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Impact of Chronic HCV Co-infection on HIV Clinical Outcomes in the
District of Columbia
Sarah Willis, MPHDepartment of Epidemiology and Biostatistics
School of Public Health and Health Services
The George Washington University
2011 National HIV Prevention Conference
A Public Health/Academic Partnership between the
District of Columbia Department of Healthand
The George Washington University School of Public Health and Health Services
Department of Epidemiology and Biostatistics
Contract Number POHC-2006-C-0030
Background
• An estimated 1/4 of those infected with HIV are also infected with hepatitis C virus (HCV)
• Estimates of HIV/HCV co-infection range from 50-90% among certain sub-populations
• Supporting evidence that HIV negatively impacts HCV disease progression and reduces the effectiveness of available treatments
Background (2)
• Less research has been conducted regarding role of HCV co-infection on HIV disease and existing studies have conflicting results– Association between HCV/HIV co-infection and
worsening liver disease and higher mortality when compared to those with HIV or HCV monoinfection (Merriman et al)
– HCV co-infection associated with blunted CD4 cell recovery after initiating HAART yet no effect on virologic response or mortality (Carmo et al)
Objectives
Utilize routinely reported surveillance data to:1. Determine the extent of HIV/HCV co-infection
in the District of Columbia between 2000-2009
2. Describe potential factors that may be associated with HIV/HCV co-infection
3. Determine the impact that HIV/HCV co-infection has on HIV clinical outcomes and mortality
Methods• Identified name-based HIV/AIDS cases diagnosed
and reported to the DCDOH between 2000 – 2009 (n=10,215)
• Identified chronic HCV cases reported to DCDOH during the same time period (n=16,235)
• Used Link Plus Probability matching program to match cases by:– First and last name
– Date of birth
– Sex
– Race
• Reviewed potential matches for accuracy
Methods (2)• Performed bivariate analyses to detect differences
among HIV/HCV co-infected and HIV mono-infected individuals based on:– Demographics
– Entrance into HIV Care (time between HIV/AIDS diagnosis and first VL or CD4 test reported to DCDOH)
– Engagement in HIV Care• Continuous Care - evidence (e.g. HIV-related lab test) of at least 2
visits to an HIV medical provider 10-14 weeks apart
• Sporadic care - one visit to a provider or 2 visits but more than 14 weeks apart
– Viral load and CD4 count (at time of diagnosis and most recent results)
– Mortality
Methods (3)• Assessed timing of HIV/HCV co-infection
• Association between HIV/HCV co-infection and mortality (time to death) examined through:– Kaplan-Meier log rank test/log rank survival plots
– Cox proportional hazard ratio model
Demographics of Co-Infected and Monoinfected Cases
HIV/HCV Co-infected(n=1,151)
HIV Monoinfected
(n=9,017)
Chi-square p-value
SexMaleFemale
67.2%32.8%
70.5%29.5%
0.0189
Race/ethnicityWhiteBlackHispanicOther*
4.5%90.4%3.1%2.0%
14.4%77.5%5.8%2.3%
<0.0001
11.3% of reported HIV cases were HCV co-infected
*Other race includes Asian, Alaska Native, American Indian, Native Hawaiian, Pacific Islander, and Mixed and Unknown race
Age and Vital Status of Co-Infected and Monoinfected Cases
HIV/HCV Co-infected(n=1,151)
HIV Monoinfected
(n=9,017)
Chi-square p-value
Age at HIV diagnosis13-1920-2930-3940-4950-59≥60
0.2%3.7%
13.9%48.1%28.8%5.3%
3.1%20.6%32.4%28.1%11.8%4.1%
<0.0001
Vital Status*AliveDead
80.5%19.5%
88.5%11.5%
<0.0001
*as of December 31st, 2009
HIV Mode of Transmission
17.6%
40.3%
4.6%
23.5%
13.8%
36.4%
12.1%
2.6%
31.6%
17.2%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
MSM IDU MSM/IDU Heterosexual Risk Not Identified
Prop
orti
on o
f Dia
gnos
ed C
ases
HIV/HCV Co-infected HIV
Timing of HIV/HCV InfectionConcurrent Infections
(< 3 months apart)27.1%
HIV Infection 3+ months
prior to HCV14.2%
HCV Infection 3+ months prior to HIV
58.7%
HIV Care Seeking BehaviorHIV/HCV
Co-infected(n=1,151)
HIV Monoinfected
(n=9,017)
Chi-square p-value
Entrance into Care< 3 months3 – 6 months6 – 12 months> 1 yearNot in care
56.9%5.7%6.3%
25.0%6.0%
59.9%4.6%5.6%
20.4%9.5%
<0.0001
Engagement in CareNo careSporadic CareContinuous Care
6.0%57.7%36.3%
9.5%61.4%29.1%
<0.0001
HIV Viral Load at Time of HIV Diagnosis
Kruskal Wallis; p = 0.3031
10,55116,406
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
HIV/HCV Co-infection HIV only
Med
ian
Vir
al L
oad
at D
iagn
osis
(c
opie
s/m
L)
Most Recent Viral Load Results
Kruskal Wallis; p = 0.0119
74 740
500
1,000
1,500
2,000
2,500
3,000
3,500
HIV/HCV coinfection HIV only
CD4 Count at HIV Diagnosis
Kruskal Wallis; p-value = 0.3986
185 192
0
50
100
150
200
250
300
350
400
450
500
HIV/HCV coinfection HIV only
Med
ian
CD4
Coun
t at D
iagn
osis
(c
ells
/µL)
Most Recent CD4 Results
Kruskal Wallis; p-value = 0.0002
389445
0
100
200
300
400
500
600
700
HIV/HCV coinfection HIV only
Med
ian
CD4
Coun
t (ce
lls/µ
L)
Survival Among HIV/HCV and HIV only cases
HIV only cases
HIV/HCV co-infected cases
Log-rank = 47.35p-value = <0.0001
Adjusted Hazard Ratio for Mortality among HIV/HCV Co-infected Cases
Adjusted Hazard Ratio†
95% Confidence Interval
HCV/HIV vs. HIV only 1.20 1.02, 1.40
† Adjusted for sex, race/ethnicity, age, engagement in care, HIV mode of transmission, and progression to AIDS
Conclusions
• More than half of HIV/HCV co-infections were infected with HCV first
• In comparison to HIV monoinfected cases, HIV/HCV co-infected cases in DC were more likely to be:– Black– Over 40 years of age– IDU
• HIV/HCV co-infected cases in DC may have poorer HIV clinical outcomes over time– Lower CD4 counts among HIV/HCV co-infected cases at
most recent test– Increased mortality among HIV/HCV co-infected cases
Limitations
• May have underestimated HIV/HCV co-infections due to errors in data entry, name changes or incorrect spelling
• Large proportion of cases with missing CD4 and viral load data at diagnosis and at follow-up (25%-75%) in eHARS, could not assess their clinical outcomes
Recommendations
• Subsequent studies should be conducted to better understand the impact of HCV co-infection on HIV disease
• Studies utilizing surveillance data for this purpose should:– Improve completeness of VL and CD4 test results data – Obtain data on ART utilization
• Prevention and treatment interventions should be developed for sub-populations with high rates of HCV/HIV co-infection, such as IDUs
Acknowledgments
DC DOH HIV/AIDS, Hepatitis, STD, TB Administration
– Angelique Griffin*– Yujiang Jia– Gregory Pappas– Rowena Samala– Tiffany West*
George Washington University School of Public Health and Health Services
– Amanda D. Castel*– Irene Kuo*– Alan Greenberg
*Co-authors