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

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AIDS-Related: Pneumonia, PCPGI: Pancreatic diseases, liver diseasesMental Health: Substance-related, affective disordersCirculatory: Carditis, hypertension

Hospitalization Rates

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