Familial Risk for Common Diseases in Primary Care
The Family Healthware™ Impact Trial
Suzanne M. O’Neill, MA, MS, PhD, Wendy S. Rubinstein, MD, PhD, Catharine Wang, PhD,
Paula W. Yoon, ScD, MPH, Louise S. Acheson, MD, MS, Nan Rothrock, PhD, Erin J. Starzyk, MPH,
Jennifer L. Beaumont, MS, James M. Galliher, PhD, Mack T. Ruffin IV, MD, MPH, for the Family Healthware™
Impact Trial group
Context:
Family history is a risk factor for many common chronic diseases, yet it remains
underutilized in primary care practice.
Background:
Family Healthware™ is a self-administered, web-based tool that assesses familial risk for
CHD; stroke; diabetes; and colorectal, breast, and ovarian cancer, and provides a
personalized prevention plan based on familial risk. The Family Healthware Impact Trial
evaluated the tool.
Design:
In this cluster RCT, participants completed baseline and 6-month follow-up surveys. The
intervention group used Family Healthware directly after the baseline survey. Controls used
the tool after completing the follow-up survey.
Setting/
Patients aged 35– 65 years with no known diagnosis of these six diseases were enrolled from
participants:
41 primary care practices.
Main
The prevalence of family-history– based risk for coronary heart disease (CHD); stroke;
outcome
diabetes; and colorectal, breast, and ovarian cancer was determined in a primary care
measures:
population.
Results:
From 2005 to 2007, 3786 participants enrolled. Data analysis was undertaken from
September 2007 to March 2008. Participants had a mean age of 50.6 years and were
primarily white (91%) women (70%). Of the 3585 participants who completed the
risk assessment tool, 82% had a strong or moderate familial risk for at least one of
the diseases: CHD (strong�33%, moderate�26%); stroke (strong�15%, moder-
ate�34%); diabetes (strong�11%, moderate�26%); colorectal cancer (strong�3%,
moderate�11%); breast cancer (strong�10%, moderate�12%); and ovarian cancer
(strong�4%, moderate�6%). Women had a significantly (p�0.04) higher familial
risk than men for all diseases except colorectal and ovarian cancer. Overweight
participants were significantly (p�0.02) more likely to have a strong family history for
CHD, stroke, and diabetes. Older participants were significantly (p�0.02) more likely
to report a strong family history for CHD and stroke as well as colorectal and breast
cancer.
Conclusions:
This self-administered, online tool delineated a substantial burden of family-history– based
risk for these chronic diseases in an adult, primary care population.
Trial
NCT00164658.
registration:
(Am J Prev Med 2009;36(6):506 –514) © 2009 American Journal of Preventive Medicine
From the Center for Medical Genetics (O’Neill, Rubinstein), the
tive Biology and the Case Comprehensive Cancer Center, University
Center on Outcomes Research and Education (Rothrock, Beau-
Hospitals and Case Western Reserve University (Acheson), Cleveland,
mont), NorthShore University HealthSystem (formerly Evanston
Ohio; the American Academy of Family Physicians’ National Re-
Northwestern Healthcare), Evanston; the Department of Medicine,
search Network (Galliher), Leawood, Kansas; and the Department
Feinberg School of Medicine, Northwestern University; the Depart-
of Family Medicine, University of Michigan (Ruffin), Ann Arbor,
ment of Epidemiology, University of Illinois at Chicago (Starzyk),
Michigan
Chicago, Illinois; Fox Chase Cancer Center (Wang), Philadelphia,
Address correspondence and reprint requests to: Suzanne M. O’Neill,
Pennsylvania; Office of Public Health Genomics, CDC (Yoon), At-
MA, MS, PhD, Center for Medical Genetics, 1000 Central Street, Suite
lanta, Georgia; the Departments of Family Medicine and Reproduc-
620, Evanston IL 60201. E-mail: s-oneill@northwestern.edu.
506
Am J Prev Med 2009;36(6)
0749-3797/09/$–see front matter
© 2009 American Journal of Preventive Medicine. All rights reserved.
doi:10.1016/j.amepre.2009.03.002

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Introduction
recommended. Talk to your health professional. Details
about the development and features of Family
Coronaryheartdisease(CHD),stroke,diabetes, Healthwarehavebeendescribedelsewhere.22
and cancer account for approximately 60% of
In 2003 the CDC selected three academic centers to
total deaths each year in the U.S.1,2 Family
evaluate the clinical utility of this new tool: Evanston
history influences the risk of developing these multifacto-
Northwestern Healthcare (ENH); the University of Mich-
rial diseases. The relative risk for CHD; stroke; diabetes;
igan; and Case Western Reserve University (CWRU) with
and colorectal, breast, and ovarian cancer is approxi-
the American Academy of Family Physicians’ (AAFP)
mately doubled if one first-degree relative is affected in
National Research Network (NRN). The goal of the
middle age, and some family-history patterns carry a
Family Healthware Impact Trial (FHITr) was to deter-
much stronger risk.3–10 Knowledge of family-health his-
mine whether providing tailored family-health his-
tory can guide risk-specific disease prevention, potentially
tory messages influenced the adoption of healthy behav-
reducing the burden of these chronic diseases.11,12 Yet,
iors, recommended health screenings, and family and
owing to constraints on time, competing demands, and
provider communication related to the six diseases. Addi-
the complexity of familial-risk interpretation, systematic
tionally, the study aimed to measure the prevalence of
collection and assessment of detailed family-health
three levels of family-history– based risk for the six
histories rarely are done in primary care practice.13–18
diseases (weak, moderate, strong) among adults with-
Thus, the effects are mostly unknown of systematically
out a personal history of any of these diseases. This
identifying and communicating the familial risk of
report presents the study methods and prevalence of
disease to healthy adults. Likewise, data are very limited
family history for each of the six diseases in this primary
on the prevalence of family-health history that increases
care population.
patients’ risk for common chronic diseases.19–21
As part of a public health initiative to evaluate the use
of family-health history for risk assessment and preven-
Methods
tion, the CDC created Family Healthware™, an interac-
Study Design
tive online tool that provides personalized familial-risk
assessments based on an individual’s family history of
The FHITr used a practice-based, cluster-randomized design.
six common chronic diseases as well as prevention
Primary care practices were randomized to either the inter-
plans with recommendations for lifestyle changes and
vention or the control arm. In the intervention arm, partici-
screening tests. The tool systematically collects and
pants first completed an online baseline survey, followed by
records family-history information for CHD; stroke;
Family Healthware, and subsequently received personalized
risk assessment and prevention messages generated by the
diabetes; and colorectal, breast, and ovarian cancer by
tool. The control group completed the baseline survey and
specifically asking about the occurrence of each disease
received standard prevention messages about screening and
(yes, no, don’t know) as well as the age of disease onset
healthy lifestyle choices recommended for the general popu-
(in 5-year increments) in every first- and second-degree
lation for the six diseases included in the tool. Approximately
relative. The software analyzes the user input, generat-
6 months later, both the intervention and control groups
ing a three-tiered family-history– based risk assessment
completed a follow-up survey. The control group then also
(see online appendix at www.ajpm-online.net) for each
completed Family Healthware to enable comparisons not
disease based on algorithms assessing the number of
only between the intervention and control groups but also
affected relatives, age at onset, and related conditions
among familial-risk levels.
(i.e., both breast and ovarian cancer in the same
lineage).12,22 In general, a weak familial risk is as-
Pretest and Posttest Surveys
signed to users with only one second-degree relative
A computer-administered baseline survey was developed that
with late-onset disease or no family history of the
measured demographics, health status, use of medical ser-
disease. Moderate familial risk is consistent with
vices, screening behaviors, lifestyle choices, and health beliefs.
either one first-degree or two second-degree relatives
Health status was measured by the 12-item Short Form Health
with late-onset disease. Strong familial risk is assigned
Survey.23 Items assessing health behaviors were based on previ-
when there is a first-degree relative with early-onset
ously validated items from population-based studies.24,25 Health
disease, multiple affected relatives, or a disease pat-
beliefs were based on a conceptual model that incorporated
tern suggesting a hereditary syndrome. The user’s
elements of prevailing health behavior theory.26–30 The assess-
ment of perceived risk, perceived severity, worry, perceived
risk behaviors, including smoking, diet, physical ac-
control, self-efficacy, and response efficacy for each disease
tivity, alcohol use, aspirin use, and current screening
was based on single items to reduce responder burden.26 The
history are used to tailor risk-based preventive health
intent to adopt healthy behaviors and reduce unhealthy
messages. For example, a woman aged 35 years with
behaviors in the future was measured using a modified
a strong familial risk who had never had a mammo-
stages-of-change model,28 –30 because discrete behavior-
gram would receive the message You may benefit from
change outcomes, such as an increase in exercise or the
breast cancer screening at a younger age than is usually
adoption of mammography screening, might not be captured
June 2009
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in the 6-month follow-up time. In addition, family-history
CWRU–AAFP NRN sites also created a survey for healthcare
communication patterns (among family members and physi-
providers to assess visit characteristics, preventive services,
cians) were assessed, using an instrument developed by the
and referrals.
investigative team. Participants were asked if they had talked
with specific family members or any medical care provider
Participants and Recruitment
about their family-health history, and then asked to indicate
whether they had discussed particular topics pertaining to the
Researchers from each site recruited primary care practices
risk and fear of getting the six diseases, medical screening,
affiliated with their organizations (ENH, the University of
lifestyle changes, and genetic testing. Participants were also
Michigan, CWRU–AAFP NRN). Within sites, each practice
asked about potential barriers to the discussion of family-
was randomly assigned to either the intervention or control
health history. Local experts at each site piloted questions for
arm, using site-specific randomization schemes. While es-
face validity. The questionnaire data-collection process was
sential aspects of study recruitment and consent were
piloted at each site by people meeting the study criteria. The
similar, site-specific differences are shown in Table 1. The
follow-up survey was modeled after the baseline survey to
41 participating practices, including 187 participating cli-
assess changes over time. The University of Michigan and
nicians in 13 states (Figure 1), enrolled patients at differ-
Table 1. Protocol details by site
ENH
CWRU
UM
Practice locations
Hospital-based medical
Primary care research network
Primary care clinics
group and affiliates of
(community practices in 11
affiliated with University
Evanston Northwestern
states: CA, CT, FL, GA, MT,
of Michigan Health
Healthcare (Chicago
NC, NJ, NV,OH, OR,VA)
System (Ann Arbor MI
suburbs)
area)
Practice types
Internal medicine (21);
Family medicine (14)
Internal medicine (1);
family medicine (17);
family medicine (5)
obstetrics/gynecology (4)
Practice randomization
Intervention/control 2:1
Intervention/control 1:1
Intervention/control 1:1
Patient identification
Upcoming scheduled office
Upcoming scheduled office
Existing patient lists; no
visit; EMR or paper chart
visit; record review for age
scheduled visit required;
review for age criteria
criteria; systematic
EMR review for age
procedure for random
criteria; physician review
selection if more eligible
of patient invitation list
patients than could be
invited in a given week
Patient recruitment: initial
Mailed invitation letter from
Mailed invitation letter from
Mailed invitation letter
contact
physician, informed-
physician, including study
from physician; opt-in
consent document, opt-out
ID and login password; web
postcard
postcard
portal for online consent
Patient recruitment: reminders
Three telephone calls at
One phone call, time
Second invitation letter
2-week intervals
permitting
after 2–4 weeks
Patient recruitment: consent
On receipt of signed consent
Online consent followed by
On receipt of opt-in
document, patient was
signed consent at time of
postcard, informed-
mailed further info, study
scheduled visit. After
consent document
ID, and login password
December 2006, only online
mailed. On receipt of
consent required
signed consent, patient
was mailed further info,
study ID, and login
password
Survey and Family Healthware™
Online at study website or
Online at study website or MD
Online at study website or
completion
telephone interview
office computer
Telephone interview
Delivery of printed prevention
In person at scheduled
In person at scheduled
Mailed or e-mailed to
messages
appointment (copy to MD
appointment (copy to
patient; patient asked to
at patient request)
patient and MD)
bring to MD at next visit
Questionnaires for physicians
Not applicable
After patient appointment,
All study participants given
paper questionnaire to
surveys to give to any
measure visit characteristics,
healthcare provider seen
preventive services, and
after baseline assessment
referrals; intervention-group
was completed in order
physicians answered
to measure visit
additional questions
characteristics,
evaluating the utility of the
preventive services, and
Family Healthware™ report
referrals
CWRU, Case Western Reserve University–American Academy of Family Physicians’ National Research Network; EMR, electronic medical record;
ENH, Evanston Northwestern Healthcare; UM, University of Michigan Health System (Ann Arbor MI area)
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ent times throughout the recruitment period. Participants
were healthy adults aged 35– 65 years. Exclusion criteria
included a personal history of CHD, diabetes, stroke, or
any cancer other than nonmelanoma skin cancer; the
inability to speak or read English; and known pregnancy.
All sites systematically identified potential participants
from the practices’ patient records (Table 1). Patients
received invitation letters signed by their primary care
physicians. Individual protocols were approved in 2004 by
IRB’s at all three centers, and a combined protocol was
approved by the CDC’s IRB. Recruitment took place from
November 2005 to March 2007.
Data Collection
The survey instruments and the Family Healthware tool
were accessed through a dedicated website; the study
databases were housed in two SQL servers maintained by
ENH. Site coordinators monitored progress, using manage-
ment databases created by ENH and AAFP. Subjects could
log on at any time using unique usernames and passwords
that enabled them to complete the instruments over mul-
tiple sessions, if needed. Automated time stamps recorded
only the start of input and the generation of the Family
Healthware report; thus, completion time for the tool was
calculated only for those who finished in �60 minutes. It
was assumed that longer time intervals represented indi-
viduals who did not complete the input in one consecutive
sitting, because pilot testing indicated a mean input time of
20 minutes even for the largest families.
For usability reasons, surveys were designed with auto-
mated skip patterns. Most entries were mandatory to
minimize missing data. Almost all participants (91%) com-
pleted all instruments online through the website or a
computer in their doctor’s office, although participants
could also respond by telephone with data entered online
by study personnel. For the most part, the latter occurred
because of the time constraint of completing the Family
Healthware tool before a participant’s provider appoint-
ment instead of a reluctance to go online. Online partici-
pants received Family Healthware risk levels and messages
instantly on-screen, but all participants were either mailed
or given printed reports. Participants received a $10 incen-
tive after completing each survey.
Main Outcome Measures and Analysis
This report presents characteristics of the study participants
and the distributions of the familial-risk classification for each
disease. Comparisons of these distributions were made on
Figure 1. Family Healthware™ impact trial enrollment and retention
Recruitment percentage: Number of individuals consented/number of individuals invited
Retention percentage: Number of individuals who completed all study instruments/number of individuals who completed baseline survey
June 2009
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Table 2. Demographics of study participants by study arm
recruitment, 89% retention from time of consent to
completion of the baseline survey, and 88% retention
Intervention
Control arm
arm n�2364
n�1422
from baseline to follow-up (Figure 1).
n (%)a
n (%)a
The study population was mostly white (91%) women
(70%) who were married (76%), insured (97%), and of
Gender
Male
688 (29)
460 (32)
relatively high SES, with a mean age of 50.6 years, as
Female
1676 (71)
962 (68)
summarized in Table 2. (Full demographic data are
Age (years)
available as Appendix B online at www.ajpm-online.
M (SD)
50.3 (8.4)
51.1 (8.0)
net.) The distribution of participants by practice type
Are you Hispanic or Latino?
was family practice, 1834 (48%); internal medicine,
Yes
58 (2)
29 (2)
Race
1485 (39%); and obstetrics/gynecology, 467 (12%).
White or Caucasian
2134 (90)
1320 (93)
There were no significant demographic differences
Black or African American
87 (4)
35 (3)
between control and intervention groups or between
Asian
70 (3)
31 (2)
online and telephone users.
Native Hawaiian or other
2 (0.1)
2 (0.1)
Pacific Islander
American Indian, Alaska
2 (0.1)
1 (0.1)
Familial-Risk Levels in the Primary
native
Care Population
Other
69 (2.9)
33 (2.3)
Marital status
Family Healthware was completed by 3585 partici-
Single, never married
203 (9)
96 (7)
pants. The mean completion time for a convenience
Married/living with partner
1857 (79)
1135 (80)
Formerly married
304 (12)
191 (13)
sample of 1170 consecutive participants was 19.6
Level of education
minutes (range: 5.5–59.6; median: 17.0; mode: 9.63).
�Grade 11
22 (1)
8 (0.6)
The distribution of familial-risk levels for each dis-
High school graduate
189 (8)
115 (8)
ease is summarized in Table 3. For both genders,
Some college or technical
453 (19)
274 (19)
CHD had the highest percentage in the strong-risk
school
College graduate
1700 (72)
1025 (72)
category, followed by stroke; diabetes; and breast,
Annual household income ($)b
ovarian, and colorectal cancer. Overall, 82% of par-
�25,000
91 (4)
41 (3)
ticipants had a strong or moderate risk for one or
25,001–50,000
320 (16)
151 (12)
more of the six diseases.
50,001–75,000
402 (19)
228 (18)
�
Women’s reported family histories placed them
75,000
1262 (61)
834 (66)
Do you currently have any kind of health insurance?
at significantly higher familial risk than men for
Yes
2276 (96)
1380 (97)
all diseases except colorectal and ovarian cancer
Number of visits to doctor in last year
(p�0.04), although men reported don’t know more
M (SD)
4.6 (5.7)
4.7 (5.7)
often when prompted for the disease history of their
BMI
relatives (p�0.001) For both genders, the prevalence
M (SD)
27.4 (6.2)
27.2 (5.7)
It is important for my own health to know if diseases like
of don’t know responses across all relatives ranged
cancer, diabetes, stroke, or heart disease run in my
from 25% (breast and ovarian cancer) to 28%
family.
(CHD). This response was significantly more likely in
Disagree
124 (5.2)
75 (5.3)
second-degree versus first-degree relatives and for
Neither agree or disagree
56 (2.4)
31 (2.2)
male versus female relatives (p�0.001) for all dis-
Agree
2184 (92.4)
1316 (92.5)
eases. Overweight participants (BMI�25) were signif-
Note: The comparisons are between the study arms for each variable
icantly more likely than normal-weight participants
and are adjusted for clustering effects. There were no significant
differences found for any of the variables.
to have a strong familial risk for CHD, stroke, and
aUnless otherwise noted
diabetes (p�0.02). Those aged �50 years were sig-
bTwelve percent not reported
nificantly more likely to be classified in the strong-
risk category for CHD, stroke, and colorectal and
breast cancer than younger participants (p�0.02).
gender, age, smoking status, BMI, and type of medical care
There was no significant difference in familial-risk
practice. Generalized estimating equation methods were used
to adjust for the clustering by practice in all comparisons
classification due to smoking status or recruitment
among groups. Data analysis was conducted from September
practice type.
2007 to March 2008 using SAS version 9.1.
Discussion
Results
This study’s results demonstrate that there is a substan-
Demographics of the Study Population
tial burden of family-history– based risk among unaf-
While 4248 subjects were enrolled, 3786 actually com-
fected adults aged 35– 65 years who are seen in primary
pleted the baseline survey. Overall the study had 18%
care practices. Although estimates of the prevalence of
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Table 3. Stratification of familial risk for common diseases as calculated by the Family Healthware™ risk assessment
tool (n�3585)
Familial risk
Weak
Moderate
Strong
Men
Women
Men
Women
Men
Women
n (%)
n (%)
n (%)
n (%)
n (%)
n (%)
p-valuea
Coronary heart disease
494 (46)
955 (38)
265 (24)
673 (27)
321 (30)
877 (35)
0.010
Stroke
620 (57)
1232 (49)
311 (29)
891 (36)
149 (14)
382 (15)
0.007
Diabetes
695 (64)
1543 (62)
294 (27)
651 (26)
91 (8)
311 (12)
0.040
Colorectal cancer
938 (87)
2146 (86)
116 (11)
294 (12)
26 (2)
65 (3)
0.382
Breast cancer
883 (82)
1906 (76)
109 (10)
335 (13)
88 (8)
264 (11)
0.001
Ovarian cancer
984 (91)
2258 (90)
48 (4)
150 (6)
48 (4)
97 (4)
0.380
aThe comparisons of percentages in each risk level are between men and women and are adjusted for clustering effects (p-values refer to the
distribution across risk levels).
family history of common diseases have been made
histories of family members. Other studies have com-
from national surveys,31–33 these data represent the first
pared men’s and women’s accuracy in reporting a
based on more-detailed family histories that have been
family history of cancer. Some studies39–41 showed no
collected in primary care settings. These data will be
difference, while others38,42–44 showed that women
invaluable in planning, implementing, and analyzing
reported their family history more accurately than men.
future studies on family-health history in primary care.
Theoretically, for unaffected individuals, family history
should not vary by gender. If men do underreport
Accuracy of Estimates and Variability
family history when using a screening tool such as
Among Subgroups
Family Healthware, the actual prevalence of the familial
risk may be higher than reported here. Verification of
Because Family Healthware risk algorithms depend on
family histories was beyond the scope of this study.
reported disease in both first- and second-degree relatives,
Although the risk algorithms did not include any
this study’s prevalence figures may underestimate actual
personal risk factors such as age, it was found that
familial risk. The accuracy of self-report of familial disease
has been examined in a number of studies34–37 using
adults aged �50 years had higher familial risks than
various standards of reference such as medical records,
their younger counterparts for most diseases. Studies of
death certificates, and confirmation by relatives. In gen-
age as a determinant of family-history accuracy have
eral, specificity is high and sensitivity is somewhat lower,
had varying results.37,45 For adult-onset diseases, older
indicating that individuals are better at reporting the
individuals may be more likely to have affected first-
absence of disease in relatives than the presence of a
and second-degree relatives than younger people and
specific type of disease. Sensitivity has been shown to be
may be better sources for familial-risk assessment when
lower in second-degree relatives compared to first-degree
using tools that do not include more-distant relatives.
relatives, a finding in agreement with the preponderance
However, prevention interventions may be more effec-
of don’t know responses for second-degree relatives in this
tive in younger individuals, pointing to a need to
study. In addition, most studies examining the accu-
encourage families to communicate about familial risk.
racy of self-reported family history have been done in
A novel although not unexpected finding is the
populations affected by the disease of interest, whereas
difference in risk classification for overweight partici-
this study’s participants were unaffected by any of the
pants. The relationship of being overweight to a family
six assessed diseases. While this might result in some
history of diabetes and cardiovascular disease reflects
underreporting of affected relatives due to decreased
the consequences of genetic susceptibilities, shared
salience, several case-control studies have shown little
environment, and common behaviors,46 consistent with
difference in accuracy between affected and unaf-
the familial aggregation of metabolic syndrome. In
fected informants.36,38,39
contrast, smoking status is not associated with familial-
Women had higher calculated family risks than men
risk classification for the diseases assessed in this study
for all diseases except colorectal and ovarian cancer.
population.
However, the percentage of subjects stratified by Family
The validity of the risk-stratification algorithms is essen-
Healthware into strong- or moderate-risk categories was
tial to the overall utility of the Family Healthware tool.
lower for these two cancers than for the other diseases,
Although population-based data to validate familial-risk
and therefore the power to detect gender differences
studies32,33,47
algorithms are few, several recent
have
may have been limited. This finding may be due to
assessed the performance of risk-stratification rules simi-
recall or informational bias, with women reporting,
lar to those used in Family Healthware. Table 4 shows
actually knowing more, or both, about the disease
that they have fairly good agreement with this study’s
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Table 4. Prevalence of familial risk in selected studies
Familial risk
Study
Disease
Ascertainment
n
Weak (%)
Moderate (%)
Strong (%)
Scheuner (2006)32
CHD
HealthStyles 2003
4,035
56.8
11.7
31.5
FHITr
CHD
Primary care
3,585
40.6
26.41
32.9
Hariri (2006)47
Diabetes
HealthStyles 2004
4,345
65
19
16
Valdez (2007)33
Diabetes
NHANES 1999–2004
16,388
70
23
7
FHITr
Diabetes
Primary care
3,585
61
28
11
CHD, coronary heart disease; FHITr, Family Healthware™ impact trial; NHANES, National Health and Nutrition Examination Survey
prevalence estimates. The FHITr prevalence of strong
Obviously, this is not always relevant to a male individ-
familial CHD risk is similar to the Scheuner study,32
ual’s personal risk, but it is important to female family
although the current study’s population has a higher
members. It is not clear how receptive men or their
percentage in the moderate-risk group. The FHITr
healthcare providers may be to messages about prevent-
prevalence of strong familial diabetes risk lies between
ing these two cancers.
the two existing studies,33,47 but again, the prevalence
of moderate risk is higher. While it is possible that an
active primary care population might have a higher
Familial-Risk Stratification for Screening
family-history burden than the general population,
and Prevention
further validation studies will be needed to replicate
Prevention measures exist for all of the diseases as-
these findings.
sessed, and familial-risk stratification can identify those
people most likely to benefit from targeted preventive
Limitations
strategies. One impetus for assessing risk for multiple
This study provides a unique insight into the distribu-
diseases in Family Healthware was the recognition that
tion of family-history– based risk for six common dis-
the same risk-reducing behaviors contribute to prevent-
eases in primary care practices. The observation that
ing a variety of chronic diseases. However, the adoption
almost all FHITr participants understand that family
of Family Healthware or other similar tools will be
history is a risk factor for disease and believe that
limited until clinical benefits can be shown. A few
knowing their family history is important for their own
population-based studies have found that having a
health is not necessarily evidence that this sample had
family history of a chronic disease was associated with a
self-selected for interest in family history. In a national
greater awareness of risk and reported risk-reducing
study48 about awareness of family history, 96% of survey
behaviors,50–52 but data from clinical practice are very
respondents reported that knowing their family history
limited.
was important for their own health. The limited diver-
Computerized family-history tools that include so-
sity of this study’s population, racially and ethnically,
phisticated risk assessment algorithms are fast being
limits the generalizability of results from the FHITr.
developed.22,53–59 The FHITr trial has demonstrated
Study participants were highly educated patients with
that a segment of the primary care population is both
health insurance who were, for the most part, able to
interested in family-health history (the study retention
use computers to access the study materials online.
was high) and able to easily use a computerized risk
Data are not available to compare the race, ethnicity,
assessment tool outside of the clinical setting (comple-
educational attainment, or health-insurance status of
tion time was quite modest). More validation studies in
patients invited versus those who participated. Replica-
diverse populations are critically needed before the
tion in more-diverse populations is needed.
widespread dissemination of these tools.
Although personal risk factors such as smoking, diet,
physical activity, and BMI are incorporated in the
The Family Healthware™ Impact Trial (FHITr) was sup-
tailored messaging, the Family Healthware risk algo-
ported through cooperative agreements between the CDC
rithms do not include these factors. In addition, other
and the Association for Prevention Teaching and Research
established risk factors are not included, such as the
(ENH-#U50/CCU300860 TS-1216) and the American Associ-
biopsy history incorporated in the Gail model49 for
ation of Medical Colleges (UM#U36/CCU319276 MM-0789
and CWR# U36/CCU319276 MM0630).
breast cancer, cholesterol levels for CHD, and even
Suzanne O’Neill and Mack Ruffin, principal investigators,
family-history details associated with hereditary colorec-
had full access to all the data in the study and take responsi-
tal cancer (polyps, hereditary nonpolyposis colorectal
bility for the integrity of the data and the accuracy of the data
cancer–associated endometrial cancer). These limita-
analysis. The data, in part, were presented as abstracts at The
tions can be addressed in future versions of the tool.
American Society of Human Genetics annual meeting in
Finally, Family Healthware provides familial-risk as-
2007, the National Prevention and Health Promotion Summit
sessment for men related to breast and ovarian cancer.
in 2007, and the Sixth American Association for Cancer
512
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Research International Conference on Frontiers in Cancer
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Appendix
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Document Outline

• Familial Risk for Common Diseases in Primary Care
  • Introduction
  • Methods
    • Study Design
    • Pretest and Posttest Surveys
    • Participants and Recruitment
    • Data Collection
    • Main Outcome Measures and Analysis
  • Results
    • Demographics of the Study Population
    • Familial-Risk Levels in the Primary Care Population
  • Discussion
    • Accuracy of Estimates and Variability Among Subgroups
    • Limitations
    • Familial-Risk Stratification for Screening and Prevention
  • References
  • Appendix
  • Supplementary Data

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