National Institutefor Public Healthand the Environment

HIV, HCV, and HBV in injecting drug users in EuropeMirjam Kretzschmar Centre for Infectious Disease Control, RIVM, andJulius Center for Health Sciences & Primary CareUniversity Medical Centre Utrecht, The Netherlands

May 2009

EMCDDA conference

HIV infections newly diagnosed in injecting drug users, by year of report, by country, cases per million, 1996–2006.

0

100

200

300

400

500

600

700

800

900

1000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Cas

es p

er m

illion

pop

ulat

ion

Belgium Bulgaria Czech Republic Denmark GermanyEstonia Ireland Greece Spain FranceItaly Cyprus Latvia Lithuania LuxembourgHungary Malta Netherlands Austria PolandPortugal Romania Slovenia Slovakia FinlandSw eden United Kingdom Croatia Turkey Norw ayPortugal

Source: EMCDDA website

HIV prevalence among injecting drug users — studies with national and subnational coverage.

%

25%

50%

75%

100%

Tu

rke

y

Slo

vaki

a

Fin

lan

d

Cro

atia

Bu

lga

ria

Cze

ch R

ep

ub

lic

Cyp

rus

Hu

ng

ary

Ma

lta

Slo

ven

ia

Gre

ece

Ro

ma

nia

Un

ited

Kin

gd

om

De

nm

ark

Lu

xem

bour

g

No

rwa

y

Be

lgiu

m

Ge

rma

ny

Lith

ua

nia

Sw

ed

en

Fra

nce

Au

stri

a

Po

lan

d

Italy

Po

rtu

ga

l

Sp

ain

Est

on

ia

National Subnational

%

25%

50%

75%

Be

lgiu

m

Bu

lga

ria

Cze

ch R

ep

ub

lic

Gre

ece

Cyp

rus

Lu

xem

bo

urg

Hu

ng

ary

Ma

lta

Slo

ven

ia

Slo

vaki

a

Un

ited

Kin

gd

om

Ro

ma

nia

Sw

ed

en

Au

stri

a

Lith

ua

nia

Sp

ain

Po

rtu

ga

l

Po

lan

d

Est

on

ia

National Subnational

All injecting drug users, 2005 and 2006 Young injecting drug users

(under age 25), 2005 to 2006

Source: EMCDDA website

100% 75%

Black dots: nationalBlue dots: subnational

Estimated HCV antibody prevalence among injecting drug users — studies with national and subnational coverage

%

25%

50%

75%

100%

Cze

ch R

ep

ub

lic

Hu

ng

ary

Bu

lga

ria

Fin

lan

d

Slo

ven

ia

Cyp

rus

Au

stri

a

Ma

lta

Un

ited

Kin

gd

om

Slo

vaki

a

Ro

ma

nia

Gre

ece

Fra

nce

Po

lan

d

Po

rtu

ga

l

Be

lgiu

m

Ne

the

rla

nd

s

De

nm

ark

Italy

Ge

rma

ny

No

rwa

y

Lu

xem

bo

urg

Lith

ua

nia

Sw

ed

en

National Subnational

%

25%

50%

75%

100%

Mal

ta

Cyp

rus

Slo

veni

a

Hun

gary

Finl

and

Bul

garia

Net

herla

nds

Uni

ted

Kin

gdom

Bel

gium

Gre

ece

Slo

vaki

a

Pol

and

Rom

ania

Luxe

mbo

urg

Por

tuga

l

Lith

uani

a

Sw

eden

National Subnational

Young injecting drug users

(under age 25), 2005 to 2006

All injecting drug users, 2005 and 2006

Source: EMCDDA website

100% 100%

Prevalence of markers of HBV infection estimated among national and subnational samples of injecting drug users 2005 to 2006, where data are available

%

25%

50%

75%

100%

Ma

lta

Slo

ven

ia*

Au

stri

a

Be

lgiu

m

Un

ited

Kin

gd

om

Gre

ece

Lu

xem

bo

urg

De

nm

ark

Italy

No

rwa

y

Po

lan

d*

Ge

rma

ny

National Subnational

Percentage positive for

ever infected (antiHBc)

Percentage positive for

current infection (HBsAg)

%

4%

8%

12%

16%

Hu

ng

ary

Be

lgiu

m

No

rwa

y

Cyp

rus

Gre

ece

Po

rtu

ga

l

Lu

xem

bo

urg

Po

lan

d

Lith

ua

nia

Ro

ma

nia

Bu

lga

ria

National Subnational

Source: EMCDDA website

100% 16%

Questions

• How are HIV and HCV prevalence related?

• And HBV?

• How do these prevalences depend on risk behaviour, duration of injecting, intervention?

• What is the impact of harm reduction on incidence and prevalence?

Use statistical methods and mathematical modelling to get some answers

Project

• First project: Sept. 2006 – Nov. 2007 tendered by EMCDDA and conducted as a collaboration between EMCDDA and School of Public Health, University of Bielefeld

- Set up team of modellers to work on analysis of European data- Produce 5 draft papers for publication in international journals- Resulted in collaboration with epidemiologists (the ‘Study group‘)

• Second phase: Collaboration with WHO Europe project: ‘Review statistical methods for estimating HIV incidence in countries with concentrated epidemics’

- Discuss other modelling issues and approaches- Continue EMCDDA work, link with WHO interests

• Background: EMCDDA EU network on drug related infectious diseases (HIV, hepatitis B/C in IDUs: experts, national focal points in 30 countries

Relationships between HIV and HCV prevalence

0%

25%

50%

75%

100%

0% 25% 50% 75% 100%HCV prevalence

HIV

pre

va

len

ce

Data Segmented linear regression model 95% CI

0%

25%

50%

75%

100%

0% 25% 50% 75% 100%

HCV prevalence

HIV

pre

va

len

ce

Asia Central Europe Western Europe North America Australasia

Vickerman et al. submitted

HIV and HCV prevalence data for 310 regionsfrom published studies

Thresholds?

Force of infection links incidence and prevalence

)()()()(

tSttSBdt

tdS

Bλ

μ

susceptible

Force of infection (FOI): risk per time unit for a susceptible person to become infected depends on exposure and therefore on prevalence can be different for different groups of IDU can change during drug use career

Link between FOI and heterogeneity

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0 0.5 1 1.5 2 2.5 3Fraily

%

Spain '02

Italy '05

Belgium '05

Czech 1st '00

E+W '02

(c)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Spain '02 Italy '05 E+W new'02

E+W exp'02

Belgium '05

HIV

FO

I (/ID

U/y

r)

(b)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Belgiu

m '0

5

Spain

'02

Italy

'05

E+W 0

3-06

new

E+W 0

3-06

exp

E+W 9

9-02

new

E+W 9

9-02

exp

Czech

two

new '0

3

Czech

two

exp '0

3

Czech

one

'00

HC

V F

OI (

/pe

rso

n/y

r)

Estimates of force of infections from seroprevalence studies in different populations

Sutton et al. J Viral Hepatitis 2008

Frailty function indicates heterogeneity with respect to exposure

Hamata et al; in preparation

Force of infection as a function of time since start of injecting: Exposure duration

Caveats: steady state assumption impact of intervention?

Hurley et al Lancet 1997 Amundsen et al Eur J Pub Health 2003

How effective have NEPs been?

How can we interpret ecological studies?

Decline HIV and HCV incidence in ACS 1985-2005

Amsterdam Cohort Studies among drug users

• Prospective HIV testing

• Retrospectively tested for HCV antibodies

• 952 ever injecting DU

58 HCV infections

90 HIV infections

Van den Berg et al. Eur J Epidemiol 2007

Is it all really the effect of harm reduction?

Possible other explanation:

Demographic changes in IDU population (e.g., ageing)

Disease related mortality in those groups at highest risk of infections in the first decade of the HIV epidemic might have led to a change in the composition of the IDU population with less risk behaviour and lower transmission rates at the population level over calendar time. Smit et al, JAIDS 2008

Conclusions

• Epidemiology of HIV and HCV is closely related, but need to understand better thresholds and transmission dynamics

• Force of infection links incidence and prevalence, can say something about heterogeneity if we have data about more than on infection

• The impact of calender time on these relationships is not yet clear, need cohort studies to analyse that

• We need to disentangle impact of harm reduction from other influences – demographic changes, behaviour changes

National Institutefor Public Healthand the Environment

Acknowledgements

• Lucas Wiessing• Peter Vickerman• Ziv Shkedy• Emma White• Andrew Sutton• Viktor Mravcik• Cathy Matheï• Maria Prins• Fernando Vallejo• Barbara Suligoi• Lillebil Norden

and all other members of the study group