Kendall Bryant, PhD for the VACS Project Team RSA: July 8th, 2007 Update on VACS: Using Observational Data to Inform an Integrated Alcohol Intervention for HIV Infected Individuals Kendall Bryant, PhD for the VACS Project Team RSA: July 8th, 2007

What Should an Integrated Alcohol Intervention Do? Address co-occuring/ interacting conditions Improve outcomes Fit seamlessly into care

Approach Patient inputs behaviors, symptoms, and attribution of symptoms on computer in waiting area Information integrated on server with electronic medical data and a targeted brief intervention is pushed back to patient based upon risk/benefit of intervention for active behaviors/conditions Patient responds to initial intervention with priorities/ preferences for change Server pushes forward note to provider summarizing individual risks, priorities for change, and recommendations Provider intervenes based upon clinical judgment (Steps 1-5 repeated at a series of follow up visits in abbreviated form, process measures and intermediate outcomes measured each visit.)

CORE QUESTIONS What is the cluster of co-occuring/interacting conditions? Risk/benefit from intervention at mid-life in the context of a complex chronic disease? How do we tailor interventions to individual patients? How do we inform/ facilitate providers’ use of individualized data?

CORE QUESTION #1: What is the Cluster of Alcohol Associated/Interacting Conditions

Methods VACS Virtual Cohort Using existing EMR data Identified HIV-infected veterans Receiving care during FY 1997 to 2004 We then identified HIV-uninfected comparators Matched to HIV-infected patients By sex, age (+/- 5yr), race/ethnicity, and geographic location With at least 1 encounter in the same year We used VA electronic medical records to construct the Virtual Cohort We identified HIV-infected veterans using ICD9 codes, who were receiving care in the VA, during FY 1997 through FY 2004. We then identified a 2:1 sample of comparators, matched to HIV-infected patients by sex, age (within +/- 5 yrs), race, and location of services. Comparators were required to have an inpatient stay or outpatient visit during the same fiscal year as the corresponding HIV-infected subject

Methods Comorbid conditions were identified using validated ICD-9 codes 12 months prior and 6 months after entry Conditions grouped based on factor analysis Medical Disorders Diabetes, HTN, etc Psychiatric Disorders MDD, PTSD, etc Substance Use Disorders Alcohol, any drug disorders Non-AIDS defining Comorbid conditions were then identified from ICD9 codes using validated methods from prior VACS work (HIGH prevalence conditions, over 1%) included conditions from the 12 months prior to and 6 months the patient entered into the cohort We grouped conditions based upon our prior factor analysis, which reduced the number of comorbid conditions into 3 ‘groups’ Group 1 was labeled Medical disorders, and includes diabetes, pulmonary disorders, hypertension, and others Group 2 was labeled Psychiatric disorders, and includes major depressive disorder, PTSD, and others And, GROUP3 was labeled any Substance Use disorder (or S.U.D.), which includes alcohol, and other drug disorders Note, A patient could have from 0 to 3 of these groupings

Results The sample consisted of 100,260 patients Overall: 33,420 HIV-infected 66,840 matched comparators Overall: 98% male 43% African-American, 33% Caucasian Mean age = 46 Results The sample consists of a total of 100,260 patients. We identified 33,420 HIV-infected patients, and 66,840 2:1 matched comparators Overall, 98% of the sample is male, and 43% are African American; 33% Caucasian Their mean age at entry into the cohort was 46 years old

Cluster analysis An algorithm to classify objects Not statistical Descriptive Differs from factor analysis Groups people, not variables Based upon similarity indices Not correlation Our previous analysis was based in part on the results of an exploratory FACTOR analysis. Factor Analysis groups variables, in this case diagnoses, based upon their inter-correlation. We wanted to validate those results and extend them using different methods. I DON’T want to get to deeply into the details of this method, but… Cluster analyses are algorithms used to classify objects, They are not statistical, they do not test a null hypothesis of NO clusters. There are used for descriptive purposes and hypothesis generating Need another analysis to test for difference in structure that (SEM or LCA) Cluster analysis differs from factor analysis in that it classifies people into similar categories, based upon similarity between 2 conditions

Results These are the dendograms of the comorbid conditions, by HIV status HIV+ patients are on the left, HIV-Negative on the right Remember that this is a descriptive result, need to do more analyses to determine if the clusters are different and how. I’d just wanted to get your impression on how these conditions group Note the Height axis The lower on the height measure that two conditions join up, the more similar the conditions are. This means that, among patients with either one or both of the conditions, they are more likely to have both. While many conditions group together in a similar fashion, the do so at a lower heights for the HIV+, suggesting that some comorbid conditions are more tightly associated for HIV+ patients. Also note that for the alcohol and psychiatric ‘clusters’ the heights are similar.

Cluster Analyses Summary 3 Major Groups: Substance Use Psychiatric Disease Medical Disease (vascular, metabolic, liver, other) Tightest group (by far!) is substance use

Prevalence by HIV and Group This table presents the prevalence of each condition, first by HIV status, and then by HIV status within each of the 3 comorbid condition groups. Overall, the individual comorbid conditions vary substantially by HIV status and by group. In general, HIV uninfected veterans were more likely to have any comorbid conditions (63% vs. 57 DATA not shown on table). HIV-uninfected patients were more likely to have a medical condition (44 vs. 39%), and a psychiatric disorder (22 vs. 18%), but less likely to have SUD (22 vs. 27%). However, note that HIV-INFECTED patients were more likely to have all 3 groups of comorbid conditions, the multi-morbidity we talked about earlier. This is substantially afected by the high rate of substance use disorders among the HIV+ patients. When prevalence is considered by HIV status and comorbidity group, relative differences in prevalence diminished, For instance, for both HIV uninfected and infected veterans with medical comorbidity, HTN (69% vs. 51%) was highly prevalent, but the relative difference of 35% was not as pronounced as overall (31 vs. 20%) or 55%. Higher in the HIV-negatives. *Virtual Cohort, Restricted to patients with either alcohol or drug abuse or dependence

Virtual Cohort Limitations No information on cigarettes ICD-9 codes rather than patient or provider report of conditions/behaviors Does not detect lower levels (hazardous alcohol, depressive symptoms, etc.)

Alcohol, Cigarettes, and Drugs Modified from Crothers K, et. al. Increased COPD among HIV-positive compared to HIV-negative veterans. Chest 2006;130(5):1326-1333.

Summary: Substance Use Travel more tightly than psychiatric or medical disease among HIV+/- Has overlapping health implications Alcohol and drugs: liver disease, risky sex, and nonadherence Alcohol and cigarettes: vascular disease, pulmonary disease, and cancer All 3: self medication for depression

Non Adherence (%) Among Drinking Categories Braithwaite RS, et.al. A temporal and dose-response association between alcohol consumption and medication adherence among veterans in care. Alcohol Clin Exp Res 2005;29(7):1190-1197.

Among Those Having Risky Sex Cook RL, et al. Intoxication before intercourse and risky sexual behavior in male veterans with and without human immunodeficiency virus infection. Med Care 2006;44(Suppl 2):S31-S36.

Conclusions, Next Steps Target behaviors Substance use (alcohol, cigarettes, drugs) Adherence Risky sex Target diagnoses Depression Medical disease (metabolic, pulmonary, liver) How do we jointly prioritize among these? Our findings suggest that As HIV infected veterans’ age they are likely to experience comorbidity and multimorbidity, that exceeds that of demographically matched uninfected veterans in care. This is likely for a variety of medical conditions, as well as substance abuse and psychiatric disorders.

Example Case Mr. T. is a 57 y/o male, past IV drug user, with HIV and HCV infection. He is on HAART (3rd round) with detectable viral load, modestly elevated lipids and AST. He has not been treated for HCV. He smokes and drinks on weekends. When drinking, he has risky sex and forgets to take his ARVs.

Issues for Mr. T Untreated HCV infection with AST elevation Elevated lipids Detectable viral load Hazardous alcohol consumption Active smoking Risky sex How do we jointly prioritize among these? Stay tuned.

General VACS Updates Enrollment Papers Grants Ongoing projects Goals for new 12 months

Enrollment As Of 2007 Baseline Surveys F/U 3 Blood DNA HIV+ 3270 2171 74% 1250 1290 HIV- 3245 1956 63% 736 722 Total %Target 6515 107% 3045 68% 1959 57% 2012 58%

31 Publications 2006-07 Detailed by Core Questions

Co-occuring/interacting conditions? McGinnis KA, Fultz SL, Skanderson M, Conigliaro J, Bryant K, Justice AC. Hepatocellular carcinoma and non-hodgkin’s lymphoma: the roles of HIV, hepatitis C infection, and alcohol abuse. J Clin Oncol 2006;24(31):5005-5009. Crothers K, Butt AA, Gibert CL, Rodriguez-Barradas MC, Crystal S and Justice AC, for the VACS 5 Project Team. Increased COPD among HIV-positive compared to HIV-negative veterans. Chest 2006;130(5):1326-1333. Cook RL, McGinnis KA, Kraemer KL, Gordon AJ, Conigliaro J, Maisto SA, Samet JH, Crystal S, Rimland D, Bryant KJ, Braithwaite RS, Justice AC. Intoxication before intercourse and risky sexual behavior in male veterans with and without human immunodeficiency virus infection. Med Care 2006;44(Suppl 2):S31-S36. Gordon AJ, McGinnis KA, Conigliaro J, Rodriguez-Barradas MC, Rabeneck L, Justice AC, for the VACS-3 Project Team. Associations between alcohol use and homelessness with healthcare utilization among HIV infected veterans. Med Care 2006;44(Suppl 2):S37-S43. Justice AC, Lasky E, McGinnis KA, Skanderson M, Conigliaro J, Fultz SL, Crothers K, Rabeneck L, Rodriguez-Barradas M, Weissman S, Bryant K, for the VACS 3 Project Team. Medical disease and alcohol use among veterans with human immunodeficiency infection. Med Care 2006;44(Suppl 2):S52-S60.

Risk/benefit from intervention at mid-life in the context of a complex chronic disease? Braithwaite RS, Conigliaro J, Roberts MS, Shechter S, Schaefer A, McGinnis K, Rodriguez-Barrada M, Rabenek L, Bryant K, Justice AC. Estimating the impact of alcohol consumption on survival for HIV+ individuals. AIDS Care 2007,19:459-466. Braithwaite RS., Shechter S, Roberts Ms, Schefer A, Bangsberg DR, Harrigan PR, Justice AC. Explaining variability in the relationship between antiretroviral adherence and HIV accumulation. J Antimicrob Chemother 2006;58:1036-1043. Oursler KK, Goulet J, Leaf D, Akingicil A, Katzel L, Justice AC, Crystal S. Association of comorbidity with physical disability in older HIV-infected adults: AIDS Patient Care and STD 2006;20(11):782-791.

How do we tailor interventions to individual patients? Braithwaite RS, Kozal MJ, Chang CCH, Roberts MS, Fultz SL Goetz MB, Gibert C, Rodriguez- Barradas M, Mole L, Justice AC. Adherence, virologic and immunologic outcomes for HIV-infected veterans starting combination antiretroviral therapies. AIDS (in press). Braithwaite RS, Concato J, Chang CC, Roberts MS, Justice AC. A framework for tailoring clinical guidelines to comorbidity at the point of care. Arch Intern Med (in press). Justice AC. Prioritizing primary care in HIV: comorbidity, toxicity, and demography. Topics in HIV Med 2006;14(5): 159-163.

How do we inform/ facilitate providers’ use of individualized data? Butt AA, Justice AC, Skanderson M, Rigsby M, Good CB, Kwoh CK. Rate and predictors of treatment prescription for hepatitis C. Gut (in press). Crothers K, Goulet JL, Rodriguez-Barradas, M, Gibert CL, Butt AA, Braithwiate RS, Peck R, Justice AC. Decreased awareness of current smoking among health care providers of HIV-positive compared to HIV-negative veterans. J Gen Intern Med (in press). Gandhi NR, Skanderson M, Gordon KS, Concato J, Justice AC. Delayed presentation for HIV care among veterans: A problem of access or screening? Med Care (in press). Butt AA, Justice AC, Skanderson M, Good C, Kwoh CK. Rate and predictors of hepatitis C virus treatment in HCV-HIV –coinfected subjects. Aliment Pharcacol Ther 2006;24(4):585-591. Butt AA, Justice AC, Skanderson M, Good C, Kwoh CK. Rates and predictors of treatment prescription for hepatitis C. Gut 2006 (Sept 27 Epub ahead of print).

VACS Affiliated Grant Summary U10 renewed 4 K23 awards 4 R21 awards 3 grants pending

Related Funding PI Type Focus of Study Berliner R01 (NHLBI/NIA) Unexplained Anemia in HIV+/- Aging Veterans Braithwaite R21 (NIAAA) Defining the Threshold for Alcohol- Induced Nonadherence in HIV+ Patients Braithwaite K23 (NIAAA) Tailoring HIV therapy to alcohol using populations Butt K23 (NIDA) Treatment Disparities/Outcomes of HCV-HIV Co-Infection Conigliaro R21 (NIAAA) Alcohol Use & HIV: Developing Computerized Interventions Freiberg K23 (NIAAA) Alcohol and Coronary Heart Disease in People with HIV (Barnett) R01 (NHLBI) Cost-benefit of Smoking Cessation in HIV

Related Funding cont’d PI Type Focus of Study Lim R21 (NIAAA) Markers of Alcohol Toxicity in HIV-infected Veterans Justice U10 (NIAAA) Alcohol Associated Outcomes Among HIV+/- Veterans Justice (Papas) R21 (NIAAA) Alcohol & HIV in Kenya: Stage 1 Trial of a Peer-led Alcohol Behavior Intervention Oursler K23 (NIA) Aging and Physical Functioning in HIV Tsevat VA HSR&D Implementing Symptom Assessment into Clinical HIV Care

Pending PI Type Focus of Study Brandt VA HSR&D OIF/OEF Women’s Cohort Merit Award Goulet VA HSR&D Cost and Outcome of CDA Anti-Depressant Treatment Lore R23 (NIAID) Risk Factors and Prediction of Liver Disease in HIV/HCV

Goals for Next 12 Months

Pilot Interventions Finish CALM and Symptom projects pilots Implement Kenyan peer led intervention

Analysis of Longitudinal Data Have 4 waves of survey data and 5 years of electronic data Develop measure of alcohol related frailty Estimate longitudinal benefit from Decreased alcohol consumption Depression treatment