1. Variation in Veterans’ Access to Internet and Potential Impact on VHA Electronic Health Communication
Thank you Joanne. Hello and welcome everyone, I am Kirsha Gordon
And I work for the Veterans Aging cohort study or VACS for short, at the Pain Research, Informatics, Medical co-morbidities, and Education (PRIME) Center
We are located at the West Haven VA in Connecticut
I will be presenting data from VACS, a 10 year study of HIV positive and HIV negative patients.
Here with me is Amy Justice, co-presenter and principle investigator of VACS.
Kirsha S Gordon; Joseph L Goulet; Cynthia A Brandt; Mona Duggal; Amy C Justice
VIReC Clinical Informatics Seminar
July 21st, 2009
Kirsha S. Gordon, MS
VA CT HCS
Columbia School of Public Health (Doctoral Student) 1

2. Introduction
Health care communication is increasingly web-based in the VA
For example, My Healthe Vet* (MHV) allows access to trusted, accurate and timely health information; and eventually PHI protected s
The VA has invested time and expense on My Healthe Vet website
It asserts, “…the best part is, you can easily access your personal health information in your My Healthe Vet account from any place you have an Internet connection.”
As you know, Health care communication is increasingly web-based in the VA
For example, My Healthe Vet (MHV) allows access to trusted, accurate and timely health information; and eventually PHI protected s
The VA has invested time and expense on My Healthe Vet website which is intended to provide veterans access to health information, their records, and re-fill of prescriptions.
It asserts that, “…the best part is, you can easily access your personal health information in your My Healthe Vet account from any place you have an Internet connection.”
Ultimately, when veterans use the tools My Healthe Vet provides, they’ll become an active partner with their provider in understanding and managing their personal healthcare.
For more information please visit * 2

3. Introduction cont’d Internet access is important for Veterans in care
It can provide quick access to information and allows communication with their provider
Patients’ access to internet and pose critical limitations
Internet access is important for Veterans in care as
it can provide quick access to information and allows communication with their providers;
patients’ access to internet and pose critical limitations to such web-base advancements as my Healthe Vet

4. Audience Poll Have you heard of My Healthe Vet before? Yes No
Here is our first audience poll regarding the example I’m used, My Healthe Vet.
I’ll give you a moment to respond.
(comment on audience result)

5. Goal
Estimate the proportion of Veterans with internet access and an address
Assess whether these outcomes varied by patients’
Socio-demographic characteristics
Healthcare utilization characteristics
We hypothesized that marginalized groups would have less access to internet and
Our goal was to estimate the proportion of veterans with internet access and an address
and to assess whether these outcomes varied by patients’ - Socio-demographic and Healthcare utilization characteristics
We hypothesized that patients in typically marginalized groups would have less access to internet and . 5

6. Methods

7. Veterans’ Aging Cohort Study
Prospective observational cohort study of HIV+ and age/race/site matched control group of HIV- veterans in care at 8 VA facilities
Study's aim is to understand the role of co-morbid medical and psychiatric disease in determining clinical outcomes in HIV infection
Special focus on the role of alcohol use and abuse in determining clinical outcomes
A few words about VACS,
VACS is a 10 year prospective, observational cohort study of HIV positive and an age/race/site matched control group of HIV negative veterans in care, at 8 VA facilities in the United States.
The study's aim is to understand the role of co-morbid medical and psychiatric disease in determining clinical outcomes in HIV infection.
With a special focus on the role of alcohol use and abuse in determining clinical outcomes.
For more information on VACS you can visit our website at

8. Methods We used the 3rd wave of VACS
Data was collected from October 2005 to January 2007
Outcomes were self reported
Survey questions were:
“Do you have access to the internet?”
“Do you have an address?”
Response: yes or no
We used data from the 3rd wave of VACS
Data was collected from October 2005 to January 2007
Access to the internet and having an address were self reported
The survey questions were “Do you have access to the internet?” and “Do you have an address?” Response
Yes or No 8

9. Analyses
Used Chi-square(χ2), t-test, Fisher's test, and Kruskal Wallis test
Multivariable logistic regression was used to determine which factors were associated with internet access and
Descriptive analysis were conducted using Chi-square test, t-test, Fisher’s and Kruskal Wallis.
And multivariable logistic regression was used to determine which factors were associated with internet access and having an address. 9

10. Data Sample and Description of Outcome Variables
Results
Data Sample and Description of Outcome Variables
Data sample and description of outcome variables

11. Patient Demographics and Utilization (N=3931)
Variables
Overall
Age (mean) 53
Race:
White
23%
Black
65%
Hispanic 9%
Other 4%
Sex, male
94%
College/Graduate school
59%
Income >= $12000
52%
Excellent health in general (good/excellent)
66%
VA Inpatient care in the last 4 months
25%
VA Outpatient care in the last 4 months
89%
Overall patients were older, mostly black, and male; and they primarily utilized VA outpatient care
The mean age of participants was 53 years old
Our study sample consisted of 23 percent whites, 65% blacks and 9% Hispanics. And 94% were male
59% reported having a college or graduate school education and 52% an annual income of 12,000 dollars or more. 66% reported they were in good to excellent health.
25% utilized VA inpatient care and 89% VA outpatient care

12. Description of Outcome Variables N=3931
Overall
Internet access
55%
address
43%
Internet access only
13%
address only
1.4%
Both internet access and address
42%
For our outcome variables, 55% reported having internet access
and 43% reported having an address.
1.4% of patients had only,
And 13% had internet access only
42% had both internet access and an address.

13. Distribution of Internet Access by Age Group and Race
I will be going through several graphs; the blue represents whites and the red represents blacks. The y-axis is percentage.
We looked at the distribution of internet access by age group and race because we had observed from previous work, that blacks compared to whites were less likely to use computers
We also observed that older patients were less likely to use computers.
The pictorial distribution seems to support that.
blacks compared to whites had less access.
And as age group increased, having internet access decreased
Moreover, the disparity between blacks and whites becomes increasingly evident with older age groups.

14. Distribution of Where Subjects had Internet Access
We then looked at where patients access the internet
Most patients access the internet from home.
Compared to whites, blacks were substantially more likely to have access outside the home, as can be seen when you look at work, library, or other, (p value was <0.001 )

15. Home Internet Access by Race and Age
When we only looked exclusively at those who access the internet from home.
We saw that younger blacks were equally, and possible, more likely to have access overall from home
But as can be seen in the 60 years old and older age group, older blacks had substantially less internet access.
There appears to be an age*race effect, with younger people having equal or more access, but older people having less access. (p value was <0.001)
A Age*race interaction was tested and when we compared black to whites, age was an effort modifier (p value 0.04).

16. Audience Poll
Do you use or are you trying to use the internet to facilitate patient care?
Yes No
Here is a second audience poll regarding interest in using the internet.
(comment on poll results)

17. Results
Internet access
Results for internet access

18. Patient Demographic by Internet Access (N=3931)
Variables
Internet
No, n=1770
Yes, n=2161
p value
Age (mean) 56 51
<0.001
Race (%)
White 31 69
Black 49
Hispanic 50
Other 41 59
Sex (%)
Female 27 73
Male 46 54
Patient demographics by internet access showed those with access were younger, white and female.
The mean age of pts who did not have internet access was significantly higher than those who did (56 vs. 51)
69% of whites verses 51% of blacks had internet access.
and 73% of women report having internet access verses 54% of men. 18

19. Patient Demographic by Internet Access cont’d
Variables
Internet
No, n=1770
Yes, n=2161
p value
Education (%)
<0.001
High school/GED or less 62 38
College/Graduate school 33 67
Income (%)
< $12000 60 40
>= $12000 32 68
address (%) No 77 23
Yes 3 97
Health in general (%)
Poor/fair 36 64
Good to excellent 50
Those with access were also college or gradate school educated, with higher annual income, had and were in fair health.
67% of pts who were college or graduate school educated
verse 38% who had a high school education or lower had access and
68% of pts who report an annual income of $12000 dollars or more
verse 40% who had an annual income less than 12,000 dollars, had access.
97% of patient who had an address verses 23% who didn’t, had internet access.
and 64% in poor to fair health verses 50% in good to excellent health in general had access.
There appears to be no difference among those who reported good to excellent health, but
there was a difference among those who reported poor or fair health.
A possible reason for this is we were/are recruiting patients from VA clinics.
So these are patients seeking care or treatment and are most likely not in good health, but are sick.
Health in general was from the SF12 measurement.
Amy can best describe SF12 measurement if asked.
Note regarding 3%:(a person can have an address, but not access because at some point an account was set-up but never used. For example, its automatic with most school and some employers, at the library for a brief period or one time on-line purchase)

20. Health Care Utilization by Internet Access N=3931
Variables
Internet
No, n=1770
Yes, n=2161
p value
VA Inpatient care in the last 4 months
<0.001   No 43 57
Yes 52 48
VA Outpatient care in the last 4 months
0.03 50 44 56
In terms of VA use, patient who did not utilize inpatient care and those who utilized outpatient care had internet access.
48% of patients who utilized VA inpatient care had access compared to 57% who did not utilized inpatient care
And 56% of patient who utilized VA outpatient care had internet access compared to 50% who did not utilized VA outpatient care.

21. Logistic Model Internet Access N=3931
Variables OR
95% CI
Black (ref. group white)
0.44
0.37
0.53
Hispanics (ref. group white)
0.33
0.58
Age/10 years
0.54
0.49
0.59
Sex
0.68
0.48
0.96
Income ≥ $12000
2.60
2.25
3.01
College/Graduate school
2.75
2.38
3.18
VA utilization in last 4mths
Inpatient care
0.82
0.69
0.97
Outpatient care
1.35
1.07
1.70
Health in general (poor/fair)
1.44
1.23
1.67
In a binary logistic regression looking at any internet access adjusted for age, sex, income, education, general health and VA health care utilization.
We found
Blacks and Hispanics compare to whites were less likely to have internet access, Odds Ratios That is, they was more than 2 times less likely to have access.
And for each 10 years increment in age the odds of having internet access decreased by 0.54
In other words, older patients were almost 2 times less likely to have access
Those with an annual income of 12,000 dollars or more were almost 3 times as likely to have internet access compared to those who made 12,000 or less annually, odds ratio 2.60.
For those with a college education or higher the odds of having access was 2.75, almost 3 times those with a high school education or lower.
VA utilization and health in general were also associated with having internet access, but they was less pronounce. 21

22. Polytomous Logistic Regression of Internet Access
Access Elsewhere
Access at Home
Variables OR
95% CI
Black (ref. group white)
0.82
0.62
1.07
0.36
0.30
0.44
Hispanics (ref. group white)
0.69
0.46
1.05
0.38
0.28
0.52
Age/10 years
0.55
0.49
0.53
0.48
0.58
Sex
0.66
0.43
1.01
0.99
Income ≥ $12000
1.52
1.24
1.85
3.29
2.81
3.85
College/Graduate school
2.24
1.83
2.76
3.02
2.57
3.54
Used VA in last 4mths ≥1 times
Inpatient care
0.86
1.08
0.80
0.67
0.96
Outpatient care
1.26
0.91
1.74
1.39
1.79
Health in general (poor/fair)
1.47
1.19
1.82
1.42
1.20
1.68
See separate note print out for this slide

23. Audience Poll
Do you use or wish to use for patient reminders, announcements, etc.?
Yes No
Here is another poll regarding use.

24. Results
Results for having an address

25. Having an Email Address by Race and Age
So We also looked at having an address as the outcome
and similar to what we observed with internet access,
We saw that younger blacks were equally, or more likely to have an address
but older blacks were substantially less likely, As can be seen in the age group 60 years and older, (p value was <0.01) to have an address

26. Patient Demographic by Email (N=3931)
Variables
No, n=2236
Yes, n=1695
p value
Age (mean) 55 51
<0.001
Race (%)
White 40 60
Black 62 38
Hispanic 61 39
Other 56 44
Sex
Female (%) 36 64
Male (%) 58 42
When we look at demographics by , our findings were similar to those of internet access; patients were younger, white and female
The mean age of subjects who did not have was significantly higher than those who did (mean age 55 vs. 51)
60% of whites reported having compares to 38% of blacks
And 64% of females compared to 42% of males had an address.

27. Patient Demographic by Email cont’d
Variables
No, n=1770
Yes, n=2161
p value
Education
<0.001
High school/GED or less 74 26
College/Graduate school 45 55
Income
< $12000 72 28
>= $12000 43 57
No internet 97 3
Internet 24 76
Health in general
Poor/fair 48 52
Good to excellent 62 38
Looking at education, income and health; those with a college and graduate school education, higher annual income and fair health, had .
Those with a high school education verse a college education were less likely to have an address, 26% vs. 55%
And having a lower annual income of 12,000 compare to 12,000 or more. Means you were less likely to have an address, 28% verses 57%.
If you had internet access you were more likely to have .
And patient who reported poor or fair health were more likely to have compared to pts who reported good to excellent health. 52% compared to 38%

28. Health Care Utilization by Email N=3931
Variables
No, n=2236
Yes, n=1695
p value
VA Inpatient care in the last 4 months
<0.001 No 54 46
yes 66 34
VA Outpatient care in the last 4 months
0.05 61 39 56 44
Those who had were less likely to utilize inpatient care and more likely to utilize outpatient care.
34% of patients who utilized VA inpatient care had compared to 46% who did not utilized VA inpatient care
And 44% of patient who utilized VA outpatient care had compared to 39% who did not utilized VA outpatient care.

29. Logistic Model Email Address N=3931
Variables OR
95% CI
Black (ref. group white)
0.37
0.31
0.44
Hispanics (ref. group white)
0.41
0.55
Age/10 years
0.53
0.48
0.58
Sex
0.62
0.45
0.86
Income ≥ $12000
2.62
2.27
3.04
College/Graduate school
2.99
2.57
3.48
VA utilization in last 4mths
Inpatient care
0.72
0.60
0.85
Outpatient care
1.28
1.01
1.63
Health in general (poor/fair)
1.46
1.25
1.70
Similar with internet access, in our model adjusting for age, sex, income, health and VA health care utilization, blacks and Hispanics compared to whites were significantly less likely to have an address. Odds ratios 0.37 and 0.41
That is, Blacks were almost 3 times less likely and Hispanic 2 times less likely, after controlling for these potential confounders.
And for each 10 years increment the odds of having an address decreased by 0.53
as in the internet access results, older patients are 2 times less likely to have an address.
Patients with high annual income and education were 3 times more likely to have . Odds ratios 2.62 and 2.99
Utilizing VA care and health was also associated with , but they were less pronounce

30. Limitations
Our sample consisted of older patients who were mostly male, which limits its generalizability
Outcomes and explanatory variables were self-reported, which can give rise to non-differential misclassification, from either over-reporting or underreporting
We did not assess the specific type of internet use, information sought or frequency of use
Our study had several limitations,
our sample consisted of older patients who were mostly male, which limits its generalizability.
Outcomes and explanatory variables were self-reported,
which can give rise to non-differential misclassification,
from either over-reporting or underreporting, and a bias towards the null
Since our findings were significant despite this limitation it is most likely that the association is even stronger.
We did not assess the specific type of internet use, information sought or frequency.
It is possible that thought blacks, Hispanics and older patients are less likely to have access, among those who do,
they access health care information similarly to whites and younger patients.
Further study is need to assess the impact on health care decisions and outcomes.

31. Conclusions and Implications

32. Conclusions
Veterans’ access to internet and varies by race and age in our sample
Among veterans in care in the VA who were surveyed in our study, blacks were
2 times less likely to have internet access compared to whites
3 times less likely to have an address
This race disparity was worse among older patients
Veterans’ access to the internet and varies by race and age in our sample
Among veterans in care in the VA who were surveyed in our study, blacks were
2 times less likely to have internet access compared to whites AND 3 times less likely to have an address.
And these strong associations were not explained by age, sex, education, income, or health (potential confounders which we controlled for).
In particular, this race disparity was worse among older patients. 32

33. Conclusions cont’d
For each 10 years increment in age, patients were 2 times less likely to have both internet access and
Higher education and annual income were strongly associated with internet access and
Poor health, and VA utilization were also associated but were less pronounced
The findings either by or internet access were the same
For each 10 years increment in age, patients were 2 times less likely to have both internet access and
Higher education and annual income were strongly associated with internet access and .
Poor health, and VA utilization were also associated but were less pronounced.
Again a possible explanation for poor health being associated with internet access and is the way veterans were recruited.
we recruited patient from VA clinics.
Therefore, they were most likely be ill and seeking treatment
The findings either by or internet access were the same

34. Implications
Minorities and older individuals are vulnerable groups that are less likely to have access to care and more likely to have health problems
Those who need access to care most are less likely to have internet access and
To expand the use of electronic communication for patient services we must address these disparities in internet access and
Disparities in access to health care information may exacerbate current disparities in health care outcomes
The Implications are…
Minorities and older individuals are vulnerable groups that are less likely to have access to care and more likely to have health problems
Those who need access to care most are less likely to have internet access and
To expand the use of electronic communication for patient services we must address these disparities in internet access and
Disparities in access to health care information may exacerbate current disparities in health care outcomes
Information disparities across race may limit their participation in a shared decision-making healthcare environment 34

35. Acknowledgements PI and Co-PI: AC Justice, DA Fiellin
Scientific Officer (NIAAA): K Bryant
Participating VA Medical Centers: Atlanta (D. Rimland, C Jones-Taylor), Baltimore (KA Oursler, R Titanji), Bronx (S Brown, S Garrison), Houston (M Rodriguez-Barradas, N Masozera), Los Angeles (M Goetz, D Leaf), Manhattan-Brooklyn (M Simberkoff, D Blumenthal, J Leung), Pittsburgh (A Butt, E Hoffman), and Washington DC (C Gibert, R Peck)
Core Faculty: K Mattocks (Deputy Director), S Braithwaite, C Brandt, K Bryant, R Cook, J Conigliaro, K Crothers, J Chang, S Crystal, N Day, J Erdos, M Freiberg, M Kozal, N Gandhi, M Gaziano, M Gerschenson, B Good, A Gordon, M Hernan, K Kraemer, J Lim, S Maisto, P Miller, L Mole, P O’Connor, R Papas, J Robins, C Rinaldo, M Roberts, J Samet, B Tierney, J Whittle, N Kim
Staff: D Cohen, A Consorte, K Gordon, F Kidwai, F Levin, K McGinnis, M Rambo, J Rogers, M Skanderson, Jan Tate
REAP/PRIME: R Kerns, AC Justice, A Heapy, C Brandt, P Leggett, D Pecirep, D Barry, J Beauvais, W Bellmore, M Carrithers, J Erdos, D Fiellin, L Fraenkel, M Ghori, J Goulet, G Grass, S Haskell, R Heimer, S Jeffrey, J King, A Lisi, A Lo, K Mattocks, R Papas, P Rosenberger, C Ruser, R Schottenfeld, M Shulman, R Sinatra, D Vogel, K Yaggi
Major Collaborators: Immunology Case Registry, Pharmacy Benefits Management, Framingham Heart Study, Women’s Interagency HIV Study, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Health Economics Research Center (HERC), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD
Funded by: National Institute on Alcohol Abuse and Alcoholism (U10 AA 13566); Robert Wood Johnson Generalist Faculty Scholar Award; an Inter-Agency Agreement between National Institute on Aging, National Institute of Mental Health, and the Veterans Health Administration; the VHA Office of Research and Development; and, VHA Public Health Strategic Health Care Group.
I’ll like to acknowledge the VACS team, HSR&D, REAP/PRIME and our funders the NIAAA.
Thank you. 35

36. Questions? Questions? Reference List:
Norris P. (2000) Digital Divide? Civic engagement, information poverty and the internet in democratic societies, John F Kennedy school of government, Harvard University.
Bucy EP. (2000) “Social Access to the Internet.” Press/Politics 5(1): 50-61
Lorence DP, Heeyoung P, Fox S. “Racial disparities in Health Information Access: Resilience of the Digital Divide.” J Med Syst (2006) 30:
Lorence, Park and Fox in 2006 wrote an article on “racial disparities in health information access: Resilience of the digital divide” in which
The showed Blacks and his panics were 2 times less likely to use computers compared to whites. Odds ratios 0.55 and 0.59 respectively.
They results were based on a cross-sectional nation wide study of internet use and information search in 2000 and 2002.
They too recognized that despite ambitious technology advancement efforts, little has been done to examine the availability of internet resources across underserved groups.