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Demand Driven Research: The HIV Research Network
Kelly A. Gebo MD MPH
for the HIV Research Network
June 8, 2004
Objectives
Focus on health services delivery to persons with HIV infection
Key issues concern: 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
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
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 (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.
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-date data on: Resource use and costs of care Clinical outcomes of care Linked clinical/resource use outcomes
21 HIV primary and specialty care sites CY 2004
HIV Research Network
Site Population 13 Adult, 2 Pediatric only, 3 Adult and Pediatric
<500 500-1000 >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 *
CORE-Chicago
Johns Hopkins
Montefiore
Parkland
St. Lukes-Roosevelt
UCSD
Wayne State
*Community-based
Sample Size
1999 2000 2001
10,852 19,410 17,582
7,887
12,345
4,342
Operations 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
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
Operations
Feedback 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
Resource Utilization Data
Acute/chronic hospital care Admission/Discharge dates Diagnoses
Outpatient Visits Dates of service Diagnoses CPT Coding
Emergency Department Substance Abuse/Mental Health Visits Insurance
Demographic Characteristics of CY 2001 Sample (N=10,556)
Median Age (Range) 40 (18 – 89)
Male 7,571 (71.7%)Race African-American Caucasian Hispanic Other/Unknown
5,070 (48.0%)3,282 (31.1%)2,017 (19.1%)187 (1.8%)
HIV Risk Factor MSM Heterosexual IDU MSM IDU Heterosexual IDU Other/UK
4,021 (38.1%)3,432 (32.5%)1,383 (13.1%)344 (3.3%)544 (5.2%)832 (7.9%)
Clinical Characteristics
CD4 Median
<50 cells/mm3
51-200
201-500
>500
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%)
Insurance Coverage
Medicaid 31.9%
Uninsured 31.4%
Medicare 16.3%
Private/HMO 11.4%
Other 9.1%
Utilization in CY 2001
OP Utilization (Visits/year)
IP Utilization (Admissions/100 PY)
Overall 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
Changes from HIVRN utilization data
“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”
Kathleen Clanon, M.D., Alameda County Medical Center
“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.”
James Hellinger, M.D. – Community Medical Alliance, Boston, MA
Pharmacy Utilization
HAART Usage (CD4<350) 91%
PI Backbone 68%
NNRTI Backbone 63% PCP (2 or more CD4<200): 88% MAC (2 or more CD4<50) 87%
Factors Associated with PCP Prophylaxis
AOR (95% CI)*(N=2,533)
Male 1.35 (1.00, 1.83) Age ≥ 40 1.28 (0.89, 1.85) Blacks 0.99 (0.71, 1.39)Hispanics
IDU’s1.03 (0.70, 1.52)
1.28 (0.89, 1.85) > 4 OP visits 2.39 (1.76, 3.24)
*Adjusted for site of care, insurance
Factors Associated with MAC Prophylaxis
AOR (95% CI)(N=754)
Male 1.10 (0.63, 1.92) Age ≥ 40 0.85 (0.54, 1.36) Blacks 0.90 (0.49, 1.63)
Hispanics 1.44 (0.69, 3.03)
IDU’s 0.68 (0.37, 1.23) > 4 OP visits 1.85 (1.02, 3.35)
*Adjusted for site of care, insurance
Clinical Changes from PCP/MAC Project “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).”
Robert Beil, MD- Montefiore Medical Center
“'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
“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” Peter Sklar, MD - Drexel University, Philadelphia, PA
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:
Case management, home care, pharmacy
Insurance Coverage
Safety
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
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
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: Allocation of healthcare resources Improvement of HIV prevention and treatment
strategies
Conclusions
Future Directions
Longitudinal Data Analysis Link treatment to clinical outcomes Evaluate complications of HAART Impact of hepatitis co-infection Impact of SA/MH diagnoses
Pediatric Issues Growth and development Reproduction Disclosure
Interview Data Evaluate QOL, HIV symptoms Assess adherence
HIVRN CollaboratorsAdult 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 Kathlen Clanon- Alameda County Consortium, Oakland Kathye Gorosh- CORE Foundation Chicago Steven Fine- Community Health Network, Rochester Peter Sklar- Drexel University, Philadelphia
Pediatric Sites Stephen Spector-UCSD, San
Diego Patricia Flynn- St. Jude’s,
Memphis Richard Rutstein- CHOP,
Philadelphia
Data Coordinating Center Richard Moore Jeanne Keruly Haya Rubin Kelly Gebo Erin Reilly Liming Zhou George Siberry
Funding Sources AHRQ SAMHSA HRSA OAR
Hospitalization Rates
0
1
2
3
4
5
6
7
8
Rat
e (p
er 1
00 P
erso
n Y
ears
)
AIDS-Related: Pneumonia, PCPGI: Pancreatic diseases, liver diseasesMental Health: Substance-related, affective disordersCirculatory: Carditis, hypertension
Hospitalization Rates
0
1
2
3
4
5
6
7
8
Rat
e (p
er 1
00 P
erso
n Y
ears
)