs) (n = 594)Older group (ages 60–75 yrs) (n = 653)
  • NS: nonsignificant; grad: graduate.

  • a

    P values were based on Student t tests for continuous dependent variables and on chi-square tests for categoric dependent variables. All comparisons were between the middle-aged subsample and the older subsample. Missing data are not shown for race (five patients), education (three patients), or marital status (four patients).

Female gender 523 (41.9)249 (41.9)274 (42.0)NS
Race    
 White1030 (82.9)487 (82.3)543 (83.5)
 Black 150 (12.1) 71 (12.0) 79 (12.2)
 Other  62 (5.0) 34 (5.7) 28 (4.3)NS
Education level    
 ≤ High school grad 310 (24.9)117 (19.7)193 (29.7)
 > High school 281 (22.6)126 (21.3)155 (23.8)
 College grad 260 (20.9)149 (25.1)111 (17.1)
 Postgraduate work 393 (31.6)201 (33.9)192 (29.5)< 0.0001
Marital status    
 Married/cohabitating 938 (75.5)466 (78.9)472 (72.4)
 Divorced/separated 141 (11.3) 73 (12.4) 68 (10.4)
 Widowed  92 (7.4) 14 (2.4) 78 (12.0)
 Never married  72 (5.8) 38 (6.4) 34 (5.2)< 0.0001
Annual household income    
 ≤ $15,000  91 (7.3) 28 (4.7) 63 (9.7)
 $15,001–30,000 161 (12.9)  44 (7.4)117 (17.9)
 $30,001–45,000 160 (12.8) 61 (10.3) 99 (15.2)
 ≥ $45,001 719 (57.7)428 (72.1)291 (44.6)
 Don't know/refused 116 (9.3) 33 (5.6) 83 (12.7)< 0.0001

Not surprisingly, middle-aged group versus older group comparisons across demographic indicators revealed three significant correlations. Older patients were less likely than middle-aged patients to have attended college or graduate school (P < 0.0001). Age and marital status also were found to be related (P < 0.0001) such that, compared with middle-aged patients, older patients were more likely to be widowed and were less likely to be in a current relationship. Overall, compared with middle-aged patients, older patients had lower annual household incomes and were more than twice as likely to report incomes < $30,000 per year (P < 0.0001). They also were more likely to refuse to report income or to check “do not know.”

Disease Factors: To What Extent do Actual and Perceived CRC Risks Differ by Age?

An initial step in examining the impact of aging on CRC risk among patients with colon adenomas was to determine whether perceptions of CRC risk differed for middle-aged patients (40–59 years) and older patients (60–75 years) (Table 2). There was no significant difference between middle-aged and older patients in the number of risk factors. By contrast, marked age differences emerged on perceived CRC risk and cognitive mediators of behavior change. First, there was a strong association between age and perceived lifetime risk of developing CRC. Compared with middle-aged patients, older patients were more likely to perceive that developing CRC in their lifetime was unlikely or very unlikely (P < 0.05) and reported significantly less concern about this possibility (P < 0.0001). Older patients also were less likely than middle-aged patients to believe that changing their health habits would reduce their CRC risk (P < 0.05). Compared with middle-aged patients, older patients reported significantly less motivation for changing all risk behaviors associated with CRC (P < 0.01).

Table 2. Age Comparison of Actual and Perceived Risk, Concern, Readiness, and Self-Efficacy for Change
Enhanced risk domainsMiddle-aged group (ages 40–59 yrs) (n = 594)Older group (ages 60–75 yrs) (n = 653)P valuea
  • SD: standard deviation; CRC: colorectal carcinoma; NS: not significant.

  • a

    P values were based on Student t tests for continuous dependent variables and on chi-square tests for categoric dependent variables. All comparisons were between the middle-aged and older subsamples. Missing data are not shown for outcome expectancy (changing health habits will reduce CRC risk; n = 159 middle-aged patients; n = 252 older patients).

  • b

    There were 39 patients from the from middle-aged group (6.7% of the data), and 95 patients from the older group (14.9% of the data) who were not asked this question because they did not think they had health habits to change. In addition, 27 middle-aged patients and 39 older patients responded “don't know.”

  • c

    Includes “disagree” and “neither.”

Actual risk:   
 No. of risk factors (0–6): (mean ± SD)2.5 ± 1.32.4 ± 1.2< 0.10
Perceived risk   
 Likelihood of getting CRC in lifetime: no. (%)   
  Unlikely218 (36.7)285 (43.9)
  50:50 chance/unknown302 (50.8)294 (45.3)
  Likely 74 (12.5) 70 (10.8)< 0.03
Concern:   
 Level of concern for CRC (0–10) in lifetime (mean ± SD)5.4 ± 3.14.4 ± 3.1< 0.0001
Outcome expectancies   
 Patients who indicated they had habits to changeb  
  No. of patients555558
  Agree (mean ± SD)435 ± 78.5401 ± 72.0
  Do not agree (mean ± SD)c119 ± 21.5156 ± 28.00.01
Motivation/readiness (stage of change)   
 No. of patients ready to change all behaviors (% yes)  
  Precontemplation/contemplation298 (50.2)384 (58.8)
  Preparation/maintenance296 (49.8)269 (41.2)< 0.002
Self efficacy   
 Confidence (1–5) in ability to change all health habits in the next 6 mos (mean ± SD)3.7 ± 0.93.7 ± 0.9NS

Aging Factors: To What Extent are Older Patients More Vulnerable?

Next, we examined a broad range of general influences (physical, emotional, social, financial) that were linked previously to health adversity in later life28 (Table 3). Within the physical realm and compared with middle-aged patients, older patients reported significantly higher levels of comorbid illnesses (P < 0.0001) and lower levels of self-rated health (P < 0.05). By contrast, and also consistent with past research,38, 39 older patients' reports of emotional functioning revealed lower vulnerability. Compared with middle-aged patients, older patients reported similar levels of quality of life and significantly lower levels of depressed feelings (P < 0.0001). Socially, older polyp patients in this sample were significantly more likely than middle-aged patients to live alone (P < 0.01). They also had fewer confidants (P = 0.06) and significantly lower levels of support for making behavior changes (P < 0.01). Finally, age group comparisons in the financial realm revealed that, although older patients reported significantly lower incomes compared with middle-aged patients (P < 0.0001), they were more likely to perceive that their financial resources were just adequate for meeting their needs (P < 0.05).

Table 3. Age Comparison of Physical, Emotional, Social, and Financial Vulnerability Factors Age-Related Vulnerability Factors, in Percentages
Age-related vulnerability factorsAge group (%)P valuea
Middle aged (40–59 yrs) (n = 594)Older (60–75 yrs) (n = 653)
  • NS: not significant.

  • a

    P values were based on Student t tests for continuous dependent variable and on chi-square tests for categoric dependent variable. All comparisons were between the middle-aged and older subsamples.

Physical   
 Higher comorbidity (≥ 2 comorbid illnesses)46.564.8< 0.0001
 Lower self-rated health (rated fair or poor)14.819.3< 0.05
Emotional   
 Feels blue (sometimes, occasionally, most or all of the time)46.434.5< 0.0001
 Lower quality of life (rated fair or poor)13.711.1NS
Social   
 Lives alone (yes)13.519.0< 0.01
 Fewer confidants (none or 1)12.015.70.06
 Lower social support for change (family/friends would help little or not at all)8.713.5< 0.01
Financial   
 Income ≤ $30,00012.127.6
 Income > $30,00082.359.7
 Don't know/refused5.612.7< 0.0001
 Lower perceived adequacy of income (can just meet bills, must cut back to meet bills, difficulty paying bills)31.726.2< 0.05

Cancer-Aging Interface: How Is CRC Risk Affected by Age-Related Vulnerability?

The following analyses were restricted to older patients (see Table 4). Compared with older patients with low levels of comorbid illness, physically vulnerable older patients (i.e., those with two or more chronic conditions) reported more risk factors for CRC (P < 0.05), a greater likelihood that they would develop CRC in their lifetime (P < 0.05), and greater concern for developing CRC in the future (P < 0.01), yet greater readiness to make changes (P < 0.05). Compared with older patients who rated their health as good or excellent, those with lower health ratings had significantly more CRC risk factors (P < 0.01), more worry about getting CRC in the future (P < 0.05), and perceived themselves at great risk for developing CRC (P < 0.01). In addition, older patients who reported fair or poor health were significantly less confident that they could take steps to change all of their health habits in the next 6 months (P < 0.05).

Table 4. Physical Vulnerability in Older Patients by Actual and Perceived Risk, Concern regarding Colorectal Carcinoma, and Readiness and Self-Efficacy for Change
Cognitive-behavioral variablesPhysical vulnerability factors
No. of comorbid illnessesSelf-rated health
≥ 2 (n = 423)< 2 (n = 230)P valueaFair/poor (n = 125)Good/excellent (n = 523)P valuea
  • SD: standard deviation; CRC: colorectal carcinoma; NS: not significant.

  • a

    P values were based on Student t tests for continuous dependent variables and on chi-square tests for categoric dependent variables. All comparisons are within the older patient subsample.

Actual risk      
 No. of risk factors (0–6) mean ± SD2.5 ± 1.22.2 ± 1.20.022.6 ± 1.22.3 ± 1.2< 0.01
Perceived risk      
 Likelihood of getting CRC in lifetime: no. (%)      
  Unlikely168 (40.0)117 (51.1)42 (33.6)240 (46.2)
  50:50 chance/don't know200 (47.6)94 (41.1)63 (50.4)230 (44.3)
  Likely52 (12.4)18 (7.9)0.0220 (16.0)49 (9.4)0.01
Concern      
 Level of concern for CRC (0–10) in the future (mean ± SD)4.6 ± 3.24.0 ± 3.0< 0.015.0 ± 3.44.3 ± 3.00.04
Outcome expectancies (changing health habits will reduce CRC risk)   
 No. of patients who indicated they had habits to change (mean ± SD)    
  Agree266 ± 78.9135 ± 74.684 ± 80.0315 ± 77.0
  Disagree/neither71 ± 21.146 ± 25.4NS21 ± 20.094 ± 23.0NS
Motivation/readiness (stage of change)    
 No. of patients ready to change all behaviors (%)    
  Precontemplation/contemplation235 (55.6)149 (64.8)78 (62.4)304 (58.1)
  Preparation/maintenance188 (44.4)81 (35.2)0.0247 (37.6)219 (41.9)NS
Self efficacy      
 Confidence (1–5) in ability ability to change all health habits in the next 6 mos (mean ± SD)3.7 ± 0.93.8 ± 0.8NS3.5 ± 1.03.8 ± 0.80.04

Older patients' reports of the number of confidants in their lives were related significantly to their readiness for change (Table 5). Those with few confidants were significantly less ready to change all of their risk behaviors (P < 0.005) and were less confident that they could make those changes (P < 0.005).

Table 5. Social Vulnerability Factors in Older Patients (Ages 60–75 yrs) by Actual and Perceived Risk, Concern Regarding Colorectal Carcinoma, and Readiness and Self-Efficacy for Change
Cognitive-behavioral variablesPhysical vulnerability factors
No. of confidantsSupport for change
0–1 (n = 102)≥ 2 (n = 547)P valueaLow (n = 86)High (n = 550)P valuea
  • SD: standard deviation; CRC: colorectal carcinoma; DK: don't know; NS: not significant.

  • a

    P values were based on Student t tests for continuous dependent variable and on chi-square tests for categoric dependent variables. All comparisons are within the older patient subsample.

Actual risk      
 No. of risk factors (0–6) (mean ± SD)2.4 ± 1.22.4 ± 1.3NS2.4 ± 1.22.4 ± 1.2NS
Perceived risk      
 Likelihood of getting CRC in lifetime: no. (%)     
  Unlikely42 (41.6)241 (44.3)43 (50.0)236 (43.1)
  50:50 chance/DK45 (44.6)247 (45.4)32 (37.2)252 (46.1)
  Likely14 (13.9)56 (10.3)NS11 (12.8)59 (10.8)NS
Concern      
 Level of concern for CRC (0–10) in the future (mean ± SD)4.6 ± 3.44.4 ± 3.NS3.9 ± 3.44.5 ± 3.10.13
Outcome expectancies (changing health habits will reduce CRC risk)    
 No. of patients who indicated they had habits to change (%)    
  Agree52 (71.2)349 (78.6)0.1640 (66.7)357 (79.7)0.02
  Disagree/neither21 (28.8)95 (21.4)20 (33.3)91 (20.3)
Motivation/readiness (stage of change)    
 No. of patients ready to change all behaviors (% yes)    
  Precontemplation/contemplation74 (72.6)306 (55.9)67 (77.9)305 (55.5)
  Preparation/maintenance28 (27.5)241 (44.1)< 0.0119 (22.1)245 (44.6)< 0.0001
Self efficacy      
 Confidence (1–5) in ability to change all health habits in the next 6 mos (mean ± SD)3.5 ± 0.93.8 ± 0.90.113.1 ± 1.03.8 ± 0.9< 0.001

The extent to which older patients felt supported by others in efforts to change CRC risk behaviors was related to their outcome expectancies, motivation for change, and self-efficacy for change. Specifically, older patients who reported low levels of support for changing their behaviors were significantly less likely to think that changing all of their risk behaviors would lower CRC risk (P = 0.02). Older patients who felt less supported also indicated the lowest levels of readiness to change (P < 0.0001) and confidence for changing all of their risk behaviors (P < 0.0005).

Because relatively few associations were found between CRC risk and financial vulnerability, these data are reported but not tabled. Objective household income related to the number of CRC risk factors, such that financially vulnerable older patients (i.e., those with incomes < $30,000) reported significantly more CRC risk factors (P < 0.01). Compared with patients who reported income, those who did not report income expressed significantly less concern for developing CRC in their lifetime. Perceived adequacy of income (i.e., ability to pay bills, purchase extras) was related to the number of CRC risk factors and self-efficacy for making behavior changes. Older patients with lower perceptions of income adequacy reported significantly more CRC risk factors (P < 0.05) and significantly less confidence that they could change their risk behaviors in the next 6 months (P < 0.01).

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Understanding aging-cancer interactions is particularly important in the context of CRC, because most CRC cases occur among individuals age ≥ 50 years. Moreover, CRC incidence nearly doubles each decade until around age 80 years, and 5-year survival rates are comparable among persons < 65 years and > 65 years, making the older population a key CRC screening target.40 Ideally, older patients with colon adenomas should have follow-up colonoscopy screening which, since July 2001, has been covered by Medicare. However, not all patients undergo surveillance colonoscopy. Therefore, and regardless of whether or not surveillance colonoscopy is performed, behavior modifications are important in decreasing the risk of recurrent polyps. Results of this study suggest that the presence of age-related vulnerability factors may enhance CRC risk and indicate special considerations in the design of interventions to control recurrent adenomas and CRC in older patients.

Preliminary evidence for enhanced risk emerged in three analyses. First, after the removal of one or more adenomatous colon polyps, older patients report less concern than middle-aged patients about developing CRC. They also have lower motivation to change and lower outcome expectancies regarding the benefits of behavior change. The second analysis confirmed the suspected existence among the older patients of age-related physical, social, and financial vulnerabilities, namely, multiple morbidities, lower self-rated health, lower social support, and lower financial resources. The final analysis focused on the cancer-aging interface and identified three subgroups of older polyp patients with a potential for enhanced CRC risk.

Members of the first subgroup were older polyp patients with higher levels of comorbidity. These chronically ill patients had more CRC risk factors and, presumably, have illness-related functional limitations that present barriers to exercise. There also is mounting evidence that unhealthy behaviors tend to cluster (i.e., those who are sedentary also are more likely to eat high amounts of animal fat, low amounts of fruits and vegetables, and vice versa) both within the general population41, 42 and in cancer patients.43 Therefore, front-line practitioners caring for older patients with a history of colon adenomas should consider targeting general “lifestyle” changes in their recommendations rather than individual CRC risk factors.

A second subgroup with enhanced risk was comprised of older patients who had few social ties or little social support for change. Compared with more socially engaged older patients, those with social supports needs held lower expectations that behavior change would reduce CRC risk, less motivation to change high-risk behaviors, and less confidence that efforts to reduce CRC would succeed. These older patients lacked key individuals who could support them, for example, in efforts to quit smoking, reduce alcohol intake, or achieve positive dietary changes. The relation of social factors to older patients' attempts to reduce their cancer risk needs systematic study, especially in patients who have inadequate support for behavior change.

The third subgroup of patients with enhanced CRC risk included older adults who perceived that their incomes were inadequate for meeting needs. Compared with patients who had “money for little extras,” those with resources that “barely met bills” or caused a “struggle to meet bills” had more CRC risk factors and less confidence that they could make the lifestyle changes needed to reduce risk.

Taken together, these findings suggest that, although behavioral risk essentially is identical among middle-aged and older individuals, older adults are more likely to underestimate that risk. Therefore, by moderating the perception of CRC risk, age also may moderate the likelihood of adopting healthier lifestyles. The correlations between age-related vulnerabilities and CRC risk also suggest that certain older patients may be less capable than others of following CRC risk-lowering recommendations. The extent to which these findings apply to other chronic conditions or cancers in which interventions may lower risk is unknown. For example, after a myocardial infarction, are older patients with physical or psychosocial vulnerabilities less likely than others to take a daily aspirin?

Research is needed to determine whether prevention efforts in older patients with certain physical, social, or financial characteristics would benefit from interventions that reach out to functionally impaired, socially isolated, or low-income seniors. For example, CRC risk reduction may be realized by identifying low-income elders for physical activity programs in community senior centers, by establishing buddy programs that link isolated elderly with walking companions, or by partnering with dietary programs, such as Meals on Wheels, to promote diets high in fruits and vegetables and low in red meat. Interventions to reduce CRC risk in elderly with low social support may be implemented more successfully within clinic support group settings or with frequent phone “visits.”

The results of the current study also suggest that intervention programs targeting older patients with polyps should make special efforts to include individuals with poor personal health perceptions. These patients need directives for behavior change and may be particularly receptive, because they tend to hold favorable outcome expectancies. However, study results suggest that providers may need to build older patients' levels of confidence that they can make these changes effectively. This may be achieved more easily by presenting risk factor reduction as a lifestyle change rather than as multiple tasks related to multiple behaviors. In light of the fact that the adenoma-carcinoma process can take a decade or more, an approach in which lifestyle change is linked to more immediate gains in overall health particularly may benefit older polyp patients who are managing multiple comorbid conditions.

The current study was limited by the use of several single-item indicators to capture aspects of health, quality of life, and social support. Similar to many large-scale behavioral intervention trials, and particularly Project Prevent, with its focus on multiple risk factors, the focus was on rigorously evaluating the intervention. Therefore, data collection resources were allocated heavily toward the use of gold standard health behavior measures (e.g., diet, physical activity), whereas other measures necessarily were brief. Although the brief measures were chosen based on their strong performances in previous trials, future work on the cancer-aging interface should include multiple-item indicators of older patients' physical and psychosocial functioning. Conversely, valid and reliable, single-item measures, such as self-rated health, are efficient and are administered easily in busy clinical settings. Additional domains that may interfere with cancer control initiatives and should be considered for measurement in older populations include self-care ability, pain, fatigue, and needs for assistance with instrumental tasks and transportation.

The expected future increase in our nation's older population calls for an integrated perspective of cancer prevention, detection, surveillance, and treatment within oncology, geriatrics, nursing, and the behavioral sciences. Common issues that cut across these perspectives include concomitant illness, quality of life, financial resources, and social support. Stronger consideration of aging factors in the design of behavior change interventions may reduce CRC risk by addressing physical and psychosocial vulnerabilities that undermine adherence to recommendations for lifestyle changes among older patients with polyps.

Acknowledgements

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

The authors thank Maureen Lahti, M.B.B.S., M.P.H., for her work on data analysis and Jennifer Chamberlain, M.B.A., M.H.A., for her assistance in preparing the current article.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES
  • 1
    Hunter CP, Johnson KA, Muss HB. Cancer in the elderly. New York: Marcel Dekker, 2000.
  • 2
    Edwards BK, Howe HL, Ries LA, et al. Annual report to the nation on the status of cancer, 1973–1999, featuring implications of age and aging on U.S. cancer burden. Cancer. 2002; 94: 27662792.
  • 3
    Cohen HJ. Geriatric principles of treatment applied to medical oncology. Semin Oncol. 1995; 22(Suppl 1): 12.
  • 4
    Cohen HJ. Cancer in the elderly: an overview. In: CasselCK, CohenHJ, LarsonEB, et al., editors. Geriatric medicine, 3rd edition. New York: Springer-Verlag, 1997: 229230.
  • 5
    Bond JH. Polyp guideline: diagnosis, treatment, and surveillance for patients with colorectal polyps. Am J Gastroenterol. 2000; 95: 30533063.
    Direct Link:
  • 6
    Winawer SJ, Shike M. Prevention and control of colorectal cancer. In: GreenwaldP, KramerBS, WeedDL, editors. Cancer prevention and control. New York: Marcel Dekker, 1995: 537560.
  • 7
    Winawer SJ, Zauber AG, O'Brien MJ, et al. Randomized comparison of surveillance intervals after colonoscopic removal of newly diagnosed adenomatous polyps. The National Polyp Study Workgroup. N Engl J Med. 1993; 328: 901906.
  • 8
    Citarda F, Tomaselli G, Capocaccia R, Barcherini S, Crespi M, The Italian Multicentre Study Group. Efficacy in standard clinical practice of colonoscopic polypectomy in reducing colorectal cancer incidence. Gut. 2001; 48: 812815.
  • 9
    Yood MU, Oliveria S, Boyer JG, Wells K, Stang P, Johnson CC. Colon polyp recurrence in a managed care population. Arch Intern Med. 2003; 163: 422426.
  • 10
    Sesink A, Termont D, Kleibeuker J, Van der Meer R. Red meat and colon cancer: dietary ham-induced colonic cytotoxicity and epithelial hypoproliferation are inhibited by calcium. Carcinogenesis. 2001; 22: 16531659.
  • 11
    Willett W. Diet and cancer: one view at the start of the millennium. Cancer Epidemiol Biomarkers Prev. 2001; 10: 38.
  • 12
    Sesink A, Termont D, Kleibeuker J, Van der Meer R. Red meat and colon cancer: dietary ham, but not fat, has cytotoxic and hyperproliferative effects on rat colonic epithelium. Carcinogenesis. 2000; 21: 19091915.
  • 13
    Bingham S. High meat diets and cancer risk. Proc Nutr Soc. 1999; 58: 243248.
  • 14
    Negri E, Bosetti C, La Vecchia C, Fioretti F, Conti E, Franceschi S. Risk factors for adenocarcinoma of the small intestine. Int J Cancer. 1999; 82: 171174.
  • 15
    Giovannucci E, Colditz G, Stampfer M, et al. A prospective study of cigarette smoking and risk of colorectal adenoma and colorectal cancer in U.S. women. J Natl Cancer Inst. 1994; 86: 192199.
  • 16
    Martínez ME, McPherson RS, Annegers JF, Levin B. Cigarette smoking and alcohol consumption as risk factors for colorectal adenomatous polyps. J Natl Cancer Inst. 1995; 87: 274279.
  • 17
    Boutron M, Faivre J, Dop M, Quipourt V, Senesse P. Tobacco, alcohol, and colorectal tumors: a multistep process. Am J Epidemiol. 1995; 141: 10381046.
  • 18
    Giovannucci E. An updated review of the epidemiological evidence that cigarette smoking increases risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2001; 10: 725731.
  • 19
    U.S. Department of Health and Human Services. Physical activity and health: a report of the Surgeon General. Atlanta: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 1996.
  • 20
    Giovannucci E, Stampfer MJ, Colditz G, et al. Multivitamin use, folate, and colon cancer in women in the Nurses' Health Study. Ann Intern Med. 1998; 129: 517524.
  • 21
    Glynn S, Albanes D. Folate and cancer: a review of the literature. Nutr Cancer. 1994; 22: 101119.
  • 22
    Rosenstock IM. Historical origins of the health belief model. In: BeckerMH, editor. The health belief model and personal health behavior. Thorofare, NJ: Charles B. Slack, 1974: 18.
  • 23
    Janz NK, Champion V, Strecher V. The health belief model. In: GlanzK, LewisF, RimerB, editors. Health behavior and health education: theory, research and practice, 3rd edition. San Francisco: Jossey-Bass, 2002: 4566.
  • 24
    Weinstein ND. The precaution adoption process. Health Psychol. 1988; 7: 355386.
  • 25
    Weinstein N. Unrealistic optimism about future life events. J Pers Soc Psychol. 1980; 39: 806820.
  • 26
    Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983; 51: 390395.
  • 27
    Bandura A. Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: Prentice Hall, 1986.
  • 28
    Besdine RW. Clinical approach: an overview. In: CasselCK, CohenHJ, LarsonEB, et al., editors. Geriatric medicine, 3rd edition. New York: Springer-Verlag, 1997: 155168.
  • 29
    Schatzkin A. Diet and colorectal cancer: still an open question. J Natl Cancer Inst. 1995; 87: 17331735.
  • 30
    Polich JM, Orvis BR. Composite quantity frequency index. In: National Institute of Alcohol Abuse and Alcoholism. NIAAA treatment handbook series 4: assessing alcohol problems. Bethesda: National Institute of Alcohol Abuse and Alcoholism, 1998:457459.
  • 31
    Stewart A, Painter P. Issues in measuring physical functioning and disability in arthritis patients. Arthritis Care Res. 1997; 10: 395405.
  • 32
    Stewart A, Verboncoeur C, Mclellan B, et al. Physical activity outcomes of CHAMPS II: a physical activity promotion program for older adults. J Gerontol A Biol Sci Med Sci. 2001; 56: M465M470.
  • 33
    Fagerstrom KO. Measuring degree of physical dependence to tobacco smoking with reference to introduction of treatment. Addict Behav. 1978; 3: 235241.
  • 34
    Emmons K, Marcus B, Linnan L, Rossi J, Abrams D. The relationship between smoking, physical activity, and dietary fat intake among manufacturing workers. Prev Med. 1994; 23: 481489.
  • 35
    Fillenbaum GG. Multidinensional functional assessment of older adults. Hillsdale, NJ: Lawrence Erlbaum Associates, 1988.
  • 36
    Breslow L. Social ecological strategies for promoting healthy lifestyles. Am J Health Promot. 1996; 10: 253257.
  • 37
    McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988; 15: 351377.
  • 38
    Cassileth B, Lusk E, Brown L, Cross P, Walsh W. Factors associated with psychological distress in chronically ill patients. Med Pediatr Oncol. 1986; 14: 251254.
  • 39
    Ganz P, Coscarelli C, Heinrich R. The psychosocial impact of cancer on the elderly: a comparison with younger patients. J Am Geriatr Soc. 1985; 33: 420435.
  • 40
    Prindiville SA. Screening for colorectal cancer in the elderly. In: HunterCP, JohnsonKA, MussHB, editors. Cancer in the elderly. New York: Marcel Dekker, 2000: 4156.
  • 41
    Serdula MK, Byers T, Mokdad AH, Simoes E, Mendlein JM, Coates RJ. The association between fruit and vegetable intake and chronic disease risk factors. Epidemiology. 1996; 7: 161165.
  • 42
    French SA, Hennrikus DJ, Jeffrey RW. Smoking status, dietary intake, and physical activity in a sample of working adults. Health Psychol. 1996; 15: 448454.
  • 43
    Demark-Wahnefried W, Peterson B, McBride C, Lipkus I, Clipp E. Current health behaviors and readiness to pursue life-style changes among men and women diagnosed with early stage prostate and breast carcinomas. Cancer. 2000; 88: 674684.