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ORIGINAL ARTICLE
Culture, Emotion, and Cancer Screening: an Integrative
Framework for Investigating Health Behavior
Patricia M. Flynn, Ph.D., M.P.H. &
Hector Betancourt, Ph.D. &Sarah R. Ormseth, M.A.
#The Society of Behavioral Medicine 2011
Abstract
Background Although health disparity research has inves-
tigated social structural, cultural, or psychological factors,
the interrelations among these factors deserve greater
attention.
Purpose This study aims to examine cancer screening
emotions and their relations to screening fatalism as
determinants of breast cancer screening among women
from diverse socioeconomic and ethnic backgrounds.
Methods An integrative conceptual framework was used to
test the multivariate relations among socioeconomic status, age,
screening fatalism, screening emotions, and clinical breast
exam compliance among 281 Latino and Anglo women, using
multi-group structural equation causal modeling.
Results Screening emotions and screening fatalism had a
negative, direct influence on clinical breast exam compli-
ance for both ethnic groups. Still, ethnicity moderated the
indirect effect of screening fatalism on clinical breast exam
compliance through screening emotions.
Conclusions Integrative conceptual frameworks and multi-
variate methods may shed light on the complex relations
among factors influencing health behaviors relevant to
disparities. Future research and intervention must recognize
this complexity when working with diverse populations.
Keywords Emotions .Culture .Fatalism .Breast cancer
screening .Health disparities
Introduction
In response to the US Preventive Services Task Force
changes to mammography screening guidelines, the
National Cancer Institute [1] recently suggested that
women should consult with their healthcare professionals
to discuss their individual benefits and risks prior to
deciding when and how often they should have mammo-
grams. These changes suggest that some patients may
choose to forgo mammography and instead elect to have a
clinical breast exam after consulting with their health
professional. Therefore, understanding the factors that
encourage or discourage women from consulting with
their healthcare professionals and having a clinical breast
exam may become more important than in the past. This
may be particularly so in the case of Latin American
(Latino)
1
women in the USA, who already report lower
ratesofscreeningcomparedtonon-LatinoWhite(Anglo)
2
women. In fact, disparities in breast cancer screening
between Latino and Anglo women have increased during
the last decade [3,4]. Latino women were also more likely
to present with breast cancer at stages III and IV [5,6],
and consistent with findings regarding socioeconomic
status (SES)-related disparities, Latinas from lower as
compared to higher income levels were more likely to be
diagnosed at later stages [7].
1
The term Latino refers to the individuals or populations of the USA
who came originally from Latin America or a region of the USA that
was once part of Latin America.
2
Anglo American refers to non-Latino White individuals or popula-
tions of the USA who came originally from the UK or other European
backgrounds, who share the English language and Anglo American
cultural heritage [2].
P. M. Flynn (*):H. Betancourt :S. R. Ormseth
Department of Psychology, Loma Linda University,
Loma Linda, CA 92354, USA
e-mail: pflynn@llu.edu
H. Betancourt
Universidad de La Frontera,
Temuco, Chile
ann. behav. med.
DOI 10.1007/s12160-011-9267-z
Understanding health behaviors such as cancer screening
within the context of a culturally diverse population
requires that the interrelations among psychological, cul-
tural, and social structural factors be considered [8]. Still,
research in this area often focuses on either social
structural, cultural, or psychological factors as determinants
of breast cancer screening. Specifically, while some studies
have focused on the role of emotions as motivators and/or
deterrents of health behavior [9,10], others have focused
on the effects of access to care [11] or cultural beliefs [3,
12]. This represents an important limitation for understand-
ing complex phenomena in this area, such as the potential
role of culture in psychological processes (e.g., emotions),
which may in turn impact health behavior. Such phenomena
may actually explain why findings from research regarding
the role of culture in cancer screening are mixed [3]. For
instance, some studies have found that fatalistic beliefs
predict lowered usage of self-breast exams, clinical breast
exams, and Pap tests among Latino women [13,14],
whereas others have not found a direct relationship between
fatalism and cancer screening [15]. Those that do not find
direct relationships between aspects of culture and health
behavior may conclude that culture does not influence
behavior. However, this view may change when research
considers the possibility that culture influences psychological
processes, such as emotions, which may in turn influence
health behavior. In fact, research suggests that variations in
emotional response among Anglo and Latino women may
account for differences in cancer screening [16].
Research has found that some ethnic minority populations
such as Latinos [3] and African Americans [17] experience
higher levels of negative cancer emotions compared to
Anglo Americans, suggesting that the experience of emo-
tions may differ as a function of ethnicity [18,19]. However,
research concerning the influence of emotions on cancer
screening often times controls for ethnicity. Such research
does not take into consideration the potential moderating role
of ethnicity on the relations between cancer emotions and
screening behavior. Furthermore, since cultural values and
beliefs have been found to influence emotions [20], it is
possible that ethnic variations in screening emotions may be
in part a function of cultural factors.
The Structure of Relations Among Social, Cultural,
and Psychological Factors Influencing Health Behavior:
an Integrative Theoretical Framework
Gallo, Smith, and Cox [8] argue that in order to better
understand the role of psychological and social determi-
nants of health behaviors, an integrative theoretical frame-
work is necessary. In fact, previous research in this area has
suggested that theoretical models and multivariate methods
are needed in order to account for the complexity of
relations among psychological, social structural, and cul-
tural determinants of health behaviors [3]. The present
research is guided by a theoretical model (see Fig. 1) for the
study of culture, psychological processes, and health
From distal... to more proximal determinants of behavior
Population Cultural Psychological Health
Categories Factors Processes Behavior
AB C D
Professionals’
Race, Ethnicity,
Gender, SES, and
Religion
---------------
Patients’
Race, Ethnicity,
Gender, SES, and
Religion
Professionals’
Socially Shared
Values, Beliefs,
and Expectations
about Patients
and Health-Care
Practices
--------------
Patients’
Socially Shared
Values, Beliefs,
and Expectations
Relevant to
Health Behaviors
and Interactions
with the Health-
Care System
Professionals’
Motivation and
Emoti ons Relevant
to Health-Care
Practices an d
Interactions with
Patients
-----------
Patients’
Motivation and
Emoti ons Relevant
to Health Behaviors
and Interactions
with the Health-
Care System
Professionals’
Health-Care Practices
and Interactions with
Patients
----------------
Patients’
Health Behaviors and
Interactions with the
Health-Care System
Fig. 1 Betancourt’smodelof
culture, psychological process-
es, and behavior adapted for the
study of health behavior [21]
ann. behav. med.
behavior that provides a theoretical framework for under-
standing ethnic and socioeconomic health disparities [21,
22]. The model can be used to better understand the health
behaviors of culturally diverse patients as well as their
health professionals. In the present study, only the aspects
of the model corresponding to the patients were examined.
An important underlying principle of the model is that
relations among the variables conceived as determinants of
health behavior are structured from most distal to more
proximal (moving from A to D), with proximity to behavior
determining a greater impact. According to the model, health
behavior (D) is a function of psychological processes such as
emotions (C), which are experienced at the individual level.
These psychological processes are the most proximal determi-
nants and therefore have the greatest influence on behavior.
Health behavior (D) is also associated with culture, which is
defined here in terms of aspects such as value orientations,
beliefs, and norms that are socially shared among individuals
from a particular population or society (B). These aspects of
culture (e.g., fatalistic beliefs) may be directly or indirectly
associated with health behavior through psychological pro-
cesses (C). Moving further away from behavior are social
categories such as race, ethnicity, age, and SES (A), which
represent sources of cultural variation. However, these social
categories are more distal determinants and may not neces-
sarily be directly associated with a particular health behavior.
A number of factors such as race/ethnicity, access to care,
education, income, age [11], and language barriers [23] have
been identified as relevant to cancer screening disparities.
According to the conceptual model represented in Fig. 1,
these social structural factors are more likely to be associated
with variations in aspects of culture rather than directly with
health behavior. In fact, research indicates that ethnicity,
income, age [24], and education [25] are all factors associated
with fatalistic beliefs about cancer and cancer prevention.
Cultural variables such as fatalism have been examined in
relation to health behavior in several studies [13–15,25,26].
Although the definitions vary [27], fatalism has been generally
described as a cultural value orientation characterized by a set
of beliefs around the view that life events are inevitable and
that one’s destiny is not within one’s own hands [28]. Much
of the research identifies this cultural belief with ethnicity.
However, based on our definition of culture, beliefs, values,
expectations, norms, and practices are considered cultural
when they are socially shared. In fact, the category defining
the population or group could be race, ethnicity, age, gender,
or any other group for that matter. Moreover, many of these
social structural categories often times overlap with one
another (e.g., many ethnic minorities are overrepresented at
lower SES levels). Therefore, fatalistic beliefs may be to a
large extent a function of social structural barriers such as
lower education and income rather than ethnicity per se [27].
Thus, research should test the role of education, income, and
age along with ethnicity as sources of variation in fatalistic
cultural beliefs rather than as direct determinants of screening
behavior. In doing so, the relative impact of these variables as
sources of cultural variation can be delineated and their
relation to emotions and health behavior can be better
understood.
Borrayo and Guarnaccia [29] indicate that a common
problem in research methodology in this area is the failure to
examine confounding relationships between social structural
variables and cultural beliefs. In fact, variables such as SES
and ethnicity are more likely to be controlled for rather than
directly investigated to better understand their influence on
health behaviors. Tov and Diener [30] argue that since these
variables are associated with cultural beliefs, controlling for
them actually results in the removal of cultural effects. As a
result, when research controls for these social structural
variables and predicts health behaviors based on cultural
variables that had some of the cultural effects removed, results
are likely to reveal less significant findings concerning the
role of culture. Still, some studies have found significant
effects for these cultural beliefs after controlling for social
structural factors [24,25], while others have not [15,31].
These nonsignificant findings, however, may have more to
do with research that does not employ methodological and
statistical approaches that specifically investigate the inter-
relations among social structural categories, aspects of
culture, psychological processes, and health behavior.
The Present Study
The purpose of this research was to examine the interrela-
tions among screening emotions, screening fatalism, and
related social structural factors as determinants of clinical
breast exam compliance among culturally diverse women in
Southern California. To ensure that the complexity of
relations among these variables were properly understood,
multi-group structural equation modeling was employed to
test both the direct and indirect effects of the study
variables on health behavior as well as the potential
moderating role of ethnicity.
Consistent with the conceptual model guiding the research,
it was hypothesized that clinical breast exam compliance
would be a function of both screening emotions and screening
fatalism for both Latino and Anglo women. Specifically,
higher levels of screening emotions and screening fatalism
were expected to negatively impact clinical breast exam
compliance. It was also hypothesized that the effect of
screening fatalism on clinical breast exam compliance was,
at least in part indirect, through its effects on screening
emotions. Higher levels of screening fatalism were expected
to positively impact negative screening emotions, which
would in turn negatively impact clinical breast exam
ann. behav. med.
compliance. Finally, consistent with research concerning the
higher level of negative screening emotions among Latino
women [3], it was expected that the role of screening
emotions and screening fatalism as determinants of clinical
breast exam compliance would be moderated by ethnicity.
Method
Participants and Procedures
Multi-stage, stratified sampling was conducted to obtain
nearly equal proportions of Latino and Anglo women from
varying demographic characteristics in Southern California.
Using US Census tract data from the Federal Financial
Institutions Examination Council, projections regarding
ethnicity, SES, and age were anticipated for potential
recruitment sites, including churches, markets, universities,
free/low-cost health clinics, mobile home parks, and
community settings. Once permission from key personnel
at the selected sites was obtained, an English and/or
Spanish recruitment flyer was posted describing the study,
eligibility for participation, and the time and on-site
location where interested women could go to participate.
Institutional Review Board approval for the study was
granted prior to data collection. When interested women
arrived at the noted settings, bilingual research assistants
explained the purpose of the study and restated that women
were eligible to participate if they were Latino or Anglo
American, at least 20 years old, able to read English or
Spanish, and had never been diagnosed with breast cancer.
After participants provided written consent, they were
administered an English or Spanish version of the instru-
ment, which took approximately 30 to 45 min to complete.
All participants were compensated $20 for their time. Once
data were collected from a number of sites, the distributions
of participants across demographics were examined and
additional settings were identified to fulfill the particular
demographic need. As a result, 326 self-identified Latino
(n=173)orAnglo(n=153) women participated in the study.
Measures
Ethnicity
Ethnicity was self-reported by participants and included as a
moderating variable in the structural equation models (SEM).
Social Structural Sources of Cultural Variation
An existing measure used in previous research with
culturally diverse populations [3] was employed to assess
income, education, and age. Participants indicated their age
in years and annual household income based on five
categories (see Table 1). Women also indicated their
number of years of education, which was then coded into
five categories to be consistent with the income categories.
Screening Fatalism
A three-item subscale of the Cultural Cancer Screening
Scale (CCSS; [3]) was used to assess breast cancer
screening fatalism. The CCSS was developed with Latino
and Anglo women based on the bottom-up methodological
approach to the study of culture, which utilizes mixed
methodologies. The approach begins with specific obser-
vations relevant to an area of research (e.g., cancer
screening), which are derived through interviews from the
Table 1 Sample demographics based on ethnicity
Variable Latino
(n=144)
Anglo
(n=137)
M(SD) M(SD)
Education* 11.28 (4.01) 14.49 (2.75)
Age in years* 42.49 (12.03) 47.20 (15.97)
n(%) n(%)
Income*
≤$14,999 49 (34.03) 28 (20.44)
$15–24,999 30 (20.83) 17 (12.41)
$25–39,999 19 (13.19) 24 (17.52)
$40–59,999 25 (17.36) 21 (15.33)
≥$60,000 21 (14.58) 47 (34.31)
Marital status
Single 27 (18.75) 25 (18.25)
Married 81 (56.25) 74 (54.02)
Divorced 23 (15.97) 24 (17.52)
Widowed 7 (4.86) 11 (8.03)
Not specified 6 (4.17) 3 (2.19)
Place of birth*
Mexico 64 (44.44) 0 (0.00)
Central America/Caribbean 5 (3.47) 0 (0.00)
South America 3 (2.08) 0 (0.00)
Canada 0 (0.00) 2 (1.46)
Europe 0 (0.00) 3 (2.19)
Not specified 9 (6.25) 3 (2.19)
USA 63 (43.75) 129 (94.16)
Spanish survey language* 63 (43.75) 0 (0.00)
Health insurance coverage* 107 (74.31) 117 (85.40)
Access to healthcare clinic 118 (81.94) 124 (90.51)
Ever diagnosed with cancer 8 (5.56) 16 (11.68)
Know anyone diagnosed with
breast cancer*
90 (62.50) 109 (79.56)
Family history of breast cancer* 23 (16.00) 54 (39.40)
*p<0.05
ann. behav. med.
populations of interest (e.g., Latinos and Anglos), and
evolves from these observations to the development of
quantitative instruments [3]. An advantage of this approach
is that it allows for the identification of aspects of culture
directly from individuals, rather than based on stereotypical
views. Moreover, because efforts are taken to preserve the
participants’interview responses when developing the scale
items and because the translation process is performed by
Spanish–English speaking experts using the double back-
translation [32] and decentering [33] procedures, measure-
ment equivalence is more likely to be achieved.
The CCSS has demonstrated adequate reliability
(Latino α=0.84; Anglo α=0.83), measurement equiva-
lence (Tucker phi= 0.98), and predictive validity with
breast and cervical cancer screening behaviors [3]. The
three items from the screening fatalism subscale include,
“It is not necessary to screen regularly for breast cancer
because everyone will eventually die of something
anyway,”“It is not necessary to screen for breast cancer
regularly because it is in God’s hand anyway,”and “If
nothing is physically wrong, then you do not need to
screen for breast cancer.”All items were based on a seven-
point Likert scale from “strongly disagree”to “strongly
agree.”The reliability for this subscale was good for both
ethnic groups (Latino total α=0.782; Latino Spanish α=
0.739; Latino English α=0.781; Anglo α=0.814).
Negative Screening Emotions
Findings from interviews with Latino and Anglo women
revealed that fear, anxiety, and embarrassment were the
most frequently identified emotions associated with clinical
breast exam screening [3]. Therefore, the three screening
emotions included in the questionnaire were, “When I think
about having a clinical breast exam I get very scared,”
“Clinical breast exams are extremely embarrassing,”and
“Thinking about having a clinical breast exam makes me
terribly anxious.”Items were placed on a seven-point Likert
scale from “strongly disagree”to “strongly agree.”The
reliability of this scale was strong (Latino total α= 0.927;
Latino Spanish α=0.922; Latino English α=0.932; Anglo
α=0.836).
Clinical Breast Exam Compliance
According to the American Cancer Society (ACS) [34],
clinical breast exams are recommended for women in
their 20–30s at least every 3 years and for women 40 and
older every year. To assess clinical breast exam compli-
ance, participants were provided with an illustration of a
woman having a clinical breast exam and a brief
description of the exam. Participants were then asked,
“Have you ever had a clinical breast exam?”followed by,
“If yes, how many have you had in the last 5 years?”
Using similar methods employed by Kundadjie-Gyamfi
and Magai [35], a screening compliance proportion was
calculated based on the total number of clinical breast
exam tests reported, divided by the maximum number that
a woman of her age should have if they were fully
compliant with screening guidelines (minimum compliance =
0; maximum compliance =1.0).
Covariates
Based on previous research [3], insurance status, knowledge
about the availability of free/low-cost healthcare, country of
birth, length of residence in the USA, language of the survey,
diagnosis of cancer other than breast cancer, acquaintance
with anyone diagnosed with breast cancer, and family
history of breast cancer were assessed.
Statistical Analyses
All hypotheses were tested using Bentler’s structural
equations program (EQS 6.1; [36]) with the ML method
of estimation. In order to maintain a simplified model
without using up model degrees of freedom [37], all
relevant covariates were partitioned from the indicators of
the noted outcomes prior to SEM analyses. Due to
theoretical considerations, age, education, and income
were included in the test of models as social structural
sources of cultural variation. Adequacy of fit was
assessed using the nonsignificant χ
2
goodness-of-fit
statistic, a ratio of less than 2.0 for the χ
2
/df [38], a
Comparative Fit Index (CFI) of 0.95 or greater [36], and a
root mean square error of approximation (RMSEA) of
less than 0.05 [39]. Modifications of the hypothesized
model were performed based on results from the
Lagrange multiplier (LM) test and the Wald test in
addition to theoretical considerations.
To test ethnicity-based differences in the magnitude of
the relations among the study variables, multi-group
structural equation modeling for Latino and Anglo
women was also conducted. If the constrained structural
model showed a decrement in fit based on a significant
Δχ
2
or ΔCFI of 0.01 or greater as compared to the
reference model, the LM test of equality constraints was
assessed for evidence of noninvariance [40]. Equality
constraints were considered noninvariant and released in a
sequential manner if doing so dramatically improved the
model fit (LM χ
2
≥5.0 per df [41]). Since it is necessary in
cross-cultural research to establish that differences observed
between groups are not due to measurement artifacts
[42], measurement equivalence was examined prior to
invariance testing. Establishing measurement equivalence
allows the researcher to more confidently assert that ethnic
ann. behav. med.
group differences are the result of the cultural factors
being tested [43].
Results
Preliminary Analyses
As a result of multi-stage stratified sampling, the sample
was well balanced between Latino and Anglo partici-
pants (n=173 and n=153, respectively). Cases with
missing values on a manifest (e.g., measured) variable or
more than half of the items on multi-item subscales were
excludedfromtheanalyses.Thereweresomedifferences
between the omitted and retained sample in regards to
education (t(317)=1.97, p=0.05) and Spanish/English
version of the survey (χ
2
(1)=14.10, p<0.001). The
retained sample reported higher levels of education (M=
12.86, SD=3.81) compared to the omitted participants (M=
11.56, SD= 4.72). Omission was also more likely among
Latinos that completed the instrument in Spanish (25.88%,
n=22) than English (9.54%, n=23). After imputing values for
26 cases using the expectation–maximization algorithm, data
from 281 (144 Latino; 137 Anglo) women were available for
analyses.
Although multi-stage stratified sampling efforts resulted
in Latino and Anglo women represented across all levels of
income, education, and age, the distribution of women
within these categories was not equal. For instance, the
Latino sample was overall younger, of lower income and
education, and more likely to be uninsured. As expected,
they were more likely to have been born outside the USA
and complete the Spanish version of the survey (see
Table 1).
Analysis of Covariates
Only one covariate was found to be statistically significant.
For Latino participants, the negative screening emotion,
“When I think about having a clinical breast exam I get
very scared”was associated with a shorter period of
residence in the USA (r=−0.251, p=0.020). The variance
explained by this covariate was partialed from the indicator
prior to SEM.
Descriptive Statistics and Correlations
Table 2includes the means and standard deviations for the
study variables. Approximately 58% of the total sample
(62% Anglos, 55% Latinos) was fully adherent to ACS
screening guidelines. Table 2also reports the correlations
among the study variables after adjustment of the covariate
noted above. Fischer’sr-to-ztest of difference revealed a
number of significantly different bivariate correlations
based on ethnicity, confirming the necessity for conducting
a test of invariance.
Structural Equation Modeling
Test of the Hypothesized Model
Prior to conducting a test of the model for the Latino and
Anglo samples independently, the data were screened
revealing a violation of multivariate normality for both
ethnic groups. Therefore, the ML robust test statistics,
which corrects for non-normal data, are reported. The
hypothesized model for Anglo women fit the data well
(CFI=0.998, χ
2
(30, n=137)=30.51, p=0.440, χ
2
/df=1.02,
RMSEA= 0.011). However, for Latinos, based on the
Lagrange test and theoretical plausibility, the disturbance
terms for screening fatalism and screening emotions were
covaried (cov=−0.457, p=0.031), which resulted in an
improvement in model fit (CFI=1.00, χ
2
(29, n=144)=
25.27, p=0.664, χ
2
/df=0.87, RMSEA= 0.000). The factor
structure, including the direction and significance of factor
loadings, appeared similar for both groups. However, there
were some differences in magnitude and significance of the
associations between factors, which were further examined
in multiple group analyses (Fig. 2).
Test of Configural Invariance (Model 1)
Testing for measurement equivalence began with the
least restrictive model in which only the factor structure
of the baseline model, namely the number of factors and
the factor-loading pattern, was checked for equality
across ethnic groups. The requirement for configural
invariance suggests that the same items must be indictors
of the same factor for Latinos and Anglos, yet differ-
ences in factor loadings are permitted across groups [44].
AsshowninTable3, the fit indices revealed an excellent
fit to the data.
Test of Measurement Invariance (Table 3, Model 2)
In the second level of measurement equivalence, the factor
loadings of the baseline model were constrained to be equal
across ethnic groups, making these coefficients invariant
between Latinos and Anglos. The fit of the constrained
measurement model was also good, indicating that con-
straining factor loadings did not result in a significant
decrement in model fit. Furthermore, a review of the LM
test of equality constraints statistics showed no significant
between-group differences in the paths of the measurement
model. Because the measurement model operated similarly
for both Latinos and Anglos, any group variations observed
ann. behav. med.
Table 2 Intercorrelations, means, and standard deviations as a function of ethnicity
1 234 56 789101112
1. Education –
2. Income 0.530***
(0.443***)
–
3. Age 0.136
(−0.316***)
0.235**
(0.031)
–
4. Screening
fatalism
−0.416***
(−0.252**)
−0.369***
(−0.213*)
−0.089
(0.302***)
–
5. Nothing
wrong
−0.299***
(−0.162*)
−0.265**
(−0.137)
−0.064
(0.195*)
0.718***
(0.644***)
–
6. In God’s
hands
−0.302***
(−0.228**)
−0.268**
(−0.193*)
−0.064
(0.274**)
0.727***
(0.906***)
0.522***
(0.584***)
–
7. Everyone
will die
−0.318***
(−0.192*)
−0.283***
(−0.162)
−0.068
(0.230**)
0.765***
(0.761***)
0.550***
(0.490***)
0.557***
(0.689***)
–
8. Screening
emotions
−0.252**
(−0.031)
−0.225**
(−0.026)
−0.054
(0.026)
0.170*
(0.122)
0.122
(0.079)
0.124
(0.111)
0.130
(0.093)
–
9. Embarrassment −0.216**
(−0.022)
−0.192*
(−0.018)
−0.046
(0.033)
0.145
(0.087)
0.104
(0.056)
0.105
(0.079)
0.111
(0.066)
0.851***
(0.709***)
–
10. Anxiety −0.248**
(−0.027)
−0.220**
(−0.023)
−0.053
(0.030)
0.166*
(0.109)
0.119
(0.070)
0.121
(0.099)
0.127
(0.083)
0.976***
(0.890***)
0.830***
(0.631***)
–
11. Scared −0.221**
(−0.025)
−0.196*
(−0.021)
−0.047
(0.034)
0.148
(0.099)
0.106
(0.064)
0.108
(0.090)
0.113
(0.075)
0.872***
(0.807***)
0.742***
(0.572***)
0.851***
(0.718***)
–
12. Clinical
breast exam
compliance
0.169*
(0.081)
0.150
(0.070)
0.036
(−0.097)
−0.275***
(−0.322***)
−0.197*
(−0.208*)
−0.200*
(−0.292***)
−0.210*
(−0.245**)
−0.340***
(−0.178*)
−0.289***
(−0.126)
−0.331***
(−0.158)
−0.296***
(−0.144)
–
M11.27
(14.49)
2.58
(3.31)
42.49
(47.20)
2.24
(1.54)
2.46
(1.65)
2.23
(1.55)
2.04
(1.41)
3.02
(2.44)
2.98
(2.83)
3.06
(2.45)
3.03
(2.02)
0.65
(0.76)
SD 4.01
(2.75)
1.47
(1.54)
12.03
(15.97)
1.70
(1.15)
2.06
(1.38)
2.01
(1.39)
2.03
(1.28)
2.14
(1.54)
2.30
(1.94)
2.32
(1.86)
2.22
(1.50)
0.37
(0.35)
Intercorrelations, means, and standard deviations for Latino participants (n= 144) are presented in upper portion of cell, and values in parentheses represent Anglo participants (n=137). Boldface
indicates that groups differ significantly at p<0.05
*p<0.05; **p< 0.01; ***p< 0.001
ann. behav. med.
in the multi-group structural model could be interpreted as
cross-cultural differences rather than the result of measure-
ment artifacts (see [43]).
Test of Structural Invariance (Table 3, Model 3)
To test for differences in the magnitude of the paths among
the study variables across ethnicity, constraints were
imposed on all structural paths. Specifically, invariance
tests for path coefficients were used to test whether the
effect of one variable on another variable differed as a
function of ethnicity. In comparison with the configural
model (model 1, Table 3), the constrained structural model
showed a decrement in fit based on the change in CFI
greater than 0.01 (ΔCFI= −0.035). The chi-square differ-
ence test (ΔS-B χ
2
(10)=15.65, p=0.110) also revealed a
similar trend. A review of the LM test of equality
constraints statistics confirmed significant between-group
differences in the path from screening fatalism to screening
emotions (LM χ
2
(1)=6.13, p=0.013).
Test of Partial Structural Invariance (Table 3, Model 4)
After releasing the path constraint from screening fatalism to
screening emotions (Latino: β=0.61, p=0.004; Anglo: β=
0.12, p=0.311), the fit of the model improved. The fit shown
in the final test of invariance was comparable to the
configural model, indicating that no additional paths should
be released.
Test of Research Hypotheses
The proposed structure of relations among SES, age,
screening fatalism, screening emotions, and clinical breast
exam compliance explained the data well for both Latino
and Anglo women. As expected, ethnicity was found to
significantly impact the magnitudes of some structural
paths resulting in different combinations of significant
direct and indirect effects for the Latino and Anglo models.
The first hypothesis concerning the direct effects of
screening emotions and screening fatalism on clinical breast
Fig. 2 Final model with estimated path coefficients and factor loadings for Latino (Anglo) subgroups. *p< 0.05; **p< 0.01; ***p<0.001
Table 3 Model summary for tests of configural, measurement, and structural invariance across ethnicity
Model S-B
χ
2
df CFI RMSEA (90% CI) ΔS-B
χ
2a
pΔdf ΔCFI
Model 1 configural 56.28 59 1.00 0.000 (0.000, 0.034) ––– –
No constraints
Model 2 measurement model (factor loadings
constrained across ethnicity)
59.91 63 1.00 0.000 (0.000, 0.033) 3.68 0.451 4 0.00
Model 3 structural model (constrained factor loadings
and 6 structural paths)
72.69 69 0.965 0.014 (0.000, 0.038) 15.65 0.110 10 −0.035
Model 4 structural model (constrained factor loadings
and 5 structural paths, released Fatalism→Screening
emotions)
67.56 68 1.00 0.000 (0.000, 0.035) 10.97 0.278 9 0.00
S-B χ
2
Satorra–Bentler scaled statistic, CFI robust CFI, RMSEA robust RMSEA, 90% CI 90% confidence interval
a
Corrected value
ann. behav. med.
exam compliance was confirmed for Latino women and
partially confirmed for Anglo women. For both ethnic
groups, screening fatalism exerted a direct and negative impact
on clinical breast exam compliance (Latinos: β=−0.22, p=
0.019; Anglos: β=−0.31, p=0.006). While the negative effect
of screening emotions on clinical breast exam compliance
was significant for Latino women (β=−0.30, p<0.001), it
was not for Anglo women.
The second hypothesis concerning the indirect effect of
screening fatalism on clinical breast exam compliance
through screening emotions was also partially confirmed.
For Latino women, screening fatalism significantly influ-
enced compliance indirectly through screening emotions
(β
indirect
=−0.184, p=0.015). Specifically, higher levels of
screening fatalism positively impacted screening emotions
(β=0.61, p=0.004), and as already indicated in the first
hypothesis, screening emotions in turn negatively influ-
enced clinical breast exam compliance (β=−0.30, p<
0.001). For Anglo women, however, this indirect effect
was not significant.
The third hypothesis, which predicted that the influence
of screening emotions and screening fatalism on clinical
breast exam compliance would be moderated by ethnicity,
was also confirmed. The test of invariance revealed that the
influence of screening fatalism on screening emotions was
stronger for the Latino sample (β=0.61, p= 0.004; Anglos:
β=0.12, p=0.311). Regarding the influence of screening
emotions on clinical breast exam compliance, though the
effect was only significant for the Latino sample (Latinos:
β=−0.30, p<0.001; Anglos: β=−0.14, p=0.151), the test of
invariance did not reveal a significant difference in the
effect between Latinos and Anglos. To further examine the
moderating role of ethnicity on the indirect effect of
screening fatalism on clinical breast exam compliance
through screening emotions, MacKinnon’s[45] procedures
for contrasting indirect effects were employed. The indirect
effects were shown to be statistically different between
Latinos and Anglos (t=2.00, p=0.046), indicating that
ethnicity significantly moderated the indirect effect of
screening fatalism on clinical breast exam compliance
through screening emotions.
In addition to the noted hypotheses, age, education, and
income were found to be social structural sources of
variation in the cultural factor screening fatalism. However,
the relative influence of these social structural categories
varied for Latino and Anglo women. For instance, among
Latino women, lower education and income was associated
with higher levels of screening fatalism (β=−0.31, p=
0.003; β=−0.21, p=0.008, respectively), but age was not.
On the other hand, among Anglos, greater age and lower
income was associated with higher levels of screening
fatalism (β=0.28, p=0.010; β=−0.19, p= 0.028, respectively),
but education was not found to be a strong social structural
source of cultural variation in screening fatalism. Interesting-
ly, the LaGrange test statistic, which provides recommenda-
tions for adding parameters not hypothesized in the causal
model, did not recommend that any direct paths be added
from these social structural factors in relation to clinical breast
exam compliance.
Discussion
Overall, this research reveals that psychological processes,
such as screening emotions, and cultural beliefs, such as
those related to fatalism, are factors relevant to disparities in
health behaviors such as clinical breast exam compliance.
Findings concerning the interrelations among social struc-
tural, cultural, and psychological phenomena demonstrate
the importance of models that can guide the investigation of
complex relations among multiple determinants of health
behavior. Specifically, consistent with the conceptual model
guiding the research, screening emotions and screening
fatalism were found to influence compliance for both ethnic
groups. These findings not only point to the interrelations
among cultural beliefs, emotions, and breast cancer screen-
ing, as proposed by the model, but also point to the
moderating role of ethnicity on these relations, which has
conceptual as well as practical implications.
The fact that the structure of relations specified by the
model is supported by the results strengthens its value as a
foundation for both research and theory. Still, findings
concerning the moderating role of ethnicity represent both a
contribution to a better understanding of the relations
among social structural, cultural, and psychological ante-
cedents of behavior as well as a challenge to the linear
representation of the relations specified in Fig. 1. In general
terms, this finding is not inconsistent with the conceptual
foundations of the model [21,22,46], in that ethnicity is
thought to represent more than socially shared beliefs.
Specifically, it is conceivable that aspects associated with
ethnicity, other than those relevant to the measured cultural
factor, can make a difference in how individuals of different
ethnic backgrounds behave in a particular situation, which
could explain in part a moderating effect like the one
observed. In sum, while it sheds light on the nature of the
culture-behavior link within the context of a multiethnic
society, the observed moderating effect is expected to
stimulate further conceptual work and research.
From a methodological perspective, these results con-
firm the importance of employing statistical techniques that
take into account the role of ethnicity such as multi-group
structural equation modeling. For instance, in the present
study, Latino women reported significantly higher levels of
anxiety and fear about clinical breast exam screening as
compared to Anglo women, which in turn impacted clinical
ann. behav. med.
breast exam compliance to a greater degree for the Latino
as compared to the Anglo sample. If these data were not
analyzed separately for Latino and Anglo women, but
rather controlled for ethnicity, the complexity of these
relations might not have been recognized. These results are
consistent with Consedine and associates’[47] suggestion
that the lower screening behaviors of African American
women may be more influenced by fear, due to finding that
this population is more afraid of cancer [48]. In fact, similar
results have been reported for Latino and Anglo women
regarding the influence of emotions on health behavior as a
result of perceptions of healthcare mistreatment [16].
Concerning the proposed influence of socially shared
fatalistic beliefs on screening emotions and clinical breast
exam compliance, the role of ethnicity is interesting,
particularly when considering the indirect influence of
those cultural beliefs. Consistent with the model for the
study of culture, screening fatalism was strongly and
directly related to clinical breast exam compliance for both
Latino and Anglo women. For Latinas, screening fatalism
also exerted a significant indirect effect through screening
emotions. These findings suggest that for Latino women,
both screening fatalism as well as screening emotions are
important determinants of cancer screening. However, for
Anglo women, screening fatalism mainly exerted a direct
effect on clinical breast exam compliance as the indirect
effect through emotions was not significant. Future research
should perhaps examine the role of screening fatalism in
relation to cognitive processes, such as perception of
control and causal attributions. It is possible that among
Anglo women, these cognitive variables may be more
directly influenced by culture than are emotions.
In addition to supporting the hypothesized structure of
relations among cultural and psychological variables asso-
ciated with clinical breast exam compliance, results also
shed light on the role of SES and age as social structural
sources of variation in cultural beliefs. While a significant
amount of research in the health disparities literature has
examined factors such as ethnicity, SES, age, and access to
care, most research in this area typically controls for such
social structural variables or test their direct effects on
health behavior. Consistent with the model, these social
structural factors are postulated to be sources of cultural
variation and are believed to influence health behavior
indirectly through psychological processes. Although in
some cases income and access to care may be directly
related to health behavior due to the instrumental impor-
tance of resources, this direct relation may not have been
observed because California provides free breast and
cervical cancer screening exams to underserved women.
In fact, the present research revealed that age, education,
and income did influence clinical breast exam compliance
but only through their effects on screening fatalism. Similar
results have been reported by Murguia and Zea [49]
revealing that Latino cultural health beliefs were better
predictors of healthcare utilization as compared to SES and
acculturation.
Despite the significance of the study findings, some
limitations of the research should be considered. For
instance, the Latino sample of this study reflected the
demographic reality of Southern California, which is
predominantly of a Mexican cultural background. There-
fore, it is unclear whether the results would be the same
with Latinos from regions of the country that represent
other national origins. The sample also included both
immigrant and US born Latinos, which was expected to
contribute to more variance in the cultural factor. Future
research could include samples of Latinos that would make
it possible to examine generation status and other potential
sources of cultural variation, which may provide more
targeted information. In addition, while the theoretical
model on which the hypothesized relations were based
provided a meaningful conceptual foundation for SEM
models, the cross-sectional design of this study limits the
test of temporal relations. Future work could employ
longitudinal data in order to examine such relations in a
more definitive manner.
The findings of the research have important implications
for interventions with culturally diverse patients. First,
results concerning the indirect effects of culture on clinical
breast exam compliance for the two ethnic groups suggest
that efforts designed to improve clinical breast exam
compliance among culturally diverse populations may have
to emphasize different variables highlighted in the model.
For instance, on the one hand, in order to be effective with
Anglo patients, health professionals may have to recognize
cultural beliefs. On the other hand, when working with
Latino patients, health professionals may be better off
paying attention to not only their cultural beliefs but also
emotions such as those related to the screening process. In
fact, research with women in Korea has indicated that
interventions that focus on both cognitions and emotions,
such as shame, embarrassment, and worry related to Pap
smear screening, are particularly successful [50]. All things
considered, intervention efforts that address both screening
fatalism as well as emotions may prove effective at
enhancing screening and reducing the noted health disparities
between Latino and Anglo women.
Acknowledgments This research was supported by a NIH grant
1R21CA101867-01A2 to H. Betancourt, PI, through the National
Cancer Institute and the Office of Research on Women’s Health and
by grant PFT-08-014-01-CPPB to P. Flynn, PI, through the American
Cancer Society.
Conflict of Interest The authors have no conflict of interest to
disclose.
ann. behav. med.
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