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Demand Driven Research: The HIV Research Network
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  1. Demand Driven Research: The HIV Research Network Kelly A. Gebo MD MPH for the HIV Research Network June 8, 2004

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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

  7. HIV Research Network 21 HIV primary and specialty care sites CY 2004

  8. Site Population • 13 Adult, 2 Pediatric only, 3 Adult and Pediatric *Community-based

  9. Sample Size

  10. 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

  11. 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

  12. 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

  13. Demographic Characteristics of CY 2001 Sample (N=10,556)

  14. Clinical Characteristics

  15. Insurance Coverage

  16. Utilization in CY 2001

  17. 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

  18. 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%

  19. Factors Associated with PCP Prophylaxis *Adjusted for site of care, insurance

  20. Factors Associated with MAC Prophylaxis *Adjusted for site of care, insurance

  21. 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

  22. 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

  23. Safety • Drug Interactions • Variations in care across sites • Intranet error reporting system

  24. 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

  25. Conclusions • 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

  26. 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

  27. 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 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 HIVRN Collaborators

  28. Hospitalization Rates AIDS-Related: Pneumonia, PCP GI: Pancreatic diseases, liver diseases Mental Health: Substance-related, affective disorders Circulatory: Carditis, hypertension Rate (per 100 Person Years)

  29. Hospitalization Rates Rate (per 100 Person Years)