1. Surveillance Databases: Clinical Case Registry (CCR), NSQUIP, and VACS
Amy C. Justice
8/17/2006

2. Outline What is a surveillance database?
What are the strengths and weaknesses of VHA for surveillance?
What does it take to develop and maintain a surveillance database?
How can you work with a surveillance database to conduct your own research?
8/17/2006

3. Surveillance Surveillance (Concise Oxford Dictionary)
“Supervision, close observation, especially of a suspected person.”
8/17/2006

4. What Do We Want to Monitor?
Infections
Epidemics: HIV, HCV, “bird flu”
Hospital infections (C. difficile; post op; MRSA)
Homeland security: plague, etc.
Cancer
Injuries
Quality of care/ patient safety
Infant mortality (cause of death)
Pharmacoepidemiology
Treatment benefit in average patients
Treatment harm in average patients
8/17/2006

5. Approaches to Health Surveillance
Population level
Gold standard
Difficult and expensive
Clinical population (mandated for some)
Sentinel clinical sites (common)
EMR based health system (emerging)
How do those in care differ from the whole?
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6. Epidemiology Vs. Clinical Epidemiology
How do folk who come in for care differ from those who don’t?
They are motivated to seek care
Prevalence/ severity/ rate of progression
They think care will do them some good
They have access to care
Economic/Education/Other Resources
Can make time to come in
Understand how to use care
8/17/2006

7. Veterans Vs. Veterans in Care
Veterans are different from the general population
More likely to be men
Health and literacy screening at enlistment
Veterans in care in VHA are different from other veterans
15% of veterans use VHA
Poorer, urban, more likely to be male
Sicker, older, more comorbidity
Less likely to have alternative health insurance
8/17/2006

8. Data on the Socioeconomic Status of Veterans
Robert E. Klein, Ph.D., Office of the Actuary
Donald D. Stockford, M.A., Veterans Health Administration
May 2001
8/17/2006

9. Male Veterans and Nonveterans*
Socioeconomic Status
Education
Male Veterans and Nonveterans*
Percent
Educational Attainment
8/17/2006
* Age 20 and Over
Source: Current Population Survey, March 1999

10. Socioeconomic Status Education:
In 1999, significant differences existed between male veterans and nonveterans in their highest level of education attained:
12% of male veterans had not graduated from high school compared to 18% for male nonveterans.
A higher proportion of male veterans (65%) than male nonveterans (56%) had at least a high school education or had completed 1 to 3 years of college.
Male nonveterans are more likely than male veterans to have completed 4 or more years of college (26% vs. 23%).
Source: Current Population Survey, March 1999
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11. Socioeconomic Status
8/17/2006

12. Socioeconomic Status Education (Cont’d):
About the same percentage of male veterans as male nonveterans had at least some some college (52% vs. 51%).
However, a higher percentage of male veterans “40-54” (mostly Vietnam era) than male nonveterans of that age had at least some college (61% vs. 57%).
Also, a higher percentage of male veterans age “20-39” (post-Vietnam and Gulf War era) than similarly aged male nonveterans had at least some college (54% vs. 52%).
Source: Current Population Survey, March 1999
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13. Median Personal Income of Male Veterans and Nonveterans by Age
Socioeconomic Status
Median Personal Income
of Male Veterans and Nonveterans by Age
March 1999
Median Income ($000s)
Age
8/17/2006
Source: Current Population Survey, March 1999

14. Socioeconomic Status Personal Income:
In general, personal income in 1999 was higher for male veterans than male nonveterans due, in part, to differences in their age and to possible differences in job skills and training.
The median income of $28,800 for male veterans was 9% higher than the median of $26,400 for their male nonveteran counterparts.
The median income of $37,100 for male veterans age “40 -54” (Vietnam era) was the highest, and the median of $14,600 for male nonveterans age “65 or over” was the lowest.
Source: Current Population Survey, March 1999
8/17/2006

15. Socioeconomic Status Unemployment Rates for Veterans and Nonveterans
by Veteran Status and Sex, Jan. 1, Dec. 31, 1999
Percent
Sex
8/17/2006
Source: Data are annual averages from the monthly Current Population Survey, 1999

16. Socioeconomic Status Unemployment:
In 1999, the annual average (of the monthly data for January through December 1999) unemployment rate of 3.2% among veterans was lower than the 3.7% rate for their nonveteran counterparts.
Similarly, the 3.1% unemployment rate among male veterans was lower than the 3.6% rate among male nonveterans.
However, the 4.6% unemployment rate for female veterans was higher than the 3.7% rate for their female nonveteran counterparts.
Source: Current Population Survey data for CY 1999
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17. Uninsurance Among Veterans and VA Users
Socioeconomic Status
Uninsurance Among Veterans and VA Users
1993
Percent Uninsured
8/17/2006
Source: National Survey of Veterans

18. Socioeconomic Status Health Insurance:
VA users of inpatient and outpatient care have greater health insurance coverage problems than veterans in general.
About 9% of all veterans, most of whom are male, were uninsured at the time of the National Survey of Veterans interview in This compares to 21% of VA users.
Among veterans “under 65”, 13% were uninsured in 1993, while among VA users “under 65”, 29% were uninsured.
Nearly all veterans age “65 or over” are covered by Medicare; only about 1% are not. However, among VA users age “65 or over” about 7% are uninsured.
Source: 1993 National Survey of Veterans
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19. Socioeconomic Status
Special Needs Veterans Percent of Male Veterans and Nonveterans in Poverty
5.7
Source: Decennial Census
8/17/2006

20. Socioeconomic Status Special Needs Veterans Poverty:
In March 1990, only 5.7% of all veterans were at or below the poverty level compared to 9.1% of all adult U.S. males.
Most states with poverty rates above 5.7% among veterans were in the South and Northwest. Generally,states in New England and the Mid-Atlantic had the lowest veteran poverty rates.
Source: Decennial Census
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21. Socioeconomic Status
Incarcerated Veterans Male Veterans and Nonveterans in Correctional Institutions 1990 and 1997
Source: Decennial Census and Bureau of Justice Statistics
8/17/2006

22. Socioeconomic Status Special Needs Veterans (Cont’d) Incarceration:
In March 1990, about 166,000 male veterans were in prisons. This represents a rate of 636 per 100,000 veterans, half the rate of nonveteran adult males.
By 1997, the rates of incarceration increased for both male veterans and nonveterans, but the rate for veterans was still about half that of nonveterans.
The lower rate for veterans is explained in part by a smaller proportion of veterans in the young age groups which make up most of the prison population.
Source: Decennial Census
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23. Socioeconomic Status
Inmates of Federal, State, and Local Correctional Facilities by Veteran Status
Violent offenses
Property offenses
Drug offenses
Public-order or other offenses
Local jails
Violent offenses
Property offenses
Drug offenses
Public-order or other offenses
Federal
prisons
Violent offenses
Property offenses
Drug offenses
Public-order or other offenses
State prisons
Percent
8/17/2006
Source: Bureau of Justice Statistics data

24. Socioeconomic Status Special Needs Veterans (Cont’d)
Incarceration (Cont’d):
More recently, the Bureau of Justice Statistics in the Department of Justice sponsored surveys in 1996 and 1997 on inmates in state, federal, and local correctional facilities. Data on veterans were included.
In 1997, 225,700 veterans, or less than 1% of all veterans, were in prisons and jails.
Males made up most of the prison population. Among veterans in all correctional facilities, 99% were male compared to 89% of nonveterans in jail, 92% of nonveterans in federal prisons, and 93% of nonveterans in state prisons.
Source: Bureau of Justice Statistics
8/17/2006 19

25. Socioeconomic Status Special Needs Veterans (Cont’d)
Incarceration: (Cont’d):
Most veterans in state prisons, 55%, were sentenced for violent offenses, compared to 46% of nonveterans.
Most veterans (51%) as well as most nonveterans (65%) in federal prisons were sentenced for drug offenses.
The most frequent offenses for veterans in local jails were public order or other offenses (31%), and for nonveterans they were property offenses (27%).
Age and socioeconomic differences account in part for the differences in types of offense committed by male veterans and nonveterans. Because male veterans are, on average, older and more likely to be employed before imprisonment, they are more likely to be sentenced for violent crimes and less likely for drug and property crimes than their nonveteran counterparts.
Source: Bureau of Justice Statistics
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26. Homelessness Male Veterans and Nonveterans in Shelters 1990
Socioeconomic Status
Homelessness Male Veterans and Nonveterans in Shelters 1990
Source: Decennial Census
8/17/2006

27. Socioeconomic Status Special Needs Veterans (Cont’d) Homelessness:
Hard data on the homeless population are difficult to obtain. The last Census counted 39,000 male veterans in emergency homeless shelters in March This understates the extent of the problem because many homeless do not reside in shelters.
The total number of male veterans in homeless shelters was about half the number of adult male nonveterans in shelters.
But the rate of residence in shelters was higher for male veterans (149 per 100,000) than for adult male nonveterans (126 per 100,000).
The rates were also higher for Black veterans than for White and Hispanic veterans.
Source: Decennial Census
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28. Mental Illness Male Veterans and Nonveterans in Mental Hospitals 1990
Socioeconomic Status
Mental Illness Male Veterans and Nonveterans in Mental Hospitals 1990
Mental Hospital Patients
8/17/2006
Source: Decennial Census

29. Socioeconomic Status Special Needs Veterans (Cont’d) Mental Illness:
The 1990 census counted 14,000 male veterans in mental hospitals compared to 50,000 adult male nonveterans.
For every 100,000 male veterans in the veteran population in 1990, there were 54 male veterans in mental hospitals compared to 87 per 100,000 adult male nonveterans.
Veterans under age 40 are somewhat more likely to be in mental hospitals than male nonveterans of that age; at age “40 or over”, however, male veterans are less likely than male nonveterans to be in mental hospitals.
Source: Decennial Census
8/17/2006 30

30. Selected VA Programs Healthcare Enrollment (Cont’d) Current Enrollees:
As of September 27, 1999, there was a total of 4,068,965 veterans enrolled in the VA Healthcare Enrollment Program.
Priority Groups 5 and 7, which include nonservice-connected veterans, account for about 59% of all enrollees.
More than 80 percent of enrolled veterans belong to Priority Groups which require no copayment.
Total enrollment is expected to peak in the near future.
Source: Veterans Health Administration data
8/17/2006 35

31. Contacts
Robert E. Klein Donald D. Stockford
8/17/2006 52

32. National Health Information System (HIS)
Veterans Health Information Systems and Technology Architecture (VistA):
Decentralized Hospital Computer Program (DHCP) employing mini-computers, MUMPS, and table-driven reusable modules, >99 Packages including: 16 infrastructure applications; 28 administrative and finanancial applications; and 55 clinical applications
Derivative Registries, Health Data Repositories, Centralized Administrative Files, etc., must pulled individually from each facility or facility complex on a regular basis.
8/17/2006

33. Examples of Derivative Datasets
National Patient Care Database (Administrative Data)
Diagnostic and procedure codes
Utilization data
Beneficiary Information Records Locator System (BIRLS)
SSNs, State and Date of death
Pharmacy Benefits Management (PBM)
Outpatient medications (inpatient in progress)
Decision Support Systems (DSS)
Plus cost estimations per patient per year
Surveillance Databases (+ input by local nurse coordinator):
HIV/HCV Registry (Clinical Case Registry)
National Surgery Quality Improvement Program (NSQUIP)
Will be covered in a separate talk
8/17/2006

34. NSQIP National Surgical Quality Improvement Program
Response to high post operative mortality rates within VHA in mid 80s
Began in 1991, in 44 VHA centers doing major surgery
Expanded subsequently
Chart extraction for
Presurgical risk factors (diabetes, hypertension etc)
Process of care during surgery
30 day surgical outcomes including
Access: IRB approval, protocol and application to NSQIP, likely data use agreement
Contact: Lozel Greenwood, RN
8/17/2006

35. The Clinical Case Registry from VHA Care Quality Management
Lawrence Mole, PharmD
8/17/2006

36. History
Immunology Case Registry started 1982 in order to track the new HIV epidemic
Originally focused on CDC reporting requirements and projecting utilization
Originally included only demographic data and CD4 cell counts
No one maintained the system
~1996 Taken over by VHA Public Health Strategic Health Care Group
Nearly 10 years have been invested in making it a usable database
8/17/2006

37. History Continued Laboratory “pointers”
Clarifying who should and shouldn’t be in the registry
Site to site variation in entry practices
Major effort to train nurse coordinators and clinic directors across the country in the use of the system
8/17/2006

38. Goal
To create a real time surveillance database of all veterans in care who are HIV or HCV infected in order to monitor
Healthcare utilization and costs
Quality of care
Treatment toxicity
Healthcare outcomes
8/17/2006

39. CCR Combined registry of HIV and HCV infected veterans in care
Positive lab tests must be confirmed by nurse coordinators at each facility
Coordinators can also enter individuals testing positive on the outside
Nightly downloads from VistA to special relational database housed in Austin, TX
8/17/2006

40. Objectives of New CCR CQM Reports
Created for both hepatitis C and HIV populations
Separate report for Co-infected in the future
Provide summary information for
Administrative decision making
Quality projects
Safety
Research (feasibility and protocols)
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41. Audience Front Line Clinicians Administrators Other Federal Agencies
How does my VA compare with other VAs?
Administrators
Budget, Staffing
Other Federal Agencies
HRSA, FDA
Researchers
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42. Report Types Population Diagnoses Registry Medications
Quality and Safety– to come
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43. Population Reports Feature demographics for Nation, VISN, and Station
Include patients “In Care” defined by utilization in:
Admissions
Lab tests
Outpatient prescriptions
Outpatient visits
Radiology
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44. Population Reports - Demographics
In Care
How many receive care at which VAs and VISNs over set time periods?
Who are the veterans in care?
Sex, Age, Race, Ethnicity
How mobile are they?
% seen at multiple VISNs and Stations
8/17/2006

45. Report Types – Selected Diagnoses
Use Inpatient and Outpatient visit sources
2 OP codes on different dates or 1 discharge diagnosis
leaving out Problem lists for now
Focus on 12 key clinical areas
Period covered: 2002 to present
Report on veterans who
Have ever had the diagnosis in VA
Have their first ever diagnosis in VA in a given year.
8/17/2006

46. Report Types – Selected Diagnoses
Anemia
Bloodborne viral Dz
CV conditions
GI conditions
Liver disease
Malignant neoplasms
Mental illness
Metabolic disorders
Pulmonary disorders
Renal disorders
Substance use
8/17/2006

47. Report Type – Registry Medications
FDA approved meds only
Hepatitis C: INF, pegINF, RBV
ARVs
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48. Report Type – Registry Medications
Number of Unique Patients who received at least one prescription fill for:
Any ARV
By Drug Class (nRTI, nnRTI, PI, FI)
Individual product
Individual ingredient
Calculated for FY and CY – 1997 to present
8/17/2006

49. Standard ARV Reports
Number of ARV experienced patients who have switched or added new ARVs in a quarter
National
By ingredient
Dispensed regimens
See examples
8/17/2006

50. Now For the Fun Part: A Project
Science
What is the primary question?
What data do you need?
What other data do you want?
Resources
Trade off between sensitivity and specificity?
What can you spend?
How much time do you have?
Methods
8/17/2006

51. Science
8/17/2006

52. Primary Question
How can we change the care provided to those with HIV infection to efficiently improve outcomes?
--“Where is the low hanging fruit?”
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53. Patient Outcomes in HIV in 2006
Aging
Comorbid Disease, Behaviors, & NonARV Toxicity
HIV
HIV Treatment
8/17/2006

54. Hypothesis Observations Primary Hypothesis
HIV is now a chronic disease complicated by comorbid illness and behaviors (comorbidity).
Comorbidity has a major modifiable impact on outcomes directly and by decreasing HAART benefit
Primary Hypothesis
We can make a substantial difference in HIV outcomes by improving the management of comorbidity
8/17/2006

55. What Do We Need? Records of all treatment (HIV and other)
VA Pharmacy Records
Outside Pharmacy Records?
Measures of comorbid diagnoses and behaviors
Diagnoses: codes vs. full chart review vs. standardized screening
Behaviors: difficult to get from medical record
Outside VA?
Measures of frailty
Functional status
Physiologic measures
Measures of outcome (usual suspects)
Mortality (BIRLS plus discharged “dead” plus SSN)
Morbidity (symptoms, function, quality of life)
Utilization (hospitalizations, clinic visits, overall costs)
Outcomes outside VA?
8/17/2006

56. What Do We Want on the Patient?
Health behaviors
Diet
Exercise
Alternative medicines
Drugs, Alcohol, Cigarettes
Health beliefs
Readiness to change behaviors
8/17/2006

57. What Data Do We Want on Provider?
Training
Experience
Health care beliefs
Assessment of patient
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58. What Can We Get on NonVA Care?
Patient report—variable accuracy
Medications
Hospitalizations
Clinic Visits
Medicare
Medicaid—very difficult
8/17/2006

59. Resources
8/17/2006

60. Sensitivity and Specificity I
Routine data is specific, but not sensitive
e.g., if a chart note says the patient has pneumonia, they likely do. However, not all pneumonias are identified.
ICD-9 codes can be made more specific if you require 2 outpatient codes
This may imply that those with more severe disease are more likely to be identified
An issue if you want to identify prevalence or incidence
Not so much if you want to compare two groups with the same source of data
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61. Sensitivity and Specificity II
In comparative analyses
Major impact may only be power
Need to consider possibility of bias
Look at Tables 2 and 4: Justice et al, Medical Disease and Alcohol Use Among Veterans with HIV infection: A Comparison of Disease Measurement Strategies Medical Care 2006;44:S52-S60
8/17/2006

62. Money Major constraint—don’t try to do more than your budget allows
Build up from foundation of “smaller” study
Size refers to budget rather than numbers
Can be small with respect to depth of data or number of subjects
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63. Time If your study requires patient contact, allow a lot more time
IRB reviews at each site
Hiring staff
Training staff
Setting up data flow if not all electronic
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64. Methods
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65. Nature of Study Analytic design implies data design
Cross sectional (simplest analysis)
Baseline to outcome
Longitudinal (can get quite complex)
Retrospective/Prospective may have varying effects on missing data
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66. 8/17/2006

67. VACS Goals
Describe effects of aging, medical and psychiatric comorbid disease, substance use, and drug toxicity on patient outcomes in HIV.
Compare these effects among those with and without HIV infection.
Identify modifiable mediators of survival; quality of life; and incidence and severity of HIV and comorbid disease.
Design and implement large scale strategy trials using the EMR to improve these outcomes.
8/17/2006

68. VACS Consistent Themes
VA ID and GIM clinics
All HIV positive patients seen are eligible
GIM patients “matched” to HIV patients
All electronic medical record (EMR) data
Self completed surveys supplement EMR
Permission to recontact
Followed for long term outcomes
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69. VACS
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70. 8/17/2006

71. Build on the Incredible Resources Available in VHA
National EMR, fully implemented and integrated across inpatient and outpatient care
Complete coverage of HIV medications
Fill data for pharmaceuticals
Easy access to appropriate comparators
Understudied special population
National system for cooperative studies
8/17/2006

72. Table 5 – VACS Data Sources and Items used in Addition to the National Veterans Health Information System
Source of Measure Sample Collected* Reference
Patient Survey Items (Self Completed by Patient)
Comorbidity Veterans Health Survey 3, 5, B, F1, F2, F3
AIDS Defining Conditions VACS 5 5, B, F1, F2, F3 Dombrowski, MC-UR
Health & Habits (Weight, Height, Exercise) Veterans Health Survey 5, B, F1, F2, F3
Alternative therapies VACS 3 3, 5, B, F1, F2, F3
Homelessness VACS 3 5, B, F1, F2, F3
Alcohol (Hazardous Use, Screening) AUDIT or AUDIT-C 3, 5, B, F1, F2, F3
Alcohol (Consequences) Short Inventory of Problems (SIPS) B, F1, F2
Alcohol (Dependency) Alcohol Dependency Scale (ADS) F1, F2
Alcohol (Expectancies) F2
Alcohol (Symptoms) F1
Alcohol (Readiness to Change) SOCRATES, RTC Ladder F2, F3
Tobacco Packs Per Day 5, B, F1, F2, F3
Tobacco Readiness to Change RTC Ladder for Smoking F2, F3
Drugs (Illicit drugs) DAST-10 3, B, F1, F2, F3
Drugs (Prescription drug abuse) F2, F3
Drugs Readiness to Change RTC Ladder for Drugs F2, F3
Risky sexual and drug use behavior Centers for Disease Control 5, B, F1, F2, F3
Social aspects of health, social contacts & coping HCSUS 5, B, F1, F2, F3
Religiosity/spirituality Elder Care Research Center 5, B, F1, F2 E. Kahana
Health care utilization & accessibility Veterans Health Survey 5, B, F1, F2
Trust in provider/quality of care Veterans Health Survey 3, 5, B, F1, F2, F3
Medical care & insurance outside VHA Veterans Health Survey 3, 5, B, F1, F2, F3
Medication adherence AIDS Clinical Trails Group 3, 5, B, F1, F2, F3
Symptom burden AIDS Clinical Trails Group 3, 5, B, F1, F2, F3
Symptoms of Depression & Anxiety PRIME MD, BECK, BECK 3, 5, B, F1, F2, F3
Health Related Quality of Life & Functional Status Short Form 12, Medical Outcomes Study 3, 5, B, F1, F2, F3
Demographics: Education/Race/Ethnicity/ Marital Status/Employment/Date of Birth/Sex/Income Veterans Health Survey 3, 5, B, F1, F2
Provider Survey Items (Self Completed by Provider)
Provider Characteristics VACS 5 5, B, F1, F2, F3
Provider Assessment of Patient VACS 3 3, 5, B, F1, F2, F3
Cause of Death CHORUS One Time Form Fusco, MC-UR
Telephone Interview UCSUR, University of Pittsburgh
Alcohol use (current and past) 30 Day TLFB, Lifetime Drinking History F1, F2
Medication Adherence Modified 30 Day TLFB F1, F2
Diagnosis of Abuse or Dependence CIDI Substance Abuse Module F1
Blood Samples (VACS 5 substudy n=252)
AST, mAST, cAST, M. Gerschenson, U. of Hawaii 5
B12, Folate, Homocystine, Cytokines D. Taub, NIA Intramural Program 5
Neuropsychiatric Testing
HNRC Neuropsychiatric Battery HIV Neurobehavioral Research Ct.,
University of California San Diego 5
*3=VACS 3; 5= VACS 5; B=Baseline Full Study; F1,F2,F3 correspond to follow up 1, 2, or 3 in the Full Study.
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73. 8/17/2006

74. Group Matching of Comparators
Done to insure that comparators are similar to those with HIV but for HIV status
Using VA data identified targets using HIV positives age/race distributions at each facility
Does not obviate the need to adjust for age and race in the analyses
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75. 8/17/2006

76. 8/17/2006

77. Use VHA Resources to Understand Sample
National data allows comparison of sample with those with condition but not in sample
Site data allows comparison with those not enrolled
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78. Follow-up on VACS Subjects as of 06/05 HIV Positive N Mortality
Rate Lost/Yr Replacement
VACS % 1.9% 5.2%
VACS % 1.9% 4.3%
Full Study % 1.9% 5.0%
HIV Negative
VACS % 5%
Full Study % 4.7%
8/17/2006

79. It Takes a Village…
8/17/2006

80. What I Didn’t Learn in Fellowship
How to make, monitor, and modify budgets
How to hire, promote, and fire
How to document and store data
How to talk to programmers
How to delegate responsibility, yet supervise effectively
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81. 8/17/2006

82. 8/17/2006

83. Veterans Aging Cohort Study
PI and CoPI: A. Justice, J. Conigliaro
Participating VA Medical Centers: Atlanta, Bronx, Baltimore, Houston, Manhattan-Brooklyn, Pittsburgh, and Washington DC
Site PI/CoPIs: D. Rimland, C. Jones-Taylor, S. Brown, S. Garrison, M. Rodriguez-Barradas, N.Masozera, A Butt, E. Hoffman
Faculty: S. Braithwaite, S. Fultz , K. Crothers, J Goulet, M Freiberg, J. Erdos, P. Miller, C. Rinaldo, M. Gaziano, R. Cook, K. Kraemer, S. Crystal, N. Day, K. Bryant, P. O’Connor, D Fiellin, J. Samet, S. Maisto, B. Tierney, J. Whittle, B. Goode
Staff: D. Gibson, M. Hall, D. Reddy, K. McGinnis, M. Skanderson, T. Griffith, K. Gordon, S. Kowalsky, L. Timko, A. Mckeon
Major Collaborators: Immunology Case Registry, Pharmacy Benefits Management, Framingham Heart Study, Womens Interagency HIV Study, MAVERIC, Health Economics Research Center (HERC), (Center for Health Equity Research and Promotion (CHERP), Collaborations in HIV Outcomes Research-US (CHORUS)
Major Funders: National Institute of Alcoholism and Alcohol Abuse, National Institutes of Medicine (NIH); Robert Wood Johnson Foundation; National Institute on Aging, NIH; National Institute for Mental Health, NIH; Public Health Strategic Healthcare Group and the VA Office for Research and Development.
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84. Availability Majority of the reports available in August 2006
Regimens, Quality and Safety
VA intranet with 13b contact list members notified
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85. How Can You Work With Existing Surveillance Databases
Get them interested in your question
Clearly define the data you need based on the data they have
Water on a rock
8/17/2006