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Reasons, assessments, and actions taken: A national study of consumer use of Internet health information
1. Reasons, assessments, and actions taken:
A national study of consumer use of Internet
health information
by
Michele Ybarra, MPH PhD*
Michael Suman, PhD **
American Public Health Association Annual Conference
December 14, 2005, Philadelphia, PA
*Center for Innovative Public Health Research
**Center for the Digital Future, University of Southern California
* Thank you for your interest in this presentation. Please note that
analyses included herein are preliminary. More recent, finalized
analyses can be found in: Ybarra, M., & Suman, M. (2006). Reasons,
assessments, and actions taken: Sex and age differences in uses of
Internet health information. Health Education Research, or by
contacting CiPHR for further information.
2. Acknowledgements
We would like to thank our colleagues working
with us on the Growing up with Media
Project:
Dr. Marie Diener West, JHSPH
Dr. Merle Hamburger, CDC
Mr. Levator Brown, CDC
Dr. Dana Markow, Harris Interactive
Dr. Suzanne Martin, Harris Interactive
Ms. Amie Kim, Harris Interactive
3. Background
Access to reliable disease information online
has been linked to
Reduced anxiety (Gufstason, Hawkins, Boberg et al., 2002),
Increased feelings of self-efficacy
48% of health information seekers indicate
that their findings help them to take better
care of themselves (Fox, Rainie, Horrigan et al., 2000).
41% of adolescents have changed their
behavior because of information they found
online (Kaiser Family Foundation, 2002).
4. Problem statement and Study
question
The Internet’s influence on health care consumers is likely
to only increase, necessitating more information about
the seeking experience.
Personal characteristics may significantly influence Internet
health information seeking behavior.
Study questions:
What are the common reasons, assessments and
actions taken as a result of Internet health information
How do the RAAs vary by sex and age?
5. Surveying the Digital Future Year 4
Methodology
Conducted by the Center for the Digital
Future, University of Southern California
N = 2,010
4th year of a longitudinal telephone survey
Nationally representative (EPSEM)
Conducted in summer 2003
Inclusion criteria:
Over the age of 12 years
Speak either English or Spanish
Consent to participation
6. Statistical methods
1. Valid answer to Internet use, N=2,007
2. Missing data imputed using best-set
regression (Stata 7)
3. Differences by sex and age were
investigated via chi-square tests.
7. Measures
1.
Reasons: Open-ended question about why
respondent chose the Internet to look for health
or medical information. Coded into 6 categories.
2.
Assessments: 10 statements were read aloud
and respondents rated how much they agreed
with each statement (5-point Likert scale).
3.
Actions / results: Respondents asked if they
engaged in each of 5 activities (yes / no).
8. Study sample characteristics
(N=2,007)
Non-Internet
users
(N=548)
Internet users, nonhealth information
seekers (N=640)
Race
White
82.1% (450)
79.7% (510)
88.6% (726)
Black
7.1% (39)
8.3% (53)
3.9% (32)
Asian
0.4% (2)
1.9% (12)
1.8% (15)
American Indian
1.8% (10)
1.4% (9)
1.2% (10)
Other
8.6% (47)
8.8% (56)
4.4% (36)
Hispanic ethnicity
11.0% (60)
8.3% (53)
5.6% (46)
X2=13.0 (2)**
Female
63.7% (349)
52.5% (336)
64.6% (529)
X2=25.2 (2)***
Age [M (SD)]
61.1 (18.1)
40.7 (19.0)
45.9 (15.2)
F=5.3 (82)***
Income ($80,000+)
3.7% (20)
18.9% (121)
24.7% (202)
Demographic
characteristics
*p-value<.05; **p-value<.01; ***p-value<.001
Health
information
seekers (N=819)
Statistical
Comparison
X2=35.7 (8)***
X2=104.5 (2)***
9. Internet use and health information
seeking
72% of all respondents were Internet
users
56% of Internet users were health
information seekers (41% of all
respondents)
10. Internet use by sex and age (N=2,007)
Health
information
seekers
(N=819)
Internet users,
non-health
information
seekers (N=640)
Non-Internet
users
(N=548)
% (N)
% (N)
% (N)
Adolescents (12-19 yrs)
23 (37)
72 (114)
5 (8)
Young adults (20-39 yrs)
47 (235)
40 (201)
13 (67)
Middle age (40-59 yes)
53 (388)
27 (203)
20 (148)
Older adults (60+ yrs)
26 (159)
20 (122)
54 (325)
Men
37 (290)
38 (304)
25 (199)
Women
44 (529)
28 (336)
29 (349)
Age
Sex
Percentages sum to 100% across the row
11. Internet health information seeking by
sex among Internet users (N=1,459)
Healt h infor m at ion seek er
Non- seek er
80%
70%
61%
60%
50%
51%
49%
39%
40%
30%
20%
10%
0%
Males
X2(1) = 21.8 p<.001
Fem ales
12. Internet health information seeking by
age among Internet users (N=1,459)
80%
Healt h infor m at ion seek er
Non- seek er
75%
66%
70%
60%
57%
54%
46%
50%
40%
30%
43%
34%
25%
20%
10%
0%
Childr en &
adolescent s ( 12- 19
yr s)
X2(3) = 84.0 p-value<.001
Young adult s ( 20-39
year s)
Middle aged adult s
( 40-59 years)
Older adult s ( 60
years+ )
13. The health information seeking
experience (N=819)
Reasons
75%: to search about a personal health problem
70%: to search about a loved one’s health problem
4%: privacy / embarrassing topic
Assessments
73% satisfied with information found
21% concerned about the quality of information
8% information too hard to understand
Action taken / results
55% contacted a healthcare provider
78% felt more comfortable about information from a
healthcare provider
14. Internet health information seeking
by sex (N=819)
80%
*p-value<.05; **p-value<.01
70%
Men
60%
50%
40%
Wom en
49%
46%
40%
37%
34%
30%
24%
23%
16%
20%
16%
9%
10%
0%
I nfor m at ion is Not enough t im e
easy t o f ind **
t o find
inform at ion*
Reasons
Took a lot of
effor t **
Assessments
Tr ied t o Dx a
pr oblem *
Seek support
fr om ot her s **
Actions taken
15. Internet health information seeking
by age: Reasons (N=819)
76% 78%
80%
Childr en and adolescent s
71%
70%
Young adult s
Middle aged adult s
60%
Older adult s
54%
50%
38% 39%
40%
34%
30%
38%
32%
30%
23%
20%
14%
10%
0%
Healt h problem loved one
has **
*p-value<.05; **p-value<.01
Wide availabilit y of
infor m at ion *
Needed inf or m at ion quick ly *
16. Internet health information seeking
by age: Assessments (N=819)
80%
70%
Childr en and adolescent s
Young adult s
60%
Middle aged adult s
Older adult s
50%
40%
31%
30%
20%
23%
20%
16%
15%
10%
10%
14%
0%
0%
Want ed m ore inform at ion but didn't k now
w her e t o find it **
**p-value<.01; ***p-value<.001
Took a lot of effort ***
17. Internet health information seeking
by age: Results / Actions taken
(N=819)
90%
79%
78%
80%
84%
Childr en and adolescent s
Young adult s
72%
Middle aged adult s
70%
Older adult s
60%
50%
41%
40%
32%
30%
32%
26%
20%
10%
0%
Felt m ore com for t able w it h infor m at ion
from healt h pr ovider *
*p-value<.05
Tr ied t o t r eat a healt h pr oblem *
18. Study Limitations
The current investigation does not
include medical conditions or
treatment outcomes.
Data do not provide enough detail to
disentangle reasons why
consumers sought medical care /
support.
19. Conclusions: Age
As age increases, so too does the
likelihood of reporting:
The reason for using the Internet was
the wide availability of information
But also,
The search took a lot of effort and
More information was wanted but the
consumer didn’t know where to find it.
20. Conclusions: Sex
Men and women were equally likely to
be searching for information about a
personal problem as well as a loved
one’s health problem.
Women were significantly more likely to
report a negative assessment of the
seeking experience than men.
21. Implications: Medical Care
One in two seekers contact a physician
because of information found online.
No difference in likelihood of seeking medical
care was noted by sex or age.
Health behavior and perceptions of health
services received are likely influenced by
information found online, which may vary by
sex and age.
22. Implications:
Intervention and prevention
Although we tend to think of the Internet
as a young person’s tool, it may be a
viable delivery method for older adults
too.
The Internet is being used by
caregivers. “Multiple hit” interventions
should be considered.
Notas del editor
Year 1 sample in 2000, as well as replacement respondents in subsequent years. In Year 1, 19,247 phone numbers were generated, resulting in 2,104 completed interviews.
In Year 4, all 1,960 respondents from the previous year who indicated they were willing to be contacted again were called. To replace dropouts, an additional 18,500 phone numbers were randomly identified via EPSEM and contacted. In total, 2,010 interviews were completed. Five hundred and seventy of the 2,104 participants from Year 1 were in the panel in Year 4.
20,460 phone numbers were dialed. Of the 6468 households contacted, 6279 were eligible (97% of households contacted). 2010 completed the interview – 32% of households identified as eligible.
Year 1 sample in 2000, as well as replacement respondents in subsequent years. In Year 1, 19,247 phone numbers were generated, resulting in 2,104 completed interviews.
In Year 4, all 1,960 respondents from the previous year who indicated they were willing to be contacted again were called. To replace dropouts, an additional 18,500 phone numbers were randomly identified via EPSEM and contacted. In total, 2,010 interviews were completed. Five hundred and seventy of the 2,104 participants from Year 1 were in the panel in Year 4.
20,460 phone numbers were dialed. Of the 6468 households contacted, 6279 were eligible (97% of households contacted). 2010 completed the interview – 32% of households identified as eligible.
Sex-specific medical conditions will have implications for the type of information male and female patients will seek. For example, women are significantly more likely than men to search for information about depression, anxiety and stress [16]. Age-specific lifestyle trends (e.g., middle-aged adults becoming caregivers for older parents as well as their children) and typical health status changes as one grows older also likely influence the decision to use the Internet as a resource. Although adolescents have more readily adopted the Internet in general, middle-aged adults are most likely to look for health information online [2].
Year 1 sample in 2000, as well as replacement respondents in subsequent years. In Year 1, 19,247 phone numbers were generated, resulting in 2,104 completed interviews.
In Year 4, all 1,960 respondents from the previous year who indicated they were willing to be contacted again were called. To replace dropouts, an additional 18,500 phone numbers were randomly identified via EPSEM and contacted. In total, 2,010 interviews were completed. Five hundred and seventy of the 2,104 participants from Year 1 were in the panel in Year 4.
20,460 phone numbers were dialed. Of the 6468 households contacted, 6279 were eligible (97% of households contacted). 2010 completed the interview – 32% of households identified as eligible.
Missing and non-responsive answers (i.e., “don’t know” and “refused”) were imputed using best-set regression [20]. This affected less than 1% of data with one exception: 11.3% of health information seekers were unresponsive to queries about their household income.
Missing and non-responsive answers (i.e., “don’t know” and “refused”) were imputed using best-set regression [20]. This affected less than 1% of data with one exception: 11.3% of health information seekers were unresponsive to queries about their household income.
Note: income was entered into the model as an ordinal variable (range: 22). The above is simply an indicator and is truncated for space. The statistical comparison reflects a comparison of medians test
Missing and non-responsive answers (i.e., “don’t know” and “refused”) were imputed using best-set regression [20]. This affected less than 1% of data with one exception: 11.3% of health information seekers were unresponsive to queries about their household income.
Note: income was entered into the model as an ordinal variable (range: 22). The above is simply an indicator and is truncated for space. The statistical comparison reflects a comparison of medians test
95% of kids are on the Internet
87% of young adults
80% of middle aged
46% of older adults
Missing and non-responsive answers (i.e., “don’t know” and “refused”) were imputed using best-set regression [20]. This affected less than 1% of data with one exception: 11.3% of health information seekers were unresponsive to queries about their household income.
Note the scale changing
Challenges include callerID, confusion with telemarketers, and saturation of surveys among the public. Our survey is additionally challenged because of its broad inclusion criteria; targeting a more select population would have likely increased the response rate but decreased the generalizability.