Revisiting the relationship between beliefs and mammography utilization.
Objective: Mammography rates have peaked and appear to be
decreasing, prompting the Centers for Disease Control and Prevention to
call for updated and expanded activities that promote breast cancer
screenings. The purpose of this study was to revisit the relationships
between perceived susceptibility, perceived benefits, perceived
barriers, and perceived access, and mammogram utilization for low-income
women 40 years and older.
Methods: A total of 99 women age 40 to 80 years (58% White, 41% African-American, and 1% Asian) were recruited from seven urban health centers.
Results: Slightly more that half the women (57%) surveyed reported receiving a mammogram in the past year. Overall, women reported high perceived susceptibility for breast cancer, positive benefits of mammography, and low perceived emotional barriers for mammography. Perceived access to mammography had a significant relationship to mammogram utilization (p = .011). The odds of having a mammogram within one year decreased by 28% for every unit increase in perceived access barriers (OR = .716, 95% CI = .553 - .927). Cost, reported by 32% of the women, was the most commonly reported perceived access barrier. Only 22% of women reported receiving a physician recommendation for a mammogram.
Conclusions: Future breast cancer education campaigns should be updated to communicate population-specific local screening resources and encourage women to initiate a conversation with their health care provider. Health care providers should also be targeted for future mammography health education campaigns.
African Americans (Surveys)
Disease susceptibility (Prevention)
Orlowski, Marietta A.
Hallam, Jeffrey S.
|Publication:||Name: American Journal of Health Studies Publisher: American Journal of Health Studies Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2010 American Journal of Health Studies ISSN: 1090-0500|
|Issue:||Date: Spring, 2010 Source Volume: 25 Source Issue: 2|
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|Organization:||Government Agency: United States. National Center for Health Statistics|
Rates of mammography have increased significantly in the past two decades. In 1987, approximately one-third (29%) of women 40 years and older reported having a mammogram within the past two years (National Center for Health Statistics, 2009). By 1999, 70.3% of women 40 years and older reported having a mammogram within the past two years (National Center for Health Statistics, 2009). Unfortunately, mammography utilization peaked in 2000, at 70.4%, and has since decreased. In 2005, the most recent reporting year, only 67% of women age 40 years and older received a mammogram in the past two years (National Center for Health Statistics, 2009).
In addition to the discouraging decreases in mammography utilization, utilization remains lower for select populations. Lower mammography rates are consistently reported for women of lower education and income levels (Schuleler, Chu, & Smith-Bindman, 2008; National Center for Health Statistics, 2009). In 2005, 58% of women over 40 years of age who had less than a high school education received a mammogram. However, 73% of women over 40 years of age who had some college or more received a mammogram within the past two years (National Center for Health Statistics, 2009). Likewise, 57% of women 40 to 64 years of age with Medicaid received a mammogram compared with 75% of women 40 to 64 years of age with private insurance. The lowest level of mammography utilization, by insurance type, was in uninsured women; 38% of uninsured women 40 to 64 years of age received a mammogram within the past two years (National Center for Health Statistics, 2009).
Tailored interventions appear to be more effective in increasing mammography utilization. Intervention strategies are tailored to a woman or a group of women by targeting specific behavioral antecedents that may either inhibit or facilitate mammography participation. Behavioral antecedents include demographic and physical traits, attitudes, behavioral capabilities, and access to resources (National Cancer Institute, 2007). For example, Latina women are more likely to report safety concerns as a barrier for breast screenings (Schueler, Chu & Smith-Bindman, 2008). However, the ability to speak English is a strong predictor of mammography utilization in Asian American women (Liang, Wang, Chen, Feng, Yi, & Mandelblatt, 2009; Wu, Bancroft & Guthrie, 2005). Tailored client-oriented activities that have produced increases in mammography-related outcomes include client reminders, small media, one-on-one education, and reducing out-of-pocket expenses (Guide to Community Preventive Services, 2009).
Given the declining rates of mammography utilization, the Centers for Disease Control and Prevention (CDC) has called for an expansion of activities that promote breast cancer awareness and screening (2007). A preliminary step in updating and expanding such awareness and screening campaigns is to reassess factors that improve or hinder mammography utilization. Current information about the factors for specific populations will allow for the development of tailored breast cancer awareness and screening programs. The purpose of this study was to revisit the relationship of perceived susceptibility, perceived benefits, perceived barriers, and perceived access to mammogram utilization in low-income women. Low-income women 40 years and older who attended local health centers were purposefully recruited for this study. Recruiting women from a community health center controlled for physician access, a documented predictor of mammography utilization (Schueler et al., 2008; Smith & Hayes, 1992; Valdini & Cargill, 1997). The focus of the study variables was on the attitudinal factors that could be targeted by health education campaigns. Physician and facility access are potential targets for health care policy interventions.
The health belief model has been used extensively to study the attitudinal factors related to mammography utilization. In the core of the model, women who perceive themselves susceptible to disease and perceive negative physical, emotional, and or social outcomes are more likely to participate in a preventive health behavior. Additional behavioral influencers are the perceived benefits and barriers of the target behavior, modifying factors such as demographic characteristics, and cues to act, such as symptoms. Historically, higher perceived susceptibility to breast cancer, higher perceived benefits of screening, and lower perceived barriers to mammography have been associated with higher screening rates (Champion & Skinner, 2003; Fulton et al., 1991; Hyman, Baker, Ephraim, Moadel & Philip, 1994; Janz & Becker, 1984; Paskett et al., 1999; Stein, Fox, Murata & Morisky, 1992; Thomas, Fox, Leake, & Roetzheim 1996; Yarbrough & Braden, 2000). Access to a physician and a physician's recommendation have also been documented as strong predictors of mammography utilization (Fulton et al, 1991; Paskett et al., 1999; Schueler et al. 2008; Smith & Hayes, 1992; Yarbrough & Braden, 2000).
PARTICIPANTS AND SETTING
The target population was low-income women 40 years and older who attended local community health centers in one southwest Ohio city. Seven centers, all located within the city limits, were used to recruit subjects. The health centers reported that the maximum income of individuals or families receiving care was 300% of the federal poverty level (FPL). This level was calculated to be less than $29,400 for one person, and less than $60,000 for a family of four. The principal investigator approached women in the waiting room of the participating local health centers and explained the purpose of the study. If the woman agreed to participate in the study, she was given the questionnaire. All women returned the anonymous questionnaire to the principal investigator. The data were collected during a 60-day period in the summer of 2007. The university's Institutional Review Board for Human Subjects approved the methodological techniques of this study.
The rule of ten was used to estimate sample size in both designing the study and in fitting the logistic regression model. The rule of ten states that the minimum number of subjects equals 10 divided by the smaller proportion of the two outcome categories multiplied by the number of independent variables (van Belle, 2002). The original design included six variables: the four attitudinal variables, education level, and family history. After the descriptive analysis of the data, race and employment status were explored. Half the women were expected to have reported a mammography within the past year; thus, approximately 120 subjects were needed in the sample to insure generalizability of the results. After descriptive analysis and using the smallest proportion of the outcome variable (0.43), up to four variables could be included in the final logistic regression model.
INSTRUMENTATION AND DATA COLLECTION
There were four attitudinal predictor variables of interest in this cross-sectional study: perceived susceptibility, perceived benefits, perceived emotional barriers, and perceived access. All variables were measured via a self-administered questionnaire. The questionnaire was developed from Champion's validated and published questionnaire (Champion, 1999). The perceived benefits scale was edited from Champion's five item scale to three items. The perceived barriers scale was edited from Champion's 11-item scale; the new scale focused on emotional barriers. The perceived access items were edited from Valdini and Cargill (1997). The attitudinal variable items used a 4-point Likert scale of strongly disagree, disagree, agree, and strongly agree (Table 1). The mammogram utilization item asked the number of screening mammograms in the past five years. The dependent variable was recoded to a binary variable: a mammogram within the last year or no mammogram within the last year.
Four demographic variables were measured as possible confounders: educational status, family history of breast cancer, race, and employment status (see Table 2). After descriptive analysis, three of the variables were recoded. Educational status was coded as high school graduate or not a high school graduate. Race was recoded as white, African American or other. Employment was recoded as currently employed or unemployed.
Face validity for the questionnaire was determined through a review by a community cancer prevention nurse specialist and an expert in health behavior theory. The questionnaire was field tested with three women, over age 40, who were seeking services at a local community center that provided food and health services to low-income individuals and families. The women completed two versions of the same questionnaire. All women preferred the same format.
Descriptive statistics as well as measures of association were calculated for all study variables. Variables that had at least a modest correlation, p < 0.15, with mammography utilization were considered for inclusion in the preliminary logistic regression model (Hosmer & Lemeshow, 2000). Rank biserial correlation coefficients were calculated for the ordinal variables, and phi coefficients were calculated for the binary variables (Khamis, 2008). Removal of variables in the final model was done using a backward stepwise likelihood ratio test based on significance (p < .05). Collinearity was analyzed via the correlation matrix of the study variables. The Hosmer and Lemeshow's Test was then analyzed for model goodness of fit. Analysis was performed using SAS Statistics 9.2.
Of the 121 questionnaires collected, 99 questionnaires were used in the analyses. Five women were excluded because they were under age 40 and 17 additional questionnaires were incomplete. The women in the remaining sample were white (58%), African-American (41%), and Asian (1%). Most of the women had graduated high school and/or gone to college (85%). One third of the population (28%) reported being married. The remaining women reported various forms of singleness: divorced, single, separated and or widowed. Slightly more than half of the women (52%) reported a family history of breast cancer (Table 2).
MAMMOGRAPHY UTILIZATION AND BELIEFS
The majority (57%) of the 99 women surveyed reported having had a mammogram in the last year (see Table 2). One-third of the women reported having had a mammogram in the past few years, but less than annually. Only 7% of the women reported never having had a mammogram.
Women reported high perceived susceptibility for breast cancer, 5.79 on a 9-point scale (see Table 1). Perceived benefits of mammograms were also high, 7.06 out of 9. Women reported low perceived emotional barriers (3.00 on a 9-point scale) and low perceived access to getting a mammogram (2.98 on a 9-point scale).
Two variables were included in the preliminary logistic regression model: perceived emotional barriers and perceived access. These variables had at least moderate relationships with mammography utilization (See Table 3). A relationship with mammography utilization was also confirmed by a Wilcoxon two-sample test that compares the median value of the variable for the two levels of mammography utilization. The median values of perceived barriers (p = 0.010) and perceived access (p = 0.006) were statistically significantly higher for mammography utilization within one year.
In the final model, perceived emotional barriers was not a significant predictor of mammography utilization (p = 0.147). Perceived access had a significant relationship to mammogram utilization (p = .011). The odds of having a mammogram within one year decreased by 28% for every unit increase in perceived access barriers, (OR = .716, 95% CI = .553 - .927). The model had acceptable goodness of fit ([chi square] (8) = 10.873, p = .209). There was not collinearity in perceived access and perceived emotional barrier, r = .303, nor was there a significant interaction of the two variables (p = 0.087).
The Healthy People 2010 goal for breast health is that at least 70% of women 40 years and older will have had a mammogram in the past two years. In the present investigation, 57% of our sample reported having had a mammogram in the past year, and 69% reported a mammogram in the past two years. By comparison, US data for 2005 indicate 48.5% of women age 40 years and over living below 100% federal poverty reported having a mammogram within the past two years. As income level increases, so do the reported levels of mammography. About three-quarters of women age 40 years and over with income at least 200% of poverty level reported having a mammogram within the past two years. Women in poverty show a decrease in reported mammography utilization since 1999 (National Center for Health Statistics, 2009).
The present study also found both perceived susceptibility for breast cancer and perceived benefits for mammography were very high: 5.79 on a 9-point scale and 7.06 on 9-point scale. The high level of susceptibility to breast cancer is noteworthy. While not specifically evaluated in this study, one might hypothesize that the high perceived risk levels may be attributed to previous breast cancer awareness campaigns, as well as heightened personal exposure to breast cancer. Breast cancer awareness campaigns have grown exponentially in the past two and a half decades. The magnitude of these awareness campaigns is demonstrated via the growth of the Susan G. Komen breast cancer awareness walks, Race for the Cure[TM]. The first Race for the Cure[TM] was held in 1983 with 800 walkers. In 2008, over 1.5 million people participated in over 100 awareness walks (Susan G. Komen Race for the Cure Foundation, 2008). Furthermore, when these campaigns were emerging, a woman's lifetime probability of being diagnosed with breast cancer was 1 in 10, whereas the current probability of being diagnosed with breast cancer is 1 in 8 (National Cancer Institute, 2006). Thus, women are more likely to have a family history or friend who was diagnosed with breast cancer. In the present study, half the women reported a family history of this disease.
Barriers are consistently reported as a predictor of mammograms and other preventative health screenings (Janz & Becker, 1984; Fulton et al., 1991; Schueler et al. 2008; Yarbrough & Braden, 2000). For greater practice implications, barriers were purposefully measured as two variables: perceived emotional barriers and perceived access barriers. Perceived emotional barriers (embarrassment, pain, and fear) in this study were low and not a significant predictor of mammography utilization (p = 0.147). The low levels of reported emotional barriers are noteworthy, and again, may represent a shift in women's attitudes toward mammography. Previous studies found embarrassment, fear/concern of safety, pain/comfort as predictors of mammography utilization (Fulton et al., 1991; Schueler et al. 2008; Yarbrough & Braden, 2000). Creating a narrowly defined scale of barriers, as well as not testing single questionnaire items, might also explain the absence of significance in the present study.
Perceived access was a significant predictor of mammogram utilization (p < 0.05). The higher the reported perceived access barriers, the less likely a woman was to have had a mammogram in the past year. Descriptively, one-third of the women in our investigation reported cost as an access issue. Access factors, particularly cost, have consistently been reported as a mammography utilization barrier by other researchers. In two separate literature reviews, from years 1988 to 2004 and from years 1990 to 1999, both researchers concluded cost as a strong predictor of mammogram frequency (Schueler et al. 2008; Yarbrough & Braden, 2000).
The cost of a mammogram has been an intervention target for the past 25 years. The Breast and Cervical Cancer Early Detection Program was established by the Centers for Disease Control and Prevention in 1991 to provide breast and cervical cancer screenings to low-income, uninsured women (U.S. Department of Health and Human Services, 2005). Between the years of 2002-2006, 1.77 million mammograms were funded. In Ohio, during the same time period, 39,702 mammograms were provided (CDC, 2008). Within this study, all seven health centers recruited for this study had programs to provide vouchers for screening and diagnostic mammograms.
The perception of cost and access appears to be emerging as a current mediating barrier. In this study, all seven health centers recruited for this study had programs to provide vouchers for screening and diagnostic mammograms. McAlearney et al. (2005) also found that women erroneously reported cost of the mammogram as a barrier. In their large randomized controlled study evaluating the impact of a health education intervention to improve mammography in rural low-income women, 53% of the women who had not received a mammogram in the past two years stated cost as the barrier; 40% of those women had an erroneous perception of their insurance coverage. In a follow-up analysis, race, income, and education level were not related to cost as a barrier (2007).
Surprisingly, only 22% of the women surveyed reported that they had received a physician recommendation to get a mammogram. We found this percentage much lower than expected, given that the women were recruited from local community health centers. For women of lower SES, a physician's recommendation is considered to be an important facilitator of mammography behavior (Fulton et al, 1991; McDonald et al. 1999). Fifteen years earlier, Fulton reported that less than half of the women reported receiving a recommendation for a mammography and that the provider's recommendation had the greatest independent effect on predicting screening status, compared with other health belief model constructs (1991). More recently, after reviewing 221 mammography utilization factor studies, Schueler concluded that a provider's recommendation was "highly predictive of not obtaining mammography (2008, p. 1477)".
Race, family history, employment, and education level were not significant predictors, nor confounders, with mammography utilization. Due to the sample characteristics, race was narrowly evaluated as African American versus White. Nationally, current mammography utilization in these two groups appears to be similar; however, differences are consistently noted for Latina and Asian Americans (National Center for Health Statistics, 2009). We were unable to test for these differences. Family history and education level findings should be interpreted with caution. Previous findings have consistently, and over time, found education level (completed high school verses less than high school) as a predictor of mammography utilization (Fulton et al., 1991; Raham, Dignan, & Shelton, 2005; Schueler et al. 2008). Previous findings also suggest that family history may have an interactive effect with age, and age was not a variable of interest in this study (Raham, Dignan, & Shelton, 2005; Schueler et al. 2008).
The convenience sample, self-selection for inclusion in the study, the use of self-report data, and the method in which select variables were measured are notable limitations of this study. The convenience sample was recruited from women attending local health centers. Thus, participants had access to a health care provider, but whether the woman had a regular physician or care provider was not assessed. Furthermore, women attending a local health center, and who agreed to complete the questionnaire, may have had more positive beliefs about health and health behaviors than low-income women recruited through other means. Women self-reported mammography utilization and time since last mammogram, and thus, these values may have been over-reported. Women also reported family history of breast cancer and thus, this method captured the perception of family history, not actual epidemiological risk. Low-income status was assumed by attendance at the local health center. Economic status operationalized by actual income level, type of assistance, or other measures might change the findings, particularly for the relationship of cofounders, education level and employment status.
IMPLICATIONS FOR HEALTH EDUCATION PRACTICE
Modern breast cancer campaigns originated in the 1970's and focused on susceptibility of breast cancer and age-related screening guidelines (Lerner, 2001). Results from this study appear to support the need to update efforts to improve mammography utilization among low-income women who attend health centers. Women in this study perceived high levels of risk for breast cancer, high levels of reported benefits, and low levels of emotional barriers. Future breast cancer awareness and education campaigns should target women's perceived access to screening services. Campaigns should communicate the availability of local resources for screening mammograms. Given the low levels of reported health care provider recommendations, campaigns may also encourage women to initiate the conversation with their health care provider.
Health care providers should also be aggressively targeted for future health education programs. Practitioners should talk to low-income women about screening mammograms, as research indicates that women are receptive to this message. Surprisingly, only a fifth of the women in this study reported receiving a physician recommendation for a screening mammogram. The patient/provider conversation should focus not on issues of risk and fear, but provide women of low-income status specific information on access to local resources.
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Marium Husain, MPH Marietta A. Orlowski, PhD Karen Wonders, PhD Jeffrey S. Hallam, PhD
Marium Husain, MPH, is a student in the School of Medicine, The Ohio State University. Marietta A. Orlowski, PhD, is affiliated with the Department of Health, Physical Education & Recreation, Wright State University. Karen Wonders, PhD, is affiliated with the Department of Health, Physical Education & Recreation, Wright State University. Jeffrey S. Hallam, PhD, Center for Health Behavior Research, The University of Mississippi. Please address all correspondence to Marietta A. Orlowski, PhD, Wright State University, 312 Nutter Center, 3640 Colonel Glenn Highway, Dayton, OH 45435. Phone: (937) 775-4023. Fax: (937) 775-4252. E-mail: firstname.lastname@example.org.
Table 1: Operational Definitions, Means, and Standard Deviations of Perceived Susceptibility, Perceived Benefits, Perceived Emotional Barriers, Perceived Access, and Mammography Utilization Variable Operational Possible Mean (SD) Definition Min/Max Values Perceived Sum of three 0-9 5.79 [+ or -] 1.62 Susceptibility items on diagnoses related to age, lifetime, and others. The higher the score, the higher the perceived susceptibility. Perceived Sum of three 0-9 7.06 [+ or -] 1.63 Benefits items on lump detection, treatment experience, and chances of survival. The higher the score, the higher the perceived benefits. Perceived Sum of three 0-9 3.00 [+ or -] 1.80 Barriers items on embarrassment, pain, and fear. The higher the score, the higher the perceived emotional barriers. Perceived Sum of three 0-9 2.98 [+ or -] 1.69 Access items on physician recommendation, cost, and transportation. The higher the score, the higher the access barriers. Mammogram Self-report of 0-1 N/A utilization mammogram utilization, within the last year, or not within the last year. Table 2: Characteristics of the Sample Characteristic Percent (%) Race White 58 African-American 41 Asian 1 Hispanic 0 Other 0 Education Status Never Went to High School 3 Went to High School, 12 but didn't graduate Graduated High School 45 Went to College 40 Employment Status Unemployed 68 Employed Full-Time 22 Employed Part-Time 10 Marital Status Single (never married) 23 Married 28 Divorced 29 Separated 8 Widowed 12 Family History of Breast Cancer Yes 52 No 48 Mammogram Utilization Last Year 57 2 years ago 12 3-4 years ago 15 5 or more years ago 9 Never 7 Table 3: Measures of Association Between Predictor Variables and Mammography Utilization Rank biserial correlation P-value coefficient Perceived Susceptibility 0.002 0.9843 Perceived Benefits 0.120 0.2341 Perceived Barriers -0.301 * 0.0004 Perceived Access -0.326 * 0.0007 Education Status -0.072 0.4771 phi coefficient P-value Family History of Breast Cancer 0.047 0.6404 Race -0.000 0.9966 Employment status -0.136 0.1820 * p < 0.05
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