Factors affecting the use of screening mammography among African ...

Vol. 1, 75-82. November/December 1991 Cancer Epidemiology, Biomarkers & Prevention 75
Factors Affecting the Use of Screening Mammography among
African American Women1
Joan R. Bloom,2 Kyle Grazier, Felicia Hodge, and
William A. Hayes
University of California School of Public Health, Berkeley, California
94720 [J. R. B.. K. G., F. H.], and Northern California Cancer Center,
Belmont, California 94002 [1. R. B., F. H., W. A. H.]
Abstract
Our objective was to determine the influence of health
consciousness in the utilization of mammography in
asymptomatic African American women. The sample
consisted of 670 women who participated in a
household interview in two cities. Logistic regression
was used to determine the independent effects of
health consciousness, holding constant other factors
believed to be related to mammography utilization.
Health insurance, income below the poverty line, and
an annual physical were not significant predictors. The
single most important predidor of having a
mammogram was the regular practice of breast selfexamination;
the group of women who practiced selfexamination
was almost twice as likely to have a
mammogram.
Introduction
Mammography as an effective means of reducing cancer
mortality through early detection is well accepted in the
medical community, particularly for women oven the age
of 50 (1-5). National studies indicate that in African
American women oven 40, 83% had heard of the procedune,
and 59% had had a mammognam, which is a
doubling of estimates made as recently as 1987 (6, 7).
These national figures differ from those of regional studies
of special populations of Hispanic and African Amenican
women (8, 9) where the rates were half as high.
Recent regional estimates of these particular populations
indicate less dramatic improvements, perhaps reflecting
greaten numbers of poorer women than may have been
reached in telephone surveys. These recent surveys also
indicate that far fewer women have had more than one
mammogram (35%) (7).
Several explanations may account for the discrepancy
between what seems desirable for cancer control
and current practice. One is cost, which can range from
$40.00 to $200.00. Insurance usually does not pay for
Received 2/13/91.
, Supported by Grant CA40651 from the National Cancer Institute.
2 To whom requests for reprints should be addressed, at University of
California School of Public Health, Department of Social and Administralive
Health Sciences, Berkeley, CA 94720.
3 The abbreviation used is: HMO, health maintenance organization.
preventive services because their demand is predictable,
although several states now mandate that screening
mammography be covered, and Medicare coverage of
mammography is now in effect(10, 11).
A second explanation of the low utilization of mammognaphy
is the reluctance of the physician to recommend
it. A decade ago the radiological risk posed by
mammography was well publicized; this frightened
women away from accepting physicians’ advice that they
be screened and discouraged physicians from recommending
mammography to asymptomatic women (12).
While current technology relies on lower doses of nadiation,
many women still shy away from radiation exposure
and, in the absence of symptoms, their physicians may
be reluctant to recommend it. A recent survey of primarycane
physicians by the American Cancer Society indicates
that the percentage of physicians reporting that they
completely agree with American Cancer Society guidelines
regarding mammography has increased in the past
5 years from 41% to 72%. Only 37% indicate that they
actually follow these guidelines. Cost (39%) and radiation
exposure (25%) were the previous major sources of
disagreement, but physicians now cite disagreement with
aspects of the guidelines. Some physicians still feel that
35-39 is too young for a baseline mammogram (35%)
and that annual screening after age 50 is too frequent
(29%). Little disagreement was expressed with the biennial
screening recommendation for ages 40-50 (13).
Since patients rarely reject their physicians’ recommendation
to have a mammognam, physician factors may play
a major role (14).
A third explanation is based on women’s health
consciousness. Health consciousness is exhibited in behaviors
and in expressions of beliefs. Performance of
other health behaviors, including breast self-examination,
is positively associated with mammography use (15). In
this study, the use of mammography was also associated
with increased knowledge of cancer detection and treatment
procedures. Within this model, health-conscious
women are assumed to be more aware of the risk of
breast cancer and more likely to request mammography
and other cancer screening tests. Closely related to consciousness
about health is the perception of an individual’s
own risk of cancer, specifically personal vulnenability
to breast cancer. One review found that increasing
self-estimates of the likelihood of breast cancer are positively
associated with mammography use (16). Perceptions
of vulnerability have also been found to influence
help-seeking, the purchase of health insurance, and other
health-related behavior in multiple plan choice situations
(17-19).
The purpose of the current study is to evaluate the
role of health consciousness in the light of two alternative
explanations for the utilization of mammography by
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Copyright © 1991 American Association for Cancer Research

76 Use of Screening Mammography among African American Women
asymptomatic women. Of particular interest is the application
of this model of screening behavior to African
American women.
Methods
Background
This study is part of an intervention program to decrease
the avoidable mortality from cancer among African Amenican
residents of an urban community in northern California.
Prior to the initiation of the intervention program,
a household survey was conducted in 1986 in two communities
with large African American populations (9).
Survey
Design
A two-stage probability sample was drawn (census blocks
and households within blocks). One hundred randomly
selected blocks were selected from each of the two
communities (San Francisco and Oakland, California).
Using the Kish (20) procedure, randomly selected mdividuals
from the sampled households participated in a
face-to-face interview. If the individuals were not at
home during the first visit, intensive follow-up efforts
were made to interview the selected respondents. Individuals
20 years old or older were eligible to participate.
The response rate was 67.6% in one community, resulting
in 568 completed interviews, and 69.1% in the second
community, resulting in 569 completed interviews.
To determine whether a response bias existed, analyses
were completed to determine the nepresentativeness of
respondents from different geographical areas in the
target community. Data from the 1980 census were
compared by age, income adjusted to 1986 dollars, and
gender. No statistically significant differences were
found. Survey findings from the entire sample have already
been reported (9, 21).
Analysis
First, descriptive analyses were performed to examine
the bivaniate relationships between ever having had a
mammogram and the socioeconomic, attitudinal, and
provider factors of the model. Health consciousness was
then examined using logistic regression. The conceptual
groups of variables were forced into the model in steps.
Only women in the “at risk” sample, over age 35, who
had even had a mammognam (n = 418) were included in
this analysis. This is consistent with the American Cancer
Society guidelines, and it recognizes local medical community
practice, which dictates that screening should
begin at this earlier age because younger African Amencan
women are at higher risk for breast cancer and have
a higher mortality rate (22). Finally, in order to evaluate
the importance of health consciousness for other screening
tests for women, additional analyses were performed
comparing mammography to breast physical exams and
Pap test for cervical cancer. To be consistent with the
screening guidelines for a physical breast exam, all the
women over age 20 were included in this analysis.
Measurement
The dependent variable is even having had a mammogram
for screening purposes. The cumulative nature of
this variable recognizes the role of increased age in the
likelihood of its occurrence; as such, age is considered a
control rather than a predictive variable. Other research
on screening mammography has used such a measure,
as well as screening within the past year (23). Preliminary
data from other portions of this study revealed that of
those who reported having had a mammognam, the test
had occurred within the previous 5 years. However, no
specific questions were asked about the pattern of use
of mammography prior to the study.
The independent variables for these analyses included
patient behaviors and attitudes and physician
behaviors. Respondents’ general health attitudes, beliefs,
and health practices; attitudes and beliefs about cancer;
insurance coverage; and sociodemognaphic descriptors
were collected. Whether the provider recommended
mammography and other screening tests during a physical
exam on at any other time was also ascertained.
Measures used in this analysis are described below.
Health Consciousness. Seven questions explored the
individual’s health consciousness. The first action-based
variable measured the respondent’s frequency of breast
self-examination: infrequently (

Cancer Epidemiology, Biomarkers & Prevention 77
Vulnerability. The respondents’ perceived vulnenability
to cancer was assessed on a 4-point Likert-type
scale. Respondents were asked how likely it was that
they would get cancer. Individuals were “very” or “somewhat
likely” to get cancer (coded as 1) or “not too likely”
on “not likely at all” (coded as 0).
Insurance. The survey probed for the existence,
type, and level of health insurance within the family. Of
importance to the study was the coverage for screening
tests, particularly those used for preventive purposes in
asymptomatic individuals. The coverage measure was
tnichotomized: belonging to a health maintenance organization
which covered all of the cost anchored one end
of the scale; having an indemnity or service benefit plan
on federal entitlements (Medicare on Medicaid) which
covered some of the cost was the intermediate value;
and having no health insurance which covered the cost
anchored the other end of the scale.
Poverty. To enhance understanding of the cost implications
of having screening tests performed, a measure
of poverty was also developed. It combines questions on
household size and income. The measure was tnichotomized:
whether the family income is more than 200% of
the poverty line (coded as 3), from 100 to 200% of the
poverty line (coded as 2), or less than 100% of the
poverty line (coded as 1) (24).
Early Detection Tests. The respondents were also
asked whether they had received any early cancer detection
tests as part of a routine physical examination on
due to symptoms. The tests assessed in this analysis
included the cervical Pap smear, the clinical breast exam,
and the mammognam.
Willingness to Be Tested. Women were also asked
whether they would have a mammognam if it were necommended,
the reasons why they would not have one,
and how safe they believed a mammogram to be. These
data are used in the descriptive analysis.
Sociodemographic Characteristics of the Respondent.
Chronological age, years of education, and several
dimensions of access to cane were also ascertained.
Physician Behavior. To determine whether physician
behavior influenced the decision, respondents were
asked whether the provider recommended a mammogram
(or “breast X-ray”) during the physical exam on at
another time, either due to symptoms or as a screening
test. Respondents were also asked whether they had had
the mammognam. The latter question was used by itself
for descriptive purposes. In calculating rates of women
who had even had a mammognam, only women who
were asymptomatic when they had a mammognam were
included. This measure was also developed for the dependent
measure in the logistic regression model.
Results
The data reported here are for the total sample of African
American female adults from the two communities (n =
670). Almost 40% of the respondents were under 35,
30% of the respondents were between 35 and 49, and
the remainder of the respondents were 50 and older.
The sample was generally well educated: 8% had had 8
years of education or less, 1 6% had had some high school
education, 36% were high school graduates, 30% had
had some college, and 9% reported they had had four
Table 1 Number and percentage of women 35 and aver who have
ever had a mammogram (saciodemagraphic variables in the model)
In = 415)’
Predictor
Insurance
Age
None
Some
HMO
35-44yr
45-54yr
55-64 yr
>65 yr
Response
Poverty” 100%
101%1a200%
>200%
Education $22,400 for a family of four) (24). These
data are representative of the area based on the 1980
census.
As seen in Table 1, women belonging to an HMO,
and thus having coverage, are only slightly more likely to
report even having had a mammogram than women who
have some health insurance on none at all.
It was also reasoned that perhaps, due to costs,
physicians would not recommend mammography to
women they did not believe could afford it. This would
be evidenced in the study in lower rates of necommendations
for mammography to women at or below the
poverty line. The data do not support this, as women
whose incomes are at or below the poverty line are just
as likely to report ever having had a mammogram as
those whose income is more than twice the poverty line.
Finally, cost is cited by only two women as the reason
they did not follow their physicians’ mammography
recommendations.
With regard to physician behavior, only 31.6% of
women over 35 stated that their physician had ever
recommended they have a mammognam. Of this group,
87% indicated that they had actually had the mammogram.
When asked if they would have a mammognam (on
another one) if the doctor recommended it, only 30
women said they possibly or definitely would not. Rcasons
given for not having a (another) mammogram were
cost (

78 Use of Screening Mammography among African American Women
Table 2 Number and percentage of women 35 and o yen who have ever ha d a mammogram (at titude and behavioral variables in the model) In = 415)
Ever had a mammogram
Predictor
Response
Number of
responses
In = 1 1 5)
Percentage
responses
of
x2
Preventive behavior
Hasn’t eaten something because unhealthy
Eat food good for you
Exercised because healthy
If I take care of myself I can avoid getting sick
Agree
Disagree
Agree
Disagree
Agree
Disagree
Agree
Disagree
Former smoker Yes
No
Breast self-exam Frequent
Regular
Rarely
Annual physical exam Yes
No
56
59
90
25
56
59
102
12
25
90
46
43
26
78
36
30. 1 1 . 1 3
25.4
27.8 0.05
26.6
29.3 0.58
26.0
28.4 1.50
20.7
34.3 2.01
26.1
37.1 9.25
25.9
20.3
41.5 5.21
21.3
0.29
0.82
0.45
0.22
0.45
0.01
0.02
Treatment efficacy’
Surgery does mare good than harm More Good
More Harm
Chemotherapy does mare good than harm More Good
More Harm
Radiation therapy does more good than harm More Good
More Harm
60
48
48
54
42
60
29.6 2.67
28.0
23.4 3.41
31.2
25.2 0.84
29.4
0.26
0.18
0.66
Negative attitudes’
Getting cancer is a death sentence Agree
73
28.2 0.16
0.69
Disagree
42
26.4
If you had cancer, you would rather not know Agree 25 24.5 0.72 0.40
about it Disagree 90 28.9
Surgery can expose cancer to the air and cause Agree 94 28.2 0.49 0.49
it to spread Disagree 20 24.4
Getting treated for cancer is often worse than Agree 77 27.1 0.01 0.93
the disease Disagree 35 27.6
Perceived vulnerability Yes
No
a Middle category excluded from table for clarity.
35
79
19.7 10.04
33.8
0.002
b x2 based an 3 categories and 2 degrees of freedom.
‘ Six-paint Likert scale dichotomized for this analysis.
directly questioned about the safety of mammography,
28.6% considered it a little risky and less than 5% considered
it very risky. Even though almost one-third of the
sample thought a mammognam was risky, only 1% said
they would not have a (another) mammognam due to
radiation risk. In other words, women are likely to have
a mammogram if the physician recommends it.
As shown in Table 2, women who have an annual
physical exam (41.5%, compared to 21.3% who don’t)
and those who frequently or regularly examine their
breasts for lumps (breast self-exam) are more likely
(31.0% compared to 20.3%) to have a mammognam. In
addition, being olden is strongly related to ever having
had a mammognam (mean, 55.78 versus 49.64; t = 4.37;
P< 0.0001).
Multivariate Analysis. To fully evaluate the factors affecting
whether a woman had even had a screening test
for breast cancer, logistic regression was also used. The
first model addresses only mammography and thus includes
only women aged 35 and olden (Table 3). The
second model expands the sample to include women 20
years old and older. Even though women between 20
and 35 are unlikely to have a screening mammognam,
this model provides the necessary comparison group to
women described below who report having a clinical
breast exam and/on a cervical Pap smear (Table 5).
To isolate the importance of health consciousness,
each group of variables was forced into the model using
a forward stepwise program with the demographic vanables
first, followed by the fiscal factors, attitudes toward
treatment, and attitudes toward cancer. The indicators of
a preventive orientation were put in last. Several interactions
were tested, an age-squared term was added to
examine the age relationship, and the effect of potential
correlations among variables was carefully examined.
Age/attitude interactions (7 items) were added to
each model and then eliminated. The contribution of the
interaction terms was not statistically significant for any
of the independent variables; therefore, the remainder
ofthe analysis is based on the assumption that interaction
effects are absent and a simple additive (on the logistic
scale) model was used.
Age squared was added to each model to explore
the improvement in fit of the models. As expected due
to the nature of the dependent variable, the model x2
was improved by its addition (65.96, df= 19 versus 46.65,
dl = 18); the coefficient on the insurance variable also
improved in significance (from P = 0.07 to P = 0.03). The
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Cancer Epidemiology, Biomarkers & Prevention 79
Table 3 Effect of health consciousness on rouli ne screening by mam mography fo r women 35 and older’ In = 380)”
-
Predictor
. -
Coefficient SE P
Logoddṣ
ratio
95% .
confidence
.
interval
Preventive behavior
Hasn’t eaten something because unhealthy
Eat food good for you
Exercised because healthy
If I take care of myself I can avoid getting sick
Former smoker
Breast self-exam’
Annual physical exam
-0.151
0.058
0.027
-0.534
0.401
0.396
0.435
0.102
0.1 13
0.093
0.41 5
0.313
0.165
0.265
0.14
0.61
0.77
0.20
0.20
0.02
0.10
0.85
1.06
0.97
0.57
1.49
1.49
1.54
(0.82-1.03)
(0.80-1.25)
(0.83-1.20)
(0.44-2.26)
(0.54-1.85)
(0.72-1.38)
(0.59-1.68)
Treatment efficacy
Surgery does mare good than harm
Chemotherapy does more goad than harm
Radiation therapy does more good than harm
-0.058
0.095
0.040
0.144
0.156
0.158
0.68
0.54
0.80
0.94
1.09
0.96
(0.75-1.33)
(0.74-1.36)
(0.74-1.36)
Negative
altitude
Getting cancer is a death sentence
Ifyou had cancer, you would rather not know about it
Surgery can expose cancer to the air and cause it to spread
Getting treated for cancer is often worse than the disease
-0.010
0.019
-0.001
-0.004
0.069
0.066
0.080
0.073
0.89
0.78
0.99
0.96
0.99
1.02
0.99
1.00
(0.87-1.14)
(0.88-1.14)
(0.85-1.17)
(0.87-1.15)
Perceived vulnerability -0.441 0.265 0.10 0.64 (0.59-1.68)
Insurance” 0.394 0.220 0.07 1.48 (0.65-1.54)
Age#{176} 0.047 0.012 0.00 1.05 10.98-1.02)
Education 0.063 0.054 0.25 1.07 (0.90-1.11)
a -2 log (likelihood ratio) = 395.43; P = 0.002.
b Listwise deletion accounts for 35 missing observations.
‘ Odds ratio from never performing self-exam to frequently performing self-exam is 2.20.
dOdds ratio from no insurance to HMO is 2.199.
#{176} Odds ratio age 35 compared to 65 is 4.096.
other significant variables in the model were not affected.
Since the exact fit of age in the model is unrelated to the
key variables (i.e., preventive behaviors) and since the
interpretability of the model is decreased, the age rather
than the age-squared term was used in the final model.
With regard to financial barriers to cane, having
health insurance to pay for screening tests was expected
to predict increased use of screening tests, especially
mammography. Closeness to the poverty line (a combination
of income and family size) was predicted to have
an independent, albeit negative, effect on having a mammognam.
Even though the correlation between the fiscal
factors (health insurance and the poverty index) was
sufficiently low to make them nonmulticolinean, because
of their potential importance they were investigated funthen.
The model was nun with both variables in the model,
with each removed separately, and with both removed.
Because there were incomplete data on income, the
poverty index could not be constructed for 48 subjects,
although 38 remained in the multivaniate model. Thus,
the models excluding the poverty index include more
subjects. When insurance was removed from the model,
the significance level of the other variables remained
unchanged; the likelihood ratio between the model including
insurance and excluding it is not significant (x2 =
1 .81 ; df = 1; P > 0.05). The exclusion of poverty level is
also not significant (x2 = 0.03; dl = 1; P > 0.05). Since
the poverty index is unimportant in the model and because
of the missing data, the final model was nun without
the poverty index, resulting in a sample size of 380.
To determine whether each variable group made a
significant contribution to the full model, the variable
groups were removed from the model separately and
contrasted with the full model using the likelihood ratio
test. Women’s health consciousness was predicted to be
independently related to having a mammognam (10, 11).
Having a mammognam not only requires a physician- or
patient-initiated request, but also the individual’s adherence
to the recommendation, which takes place at a
different time and location. The x2 resulting from the
likelihood ratio test between the full model and the
model without the preventive behaviors was 14.17 (df=
7; P < 0.05). When both the insurance variable and
poverty variables were removed, none of the individual
variables in the model was affected, and the likelihood
ratio for the model was not statistically significant (x2 =
1 .79; dl = 2; P > 0.05). When the contrast was made
between the full model and the model resulting from the
removal of the negative attitudes toward cancer, the
likelihood ratio test was not significant (x2 = 3.51; dl =
4; P > 0.05). Finally, the test was not significant when the
model eliminating attitudes toward treatment efficacy
was compared to the full model (x2 = 0.68; dl = 3; P>
0.05).
Logistic Model for Mammography. The multiple logistic
model indicates that five factors predict having a mammogram:
(a) Olden women are more likely to have ever
had a mammogram (P < 0.00001). Since the outcome
measure is “ever had” a mammogram, it was expected
that age, which was highly significant, would be positively
related to mammography use. (b) Women who examine
their breasts are significantly more likely to have a screening
mammogram (P < 0.02). This was the only indicator
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80 Use ofscreeningMarnmography among African American Women
Table 4 Probability’ of ever having had a mammognam as a function of
having a routine physical exam, having health insurance, or practicing
breast self-exam for each age group (n = 380)”
Age
40 50 60 70
Insurance
None 0.05 0.08 0.13 0.19
Some 0.08 0.12 0.18 0.26
HMO 0.11 0.17 0.25 0.34
Had a physical exam in the last year
Yes 0.08 0.13 0.19 0.27
No 0.05 0.08 0.13 0.19
Perceived vulnerability to cancer
Yes 0.04 0.06 0.09 0.13
No 0.05 0.08 0.13 0.19
Performs breast self-exam
Never 0.05 0.08 0.13 0.19
Regularly 0.08 0.12 0.18 0.26
More frequently 0.1 1 0.17 0.25 0.35
Practices regular breast self-exam’
No health insurance 0.08 0.12 0.18 0.26
Some health insurance 0.11 0.17 0.25 0.34
HMO member 0.16 0.23 0.33 0.44
a Based on estimated values from Table 3.
b Thirty-five missing observations due to listwise deletion in logistic regressian
(Table 3).
‘ For this analysis breast self-exam frequency = 1.
of preventive behavior that was significant. Three vanables
were marginally significant. (c) Those with more
insurance coverage are more likely to get a mammognam
(P < 0.07). (d) Women who believe that they are more
likely to get cancer are less likely to have a screening
mammognam (P < 0.10). (e) Having an annual physical
exam also improves the chance of having a mammognam
(P < 0.10). The same relationships were reflected for the
odds ratios presented in the table.
The probabilities of having a mammognam as a function
of several key attributes estimated from the model
were also calculated. The probability of having a mammognam,
given increasing age, presence of health insunance,
perceived vulnerability to cancer, having a routine
physical exam, and self-examination of one’s breasts, is
presented in Table 4.
Having insurance which covers the cost of a mammognam
increases the probability of having a mammogram
by 2-fold oven no insurance. A 70-year-old woman
with HMO coverage has a 33% chance of having a
mammogram, whereas a 40-year-old woman with no
health insurance has only a 5% chance.
Surprisingly, perceiving oneself to be vulnerable to
cancer actually decreases the probability of having a
mammogram. The differences here, however, are less
marked at the earlier ages.
Finally, there is slightly more than a 2-fold increase
in the probability that an individual who tries to examine
her breasts will have a mammognam. Practicing breast
self-exam regularly is as important as having health insurance
on seeing a physician for a yearly physical exam.
A combination of practicing breast self-exam regularly
and having health insurance doubles the probability of
having a mammognam (the intermediate level, examining
one’s breasts regularly, rather than the highest level of
the variable is used in this calculation). The strong effect
of age is seen in each row of the table, increasing the
probability up to 3-fold of ever having had a
mammognam.
Logistic Model of Screening Tests. Expanding the logistic
analysis to include Pap tests and clinical breast exams
indicated that having a routine Pap test in the past year
(Table 5) is predicted by having a routine annual physical
exam, being younger, and having no health insurance.
Having a routine clinical breast examination is also predicted
by having a routine physical exam and examining
one’s breasts for lumps. Years of education and age (less
so) are also predictors. In addition, disagreeing with the
attitude that “if you had cancer, you would rather not
know about it” is positively related to getting an exam.
This final analysis compares the predictors of ever
having had a routine mammogram with clinical breast
exams and Pap tests. Both age and education are positively
and significantly related to having had a mammogram.
In addition, one measure of a preventive onientation
(practicing breast self-examination) also predicts who
will have an exam. In contrast to the other screening
tests, seeing the physician for a routine physical exam is
not related to having had a mammogram.
Discussion
These analyses allowed commonly held explanations of
the utilization of mammography to be explored singularly
and with regard to the role of being health conscious.
One of the most widely accepted beliefs is that the cost
of a mammognam prohibits women from getting one. If
this explanation is valid, then women who have health
insurance should be more likely to receive a mammogram.
This study shows that having health insurance that
covers screening services is strongly associated with,
although it does not by itself predict, mammography.
The physician may also play a significant role in
determining who gets a mammogram. It is plausible that
the physician screens female patients for their ability to
pay for mammography. Thus, if a woman is not believed
to be able to afford a mammognam and/on is uninsured,
the test is not recommended unless she is symptomatic.
If this is true, one might expect that women at on below
the poverty line would be less likely to have undergone
mammography. However, as indicated, the poverty indcx
variable does not have a strong effect on the probability
of having a mammogram.
Finally, the model posits that women who are
“health conscious” are more likely to have a mammogram
than are women in general. As indicated in Table 3, only
one of the seven indicators of health-conscious behavions,
examining one’s breasts, is significantly related to
having a mammognam. Women who routinely practice
breast self-exam are twice as likely to get a mammogram.
Examining one’s breasts increases the probability of haying
a mammognam to an extent equivalent to that of full
insurance coverage and more than that of undergoing a
physical exam (Table 4).
Another explanation, not examined in the logistic
regression, is the physician’s reluctance to recommend
mammography. The descriptive data from this study support
this explanation. The data suggest that mammography
is not widely recommended to asymptomatic
women. Only 31.6% of women over 35 and 38.8% of
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Copyright © 1991 American Association for Cancer Research

cancer Epidemiology, Biomarkers & Prevention 81
Table 5 Effect of preventive behavior, attitudes regarding canc er, and sociademographic fa ctars an routine screening fa r women 20 years old and
older In = 604)’
Predictor
Coefficient
Mammography
SE
Caefficient
Breast
exam
SE
Coefficient
Pap
smear
SE
Preventive
behavior
Hasn’t eaten something because unhealthy
Eat food good for you
Exercised because healthy
If I take care of myself I can avoid getting sick
Former smoker
Breast self-exam
Annual physical exam
-0.1 12 0.095
0.040 0.106
0.014 0.088
-0.455 0.395
0.461 0.291
0.447 0. 1 56b
0.307 0.247
-0.087 0.073
0.030 0.078
0.044 0.067
0.371 0.270
-0.207 0.233
0.253 0. 1 1 7’
0.872 0,180d
-0.061 0.076
-0.006 0.081
-0.046 0.070
-0.475 0.274
0.078 0.243
0. 1 1 8 0.122
0.860 0190d
Treatment efficacy
Surgery does mare good than harm
Chemotherapy does more goad than harm
Radiation therapy does more goad than harm
-0.126 0.135
-0.018 0.143
-0.031 0.144
0.032 -0.101
0.086 0.105
-0.149 0.106
-0.091 0.106
0.069 0.110
0.034 0.111
Negative attitudes
Getting cancer is a death sentence
If you had cancer, you would rather not know about it
Surgery can expose cancer to the air and cause it to spread
Getting treated for cancer is often worse than the disease
-0.022 0.065
-0.008 0.063
-0.055 0.074
-0.036 0.069
0.045 0.048
-0. 1 55 0,049b
-0.014 0.052
-0.067 0.051
0.063 0.050
-0.069 0.050
0.064 0.054
-0.027 0.053
Perceived vulnerability -0.385 0.246 -0.021 0.178 -0.059 0.187
Insurance 0.317 0.206 0.134 0.143 -0.303 0.149’
Age 0.063 0,009” 0.01 1 0.006 -0.055 0,007”
Education 0.087 0.051’ 0.112 0,042” 0.015 0.44
-2 log (likelihood ratio)
47403d 76363d 71457d
a Sixty-six observations missing due to Iistwise deletion.
bp

82 Use of Screening MammographyamongAfrican American Women
self-exam precedes and is instrumental to having a mammogram.
Before such a conclusion is warranted, longitudinal
data would be necessary to test for a causal
relationship.
Conclusion. For the majority of women, cancer control
efforts for this decade are directed toward encouraging
rescreening. However, since so few minority women
have heeded the message to get a mammognam for the
first time, efforts will have to be made to increase the
number of women who get a first mammogram as well
as to encourage repeat screening.
Acknowledgments
Appreciation is extended to Dr. Steve Selvin for his assistance on the
analytic techniques and to Drs. S. Leonard Syme and Patrick Romano for
their critical review of an earlier draft. The technical assistance of Joan
Chamberlain is gratefully acknowledged.
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