Objectives
„
Demand Driven Research:
The HIV Research Network
„
Focus on health services delivery to persons
with HIV infection
Key issues concern:
„
Kelly A. Gebo MD MPH
for the HIV Research Network
„
„
June 8, 2004
„
HCSUS
„
„
„
„
„
„
HIV Costs and Services Utilization Study
Preceded HIVRN
Collected data in 1996
- 1998
Obtained nationally representative sample of
2,864 HIV+ patients in care
Probability sample permits strong inferences to
national population
Unique data on utilization, clinical symptoms,
outcomes in HIV patients
HIV Research Network (HIVRN)
„
„
„
„
Trade
- off representativeness for efficiency and
large sample size.
From HCSUS: Most HIV+ seen by providers
with relatively large HIV caseloads.
Recruit providers of HIV care and extract
information from medical records.
Supplement records data with personal
interviews.
Frequency of use of inpatient and outpatient care,
and the costs of providing these services
Use of and adherence to antiretroviral
medications
Access to care and socioeconomic disparities in
utilization
Quality of care and patient safety
HCSUS-- Limitations
„
„
„
„
Recruiting a nationally representative sample
is extremely expensive and time
- consuming
Over 1 year to accrue baseline sample
Sample becomes unrepresentative of
population over time, unless refreshed.
Difficult to obtain medical records from
providers not linked to study
HIV Research Network
„
„
Network of HIV care providers who can collect
and transmit clinical and health services
utilization for aggregate analyses to a
coordinating center
Provide up
- to
- d
ate data on:
„ Resource use and costs of care
„ Clinical outcomes of care
„ Linked clinical/resource use outcomes
1
HIV Research Network
Site Population
„
<500
13 Adult, 2 Pediatric only, 3 Adult and Pediatric
500
- 1000
>1000
Alameda, Oakland*
Alliance, Boston *
Nemecheck, Kansas
City*
CHOP-Peds
St. Judes-Peds
St. Lukes-Peds
UCSD-Peds
CORE-Peds
Phoenix, AZ *
Drexel
Henry Ford
Montefiore*
OHSU
Rochester *
21 HIV primary and specialty care sites CY 2004
Sample Size
*Community-based
Operations
„
1999
2000
2001
„
10,852
19,410
17,582
„
„
7,887
„
12,345
4,342
„
„
Sites individually collect information electronically
and by chart abstraction
De-identified information sent to Central Data
Coordinating Center (DCC)
Data cleaned, quality assured
Reports sent back to sites for confirmation of data
Compatible, multisite database created
Preliminary data analysis at DCC
Data Dissemination
„
„
„
Operations
„
Feedback
„
„
„
„
CORE-Chicago
Johns Hopkins
Montefiore
Parkland
St. LukesRoosevelt
UCSD
Wayne State
Project officers meeting monthly
Data Subcommittee calls 6x per year
Full Committee calls quarterly
Intranet website
Abstracts, posters, papers
„ Submission of research ideas, ideas for new
variables
„ Interview questions
„ All contact information
Data disseminated to investigators after research
question proposed, data analysis approved by data
subcommittee
Interactive data querying system on the internet
Public use data available
Resource Utilization Data
„
Acute/chronic hospital care
„
„
„
Outpatient Visits
„
Dates of service
Diagnoses
„
CPT Coding
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„
„
„
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Admission/Discharge dates
Diagnoses
Emergency Department
Substance Abuse/Mental Health Visits
Insurance
2
Demographic Characteristics of
CY 2001 Sample (N=10,556)
Median Age (Range)
Male
Race
African-American
Caucasian
Hispanic
Other/Unknown
HIV Risk Factor
MSM
Heterosexual
IDU
MSM IDU
Heterosexual IDU
Other/UK
40 (18 – 89)
7,571 (71.7%)
5,070 (48.0%)
3,282 (31.1%)
2,017 (19.1%)
187 (1.8%)
4,021 (38.1%)
3,432 (32.5%)
1,383 (13.1%)
344 (3.3%)
544 (5.2%)
832 (7.9%)
Insurance Coverage
Medicaid
31.9%
Uninsured
31.4%
327
1,076 (10.2%)
2,076 (19.7%)
4,370 (41.4%)
3,034 (28.8%)
Viral Load
Median
<10K
10-100,000K
>100K
1,311 copies/ml
6,774 (64.2%)
2,386 (22.6%)
1,396 (13.2%)
Medicare
16.3%
Private/HMO
11.4%
Other
9.1%
Overall
“We are currently utilizing data from E.R. visits to ascertain
various modes which patients use to access care:
(1) those who use E.R. and
(2) those who use the [urgent care] clinic for primary care.
With this data we will be able to identify clients who need help
in obtaining primary care in our clinic”
„
“Our monthly collection of CD4 count, viral load values, and
missing values has encouraged clinicians to more closely track
both the patients in the clinic, and patients who have missed
appointments and are late for quarterly clinical and lab
monitoring. This has resulted in additional efforts to track
patients who have missed visits.”
OP Utilization
IP Utilization
(Visits/year) (Admissions/100 PY)
5.15
35.8
Blacks
Whites
4.58
5.37
40.5
28.8
Women
Men
4.80
5.28
42.5
33.1
Age>40
Age<40
5.57
4.64
38.4
32.6
Pharmacy Utilization
Kathleen Clanon, M.D., Alameda County Medical Center
„
CD4 Median
<50 cells/mm3
51-200
201-500
>500
Utilization in CY 2001
Changes from HIVRN utilization data
„
Clinical Characteristics
„
HAART Usage (CD4<350)
PI Backbone
NNRTI Backbone
PCP (2 or more CD4<200):
MAC (2 or more CD4<50)
91%
68%
63%
88%
87%
James Hellinger, M.D. – Community Medical Alliance, Boston, MA
3
Factors Associated with
PCP Prophylaxis
Factors Associated with
MAC Prophylaxis
AOR (95% CI)*
AOR (95% CI)
(N=2,533)
Male
Age ≥ 40
Blacks
Hispanics
IDU’s
> 4 OP visits
*Adjusted for site of care, insurance
Clinical Changes from PCP/MAC Project
„
(N=754)
1.35 (1.00, 1.83)
1.28 (0.89, 1.85)
0.99 (0.71, 1.39)
1.03 (0.70, 1.52)
1.28 (0.89, 1.85)
2.39 (1.76, 3.24)
“Projects
in the works now include a red flag letter that notifies docs of
particular deficiencies (such as lack of PCP or MAI prophylaxis, patients
on triple nuke therapy and regimens that have incorrect dosing or
contains meds that shouldn't be used together).”
Male
Blacks
0.90 (0.49, 1.63)
Hispanics
1.44 (0.69, 3.03)
IDU’s
0.68 (0.37, 1.23)
1.85 (1.02, 3.35)
*Adjusted for site of care, insurance
Interview
„
„
950 adult and 300 pediatric
Topics assessed include
„
HIV related symptoms and quality of life
Adherence to ART
Mental Health and Substance Abuse treatment
Adverse Drug Events
More detailed utilization data:
„
Insurance Coverage
„
„
“'The
data obtained.…has been helpful in identifying other opportunities to
improve and comply with HIV/AIDS national guidelines. Tracking the
CD4 and meds listed on the same page is a reminder to start the patient
on prophylaxis as needed.”
„
„
John Jovanovitch, MD - Henry Ford Hospital System, Detroit, MI
„
1.10 (0.63, 1.92)
0.85 (0.54, 1.36)
> 4 OP visits
Robert Beil, MD- Montefiore Medical Center
„
Age ≥ 40
“Participation
in the HIV Research Network has been a major stimulus
driving our data collection into the clinical realm. It is incredibly
productive to reflect upon our own experience, as measured against our
peers and national guidelines, as we strive to improve the care we deliver
both at systemic and individual levels”
„
Case management, home care, pharmacy
Peter Sklar, MD - Drexel University, Philadelphia, PA
Safety
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Drug Interactions
Variations in care across sites
Intranet error reporting system
Manuscripts
„
„
„
2002 JAIDS Manuscript on Utilization
2004 JAIDS Disparities in Access to HAART
(in press)
Under Review
„
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2000/2001 IP/OP Utilization
2001 IP Diagnoses
High rates of OI prophylaxis
Variations in Quality of Care
Pediatric IP/OP Utilization
Pediatric VL suppression
4
Conclusions
„
„
„
Future Directions
Near real time data collection with quick
feedback to sites
Addresses disparities in care and safety
issues
Data from the HIVRN may be useful for:
„
„
„
Longitudinal Data Analysis
„
„
„
„
„
Pediatric Issues
„
Allocation of healthcare resources
Improvement of HIV prevention and treatment
strategies
„
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„
Evaluate QOL, HIV symptoms
Assess adherence
Hospitalization Rates
„
AIDS-Related: Pneumonia, PCP
GI: Pancreatic diseases, liver diseases
Mental Health: Substance-related, affective disorders
Circulatory: Carditis, hypertension
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Funding Sources
AHRQ
SAMHSA
„
HRSA
„
OAR
„
7
ted
Data Coordinating Center
„
Richard Moore
„
Jeanne Keruly
„
Haya Rubin
„
Kelly Gebo
„
Erin Reilly
„
Liming Zhou
„
George Siberry
8
Rate (per 100 Person Years)
Pediatric Sites
Stephen Spector-UCSD,
San Diego
„
Patricia Flynn- St. Jude’s,
Memphis
„
Richard Rutstein- CHOP,
Philadelphia
„
AID
S-R
ela
Adult Sites
„
Victoria Sharp- St. Luke’s Roosevelt, NY
„
W. Christopher Mathews- UCSD, San Diego
„
Philip Keiser- Parkland Hospital, Dallas
„
James Hellinger- Community Medical Alliance, Boston
„
Patrick Nemecheck- Nemecheck Health Renewal,
Kansas City
„
P. Todd Korthuis- OHSU, Portland
„
Jeff Nadler- Tampa General Health Care, Tampa
„
Robert Beil- Montefiore Medical Center, NY
„
Lawrence Hanau- Montefiore Medical Center, NY
„
John Post- McDowell Health Care Center, Phoenix
„
Lawrence Crane- Wayne State University, Detroit
„
John Jovanovitch- Henry Ford Hospital, Detroit
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Kathlen Clanon- Alameda County Consortium, Oakland
„
Kathye Gorosh- CORE Foundation Chicago
„
Steven Fine- Community Health Network, Rochester
„
Peter Sklar- Drexel University, Philadelphia
Growth and development
Reproduction
Disclosure
Interview Data
„
HIVRN Collaborators
Link treatment to clinical outcomes
Evaluate complications of HAART
Impact of hepatitis co-infection
Impact of SA/MH diagnoses
Hospitalization Rates
Rate (per 100 Person Years)
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