Frew Owens Saint-Victor et al 2014

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Factors associated with maternal influenza
immunization decision-making
Paula M Frew
a
, Lauren E Owens
b
, Diane S Saint-Victor
a
, Samantha Benedict
b
, Siyu Zhang
b
&
Saad B Omer
c
a
Emory University School of Medicine; Department of Medicine; Division of Infectious
Diseases; Atlanta, GA USA
b
Emory University; Rollins School of Public Health; Department of Epidemiology; Atlanta,
GA USA
c
Emory University; Rollins School of Public Health; Hubert Department of Global Health;
Atlanta, GA USA
Accepted author version posted online: 01 Nov 2014.Published online: 06 Nov 2014.
To cite this article: Paula M Frew, Lauren E Owens, Diane S Saint-Victor, Samantha Benedict, Siyu Zhang & Saad B Omer
(2014) Factors associated with maternal influenza immunization decision-making, Human Vaccines & Immunotherapeutics,
10:9, 2576-2583, DOI: 10.4161/hv.32248
To link to this article: http://dx.doi.org/10.4161/hv.32248
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Factors associated with maternal influenza
immunization decision-making
Evidence of immunization history and message
framing effects
Paula M Frew1,*, Lauren E Owens2
, Diane S Saint-Victor1
, Samantha Benedict2
, Siyu Zhang2
, and Saad B Omer3
1
Emory University School of Medicine; Department of Medicine; Division of Infectious Diseases; Atlanta, GA USA; 2
Emory University; Rollins School of Public Health;
Department of Epidemiology; Atlanta, GA USA; 3
Emory University; Rollins School of Public Health; Hubert Department of Global Health; Atlanta, GA USA
Keywords: influenza vaccination, message framing, immunization coverage, pregnant women, racial/ethnic minorities
Objective: We examined pregnant women’s intention to
obtain the seasonal influenza vaccine via a randomized
controlled study examining the effects of immunization
history, message exposure, and sociodemographic correlates.
Methods: Pregnant women ages 18–50 participated in a
randomized message framing study from September 2011
through May 2012. Venue-based sampling was used to recruit
racial and ethnic minority women throughout Atlanta,
Georgia. Key outcomes were evaluated using bivariate and
multivariate analyses.
Results: History of influenza immunization was positively
associated with intent to immunize during pregnancy [OR D
2.31, 90%CI: (1.06, 5.00)]. Significant correlates of intention to
immunize included perceived susceptibility to influenza
during pregnancy [OR D 3.8, 90% CI: (1.75, 8.36)] and vaccine
efficacy [OR D 10.53, 90% CI: (4.34, 25.50)]. Single message
exposure did not influence a woman’s intent to vaccinate.
Conclusions: Prior immunization, perceived flu
susceptibility and perceived vaccine effectiveness promoted
immunization intent among this population of pregnant
minority women. Vaccine efficacy and disease susceptibility
are critical to promoting immunization among women with
no history of seasonal influenza immunization, while those
who received the vaccine are likely to do so again. These
findings provide evidence for the promotion of repeated
exposure to vaccine messages emphasizing vaccine efficacy,
normative support, and susceptibility to influenza.
Introduction
Despite recommendations that pregnant and expecting
women receive the trivalent inactivated influenza vaccine,1-3
racial and ethnic minority groups have significantly lower rates of
immunization, as well higher rates of influenza-related deaths
and complications, as compared with Whites.4-7
Black/African
American as well as Hispanic pregnant women and infants
experience a higher burden of morbidity, mortality, and hospital-
izations due to influenza.4,8-13
Incidence data from 2010–2011
points to greater influenza-related mortality among Hispanic
infants (68 cases per 100,000 population) compared with non-
Hispanics (39 cases per 100,000).8,14-16
Despite improvements in access to care (i.e., free vaccinations
and free prenatal care), disparities in immunization coverage
have not been eliminated. One method that has been studied as a
way to change health behavior is message framing, backed by
Prospect Theory,17-20
which posits that individuals tend to avoid
risks when considering gains and prefer risks when considering
losses.21
Thus, successful gain-framed messages promote the ben-
efits of taking a risk (i.e., immunization during pregnancy), while
loss-framed messages depict the costs of forgoing risk.22
Studies
have found that gain-framed messages are most effective in pro-
moting preventive health behavior such as immunization, com-
pared with loss-framed messages, which are most effective in
promoting detection practices such as Pap smears.22-26
To our
knowledge, message framing has not been systematically evalu-
ated on maternal and infant immunization outcomes among
minority women.27-29
In addition, marketing and communication analyses indi-
cate that individuals are more likely to acquire the product of
interest after repeated message exposure.30
While single expo-
sure may draw attention to the product, decision to engage
in the proposed behavior comes after a second or third expo-
sure to the message.31
Regular communication with a target
population “early, often, and fully”31
establishes trust and
builds credibility, in addition to fostering a positive attitude
toward the proposed behavior or product. In order to pro-
mote immunization among women with no history of sea-
sonal influenza immunization, single message exposure is
expected to be less effective than repeated exposure to vaccine
promotion messages.
Results
Overall, most women reported that they were unlikely to
obtain influenza immunization (74.1%, n D 186) and a
*Correspondence to: Paula M Frew; Email: pfrew@emory.edu
Submitted: 05/16/2014; Revised: 07/23/2014; Accepted: 08/03/2014
http://dx.doi.org/10.4161/hv.32248
2576 Volume 10 Issue 9Human Vaccines & Immunotherapeutics
Human Vaccines & Immunotherapeutics 10:9, 2576--2583; September 2014; © 2014 Taylor & Francis Group, LLC
RESEARCH PAPER
Downloadedby[EmoryUniversity]at14:1215June2015

smaller proportion (25.9%, n D 65) were likely to obtain it
during pregnancy. The study population consisted primarily
of Black/African American women (88.8%, n D 221), as well
as Hispanic/Latina women (6.8%, n D 17) and Multiracial/
other women (4.4%, n D 11). (Table 1) The majority of
participants were between 18 to 25 y old (54.8%, n D 136)
and living in lower-income households with total earnings
among family members comprising $20,000 per year
(68.4%, n D 162). Fifty-six percent of participants
(n D 139) indicated that they were unemployed and 51.2%
(n D 128) achieved high school or equivalent education.
Most participants reported their relationship status as single
or never married (72.4%, n D 181).
We do acknowledge that there is a significant difference
observed in women’s relationship status among the gain-frame,
loss-frame, and control group. Fewer married participants or par-
ticipants with domestic partners were in loss-frame group. We
also note that 8 participants who were divorced or separated were
also from the loss-frame group.
Main study findings
Among women who believed that the influenza vaccine was
over 80 percent effective, there was a 10-fold increase in intention
to obtain an influenza vaccine during their pregnancy compared
with those who believed the vaccine to be less effective against
influenza [OR D 10.53, 90% CI: (4.34, 25.50)] (Table 2). For
example, a woman who believed the vaccine was highly effective
had a 30% to 60% increase in likelihood of later obtaining it.
Respondents were more likely to indicate intent to immunize if
they perceived higher susceptibility of becoming ill with influenza
during pregnancy [OR D 3.83, 90% CI: (1.75, 8.36)]. Women
with normative support surrounding immunizations expressed
greater intent to obtain the seasonal influenza vaccine than those
who did not [OR D 3.27, 90% CI: (1.48, 7.26)]. There was a
2.3-fold increase in intention to immunize during pregnancy
among women who had been immunized against influenza
within the past five y compared with those who had not obtained
influenza immunization in the past 5 y [OR D 2.31, 90% CI:
(1.06, 5.00)].
Table 1. Sociodemographics by treatment group of those who ranked their likelihood to vaccinate while pregnant
Characteristic Total (n D 251) Control (n D 79) Gain (n D 85) Loss (n D 87) p-value
Number (%) Number (%) Number (%) Number (%)
Age (missing D 3) 0.484
18–25 136 (54.8%) 40 (51.9%) 45 (53.6%) 51 (58.6%)
26–35 93 (37.5%) 33 (42.9%) 33 (39.3%) 27 (31.0)
36–45 19 (7.7%) 4 (5.2%) 6 (7.1%) 9 (10.3%)
Educational Attainment (missing D 1) 0.657
Less than High School 45 (18.0%) 14 (18.0%) 19 (22.3%) 12 (13.8%)
High School 128 (51.2%) 39 (50.0%) 43 (50.6%) 46 (52.9%)
More than High School 77 (30.8%) 25 (32.1%) 23 (27.1%) 29 (33.2%)
Racial/Ethnic Background (missing D 2) 0.401
African American/Black 221 (88.8%) 70 (89.7%) 77 (91.7%) 74 (85.1%)
Hispanic/Latino/Chicano 17 (6.8%) 4 (5.1%) 6 (7.1%) 7 (8.0%)
Multiracial/Other 11 (4.4%) 4 (5.1%) 1 (1.2%) 6 (6.9%)
Employment Status (missing D 2) 0.992
Employed 95 (38.2%) 28 (36.4%) 34 (40.0%) 33 (37.9%)
Unemployed 139 (55.8%) 44 (57.1%) 46 (54.1%) 49 (66.3%)
Other 15 (6.0%) 5 (6.5%) 5 (5.7%) 5 (6.0%)
Annual Household Income (missing D 14) 0.797
Less than $20,000 162 (68.4%) 53 (70.7%) 54 (71.1%) 55 (64.0%)
$20,001-$40,000 41 (17.3%) 11 (14.7%) 13 (17.1%) 17 (19.8%)
$40,001-$80,000 27 (11.4%) 10 (13.3%) 7 (9.2%) 10 (11.6%)
More than $80,000 7 (3.0%) 1 (1.3%) 2 (2.6%) 4 (4.7%)
Relationship Status (missing D 1)* 0.019
Single/Never Married 181 (72.4%) 58 (74.4%) 62 (72.9%) 61 (70.1%)
Married/Domestic Partner 49 (19.6%) 16 (20.5%) 19 (22.4%) 14 (16.1%)
Divorced/Separated 8 (3.2%) 0 (0.0%) 0 (0.0%) 8 (9.2%)
Widowed 1 (0.4%) 1 (1.3%) 0 (0.0%) 0 (0.0%)
Other 11 (4.4%) 3 (3.8%) 4 (4.7%) 4 (4.6%)
Health Insurance 0.477
Yes 182 (72.5%) 58 (73.4%) 59 (69.4%) 65 (74.7%)
No 63 (25.1%) 21 (26.6%) 23 (27.1%) 19 (21.8%)
Don’t Know 6 (2.4%) 0 (0.0%) 3 (3.5%) 3 (3.4%)
Likelihood of Obtaining Influenza Immunization During Pregnancy 0.104
Likely 65 (25.9%) 17 (21.5%) 29 (34.1%) 19 (21.8%)
Unlikely 186 (74.1%) 62 (78.5%) 56 (65.9%) 68 (78.2%)
*significant differences between groups at the 0.05 a level.
www.landesbioscience.com 2577Human Vaccines Immunotherapeutics

Because the majority of respondents in the study were minor-
ity (Black/African American and Hispanic/Latina) women, race
and ethnicity were not a significant factor affecting intent to
immunize during pregnancy. In addition to controlling for all of
the variables tested, the final multivariate model for this outcome
also controlled for variables such as perceived severity of flu dur-
ing pregnancy, perceived or anticipated side effects associated
with influenza immunization, myths and misperceptions such as
potential to get influenza from flu shots, and perceived potential
for household transmission. These additional variables were not
significant in the analyses and were therefore not detailed in the
table although they were included in the final model to control
for confounding.
Message framing outcomes
The results of the logistic regression models between paired
groups (model 1: gain v. loss, model 2: gain v. control, model 3:
loss v. control) are described in Table 3. The most robust predic-
tor of intention to obtain influenza immunization during preg-
nancy is seen among women who believe that the vaccine is over
80 percent effective against influenza virus [Model 1: OR D
14.66, 95% CI: (4.50, 47.78); Model 2: OR D 10.64, 95% CI:
(3.78, 29.94)]; Model 3: [OR D 7.43, 95% CI: (2.45, 22.55)].
Mothers who obtained influenza vaccines in the past five years
indicated a stronger likelihood to obtain immunization during
pregnancy than those who did not obtain any flu shots in the
past five years [model 1: OR D 3.00, 95% CI: (1.17, 7.73)].
Mothers who believed that it is likely they will get influenza while
pregnant were more likely to indicate intention to obtain the vac-
cine during their pregnancy as demonstrated in model 1 [OR D
3.72, 95% CI: (1.45, 9.59)], model 2 [OR D 3.21, 95% CI:
(1.23, 8.35)], and model 3 [OR D 5.38, 95% CI: (1.90, 15.29)].
Additionally, mothers with normative support for immunization
indicated a stronger likelihood to immunize themselves during
pregnancy as shown in model 1 [OR D 2.87, 95% CI: (1.10,
7.53)], model 2 [OR D 2.98, 95% CI: (1.12, 7.93)], and model
3 [OR D 4.22, 95% CI: (1.48, 12.01)].
Discussion
These findings demonstrate that history of seasonal influenza
immunization is predictive of intention to vaccinate during preg-
nancy among this population of pregnant minority women. Else-
where we have indicated that influenza immunization during
pregnancy has a strong effect on women’s subsequent intention
to immunize infants.32
Accordingly, we can expect that women
with immunization experience are likely to be immunized in the
future, including during pregnancy. In order to improve immu-
nization rates among this population, vaccine messages must tar-
get women who have not received the seasonal influenza vaccine,
encouraging these women to enter a regular pattern of influenza
immunization.
As expected, single message exposure was not a significant fac-
tor in determining women’s intent to immunize during preg-
nancy. These results provide further compelling evidence that
effective health communication must occur regularly and repeat-
edly30,31
in order to encourage a target population to engage in
the proposed behavior. Prior advertising research suggests that an
individual may need to see an advertisement or message multiple
times, think about it, discuss it with friends, family and/or com-
munity members and then be persuaded to adopt the proposed
behavior or product.30
As a result, single exposure to a message,
whether gain- or loss-framed, is unlikely to change individual
intention and decision-making behavior within a target
population.33
Women who thought the vaccine was 80% effective or greater
(the highest OR) were far more likely to obtain immunization
while pregnant compared with those who thought that the vac-
cine was 70% effective or less. While it is unsurprising that a per-
ception of higher efficacy is more persuasive in obtaining
immunization, seasonal influenza vaccine efficacy is less than
80% for pregnant women,34
and the perception of high efficacy
Table 2. Factors associated with the likelihood of obtaining influenza immunization during pregnancy
Adjusted OR 90%CI
Exposure of Interest
Gain-frame group 1.19 (0.45, 3.14)
Loss-frame group 0.58 (0.22, 1.55)
Control Group Referent
Additional Factors
Flu Vaccine Received in Last 5 Years
Yes 2.31 (1.06, 5.00)*
No/Don’t Know Referent
Ranked Likelihood of Getting the Flu while Pregnant
High (5 when 10 D definitely so) 3.83 (1.75, 8.36)*
Low (4 when 10 D definitely so) Referent
Normative Support (from family, friends, health care providers, and community) 3.27 (1.48, 7.26)*
Vaccine Protectiveness
Very Protective (80%) 10.53 (4.34, 25.50)*
Not Protective (70%) Referent
*Odds Ratio in BOLD means the variable is significant in the model.
2578 Volume 10 Issue 9Human Vaccines Immunotherapeutics

is unrealistic. This suggests that if vaccine efficacy increases, more
pregnant women would be encouraged to obtain immunization.
In addition, the evidence suggests that increased, repeated com-
munication about the benefits of immunization offers the best
chance to persuade pregnant women to obtain immunization.
Although age was not found to be a significant confounding
factor among this population, the large proportion of those in
the younger age range (18–25 y) in our cohort may have influ-
enced their view of presented health messages. This is especially
important in view of their overall vulnerability to influenza infec-
tion and associate severity of disease.35,36
Prior studies have indi-
cated challenges with processing gain- and loss-framed messages
in light of lower perceived risk among younger women and ado-
lescents.37-39
Thus, compared with the older counterparts, a
body of literature points to the fact that younger persons tend to
be more willing to engage in risk-taking behaviors that may trans-
late into “collective ambivalence”38
toward preventive behavior.
Consequently, this attitude toward risk may result in a decision
to forego immunization until a higher (50%) illness suscepti-
bility threshold is crossed.38,40
Therefore, the relatively young
age of women in our population and perception of immunization
effectiveness in the face of perceived illness risk may have altered
their view of our messages.41
Both gain- and loss-frame messages focused on pregnant
women’s perceived susceptibility to influenza virus, but lacked
other critical indicators of intention to immunize during preg-
nancy. Our findings indicate that perceptions of vaccine efficacy
and presence of normative support, in addition to perceived sus-
ceptibility to influenza during pregnancy, were significant factors
in determining intent to immunize during pregnancy. Based on
our findings and existing risk communication literature,30,31
we
suggest that vaccine education materials be presented to pregnant
and expecting minority women multiple times at regular inter-
vals. These messages should incorporate important determinants
of pregnant minority women’s vaccine decision-making behavior,
such as effects associated with influenza illness during pregnancy
and vaccine efficacy, as well as promotion of immunization as a
women’s health preventive norm.42-44
Results from this study suggest that immunization materials
need to target partners, non-pregnant family, friends, or commu-
nity members. Similar studies have demonstrated their critical
role on achieving healthy pregnancy outcomes related to behav-
ioral change such as tobacco use elimination and breastfeeding
practice.45,46
Social support from these individuals and groups
can be linked to influenza immunization that may avert preterm
and low birth weight outcomes.47-49
Younger women in particu-
lar, and those who are primigravida, often discuss pregnancy-
related decisions with trusted family members and friends and
rely heavily on their social support.50-52
Such discussions may
play a significant role in shaping women’s attitude toward immu-
nization during pregnancy.
Limitations
There are some limitations to this study. Convenience sam-
pling of minority women from one southeastern city is not repre-
sentative of other cities in the United States. We also had a larger
proportion of the study cohort who are younger (18–25 y) and
are not entirely representative of the actual population of preg-
nant Hispanic or African American women. Arguably, this factor
may have influenced our cohort’s overall perception of risk
Table 3. Factors associated with the likelihood of obtaining influenza immunization during pregnancy by message type
MODEL 1:
GAIN-group v. LOSS-group
MODEL 2:
GAIN-group v. CONTROL-group
MODEL 3:
LOSS-group v. CONTROL-group
Adjusted OR 95% CI Adjusted OR 95% CI Adjusted OR 95% CI
Exposure of Interest
Gain-frame group 2.01 (0.79, 5.11) 1.25 (0.49, 3.25) N/A N/A
Loss-frame group Referent N/A N/A 0.48 (0.17, 1.35)
Control Group N/A N/A Referent Referent
Additional Factors
Flu Vaccine Received in Last 5 Years
Yes 3.00 (1.17, 7.73) n/a n/a
No/Don’t Know Referent n/a n/a
Ranked Likelihood of Getting the Flu
While Pregnant
High ( 5 when 10 D definitely so) 3.72 (1.45, 9.59) 3.21 (1.23, 8.35) 5.38 (1.90, 15.29)
Low ( 4 when 10 D definitely so) Referent Referent Referent
Vaccine Protectiveness
Very Protective ( 80%) 14.66 (4.50, 47.78) 10.64 (3.78, 29.94) 7.43 (2.45, 22.55)
Not Protective ( 70%) Referent Referent Referent
Normative Support 2.87 (1.10, 7.53) 2.98 (1.12, 7.93) 4.22 (1.48, 12.01)
Vaccine Worth
Would pay n/a n/a 3.91 (1.20, 12.78)
Don’t Know n/a n/a 2.20 (0.62, 7.76)
Would Not Pay n/a n/a Referent
Odds Ratio in BOLD means the variable is significant in the model.

associated with influenza illness during pregnancy. Additionally,
we acknowledge the potential for participatory bias as women
who were agreeable to participating in the study may hold stron-
ger views on immunization and health behaviors compared with
those who did not participate in this study.
Conclusions
Promoting immunization intent among pregnant minority
women, who are at significantly higher risk for influenza-related
deaths and complications, is a critical public health issue. Our
study provides compelling evidence that women who have
received the seasonal influenza vaccine in the past are likely to do
so again. Future immunization campaigns need to focus on alter-
ing negative or ambivalent attitudes, perceptions, and behaviors
of women with little or no history of influenza immunization.
Our study demonstrates the critical role of perceived susceptibil-
ity and efficacy, as well as normative approval from relatives and
peers on vaccine decision-making. In order to influence women
to accept influenza immunization the findings indicate a need to
develop more effective vaccines. Finally, this study reflects inade-
quacies associated with single message exposure to motivate
immunization behavior. Instead, we argue the need for repeated
message exposure to emphasize influenza susceptibility and
related vaccine benefits to realize greater immunization uptake
among pregnant minority women.
Materials and Methods
Formative research
We conducted 20–30 min semi-structured interviews with
pregnant Black/African American and Hispanic women ($20
compensation per subject) at clinics throughout Atlanta. Data
were collected until saturation was achieved on emergent themes
that informed the development of two intervention message
types. Based on these formative interviews, the following infor-
mation was presented all to women, including those in the con-
trol condition, on the first page of their questionnaire:
“Information about the Flu Shot
Although pregnant women are about 1% of the US popula-
tion, they made up 5% of US deaths from 2009 H1N1 (swine
flu) reported to the Centers for Disease Control (CDC) from
April 14 – August 21, 2009. According to a study done during
the first month of the outbreak, the rate of hospitalizations for
2009 H1N1 was four times higher in pregnant women than
other groups.
Seasonal flu shots have been given safely to millions of preg-
nant women over many years. As in previous years, vaccine com-
panies are making plenty of preservative-free flu vaccine as an
option for pregnant women and small children. The flu shot (not
the nasal spray) is safe for pregnant women during any trimester.
Nursing mothers can receive a flu shot or the nasal spray. One
shot will last all flu season, even if you get it early in the season.”
The resulting gain-framed message included approximately
four lines of information about influenza vaccination with a
background visual depiction of a pregnant woman. The loss-
framed message included four lines of text emphasizing the risks
of not protecting oneself and the unborn child(ren) from
influenza.
Study design and sample
Cohort recruitment began at the inception of influenza season in
September 2011 and concluded in May 2012. This enabled the
study team to evaluate message framing under normal conditions
where the women may or may not have exposure to other influenza
immunization campaign messages. Eligible individuals were
women aged 18–50 y who self-identified as Black/African American
or Hispanic, had not already received an influenza or T-dap vaccine
during the 2011–2012 influenza season, and were able to provide
written informed consent. Project staff conducted sampling in a
variety of consenting venues across metropolitan Atlanta.
Women who met the eligibility criteria and agreed to partici-
pate (n D 251) were randomized to one of three conditions: sin-
gle exposure to gain-framed, loss-framed, or control messages.
They were interviewed that day and were compensated $20 for
time and inconvenience. No information was kept for ineligible
participants. All participants were pregnant at the time of the
interview.
Measurement
Study materials were developed in English and Spanish. Prior
to administration, bilingual community members reviewed these
documents to ensure item comprehension and readability. The
final survey had a Flesch-Kincaid reading score of 7.4, in either
language, which met the acceptable criteria of 6–8th grade read-
ing level for our target population.53,54
Assessment of intent
Intent to immunize was assessed by asking “On a scale of 0
(definitely not) to 10 (definitely so), please rank your likelihood of
getting a flu shot during your pregnancy.” Subsequently, we dichot-
omized variable that allowed us to evaluate those “likely”
(responded 6 through 10) and “not likely” (responded 0 through
5) to obtain influenza immunization during pregnancy.
Assessment of demographic and behavioral correlates
Initial survey questions assessed sociodemographic measures
(e.g., age, race/ethnicity, education, healthcare utilization,
employment status). Key behavioral indicators were assessed,
including immunization history for illnesses other than seasonal
or pandemic influenza (e.g., Hepatitis B), healthcare seeking
motivation, and willingness-to-pay for the seasonal influenza vac-
cine (i.e., $0/free to $30) using a 5-point scale.
Given the extent of evidence suggesting the importance of nor-
mative approval in vaccine decision-making,55,56
we designed a
composite measure comprised of three items to assess the perceived
approval of doctors, work colleagues, family, and friends in decid-
ing to obtain influenza immunization during pregnancy. The scale
included the following items: “I think my doctor would approve of

me getting the flu shot while pregnant,“ “I think people I work with
would be supportive of me getting a flu shot while pregnant,” and
“My friends and family would support my decision to get a flu shot
while pregnant.” Each scale item was measured by a 5-point Likert
scale (1-strongly disagree agree to 5-strongly disagree), designed to
assign meaningful values to an underlying continuum of ratings. A
team of clinicians and behavioral researchers reviewed the instru-
ment for adequacy of the measures prior to its use. The resulting
scale demonstrated strong internal consistency (Cronbach’s a D
0.771) and therefore functioned as a reliable composite measure of
normative support for this study.
Assessment of vaccine efficacy perception
Because this is a community-based study the clinical term
“vaccine effectiveness” is often interchangeably used with the col-
loquial phrase of “vaccine protectiveness.”36,37
We therefore
adopted this term in our survey as women perceive vaccine effec-
tiveness as a means to protect themselves from becoming infected
rather than experiencing a reduction of risk.57,58
The perception
of influenza vaccine efficacy was therefore assessed by asking
“Please indicate how protective you think the flu vaccine will be for
pregnant women,” with a an associated range of 0% (“Not
Protective”) to 100% (“Completely Protective”) on the contin-
uum scale. Subsequently, we performed a median split procedure
on values women assigned to this question. The resulting dichot-
omized variable enabled us to evaluate the threshold at which
influenza immunization was perceived as “effective” (i.e., result-
ing values of 80–100%) compared with the range at which it was
considered to “not or less effective” (i.e., resulting values of 0–
70%) among our cohort.
Statistical analyses
We conducted descriptive analyses, two sample t tests, and
cross-tabulations to evaluate characteristic differences observed
among enrolled arms. Multiple logistic regression analyses were
performed to evaluate the association between predictor variables
(i.e., psychosocial indicators of health, message framing, past
immunization history) and intent to immunize, while accounting
for the influence of confounding. Confounding variables were
selected based on the relationship between outcome and exposure
variables. If any of the related variables changed the influence of
different messages on vaccine intention by at least 10%, we con-
sidered these confounders and subsequently excluded the items
from the final model. We also assessed the potential for multicol-
linearity using a condition index of 20 and a VDP level of 0.5
when full model was determined.59,60
Rigorous testing indicated
that no collinearity was found in the model.61
In addition, we generated three multiple logistic regression
models in order to compare effects across exposure groups (i.e.,
gain frame vs. control, loss frame vs. control, and gain frame vs.
loss frame). For each paired-group comparison, we ran a multi-
variate logistic regression that assessed the relationship between
the study group and our primary outcome – intention to obtain
seasonal influenza immunization during pregnancy. This allowed
us to analyze potential variations between intervention arms in
the association between predictor variables and intent to immu-
nize during pregnancy.
Disclosure of Potential Conflicts of Interest
There were no potential conflicts of interest.
Acknowledgments
The authors would like to offer our gratitude to Drs. Robert
Davis and Ruth Berkelman, and Ms. Ellen Whitney for their
support and guidance throughout this study. Special thanks to all
participants for their involvement in the study and to Mr. Rick
Kern, MixIt Marketing, for assistance with message concepts. Its
contents are solely the responsibility of the authors and do not
necessarily represent the official views of the CDC.
Funding
This study was partially supported by a Kaiser Permanente
Georgia community benefits grant and a grant from the Cen-
ters for Disease Control and Prevention (CDC) grant
5P01TP000300 to the Emory Preparedness and Emergency
Response Research Center, Emory University.
References
1. Ahluwalia IB, Jamieson DJ, Rasmussen SA, D’Angelo
D, Goodman D, Kim H. Correlates of seasonal influ-
enza vaccine coverage among pregnant women in Geor-
gia and Rhode Island. Obstet Gynecol 2010; 116:949-
55; PMID:20859160; http://dx.doi.org/10.1097/
AOG.0b013e3181f1039f
2. Beigi RH, Wiringa AE, Bailey RR, Assi TM, Lee BY.
Economic value of seasonal and pandemic influenza
vaccination during pregnancy. Clin Infect Dis 2009;
49:1784-92; PMID:19911967; http://dx.doi.org/
10.1086/649 013
3. Moro PL, Tepper NK, Grohskopf LA, Vellozzi C,
Broder K. Safety of seasonal influenza and influenza
A (H1N1) 2009 monovalent vaccines in pregnancy.
Expert Rev Vaccines 2012; 11:911-21;
PMID:23002972; http://dx.doi.org/10.1586/
erv.12.72
4. Louie JK, Acosta M, Jamieson DJ, Honein MA; Cali-
fornia Pandemic (H1N1) Working Group. Severe
2009 H1N1 influenza in pregnant and postpartum
women in California. N Engl J Med 2010; 362:27-35;
NEJMoa0910444
5. Read JS, Riley L. Progress in overcoming barriers to
influenza immunization of pregnant women. Am J
Obstet Gynecol 2012; 207(Suppl):S1-2;
PMID:22920052; http://dx.doi.org/10.1016/j.
ajog.2012.06.067
6. Flowers L. Racial and ethnic disparities in influenza and
pneumococcal immunization rates among Medicare
beneficiaries. Issue Brief (Public Policy Inst (Am Assoc
Retired Pers)) 2007; (IB83): 1-6; PMID:17657906
7. Logan JL. Disparities in influenza immunization
among US adults. J Natl Med Assoc 2009; 101:161-
arr482483 ; PMID:19378634
8. Centers for Disease Control and Prevention (CDC).
Maternal and infant outcomes among severely ill preg-
nant and postpartum women with 2009 pandemic
influenza A (H1N1)–United States, April 2009-August
2010. MMWR Morb Mortal Wkly Rep 2011;
60:1193-6; PMID:21900872
9. Centers for Disease Control and Prevention. 2009
H1N1 and Seasonal Influenza and Hispanic Commu-
nities: Questions and Answers. 2010; February 16,
2010: http://www.cdc.gov/h1n1flu/qa_hispanic.htm.
10. Georgia Department of Health. Annual Vaccine Pre-
ventable Diseases Surveillance Report - Georgia, 2010.
2010; http://health.state.ga.us/pdfs/epi/vpd/2010%
20VPD%20Report.pdf.
11. Ahluwalia IB, Singleton JA, Jamieson DJ, Rasmussen
SA, Harrison L. Seasonal influenza vaccine coverage
among pregnant women: pregnancy risk assessment
monitoring system. J Womens Health (Larchmt) 2011;

10.1089/jwh.2011.2794
12. Fiscella K, Franks P, Doescher MP, Saver BG. Dispar-
ities in health care by race, ethnicity, and language
among the insured: findings from a national sample.
Med Care 2002; 40:52-9; PMID:11748426; http://dx.
doi.org/10.1097/00005650-200201000-00007
13. Neuzil KM, Reed GW, Mitchel EF, Simonsen L, Grif-
fin MR. Impact of influenza on acute cardiopulmonary
hospitalizations in pregnant women. Am J Epidemiol
1998; 148:1094-102; PMID:9850132; http://dx.doi.
org/10.1093/oxfordjournals.aje.a009587
14. Tucker Edmonds BM, Coleman J, Armstrong K, Shea
JA. Risk perceptions, worry, or distrust: what drives
pregnant women’s decisions to accept the H1N1 vac-
cine? Matern Child Health J 2011; 15:1203-9;
PMID:20936337; http://dx.doi.org/10.1007/s10995-
010-0693-5
15. Groseclose SL, Brathwaite WS, Hall PA, Connor FJ,
Sharp P, Anderson WJ, Fagan RF, Aponte JJ, Jones
GF, Nitschke DA, et al.; Centers for Disease Control
and Prevention (CDC). Summary of notifiable dis-
eases–United States, 2002. MMWR Morb Mortal
Wkly Rep 2004; 51:1-84; PMID:15123988
16. Martin JA, Hamilton BE, Sutton PD, Ventura SJ,
Menacker F, Munson ML. Births: final data for 2002.
National Vital Statistics Report. 2003; 52(10):1-113.
17. Tversky A, Kahneman D, Kahneman D. Advances in
Prospect Theory: Cumulative Representation of Uncer-
tainty. J Risk Uncertain 1992; 5(4):297-323; http://dx.
doi.org/10.1007/BF00122574
18. Satia JA, Barlow J, Armstrong-Brown J, Watters JL.
Qualitative study to explore Prospect Theory and mes-
sage framing and diet and cancer prevention-related
issues among African American adolescents. Cancer
Nurs 2010; 33:102-9; PMID:20142738; http://dx.doi.
org/10.1097/NCC.0b013e3181be5e8a
19. Tversky A, Kahneman D. The framing of decisions and
the psychology of choice. Science 1981; 211:453-8;
science.7455683
20. Kahneman D, Tversky A. Prospect Theory - Analysis of
Decision under Risk. Econometrica 1979; 47(2):263-
91; http://dx.doi.org/10.2307/1914185
21. Abhyankar P, O’Connor DB, Lawton R. The role of
message framing in promoting MMR vaccination: evi-
dence of a loss-frame advantage. Psychol Health Med
2008; 13:1-16; PMID:18066916; http://dx.doi.org/
10.1080/13548500701235732
22. Bartels RD, Kelly KM, Rothman AJ. Moving beyond
the function of the health behaviour: the effect of mes-
sage frame on behavioural decision-making. Psychol
Health 2010; 25:821-38; PMID:20204967; http://dx.
doi.org/10.1080/08870440902893708
23. Gallagher KM, Updegraff JA. Health message framing
effects on attitudes, intentions, and behavior: a meta-
analytic review. Ann Behav Med 2012; 43:101-16;
011-9308-7
24. Jung ME, Martin Ginis KA, Phillips SM, Lordon CD.
Increasing calcium intake in young women through
gain-framed, targeted messages: a randomised con-
trolled trial. Psychol Health 2011; 26:531-47;
08870441003611544
25. Gallagher KM, Updegraff JA, Rothman AJ, Sims L.
Perceived susceptibility to breast cancer moderates the
effect of gain- and loss-framed messages on use of
screening mammography. Health Psychol 2011;
10.1037/a0022264
26. Hull SJ. Perceived risk as a moderator of the effective-
ness of framed HIV-test promotion messages among
women: a randomized controlled trial. Health Psychol
10.1037/a0024702
27. Snyder LB. Health communication campaigns and
their impact on behavior. J Nutr Educ Behav 2007; 39
(Suppl):S32-40; PMID:17336803; http://dx.doi.org/
10.1016/j.jneb.2006.09.004
28. Snyder LB, Hamilton MA, Mitchell EW, Kiwanuka-
Tondo J, Fleming-Milici F, Proctor D. A meta-analysis
of the effect of mediated health communication cam-
paigns on behavior change in the United States. J
Health Commun 2004; 9(Suppl 1):71-96;
10810730490271548
29. National Cancer Institute. Making Health Communica-
tion Programs Work. 1989.
30. Tellis GJ. Effective advertising: understanding when,
how, and why advertising works. Thousand Oaks, Calif:
Sage Publications; 2004.
31. Dean DH, Biswas A. Third-Party Organization
Endorsement of Products: An Advertising Cue Affect-
ing Consumer Prepurchase Evaluation of Goods and
Services. Journal of Advertising. Winter 2001 2001;30
(4):41-57.
32. Frew PM, Zhang S, Saint-Victor DS, Schade AC, Ben-
edict S, Banan M, Ren X, Omer SB. Influenza vaccina-
tion acceptance among diverse pregnant women and its
impact on infant immunization. Hum Vaccin Immun-
other 2013; 9:2591-602; PMID:24172064; http://dx.
doi.org/10.4161/hv.26993
33. O’Connor AM, Pennie RA, Dales RE. Framing effects
on expectations, decisions, and side effects experienced:
the case of influenza immunization. J Clin Epidemiol
10.1016/S0895-4356(96)00177-1
34. Thompson MG, Li DK, Shifflett P, Sokolow LZ,
Ferber JR, Kurosky S, Bozeman S, Reynolds SB,
Odouli R, Henninger ML, et al.; Pregnancy and Influ-
enza Project Workgroup. Effectiveness of seasonal triva-
lent influenza vaccine for preventing influenza virus
illness among pregnant women: a population-based
case-control study during the 2010-2011 and 2011-
2012 influenza seasons. Clin Infect Dis 2014; 58:449-
57; PMID:24280090; http://dx.doi.org/10.1093/cid/
cit750
35. Brewer NT, Weinstein ND, Cuite CL, Herrington JE
Jr. Risk perceptions and their relation to risk behavior.
Ann Behav Med 2004; 27:125-30; PMID:15026296;
http://dx.doi.org/10.1207/s15324796abm2702_7
36. Weinstein ND, Kwitel A, McCaul KD, Magnan RE,
Gerrard M, Gibbons FX. Risk perceptions: assessment
and relationship to influenza vaccination. Health Psy-
chol 2007; 26:146-51; PMID:17385965; http://dx.
doi.org/10.1037/0278-6133.26.2.146
37. Van ’t Riet J, Cox AD, Cox D, Zimet GD, De
Bruijn GJ, Van den Putte B, De Vries H, Werrij
MQ, Ruiter RA. Does perceived risk influence the
effects of message framing? A new investigation of a
widely held notion. Psychol Health 2014; 29:933-
49; PMID:24579986; http://dx.doi.org/10.1080/
08870446.2014.896916
38. Schneider SL. Framing and conflict: aspiration level
contingency, the status quo, and current theories of
risky choice. J Exp Psychol Learn Mem Cogn 1992;
10.1037/0278-7393.18.5.1040
39. Ramirez JE, Ramos DM, Clayton L, Kanowitz S,
Moscicki AB. Genital human papillomavirus infec-
tions: knowledge, perception of risk, and actual risk in
a nonclinic population of young women. J Womens
Health 1997; 6:113-21; PMID:9065380; http://dx.
doi.org/10.1089/jwh.1997.6.113
40. Figner B, Mackinlay RJ, Wilkening F, Weber EU.
Affective and deliberative processes in risky choice: age
differences in risk taking in the Columbia Card Task. J
Exp Psychol Learn Mem Cogn 2009; 35:709-30;
a0014983
41. Williams DJ, Noyes JM. How does our perception of
risk influence decision-making? Implications for the
design of risk information. Theor Issues Ergon Sci
2007; 8:1-35; http://dx.doi.org/10.1080/
14639220500484419
42. Gerend MA, Shepherd JE. Predicting human papillo-
mavirus vaccine uptake in young adult women: com-
paring the health belief model and theory of planned
behavior. Ann Behav Med 2012; 44:171-80;
012-9366-5
43. Hopfer S, Clippard JR. College women’s HPV vaccine
decision narratives. Qual Health Res 2011; 21:262-77
PMID:20841433
44. Black SB, Shinefield HR, France EK, Fireman BH,
Platt ST, Shay D; Vaccine Safety Datalink Workgroup.
Effectiveness of influenza vaccine during pregnancy in
preventing hospitalizations and outpatient visits for
respiratory illness in pregnant women and their infants.
Am J Perinatol 2004; 21:333-9; PMID:15311370;
http://dx.doi.org/10.1055/s-2004-831888
45. Pollak KI, Mullen PD. An exploration of the effects of
partner smoking, type of social support, and stress on
postpartum smoking in married women who stopped
smoking during pregnancy. Psychol Addict Behav
1997; 11(3):182-9; http://dx.doi.org/10.1037/0893-
164X.11.3.182
46. Jenkins AL, Tavengwa NV, Chasekwa B, Chatora K,
Taruberekera N, Mushayi W, Madzima RC, Mbuya
MN. Addressing social barriers and closing the gender
knowledge gap: exposure to road shows is associated
with more knowledge and more positive beliefs, atti-
tudes and social norms regarding exclusive breastfeed-
ing in rural Zimbabwe. Matern Child Nutr 2012;
10.1111/j.1740-8709.2011.00325.x
47. Rini C, Schetter CD, Hobel CJ, Glynn LM, Sandman
CA. Effective social support: Antecedents and conse-
quences of partner support during pregnancy. Pers
Relatsh 2006; 13(2):207-29; http://dx.doi.org/
10.1111/j.1475-6811.2006.00114
48. Brown M-A. Marital discord during pregnancy: A fam-
ily systems approach. Fam Syst Med 1994; 12(3):221-
34; http://dx.doi.org/10.1037/h0089130
49. Richards JL, Hansen C, Bredfeldt C, Bednarczyk RA,
Steinhoff MC, Adjaye-Gbewonyo D, Ault K, Gallagher
M, Orenstein W, Davis RL, et al. Neonatal outcomes
after antenatal influenza immunization during the
2009 H1N1 influenza pandemic: impact on preterm
birth, birth weight, and small for gestational age birth.
Clin Infect Dis 2013; 56:1216-22; PMID:23378281;
http://dx.doi.org/10.1093/cid/cit045
50. Cronenwett LR. Social networks and social support of
primigravida mothers and fathers. Birth Defects Orig
Artic Ser 1984; 20:167-203; PMID:6536335
51. Brown MA. Social support during pregnancy: a unidi-
mensional or multidimensional construct? Nurs Res
10.1097/00006199-198601000-00002
52. Unger DG, Wandersman LP. The relation of family
and partner support to the adjustment of adolescent
mothers. Child Dev 1988; 59:1056-60;
PMID:3168613; http://dx.doi.org/10.2307/1130271
53. Davis TC, Mayeaux EJ, Fredrickson D, Bocchini JA
Jr., Jackson RH, Murphy PW. Reading ability of
parents compared with reading level of pediatric patient
education materials. Pediatrics 1994; 93:460-8;
PMID:8115206
54. Miller B, Bodie M. Determination of reading compre-
hension level for effective patient health-education
materials. Nurs Res 1994; 43:118-9; PMID:8152936
55. Frew PM, Saint-Victor DS, Owens LE, Omer SB. Soci-
oecological and message framing factors influencing
maternal influenza immunization among minority
women. Vaccine 2014; 32:1736-44; PMID:24486366;
http://dx.doi.org/10.1016/j.vaccine.2014.01.030
56. Marsh HA, Malik F, Shapiro E, Omer SB, Frew PM.
Message Framing Strategies to Increase Influenza
Immunization Uptake Among Pregnant African Amer-
ican Women. Matern Child Health J 2013; 12;
PMID:24337776
57. Honda-Okubo Y, Kolpe A, Li L, Petrovsky N. A single
immunization with inactivated H1N1 influenza vaccine

formulated with delta inulin adjuvant (AdvaxTM
) over-
comes pregnancy-associated immune suppression and
enhances passive neonatal protection. Vaccine 2014;
10.1016/j.vaccine.2014.06.057
58. Ohmit SE, Thompson MG, Petrie JG, Thaker SN,
Jackson ML, Belongia EA, Zimmerman RK, Gaglani
M, Lamerato L, Spencer SM, et al. Inﬂuenza vaccine
effectiveness in the 2011-2012 season: protection
against each circulating virus and the effect of prior vac-
cination on estimates. Clin Infect Dis 2014; 58:319-27;
PMID:24235265; http://dx.doi.org/10.1093/cid/
cit736
59. Markovitz BP. The principle of multicollinearity.
Pediatr Crit Care Med 2005; 6:94, author reply 94-5;
PMID:15643312; http://dx.doi.org/10.1097/01.
PCC.0000149232.77830.E0
60. Feher K, Whelan J, M€uller S. Exploring multicollinear-
ity using a random matrix theory approach. Stat Appl
Genet Mol Biol 2012; 11:15; PMID:22611593
61. York R. Residualization is not the answer: Rethinking
how to address multicollinearity. Soc Sci Res 2012;
10.1016/j.ssresearch.2012.05.014

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