TO TEST OR NOT TO TEST?
THE ROLE OF ATTITUDES, RELIGIOUS INVOLVEMENT, KNOWLEDGE
AND PREVIOUS EXPERIENCE IN FORMING INTENT-TO-OBTAIN
PREDICTIVE ADULT GENETIC TESTING – A STRUCTURAL
EQUATION MODELING APPROACH
Anda Botoseneanu, MD/MBA
Jeffrey A. Alexander, Ph.D
Jane Banaszak-Holl, Ph.D
Health Management and Policy Department
University of Michigan
Background
Genetic susceptibility testing:
Cancers –breast, colon, prostate
Other dx – SSA, cystic fibrosis, etc
Risk:
HNPCC genes – 70-80% early onset-cancer
BRCA 1 and 2 – 85% breast Ca and 65% ovarian Ca by age 70y
Risk-reduction surveillance and interventions:
Prophylactic mastectomy – 80-90% risk-reduction
BPO – 85-96% risk-reduction
(Ackermann et al., 2006)
Study Justification
Uptake of genetic testing among low- and high-risk
groups is low.
Predictions on GT uptake intentions scanty
(Li et al.,
2004).
Factors influencing consumers’ choices re: GT uptake
? (Ackermann et al., 2006).
Model of Theory of Reasoned Action
(TRA) & Objective
• Attitudes/knowledge/beliefs behavioral intentions 
actual behaviors
(Ajzen and Fishbein, 1980, 1985, 1991)
• Sociodemographics, culture/religion/stereotypes & biases
towards targets, psychological factors (social pressures)
 attitudes.
(Fishbein and Yzer, 2003)
Examine role of ATTITUDES, KNOWLEDGE, PREVIOUS EXPERIENCE,
RELIGIOUS INVOLVEMENT for intent to obtain genetic testing
O1: Test role of religion in forming intent-to-test.
O2: Test model of direct and indirect (mediated) effects.
MODEL Development - RELIGION
Religion health outcomes (physical, mental, functional).
(Koenig et al, 2001; Moreira-Almeida et al., 2006, Hummer et al.,
2004)
Religion  lower utilization of hospital services BUT higher
utilization of SOME preventive services.
(Koenig et al., 2004, 1998; Benjamins and Brown, 2003 )
Religion  GT interest, attitudes, knowledge (-)
(Bowen, 2003; Chatters, 2000)
Why religion?
Faith-based venues for health prevention & screening
adherence (Peterson et al., 2002)
Importance of religion in life decisions (GSS findings)
PROPOSED MODEL
Religious
Involvement
(-)
Knowledge
on GT
(-)
(+)
(-)
Attitudes
vs. GT
(+)
Previous
Experience
w. GT
(+)
(+)
(+)
Behavioral
Intentions
Data Source & Sample
UNITED STATES PUBLIC KNOWLEDGE AND
ATTITUDES ABOUT GENETIC TESTING, 2000 (Robert
Wood Johnson Foundation, April 2000 to November
2000 (updated 2005) - ICPSR ).
Cross-sectional.
Sample: nationally representative sample of adult US
population – 1,824 adults ages 18 and over.
Analytic Approach
SEM 2-step analysis (Loehlin, 2004):
1.
2.
Confirmatory Factor Analysis (CFA) – test measurement
model.
Structural Equation Model (SEM) – test the paths (direct and
indirect effects) model.
M-plus software
Robust WLS estimation method (asymptotically distribution-free
estimation method), with FIML for missing data.
Results – Final Model
Results – Variance
Variance explained (1-residual) :
BEHINT
15.2%
RelInv on ATT
1.7%
MI – no modification warranted.
Conclusions:
1.
Attitudes (positive), experience (positive) and
knowledge (negative) had DIRECT effects on
Intention-to-Test.
2.
Religious Involvement has INDIRECT effect
(negative) via held attitudes.
3.
15% of the variance in intention-to-test explained  other factors.
Limitations
1. Measures have narrow theoretical boundaries:
Attitudes not considered: (1) perceptions about personal risk for adult
genetic disorders (Shiloh and Ilan, 2005), (2) attitudes about autonomy
and confidentiality (Benkendorf et al., 1997), (3) attitudes towards
uncertainty (Braithwaite et al., 2002), (4) beliefs about the normative
expectations of others (family, friends, fellow church-goers, etc) (Ajzen,
2002)
Religious involvement measure vs. denomination, vs. religious
identification, vs. etc…
Knowledge: accuracy of test results, complications/side effects, Tx
options
2. Behavioral intentions predict actual behavior?
3. Causality Inference?
Study Implications
Understanding the mechanisms of GT behavioral intentions
formation will help with:
(1)
Identifying groups (based on levels of religious involvement)
which are more or less receptive to participation in cancer
genetic testing.
(2)
Identifying attitudinal barriers to participation in GT and
suggest ways to overcome them.
(3)
Allowing for tailoring genetic screening messages, information
and interventions to address attitudinal barriers.
Genetic Testing Behaviors: Where Do We (Hope
to) Go from Here?
Study 1: GENDER differences:
Screening programs/policies for breast cancer (females mostly), prostate
cancer (males-only) and colon cancer (both genders) should take
into account gender differences in formation of behavioral
intentions.
Study 2: ATTITUDES:
(1)
Perceptions about personal risk for adult genetic disorders/Risk
tolerance.
(2)
Attitudes about autonomy and confidentiality.
(3)
Attitudes towards uncertainty.
(4)
Beliefs about the normative expectations of others.
Study 3: What is it about religion?
Study 4: BEHAVIORAL INTENTIONS PREDICT ACTUAL
BEHAVIOR?