Global, regional, and country-level estimates of hepatitis C infection among people who have recently injected drugs Jason Grebely1 , Sarah Larney2 , Amy Peacock2 , Samantha Colledge2 , Janni Leung2,3 , Matthew Hickman4 , Peter Vickerman4 , Sarah Blach5 , Evan B. Cunningham1 , Kostyantyn Dumchev6 , Michael Lynskey7 , Jack Stone4 , Adam Trickey4 , Homie Razavi5 , Richard P. Mattick2, Michael Farrell2 , Gregory J. Dore1 & Louisa Degenhardt2 The Kirby Institute, UNSW Sydney, Sydney, NSW, Australia,1 National Drug and Alcohol Research Centre, UNSW Sydney, Sydney, NSW, Australia,2 School of Public Health, Faculty of Medicine, University of Queensland, QLD, Australia,3 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK,4 CDA Foundation, Lafayette, CO, USA,5 Ukrainian Institute on Public Health Policy, Kiev, Ukraine6 and National Addiction Centre, King’s College London, London, UK7 ABSTRACT Background and Aims People who have recently injected drugs are a priority population in efforts to achieve hepatitis C virus (HCV) elimination. This study estimated the prevalence and number of people with recent injecting drug use living with HCV, and the proportion of people with recent injecting drug use among all people living with HCV infection at global, regional and country-levels. Methods Data from a global systematic review of injecting drug use and HCV antibody prevalence among people with recent (previous year) injecting drug use were used to estimate the prevalence and number of people with recent injecting drug use living with HCV. These datawere combined with a systematic review of global HCV prevalence to estimate the proportion of people with recent injecting drug use among all people living with HCV. Results There are an estimated 6.1 million [95% uncertainty interval (UI) = 3.4–9.2] people with recent injecting drug use aged 15–64 years living with HCV globally (39.2% viraemic prevalence; UI = 31.6–47.0), with the greatest numbers in East and Southeast Asia (1.5 million, UI = 1.0–2.1), eastern Europe (1.5 million, UI = 0.7–2.4) and North America (1.0 million, UI = 0.4–1.7). People with recent injecting drug use comprise an estimated 8.5% (UI = 4.6–13.1) of all HCV infections globally, with the greatest proportions in North America (30.5%, UI = 11.7–56.7), Latin America (22.0%, UI = 15.3–30.4) and eastern Europe (17.9%, UI = 8.2–30.9). Conclusions Although, globally, 39.2% of people with recent injecting drug use are living with hepatitis C virus (HCV) and 8.5% of all HCV infections occur globally among people with recent injecting drug use, there is wide variation among countries and regions. Keywords Estimates, HCV, IDU, injecting drug use, PWID, viraemic. Correspondence to: Jason Grebely, The Kirby Institute, Level 6, Wallace Wurth Building, UNSW Sydney, Sydney, NSW 2052, Australia. E-mail: [email protected] Submitted 8 January 2018; initial review completed 29 March 2018; final version accepted 12 J ...

1. Global, regional, and country-level estimates of hepatitis
C infection among people who have recently injected
drugs
Jason Grebely1 , Sarah Larney2 , Amy Peacock2 , Samantha
Colledge2 , Janni Leung2,3 ,
Matthew Hickman4 , Peter Vickerman4 , Sarah Blach5 , Evan B.
Cunningham1 ,
Kostyantyn Dumchev6 , Michael Lynskey7 , Jack Stone4 ,
Adam Trickey4 , Homie Razavi5 ,
Richard P. Mattick2, Michael Farrell2 , Gregory J. Dore1 &
Louisa Degenhardt2
The Kirby Institute, UNSW Sydney, Sydney, NSW, Australia,1
National Drug and Alcohol Research Centre, UNSW Sydney,
Sydney, NSW, Australia,2 School of Public
Health, Faculty of Medicine, University of Queensland, QLD,
Australia,3 Population Health Sciences, Bristol Medical School,
University of Bristol, Bristol, UK,4 CDA
Foundation, Lafayette, CO, USA,5 Ukrainian Institute on Public
Health Policy, Kiev, Ukraine6 and National Addiction Centre,
King’s College London, London, UK7
ABSTRACT
Background and Aims People who have recently injected drugs
are a priority population in efforts to achieve hepatitis C
virus (HCV) elimination. This study estimated the prevalence
and number of people with recent injecting drug use living
with HCV, and the proportion of people with recent injecting
drug use among all people living with HCV infection at global,
regional and country-levels. Methods Data from a global

2. systematic review of injecting drug use and HCV antibody
prevalence among people with recent (previous year) injecting
drug use were used to estimate the prevalence and number
of people with recent injecting drug use living with HCV. These
datawere combined with a systematic review of global HCV
prevalence to estimate the proportion of people with recent
injecting drug use among all people living with HCV.
Results There are an estimated 6.1 million [95% uncertainty
interval (UI) = 3.4–9.2] people with recent injecting drug
use aged 15–64 years living with HCV globally (39.2% viraemic
prevalence; UI = 31.6–47.0), with the greatest numbers
in East and Southeast Asia (1.5 million, UI = 1.0–2.1), eastern
Europe (1.5 million, UI = 0.7–2.4) and North America (1.0
million, UI = 0.4–1.7). People with recent injecting drug use
comprise an estimated 8.5% (UI = 4.6–13.1) of all HCV
infections globally, with the greatest proportions in North
America (30.5%, UI = 11.7–56.7), Latin America (22.0%,
UI = 15.3–30.4) and eastern Europe (17.9%, UI = 8.2–30.9).
Conclusions Although, globally, 39.2% of people with
recent injecting drug use are living with hepatitis C virus
(HCV) and 8.5% of all HCV infections occur globally among
people with recent injecting drug use, there is wide variation
among countries and regions.
Keywords Estimates, HCV, IDU, injecting drug use, PWID,
viraemic.
Correspondence to: Jason Grebely, The Kirby Institute, Level 6,
Wallace Wurth Building, UNSW Sydney, Sydney, NSW 2052,
Australia.
E-mail: [email protected]
Submitted 8 January 2018; initial review completed 29 March
2018; final version accepted 12 July 2018
INTRODUCTION

3. The World Health Organization (WHO) has set a goal to
eliminate hepatitis C virus (HCV) as a global public health
threat by 2030 [1]. Between 2015 and 2030, WHO
targets include reducing new HCV infections by 80%, the
number of HCV deaths by 65% and increasing HCV diag-
noses from 20 to 90%, and eligible people receiving HCV
treatment from < 5 to 80%. People who inject drugs
represent a priority population for HCV elimination, given
the high prevalence and incidence in this group [2–7].
We previously estimated the global, regional and
country-level prevalence of HCV (viraemic infections)
[8]. In 2015, the global prevalence of HCV infection
was estimated to be 1.0% [95% uncertainty interval
(UI) = 0.8–1.1], corresponding to 71.1 million (62.5–
79.4) people living with HCV [8]. We also estimated
the global, regional and country-level HCV antibody
prevalence among people with recent injecting drug
use (previous 12 months). Among the estimated 15.6
million (UI = 10.2–23.7 million) people with recent
injecting drug use aged 15–64 years globally, it is estimated
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
HCV PREVENTION doi:10.1111/add.14393
http://orcid.org/0000-0002-1833-2017
http://orcid.org/0000-0002-5602-4963
http://orcid.org/0000-0002-5705-2026
http://orcid.org/0000-0003-3571-0136
http://orcid.org/0000-0001-5816-2959
http://orcid.org/0000-0001-9864-459X
http://orcid.org/0000-0002-8291-5890
http://orcid.org/0000-0002-9252-7576

4. http://orcid.org/0000-0002-8048-3473
http://orcid.org/0000-0003-2862-4977
http://orcid.org/0000-0001-9989-737X
http://orcid.org/0000-0002-4584-0068
http://orcid.org/0000-0003-3462-2898
http://orcid.org/0000-0002-2658-6930
http://orcid.org/0000-0001-7008-8130
http://orcid.org/0000-0002-4741-2622
http://orcid.org/0000-0002-8513-2218
that 52.3% (UI = 42.4–62.1%) are HCV-antibody positive,
representing 8.2 million people who have recently injected
drugs (UI = 4.7–12.4 million) with past or present HCV [7].
Given that 25% of people clear HCV infection spontane-
ously [9], estimates are needed on the prevalence and num-
bers of people with recent injecting drug use who are living
with HCV infection (viraemic infection).
There are no previous estimates at the global, regional
and country levels of the HCV RNA (ribonucleic acid) prev-
alence among people with recent injecting drug use, the
number of people with recent injecting drug use who are
living with HCV infection (HCV RNA detectable or
viraemic) or the proportion of people with recent injecting
drug use among all people living with HCV infection. These
data are crucial to monitor progress of global HCVelimina-
tion efforts and identify high-burden settings to enable ap-
propriate targeting of prevention and treatment strategies
to achieve the WHO HCV targets.
The aim of this study was to estimate the global HCV
RNA prevalence (viraemic infections) among people who
have recently injected drugs; the numbers of people with
recent injecting drug use living with HCV infection; and
the proportion of people who have recently injected drugs

5. among all people living with HCV at global, regional and
country levels.
METHODS
Study design and procedures
This analysis utilized data from two published studies. The
first study was a systematic review to estimate the number
of people with recent injecting drug use and the HCV
antibody (anti-HCV) prevalence among people who have
recently (previous 12 months) injected drugs [7]. The
second study was a systematic review and modelling study
to estimate the global viraemic HCV prevalence [8].
The first systematic review estimated global, regional
and country-level prevalence of injecting drug use among
people aged 15–64 years and the prevalence of HIV, HCV
and hepatitis B virus (HBV) among people with recent
injecting drug use in 2015 [7]. This review was performed
consistent with the GATHER (Guidelines for Accurate and
Transparent Health Estimates Reporting) and PRISMA
(Preferred Reporting Items for Systematic Reviews and
Meta-Analyses) guidelines. Multiple search strategies [7]
were used to identify papers and reports published since
previous reviews of IDU prevalence (from 2008) [10] and
of HCV among people who inject drugs (PWID) (from
2011) [2]. Without language restrictions, peer-reviewed
databases (MEDLINE, Embase and PsycINFO) and grey lit-
erature were searched systematically, and data requests
disseminated to international experts and agencies. We
searched for data on IDU prevalence and the prevalence
of HIV, HCV and HBV among people with recent injecting
drug use. Eligible data on prevalence of IDU, HIVantibody,
HBsAg and HCVantibody among PWID were selected and,

6. where multiple estimates were available, pooled for each
country via random-effects meta-analysis. Data on HCV
RNA prevalence among people with recent injecting drug
use were also extracted. Global, regional and country-level
estimates of the HCV antibody (anti-HCV) prevalence
among people with recent injecting drug use were used
for the current study [7].
The second systematic review estimated global, re-
gional and country levels of viraemic HCV prevalence in
2015 [8]. Data published between 1 January 2000 and
31 March 2016 were identified through searches of elec-
tronic peer-reviewed literature databases, PubMed and
Embase [8]. Non-indexed government reports, personal
communication with country experts and additional stud-
ies identified through manual searches of references noted
in publications were included where better data were not
available. Papers were scored on the degree to which they
could be extrapolated to the general population, the sam-
ple size and the year of analysis. A Microsoft Excel-based
(version 2007) Markov-type model was populated with
the highest-scoring epidemiological data for each country,
used to estimate HCV prevalence over time (including in
2015). A Delphi process was used to gain country expert
consensus and validate inputs. Further details of data
extraction, scoring of data sources, Delphi process and
modelling have been published [8]. Global, regional and
country-level estimates of the numbers of people
with viraemic HCV infection were used for the current
study [7].
Statistical analysis
First, we sought to estimate the prevalence of viraemic
HCV infection (detectable HCV RNA) among people with
recent injecting drug use at global, regional and country

7. levels. As shown in Table 1, 48% (98 of 206) of countries
had available data on HCV antibody prevalence among
people with recent injecting drug use (n = 374 studies)
compared to only 9% (19 of 206) of countries with avail-
able data on HCV RNA prevalence among people with
recent injecting drug use (n = 32 studies). Compared to
studies of HCV antibody prevalence among people with
recent injecting drug use (n = 374), studies of HCV RNA
prevalence among people with recent injecting (n = 32)
were less often estimate-grade A (multi-site seroprevalence
study with > 1 sample types) (6.3 versus 21.9%) and
national samples (6.2% versus 20.6%). Given the poor
availability of data on HCV RNA prevalence, we sought to
estimate the HCV viraemic proportion (those living with
HCV infection) by using estimates of the prevalence of
HCV antibodies among people with recent injecting drug
use within each country [7] and multiplying by an
151
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
estimate of the proportion developing viraemic HCV infec-
tion [9]. The proportion with viraemic HCV infection
among those who were HCV antibody-positive [75%;
95% confidence interval (CI) = 71%, 79%] was estimated
using data from a well-characterized merged data set of
nine international cohorts of people who had recently
injected drugs who had acquired acute HCV infection,
and were followed prospectively for spontaneous HCV
clearance and viraemic infection [9]. The number of people
with recent injecting drug use with viraemic HCV infection
was then estimated by multiplying the number of people

8. with recent injecting drug use who were HCV antibody
positive by the HCV viraemic prevalence.
Ninety-five per cent UIs were estimated using Monte
Carlo simulation taking 100 000 draws. A binomial distri-
bution was used because the parameters of interest were
proportions (product of IDU proportion among the popula-
tion and HCV proportion among PWID). Estimated sample
sizes were derived based on the 95% CIs and standard er-
rors of proportion estimates in each country. The simulated
UIs incorporated the uncertainty of estimates.
Following the collation of country-specific estimates, es-
timates of regional and global viraemic HCV infection
among people with recent injecting drug use were derived.
Region-specific, weighted estimates of the prevalence of
HCV were made using all the observed estimates and
95% CI of estimates in each country within that region
and deriving a weighted estimate and UI taking into ac-
count country population size. Regional estimates were
then used to estimate the global prevalence.
The proportion of people with recent injecting drug use
among all people living with HCV infection was computed
by dividing the total number of people with recent injecting
drug use living with HCV by the total number of all people
living with HCV for countries where both estimates were
available. As above, 95% UIs were simulated taking
100 000 draws carrying forward the standard errors for
both people with recent injecting drug use living with
HCV and the total HCV viraemic infection prevalence
estimates.
RESULTS

9. Sufficient data were identified to enable estimates of the
HCV viraemic prevalence among people with recent
injecting drug use in 98 countries, and to estimate the pop-
ulation size of people with recent injecting drug use living
with HCV in 76 countries. Sufficient data were identified
to enable estimates of the number of people living with
HCV overall in 98 countries. There were sufficient data to
estimate the number of people with recent injecting drug
use as a proportion of all people living with HCV in 55
countries.
Results are shown by region in Table 2 and by country
in Table 3. Globally, we estimate that in 2015, 39.2%
(UI = 31.6–47.0) of people with recent injecting drug use
have HCV viraemic infection, representing 6.1 million
(UI = 3.4–9.2) people with recent injecting drug use living
with HCVinfection globally. Of the 71.1 million (UI = 62.5–
79.4 million) people living with HCV infection (Table 2), we
estimate that 8.5% (UI = 4.6–13.1) are people with recent
injecting drug use (Table 2).
At the regional level, HCV viraemic prevalence among
people with recent injecting drug use varied from 16.3%
(UI = 12.7–20.1) in sub-Saharan Africa to 48.6%
(UI = 42.0–55.2) in eastern Europe (Table 2). The largest
estimated numbers of people with recent injecting drug
use living with HCV infection were in East and Southeast
Asia (1.5 million, UI = 1.0–2.1), eastern Europe (1.5 mil-
lion, UI = 0.7–2.4) and North America (1.0 million,
Table 1 Quality of evidence of countries with available hepatitis
C
virus (HCV) antibody prevalence and HCV RNA prevalence
data
among recent people who inject drugs (PWID).

10. HCV antibody
prevalence among
recent PWID
(n = 374)
HCV RNA
prevalence among
recent PWID
(n = 32)
Countries with available
data
98/206
(47.6%)
19/206 (9.2%)
Estimate-gradeb
A 82 (21.9%) 2 (6.3%)
B1 225 (60.2%) 20 (62.5%)
B2 13 (3.6%) 1 (3.1%)
C 54 (14.4%) 8 (25.0%)
U – 1 (3.1%)
Geographic coverage
National sample 77 (20.6%) 2 (6.2%)
Subnational sample 87 (23.3%) 11 (34.4%)
City sample 210 (56.1%) 19 (59.4%)
Literature typea
A1 128 (34.2%) 30 (93.75%)
A2 4 (1.1%) –
B2 147 (39.3%) –

11. B3 81 (21.7%) –
C 8 (2.2%) 2 (6.25%)
D 6 (1.6%) –
aGrading for literature type: A1 = peer-reviewed journal article;
A2 = ab-
stract of published article only; B1 = published
book/report/monograph
from scholarly or commercial publisher; B2 = published
book/report/mono-
graph from international governmental or monitoring
organization (e.g.
UN, WHO, EMCDDA); B3 = published book/report/monograph
from other
source [e.g. government, non-governmental organization
(NGO), university,
research centre]; C = conference abstract; D = other
unpublished report (in-
cluding website downloads); E = e-mail and private
correspondence;
F = ARQ. bGrading for estimate grade: A = multi-site
seroprevalence study
with > 1 sample types (e.g. needle-syringe programmes, drug
treatment
centres, incarcerated IDUs); B1 = seroprevalence study, single
sample type
and multiple sites; B2 = seroprevalence study, multiple sample
types and a
single site; C = seroprevalence study, single sample type; D =
registration
or notification of cases of hepatitis/HIV infection; E =
prevalence study using
self-reported hepatitis/HIV status; ungraded = estimate with
methodology
unknown.

12. 152 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
T
ab
le
2
R
eg
io
n
al
an
d
gl
ob
al
es
ti
m
at
es
of
th
e
pr
ev
al
en

13. ce
of
h
ep
at
it
is
C
vi
ru
s
(H
C
V
)
vi
ra
em
ic
in
fe
ct
io
n
am
on
g
pe
op
le
w
it
h

14. re
ce
n
t
in
je
ct
in
g
dr
u
g
u
se
,t
h
e
n
u
m
be
r
of
pe
op
le
w
it
h
re
ce
n
t
in
je
ct

15. in
g
dr
u
g
u
se
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
,t
h
e
to
ta
lp
op
u

16. la
ti
on
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
an
d
th
e
pr
op
or
ti
on
of
pe

17. op
le
w
it
h
re
ce
n
t
in
je
ct
in
g
dr
u
g
u
se
am
on
g
th
e
to
ta
lp
op
u
la
ti
on
w

18. it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
.
P
re
va
le
nc
e
of
H
C
V
vi
ra
em
ic
in
fe
ct
io

19. n
am
on
g
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
%
(U
I)
N
um
be
r

20. of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in
g
w
it
h
H
C
V
vi
ra
em

21. ic
in
fe
ct
io
n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct

22. io
n
(U
I)
P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
am
on

23. g
th
e
to
ta
lp
op
ul
at
io
n
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
%
(U
I)
Ea
st

24. er
n
Eu
ro
pe
4
8
.6
(4
2
.0
,5
5
.2
)
1
4
6
6
5
0
0
(6
9
9
5
0
0
,2
3

25. 7
7
0
0
0
)
8
1
8
1
0
0
0
(6
3
0
4
0
0
0
,8
2
5
0
0
0
0
)
1
7
.9

26. (8
.2
,3
0
.9
)
W
es
te
rn
Eu
ro
pe
3
9
.9
(3
5
.7
,4
4
.1
)
4
0
2
5
0
0

27. (2
6
4
5
0
0
,5
5
7
0
0
0
)
2
3
4
7
0
0
0
(1
9
6
9
0
0
0
,3
2
8
9
0

28. 0
0
)
1
7
.2
(9
.9
,3
0
.4
)
Ea
st
an
d
So
u
th
ea
st
A
si
a
3
7
.7
(2
8

29. .2
,4
7
.5
)
1
5
0
6
0
0
0
(1
0
1
9
5
0
0
,2
0
7
8
5
0
0
)
1
6
3
1

30. 3
0
0
0
(1
2
6
3
6
0
0
0
,1
7
2
4
2
0
0
0
)
9
.2
(5
.8
,1
3
.8
)
So
u

31. th
A
si
a
2
8
.9
(1
3
.4
,4
7
.5
)
2
9
6
0
0
0
(1
1
4
5
0
0
,5
1
8
0

32. 0
0
)
1
5
6
1
7
5
0
0
(1
3
3
4
1
0
0
0
,2
0
1
8
2
0
0
0
)
1
.9
(0
.7

33. ,3
.6
)
C
en
tr
al
A
si
a
4
0
.5
(3
6
.5
,4
4
.5
)
1
1
4
0
0
0
(6
9
0
0

34. 0
,1
6
5
0
0
0
)
2
5
1
6
0
0
0
(2
0
1
0
0
0
0
,2
7
4
9
0
0
0
)
4

35. .5
(2
.6
,6
.9
)
C
ar
ib
be
an
4
7
.6
(4
0
.2
,5
5
.1
)
3
7
5
0
0
(2
2
5

36. 0
0
,5
5
0
0
0
)
2
2
5
5
0
0
(1
8
3
0
0
0
,3
1
5
0
0
0
)
1
6
.7
(8

37. .9
,3
0
.6
)
La
ti
n
A
m
er
ic
a
4
6
.4
(4
3
.1
,4
9
.8
)
8
4
6
0
0
0
(6

38. 1
7
5
0
0
,1
0
9
2
5
0
0
)
3
8
5
4
0
0
0
(3
1
3
1
0
0
0
,3
9
4
8
0

39. 0
0
)
2
2
.0
(1
5
.3
,3
0
.4
)
N
or
th
A
m
er
ic
a
4
0
.5
(2
9
.2
,5

40. 1
.7
)
9
6
0
0
0
0
(3
9
8
0
0
0
,1
6
7
9
5
0
0
)
3
1
4
8
0
0
0
(2
4

41. 2
9
0
0
0
,4
0
3
4
0
0
0
)
3
0
.5
(1
1
.7
,5
6
.7
)
P
ac
ifi
c
Is
la
n
d

42. st
at
es
an
d
Te
rr
it
or
ie
sa
4
1
.4
(3
2
.4
,5
0
.5
)
9
0
0
0
(5
5
0
0
,1

43. 4
0
0
0
)
1
1
7
5
0
0
(1
0
1
0
0
0
,3
7
6
0
0
0
)
7
.9
(1
.9
,1
1
.1

44. )
A
u
st
ra
la
si
a
4
2
.8
(3
8
.9
,4
6
.8
)
4
9
5
0
0
(3
5
5
0
0
,6
5

45. 0
0
0
)
2
7
8
5
0
0
(2
2
0
0
0
0
,2
9
7
0
0
0
)
1
7
.7
(1
2
.1
,2
5

46. .2
)
Su
b-
Sa
h
ar
an
A
fr
ic
a
1
6
.3
(1
2
.7
,2
0
.1
)
2
2
5
0
0
0
(4
5

47. 5
0
0
,4
5
8
5
0
0
)
9
8
9
3
5
0
0
(7
6
0
5
0
0
0
,1
5
1
1
2
0
0
0

48. )
2
.3
(0
.5
,5
.9
)
M
id
dl
e
Ea
st
an
d
N
or
th
A
fr
ic
a
3
6
.1
(2
9

49. .2
,4
3
.2
)
1
2
6
0
0
0
(6
5
0
0
0
,1
9
9
5
0
0
)
8
6
2
5
5
0
0
(6

50. 8
3
8
0
0
0
,9
,1
5
5
0
0
0
)
1
.5
(0
.7
,2
.4
)
G
lo
ba
l
3
9
.2
(3

51. 1
.6
,4
7
.0
)
6
0
6
3
5
0
0
(3
4
3
4
5
0
0
,9
2
4
6
0
0
0
)
7
1
1

52. 4
6
0
0
0
(6
2
4
7
2
0
0
0
,7
9
4
0
4
0
0
0
)
8
.5
(4
.6
,1
3
.1
)
U

53. I=
u
n
ce
rt
ai
n
ty
in
te
rv
al
(s
ee
M
et
h
od
s
fo
r
de
ta
ils
of
es
ti
m
at
io
n
).
N

54. u
m
be
r
of
pe
op
le
w
it
h
re
ce
n
ti
n
je
ct
in
g
dr
u
g
u
se
w
it
h
vi
ra
em
ic
H
C
V

55. in
fe
ct
io
n
ar
e
ro
u
n
de
d
to
th
e
n
ea
re
st
5
0
0
.T
ot
al
po
pu
la
ti
on
n
u
m
be

56. r
w
it
h
vi
ra
em
ic
H
C
V
in
fe
ct
io
n
ar
e
ro
u
n
de
d
to
th
e
n
ea
re
st
1
0
0
0

57. .a
N
ot
e
th
at
n
o
es
ti
m
at
es
of
th
e
pr
ev
al
en
ce
of
an
ti
-H
C
V
am
on
g
pe

58. op
le
w
h
o
in
je
ct
dr
u
gs
h
av
e
be
en
lo
ca
te
d
fo
r
th
e
P
ac
ifi
c
Is
la
n
ds
an

59. d
Te
rr
it
or
ie
s,
so
th
e
w
ei
gh
te
d
ob
se
rv
ed
gl
ob
al
pr
ev
al
en
ce
w
as
u
se

60. d
h
er
e.
C
on
si
de
ra
bl
e
ca
u
ti
on
sh
ou
ld
be
u
se
d
w
it
h
th
es
e
es
ti
m
at

61. es
.
153
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
T
ab
le
3
C
ou
n
tr
y-
le
ve
le
st
im
at
es
of
th
e
pr
ev
al

62. en
ce
of
h
ep
at
it
is
C
vi
ru
s
(H
C
V
)
vi
ra
em
ic
in
fe
ct
io
n
am
on
g
pe
op
le
w
it

63. h
re
ce
n
t
in
je
ct
in
g
dr
u
g
u
se
,t
h
e
n
u
m
be
r
of
pe
op
le
w
it
h
re
ce
n
t
in
je

64. ct
in
g
dr
u
g
u
se
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
,t
h
e
to
ta
lp
op

65. u
la
ti
on
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
an
d
th
e
pr
op
or
ti
on
of

66. pe
op
le
w
it
h
re
ce
n
t
in
je
ct
in
g
dr
u
g
u
se
am
on
g
th
e
to
ta
lp
op
u
la
ti
on

67. w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
.
R
eg
io
n
an
d
co
un
tr
y
P
re
va
le
nc
e
of

68. H
C
V
vi
ra
em
ic
in
fe
ct
io
n
am
on
g
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug

69. us
e
%
(U
I)
N
um
be
r
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in
g

70. w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h
H

71. C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct

72. in
g
dr
ug
us
e
am
on
g
th
e
to
ta
lp
op
ul
at
io
n
w
it
h
H
C
V
vi
ra
em
ic
in
fe

73. ct
io
n
%
(U
I)
Ea
st
er
n
Eu
ro
pe
A
rm
en
ia
3
2
.0
(2
2
.0
,4
2
.3
)

74. 4
0
0
0
(1
5
0
0
,8
5
0
0
)
N
K
N
G
A
ze
rb
ai
ja
n
4
6
.6
(3
4
.9
,5

75. 8
.0
)
2
0
0
0
0
(1
4
0
0
0
,2
7
0
0
0
)
1
9
0
0
0
0
(1
2
5
0
0
0
,2

76. 1
2
0
0
0
)
1
0
.6
(6
.6
,1
7
.3
)
B
el
ar
u
s
4
3
.7
(3
2
.3
,5
5

77. .1
)
1
8
0
0
0
(7
0
0
0
,3
1
5
0
0
)
N
K
N
G
B
os
n
ia
an
d
H
er
ze

78. go
vi
n
a
3
0
.0
(2
0
.7
,3
9
.5
)
N
K
N
K
N
K
B
u
lg
ar
ia
5
1
.5

79. (4
7
.3
,5
5
.8
)
9
5
0
0
(7
5
0
0
,1
1
5
0
0
)
8
7
0
0
0
(4
6
0
0
0

80. ,1
1
2
0
0
0
)
1
1
.0
(6
.9
,2
0
.4
)
C
ze
ch
R
ep
u
bl
ic
1
3
.7
(1

81. 0
.9
,1
6
.7
)
6
5
0
0
(5
0
0
0
,8
0
0
0
)
4
3
0
0
0
(2
2
0
0
0
,4
8

82. 5
0
0
)
1
5
.0
(9
.2
,2
8
.6
)
Es
to
n
ia
5
9
.4
(4
9
.8
,6
8
.4
)
5

83. 0
0
0
(2
5
0
0
,8
5
0
0
)
1
8
0
0
0
(1
1
5
0
0
,2
0
0
0
0
)
2
8
.2

84. (1
2
.6
,5
3
.0
)
G
eo
rg
ia
5
1
.8
(4
2
.9
,6
0
.5
)
5
9
5
0
0
(1
2
5
0

85. 0
,1
1
9
5
0
0
)
1
6
5
0
0
0
(1
2
0
0
0
0
,1
6
9
0
0
0
)
3
6
.1
(7

86. .4
,7
6
.9
)
H
u
n
ga
ry
3
5
.0
(2
2
.9
,4
7
.2
)
1
5
0
0
(5
0
0
,2
5

87. 0
0
)
5
2
5
0
0
(2
8
5
0
0
,5
5
5
0
0
)
2
.7
(1
.1
,5
.6
)
La
tv
ia

88. 5
5
.8
(4
9
.8
,6
1
.7
)
8
0
0
0
(6
0
0
0
,1
0
0
0
0
)
4
3
0
0
0
(2
8

89. 0
0
0
,5
0
0
0
0
)
1
8
.1
(1
1
.8
,2
9
.1
)
Li
th
u
an
ia
3
0
.8
(2
8

90. .1
,3
3
.7
)
1
5
0
0
(5
0
0
,2
5
0
0
)
3
2
5
0
0
(2
0
0
0
0
,3
8
5
0

91. 0
)
4
.5
(2
.0
,8
.6
)
M
ol
do
va
3
7
.5
(2
5
.5
,4
9
.7
)
4
5
0
0
(2

92. 5
0
0
,7
0
0
0
)
N
K
N
G
P
ol
an
d
4
4
.0
(4
0
.5
,4
7
.6
)
N
K

93. 1
8
4
0
0
0
(1
3
6
0
0
0
,2
2
4
0
0
0
)
N
K
R
om
an
ia
6
2
.9
(5

94. 8
.7
,6
7
.0
)
5
1
0
0
0
(3
6
0
0
0
,6
7
5
0
0
)
5
4
7
0
0
0
(3
9
7
0

95. 0
0
,5
6
6
0
0
0
)
9
.3
(6
.0
,1
4
.2
)
R
u
ss
ia
n
Fe
de
ra
ti
on
5
1
.6

96. (4
4
.2
,5
8
.9
)
9
6
9
5
0
0
(4
6
3
0
0
0
,1
5
7
0
5
0
0
)
4
7
4
8

97. 0
0
0
(3
,2
3
8
0
0
0
,4
9
6
0
0
0
0
)
2
0
.4
(9
.3
,3
7
.2
)
Sl
ov
ak
ia

98. 4
2
.1
(2
6
.6
,5
7
.7
)
8
5
0
0
(3
5
0
0
,1
4
5
0
0
)
3
3
0
0
0
(2

99. 0
0
0
0
,3
7
5
0
0
)
2
5
.4
(9
.6
,5
2
.0
)
U
kr
ai
n
e
4
0
.4
(3
6

100. .3
,4
4
.6
)
1
2
9
0
0
0
(5
4
0
0
0
,2
2
2
0
0
0
)
N
K
N
G
W
es
te

101. rn
Eu
ro
pe
A
lb
an
ia
2
5
.5
(2
0
.1
,3
1
.1
)
1
5
0
0
(1
0
0
0
,2
5
0

102. 0
)
N
K
N
G
A
n
do
rr
a
N
K
N
K
N
K
N
K
A
u
st
ri
a
4
5
.7

103. (4
0
.6
,5
0
.9
)
8
5
0
0
(6
0
0
0
,1
1
5
0
0
)
2
1
0
0
0
(6
0
0
0
,3

104. 0
5
0
0
)
4
0
.2
(2
0
.1
,1
0
0
.0
)
B
el
gi
u
m
4
3
.8
(3
4
.9
,5

105. 2
.6
)
1
1
5
0
0
(7
0
0
0
,1
6
5
0
0
)
6
4
5
0
0
(2
3
0
0
0
,7
5
5
0

106. 0
)
1
7
.8
(8
.7
,4
5
.8
)
C
ro
at
ia
2
7
.5
(2
1
.0
,3
4
.2
)
1
5
0

107. 0
(1
0
0
0
,2
5
0
0
)
2
6
0
0
0
(1
6
5
0
0
,2
8
5
0
0
)
6
.7
(4
.0
,1

108. 1
.3
)
D
en
m
ar
k
3
1
.9
(2
6
.8
,3
7
.2
)
5
5
0
0
(4
0
0
0
,6
5
0

109. 0
)
1
9
5
0
0
(1
4
5
0
0
,1
9
5
0
0
)
2
7
.2
(1
8
.8
,3
9
.5
)
En
gl

110. an
d
2
3
.1
(2
0
.0
,2
6
.3
)
4
8
5
0
0
(4
1
5
0
0
,5
6
0
0
0
)
1
6

111. 8
0
0
0
(9
1
0
0
0
,2
1
1
0
0
0
)
2
8
.9
(1
8
.8
,5
1
.8
)
Fi
n
la
n
d

112. 5
5
.2
(5
1
.2
,5
9
.4
)
9
5
0
0
(7
0
0
0
,1
2
5
0
0
)
2
2
5
0
0
(1

113. 6
0
0
0
,2
6
0
0
0
)
4
1
.6
(2
7
.4
,6
2
.8
)
Fr
an
ce
4
8
.0
(4
4
.5

114. ,5
1
.5
)
3
9
5
0
0
(3
1
5
0
0
,4
7
5
0
0
)
1
9
4
0
0
0
(9
2
5
0
0
,2

115. 2
2
0
0
0
)
2
0
.2
(1
2
.4
,4
0
.1
)
FY
R
(F
or
m
er
Y
u
go
sl
av
R
ep

116. u
bl
ic
)
M
ac
ed
on
ia
4
6
.6
(4
3
.4
,4
9
.9
)
2
5
0
0
(1
5
0
0
,3
0
0
0

117. )
N
K
N
G
(C
on
ti
n
u
es
)
154 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
T
ab
le
3
.
(C
on
ti
n
u
ed

118. )
R
eg
io
n
an
d
co
un
tr
y
P
re
va
le
nc
e
of
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
am
on

119. g
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
%
(U
I)
N
um
be
r
of
pe
op
le

120. w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io

121. n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)

122. P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
am
on
g
th
e
to
ta

123. lp
op
ul
at
io
n
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
%
(U
I)
G
er
m
an
y
4
8

124. .7
(4
4
.6
,5
3
.0
)
6
4
0
0
0
(1
3
5
0
0
,1
2
9
0
0
0
)
2
0
5
0
0
0

125. (9
0
0
0
0
,3
1
3
0
0
0
)
3
1
.3
(6
.2
,8
0
.6
)
G
re
ec
e
4
9
.2
(4

126. 5
.4
,5
3
.1
)
2
5
0
0
(2
0
0
0
,3
0
0
0
)
1
3
2
0
0
0
(8
2
0
0
0
,1

127. 6
9
0
0
0
)
1
.9
(1
.2
,3
.1
)
G
re
en
la
n
d
–
–
N
K
–
Ic
el
an

128. d
4
7
.3
(4
3
.8
,5
0
.8
)
5
0
0
(<
5
0
0
,5
0
0
)
1
0
0
0
(1
0
0
0

129. ,1
0
0
0
)
N
R
Ir
el
an
d
5
6
.0
(5
2
.5
,5
9
.4
)
5
0
0
0
(3
5
0
0

130. ,6
0
0
0
)
2
9
5
0
0
(2
0
0
0
0
,4
2
5
0
0
)
1
6
.2
(1
0
.0
,2
8
.9

131. )
It
al
y
4
3
.4
(3
8
.8
,4
8
.1
)
1
4
8
5
0
0
(9
8
5
0
0
,2
0
5
0
0

132. 0
)
6
8
0
0
0
0
(4
5
5
0
0
0
,1
6
4
1
0
0
0
)
2
1
.8
(7
.6
,3
3
.9
)

133. Li
ec
h
te
n
st
ei
n
–
–
N
K
–
Lu
xe
m
bo
u
rg
6
1
.0
(5
5
.9
,6
6
.1

134. )
1
5
0
0
(1
0
0
0
,1
5
0
0
)
5
5
0
0
(3
5
0
0
,6
0
0
0
)
2
5
.2

135. (1
6
.6
,4
1
.1
)
M
al
ta
1
8
.9
(1
0
.4
,2
8
.4
)
<
5
0
0
(<
5
0
0
,5

136. 0
0
)
1
0
0
0
(1
0
0
0
,1
5
0
0
)
N
R
M
on
ac
o
N
K
N
K
N

137. K
N
K
M
on
te
n
eg
ro
3
2
.6
(2
9
.4
,3
5
.9
)
5
0
0
(5
0
0
,5
0
0

138. )
N
K
N
K
N
et
h
er
la
n
ds
4
1
.5
(3
6
.7
,4
6
.3
)
1
5
0
0
(1
0
0

139. 0
,2
0
0
0
)
1
6
5
0
0
(5
0
0
0
,2
5
5
0
0
)
8
.3
(4
.2
,2
2
.9
)

140. N
or
th
er
n
Ir
el
an
d
N
K
N
K
N
K
N
K
N
or
w
ay
4
8
.6
(4
4
.5
,5

141. 2
.8
)
4
0
0
0
(3
5
0
0
,5
0
0
0
)
2
1
0
0
0
(1
5
0
0
0
,2
4
5
0
0
)

142. 1
9
.4
(1
3
.8
,2
8
.1
)
P
or
tu
ga
l
6
5
.8
(5
9
.1
,7
2
.2
)
1
0
5

143. 0
0
(9
0
0
0
,1
2
0
0
0
)
8
9
0
0
0
(7
4
0
0
0
,1
2
0
0
0
0
)
1
1
.7

144. (8
.2
,1
8
.1
)
Sa
n
M
ar
in
o
N
K
N
K
N
K
N
K
Sc
ot
la
n
d
3
9

145. .1
(3
3
.8
,4
4
.5
)
6
0
0
0
(5
0
0
0
,7
5
0
0
)
N
K
N
G
Se
rb
ia

146. 1
9
.4
(1
6
.5
,2
2
.6
)
5
5
0
0
(4
5
0
0
,7
0
0
0
)
N
K
N
G
Sl
ov

147. en
ia
2
2
.9
(1
9
.6
,2
6
.2
)
1
5
0
0
(1
0
0
0
,2
0
0
0
)
6
5
0
0
(4

148. 5
0
0
,7
0
0
0
)
2
1
.3
(1
3
.3
,3
3
.5
)
Sp
ai
n
5
3
.3
(5
0
.2
,5

149. 6
.3
)
5
5
0
0
(2
0
0
0
,9
5
0
0
)
3
8
6
0
0
0
(2
0
2
0
0
0
,6
2
0
0

150. 0
0
)
1
.4
(0
.5
,3
.6
)
Sw
ed
en
6
1
.3
(5
7
.6
,6
4
.9
)
5
0
0
0
(<

151. 5
0
0
,2
0
0
0
0
)
3
7
5
0
0
(2
8
0
0
0
,4
3
5
0
0
)
1
3
.3
(0
.0

152. ,4
6
.6
)
Sw
it
ze
rl
an
d
5
5
.9
(5
1
.0
,6
0
.9
)
7
5
0
0
(6
0
0
0
,9

153. 5
0
0
)
7
8
0
0
0
(4
5
5
0
0
,8
7
0
0
0
)
9
.7
(6
.3
,1
6
.7
)
W
al

154. es
2
0
.1
(1
7
.2
,2
3
.0
)
N
K
N
K
N
K
Ea
st
an
d
So
u
th
Ea
st
A

155. si
a
B
ru
n
ei
D
ar
u
ss
al
am
N
K
N
K
N
K
N
K
C
am
bo
di
a
N
K

156. N
K
2
5
7
0
0
0
(1
4
7
0
0
0
,2
7
2
0
0
0
)
N
K
C
h
in
a
3
2
.3

157. (2
0
.8
,4
4
.3
)
8
2
8
0
0
0
(4
9
3
0
0
0
,1
2
2
8
5
0
0
)
9
7
9
5
0

158. 0
0
(6
6
7
5
0
0
0
,1
0
8
3
2
0
0
0
)
8
.5
(4
.6
,1
4
.3
)
In
do
n
es

159. ia
6
6
.9
(6
2
.2
,7
1
.5
)
1
2
7
5
0
0
(1
0
3
0
0
0
,1
5
3
0
0
0
)

160. 1
2
8
9
0
0
0
(4
4
3
0
0
0
,2
0
4
6
0
0
0
)
9
.9
(5
.5
,2
4
.9
)
Ja
pa

161. n
4
8
.6
(4
0
.8
,5
6
.3
)
1
7
9
0
0
0
(1
3
0
5
0
0
,2
3
4
5
0
0
)

162. 8
5
7
0
0
0
(3
6
4
0
0
0
,1
0
2
4
0
0
0
)
2
0
.9
(1
1
.7
,4
6
.5
)
La

163. o
P
D
R
N
K
N
K
N
K
N
K
M
al
ay
si
a
5
0
.3
(4
6
.2
,5
4
.5
)

164. 1
4
2
0
0
0
(1
1
6
0
0
0
,1
6
9
5
0
0
)
3
8
2
0
0
0
(2
4
0
0
0
0
,4
0

165. 5
0
0
0
)
3
7
.1
(2
5
.2
,5
9
.2
)
(C
on
ti
n
u
es
)
155
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
T

166. ab
le
3
.
(C
on
ti
n
u
ed
)
R
eg
io
n
an
d
co
un
tr
y
P
re
va
le
nc
e
of
H
C
V
vi
ra

167. em
ic
in
fe
ct
io
n
am
on
g
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
%
(U

168. I)
N
um
be
r
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in
g
w
it
h
H

169. C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h
H
C
V
vi
ra
em

170. ic
in
fe
ct
io
n
(U
I)
P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug

171. us
e
am
on
g
th
e
to
ta
lp
op
ul
at
io
n
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
%
(U

172. I)
M
on
go
lia
N
K
N
K
1
9
4
0
0
0
(1
3
1
0
0
0
,2
3
7
0
0
0
)

173. N
K
M
ya
n
m
ar
2
2
.2
(1
9
.9
,2
4
.5
)
3
8
5
0
0
(2
5
5
0
0
,5
3
0

174. 0
0
)
N
K
N
G
N
or
th
K
or
ea
–
–
N
K
–
P
h
ili
pp
in
es
2
6
.4

175. (1
2
.8
,4
1
.6
)
6
5
0
0
(3
0
0
0
,1
1
5
0
0
)
6
1
4
0
0
0
(3
5
3
0

176. 0
0
,6
5
1
0
0
0
)
1
.1
(0
.4
,2
.3
)
R
ep
u
bl
ic
of
K
or
ea
3
6
.3
(3

177. 1
.7
,4
1
.0
)
N
K
2
3
1
0
0
0
(1
4
8
0
0
0
,2
6
1
0
0
0
)
N
K
Si

178. n
ga
po
re
3
1
.9
(2
8
.9
,3
5
.0
)
N
K
N
K
N
K
T
ai
w
an
6
8
.2

179. (6
4
.4
,7
2
.0
)
N
K
4
8
9
0
0
0
(3
1
0
0
0
0
,8
7
7
0
0
0
)
N
K

180. T
h
ai
la
n
d
6
6
.4
(6
0
.6
,7
1
.9
)
3
4
0
0
0
(1
2
5
0
0
,6
0
0
0
0

181. )
4
6
3
0
0
0
(2
5
5
0
0
0
,4
8
7
0
0
0
)
7
.4
(2
.6
,1
6
.1
)
T
im

182. or
Le
st
e
N
K
N
K
N
K
N
K
V
ie
t
N
am
4
3
.8
(3
1
.8
,5
5
.7
)

183. 7
0
5
0
0
(4
7
0
0
0
,9
8
0
0
0
)
1
0
6
6
0
0
0
(5
8
0
0
0
0
,1
1
1

184. 6
0
0
0
)
6
.6
(3
.7
,1
2
.5
)
So
u
th
A
si
a
A
fg
h
an
is
ta
n
2
8

185. .4
(2
0
.7
,3
6
.3
)
3
9
5
0
0
(2
3
0
0
0
,6
0
0
0
0
)
1
8
3
0
0
0
(8

186. 5
0
0
0
,2
5
8
0
0
0
)
2
1
.5
(1
0
.5
,4
6
.9
)
B
an
gl
ad
es
h
2
5

187. .4
(1
6
.9
,3
4
.4
)
1
7
5
0
0
(1
1
5
0
0
,2
4
0
0
0
)
N
K
N
G
B

188. h
u
ta
n
N
K
N
K
N
K
N
K
In
di
a
3
0
.0
(2
5
.2
,3
4
.9
)
5
9

189. 0
0
0
(3
8
0
0
0
,8
4
0
0
0
)
6
,2
4
5
0
0
0
(4
7
4
8
0
0
0
,1
0
9
5

190. 7
0
0
0
)
0
.9
(0
.4
,3
.0
)
Ir
an
3
3
.1
(2
1
.4
,4
5
.2
)
5
2
0
0

191. 0
(2
9
5
0
0
,8
1
0
0
0
)
1
9
9
0
0
0
(1
2
9
0
0
0
,2
2
6
0
0
0
)
2

192. 6
.2
(1
3
.2
,4
7
.0
)
M
al
di
ve
s
0
.5
(0
.0
,1
.4
)
<
5
0
0
(<
5
0

193. 0
,<
5
0
0
)
N
K
N
G
N
ep
al
3
3
.4
(2
3
.1
,4
3
.8
)
1
2
0
0
0

194. (8
0
0
0
,1
5
5
0
0
)
N
K
N
G
P
ak
is
ta
n
2
7
.4
(0
.0
,6
0
.6
)

195. 1
1
6
0
0
0
(<
5
0
0
,1
7
3
5
0
0
)
7
1
7
2
0
0
0
(5
3
6
3
0
0
0
,7

196. 4
8
7
0
0
0
)
1
.6
(0
.5
,3
.1
)
Sr
iL
an
ka
N
K
N
K
N
K
N
K

197. C
en
tr
al
A
si
a
K
az
ak
h
st
an
4
4
.1
(3
9
.8
,4
8
.4
)
4
9
5
0
0
(3
0
0

198. 0
0
,7
1
5
0
0
)
5
0
8
0
0
0
(3
3
4
0
0
0
,5
7
2
0
0
0
)
9
.8
(5
.4

199. ,1
6
.7
)
K
yr
gy
zs
ta
n
3
2
.9
(2
9
.9
,3
6
.0
)
9
5
0
0
(5
5
0
0
,1

200. 3
5
0
0
)
N
K
N
G
T
aj
ik
is
ta
n
4
6
.0
(4
1
.9
,5
0
.2
)
1
1
0
0

201. 0
(6
5
0
0
,1
5
5
0
0
)
N
K
N
G
T
u
rk
m
en
is
ta
n
N
K
N
K
N
K

202. N
K
U
zb
ek
is
ta
n
3
8
.8
(3
4
.7
,4
3
.0
)
3
6
5
0
0
(2
2
5
0
0
,5

203. 3
0
0
0
)
1
2
9
2
0
0
0
(9
0
2
0
0
0
,1
5
2
4
0
0
0
)
2
.8
(1
.6
,4

204. .6
)
C
ar
ib
be
an
A
n
ti
gu
a
an
d
B
ar
bu
da
–
–
N
K
–
B
ah
am
as

205. N
K
N
K
N
K
N
K
B
ar
ba
do
s
–
–
N
K
–
B
er
m
u
da
N
K

206. N
K
N
K
N
K
(C
on
ti
n
u
es
)
156 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
T
ab
le
3
.
(C
on
ti
n
u

207. ed
)
R
eg
io
n
an
d
co
un
tr
y
P
re
va
le
nc
e
of
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
am

208. on
g
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
%
(U
I)
N
um
be
r
of
pe
op

209. le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct

210. io
n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)

211. P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
am
on
g
th
e
to

212. ta
lp
op
ul
at
io
n
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
%
(U
I)
C
om
m
on
w

213. ea
lt
h
of
P
u
er
to
R
ic
o
5
8
.8
(5
3
.9
,6
3
.7
)
1
6
5
0
0
(1
0
0
0
0

214. ,2
4
0
0
0
)
3
5
5
0
0
(2
3
0
0
0
,6
0
5
0
0
)
4
6
.6
(2
2
.1
,1
0

215. 0
.0
)
C
u
ba
–
–
3
5
0
0
0
(1
3
5
0
0
,7
7
0
0
0
)
–
D
om
in
ic

216. a
–
–
N
K
–
D
om
in
ic
an
R
ep
u
bl
ic
N
K
N
K
6
8
0
0
0
(4
1

217. 5
0
0
,1
0
8
0
0
0
)
N
K
G
re
n
ad
a
–
–
N
K
–
H
ai
ti
N
K

218. N
K
N
K
N
K
Ja
m
ai
ca
N
K
N
K
N
K
N
K
Sa
in
t
K
it
ts
an
d
N

219. ev
is
–
–
N
K
–
Sa
in
t
Lu
ci
a
–
–
N
K
–
St
V
in
ce
n
t
an
d

220. th
e
G
re
n
ad
in
es
–
–
N
K
–
T
ri
n
id
ad
an
d
To
ba
go
–
–
N
K

221. –
La
ti
n
A
m
er
ic
a
A
rg
en
ti
n
a
4
1
.0
(3
7
.5
,4
4
.4
)
3
3
0
0
0

222. (3
0
0
0
0
,3
6
0
0
0
)
3
2
6
0
0
0
(1
4
4
0
0
0
,4
9
0
0
0
0
)
1
0

223. .1
(6
.3
,2
1
.0
)
B
el
iz
e
–
–
N
K
–
B
ol
iv
ia
N
K
N
K
N
K

224. N
K
B
ra
zi
l
4
7
.9
(4
4
.3
,5
1
.5
)
4
6
1
0
0
0
(3
3
6
5
0
0
,5

225. 9
6
5
0
0
)
1
7
8
7
0
0
0
(1
2
9
3
0
0
0
,1
8
9
6
0
0
0
)
2
5
.8

226. (1
7
.2
,3
8
.5
)
C
h
ile
N
K
N
K
5
6
5
0
0
(3
1
0
0
0
,9
4
0
0
0
)

227. N
K
C
ol
om
bi
a
2
1
.6
(1
9
.3
,2
4
.0
)
N
K
4
0
9
0
0
0
(2
7
2

228. 0
0
0
,4
3
6
0
0
0
)
N
K
C
os
ta
R
ic
a
N
K
N
K
N
K
N
K
Ec

229. u
ad
or
N
K
N
K
N
K
N
K
El
Sa
lv
ad
or
N
K
N
K
N
K
N
K
G
u

230. at
em
al
a
N
K
N
K
N
K
N
K
G
u
ya
n
a
N
K
N
K
N
K
N
K

231. H
on
du
ra
s
N
K
N
K
N
K
N
K
M
ex
ic
o
7
1
.5
(6
7
.3
,7
5
.5
)

232. 1
0
7
5
0
0
(7
0
5
0
0
,1
4
9
0
0
0
)
5
3
2
0
0
0
(3
0
4
0
0
0
,5
5

233. 7
0
0
0
)
2
0
.2
(1
1
.4
,3
7
.2
)
N
ic
ar
ag
u
a
N
K
N
K
N
K
N

234. K
P
an
am
a
N
K
N
K
1
2
5
0
0
(7
5
0
0
,1
3
5
0
0
)
N
K
P
ar

235. ag
u
ay
7
.4
(5
.8
,9
.0
)
N
K
N
K
N
K
P
er
u
N
K
N
K
1
6
7

236. 0
0
0
(9
9
0
0
0
,1
8
2
0
0
0
)
N
K
(C
on
ti
n
u
es
)
157
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166

237. T
ab
le
3
.
(C
on
ti
n
u
ed
)
R
eg
io
n
an
d
co
un
tr
y
P
re
va
le
nc
e
of
H
C
V
vi

238. ra
em
ic
in
fe
ct
io
n
am
on
g
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
%
(U

239. I)
N
um
be
r
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in
g
w
it
h

240. H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h
H
C
V
vi
ra

241. em
ic
in
fe
ct
io
n
(U
I)
P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug

242. us
e
am
on
g
th
e
to
ta
lp
op
ul
at
io
n
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
%

243. (U
I)
Su
ri
n
am
e
N
K
N
K
N
K
N
K
U
ru
gu
ay
1
6
.4
(1
4
.1
,1

244. 8
.9
)
1
0
0
0
(<
5
0
0
,2
5
0
0
)
N
K
N
G
V
en
ez
u
el
a
N
K

245. N
K
1
1
8
0
0
0
(5
8
5
0
0
,1
2
6
0
0
0
)
N
K
N
or
th
A
m
er
ic
a

246. C
an
ad
a
5
2
.9
(4
4
.5
,6
1
.2
)
6
5
0
0
0
(5
0
0
0
0
,8
2
0
0
0
)

247. 2
1
2
0
0
0
(1
3
6
0
0
0
,2
4
6
0
0
0
)
3
0
.7
(2
0
.2
,4
9
.3
)
U

248. n
it
ed
St
at
es
3
9
.8
(2
8
.4
,5
1
.3
)
8
9
5
0
0
0
(3
5
3
5
0
0
,1
6

249. 0
1
5
0
0
)
2
9
3
6
0
0
0
(2
2
3
1
0
0
0
,3
8
2
6
0
0
0
)
3
0
.5

250. (1
0
.9
,5
8
.9
)
P
ac
ifi
c
Is
la
n
d
St
at
es
an
d
Te
rr
it
or
ie
s
A
m
er
ic
an

251. Sa
m
oa
N
K
N
K
N
K
N
K
Fe
de
ra
lS
ta
te
s
of
M
ic
ro
n
es
ia
N
K
N
K

252. N
K
N
K
Fi
ji
N
K
N
K
5
0
0
(<
5
0
0
,3
0
0
0
)
N
K
Fr
en

253. ch
P
ol
yn
es
ia
N
K
N
K
N
K
N
K
G
u
am
N
K
N
K
N
K
N
K

254. K
ir
ib
at
i
N
K
N
K
N
K
N
K
M
ar
sh
al
lI
sl
an
ds
N
K
N
K
N
K

255. N
K
N
au
ru
–
–
N
K
–
N
ew
C
al
ed
on
ia
N
K
N
K
N
K
N
K

256. N
or
th
er
n
M
ar
ia
n
a
Is
la
n
ds
N
K
N
K
N
K
N
K
P
al
au
N
K
N

257. K
N
K
N
K
P
ap
u
a
N
ew
G
u
in
ea
N
K
N
K
9
4
5
0
0
(7
0
5
0
0

258. ,3
2
8
0
0
0
)
N
K
Sa
m
oa
N
K
N
K
<
5
0
0
(<
5
0
0
,5
0
0
)

259. N
K
So
lo
m
on
Is
la
n
ds
N
K
N
K
N
K
N
K
To
n
ga
N
K
N
K

260. N
K
N
K
T
u
va
lu
–
–
N
K
–
V
an
u
at
u
N
K
N
K
N
K
N

261. K
A
u
st
ra
la
si
a
A
u
st
ra
lia
4
0
.1
(3
6
.9
,4
3
.5
)
3
7
5
0
0
(2
7

262. 5
0
0
,4
8
5
0
0
)
2
3
0
0
0
0
(1
7
8
0
0
0
,2
4
4
0
0
0
)
1
6
.2

263. (1
1
.1
,2
3
.2
)
N
ew
Ze
al
an
d
5
3
.9
(4
6
.8
,6
1
.1
)
1
2
0
0
0
(8

264. 0
0
0
,1
6
5
0
0
)
4
8
5
0
0
(3
0
0
0
0
,6
2
5
0
0
)
2
5
.0
(1
4
.7

265. ,4
2
.9
)
Su
b-
Sa
h
ar
an
A
fr
ic
a
A
n
go
la
N
K
N
K
N
K
N
K
B

266. en
in
N
K
N
K
N
K
N
K
B
ot
sw
an
a
–
–
N
K
–
(C
on
ti
n
u

267. es
)
158 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
T
ab
le
3
.
(C
on
ti
n
u
ed
)
R
eg
io
n
an
d
co
un
tr
y
P

268. re
va
le
nc
e
of
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
am
on
g
pe
op
le
w
it
h
re
ce
nt
in
je

269. ct
in
g
dr
ug
us
e
%
(U
I)
N
um
be
r
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug

270. us
e
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in

271. g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re

272. ce
nt
in
je
ct
in
g
dr
ug
us
e
am
on
g
th
e
to
ta
lp
op
ul
at
io
n
w
it
h
H
C
V
vi

273. ra
em
ic
in
fe
ct
io
n
%
(U
I)
B
u
rk
in
a
Fa
so
N
K
N
K
2
4
7
0
0
0
(1
8

274. 9
0
0
0
,2
5
6
0
0
0
)
N
K
B
u
ru
n
di
N
K
N
K
1
2
0
0
0
0
(9
3

275. 0
0
0
,4
5
9
0
0
0
)
N
K
C
am
er
oo
n
N
K
N
K
1
6
4
0
0
0
(1
1

276. 7
0
0
0
,1
8
4
0
0
0
)
N
K
C
ap
e
V
er
de
N
K
N
K
N
K
N
K
C

277. en
tr
al
A
fr
ic
an
R
ep
u
bl
ic
–
–
1
5
5
0
0
(1
1
0
0
0
,1
7
5
0
0
)
–

278. C
h
ad
N
K
N
K
1
6
2
0
0
0
(1
1
1
0
0
0
,1
8
4
0
0
0
)
N
K
C

279. om
or
os
–
–
N
K
–
C
on
go
(K
in
sh
as
a)
N
K
N
K
N
K
N
K
C

280. ot
e
d’
Iv
oi
re
1
.3
(0
.0
,7
.1
)
<
5
0
0
(<
5
0
0
,<
5
0
0
)
N
K

281. N
G
D
jib
ou
ti
N
K
N
K
N
K
N
K
Eq
u
at
or
ia
lG
u
in
ea
–
–
N
K

282. –
Er
it
re
a
–
–
N
K
–
Et
h
io
pi
a
N
K
N
K
6
4
7
0
0
0
(4
1

283. 0
0
0
0
,7
2
6
0
0
0
)
N
K
G
ab
on
N
K
N
K
1
2
4
0
0
0
(9
0
0
0

284. 0
,1
2
9
0
0
0
)
N
K
G
am
bi
a
N
K
N
K
1
7
0
0
0
(1
0
0
0
0
,2

285. 7
0
0
0
)
N
K
G
h
an
a
3
0
.1
(2
5
.8
,3
4
.4
)
N
K
3
9
9
0
0

286. 0
(3
0
5
0
0
0
,9
4
4
0
0
0
)
N
K
G
u
in
ea
N
K
N
K
N
K
N
K

287. G
u
in
ea
-B
is
sa
u
–
–
N
K
–
K
en
ya
1
2
.3
(7
.4
,1
7
.7
)
4
0
0

288. 0
(1
0
0
0
,7
5
0
0
)
1
1
5
0
0
0
(4
2
5
0
0
,1
2
6
0
0
0
)
3
.3
(0

289. .7
,9
.7
)
Le
so
th
o
–
–
N
K
–
Li
be
ri
a
N
K
N
K
N
K
N
K

290. M
ad
ag
as
ca
r
4
.2
(1
.8
,7
.0
)
5
0
0
(<
5
0
0
,3
0
0
0
)
5
6
0
0

291. 0
(3
9
0
0
0
,8
1
0
0
0
)
1
.2
(0
.0
,5
.2
)
M
al
aw
i
N
K
N
K
N

292. K
N
K
M
al
i
N
K
N
K
N
K
N
K
M
au
ri
ta
n
ia
–
–
N
K
–

293. M
au
ri
ti
u
s
7
2
.8
(6
8
.8
,7
6
.7
)
5
0
0
0
(1
5
0
0
,9
5
0
0
)

294. N
K
N
G
M
oz
am
bi
qu
e
5
0
.3
(4
6
.2
,5
4
.4
)
1
4
5
0
0
(<
5

295. 0
0
,3
1
0
0
0
)
N
K
N
G
N
am
ib
ia
–
–
N
K
–
N
ig
er
N
K

296. N
K
N
K
N
K
N
ig
er
ia
4
.3
(2
.1
,6
.8
)
1
1
5
0
0
(2
0
0
0
,2

297. 7
0
0
0
)
2
5
5
3
0
0
0
(1
9
0
2
0
0
0
,2
6
5
1
0
0
0
)
0
.5
(0
.0

298. ,1
.1
)
R
ep
u
bl
ic
of
th
e
C
on
go
–
–
N
K
–
(C
on
ti
n
u
es
)
159

299. © 2018 Society for the Study of Addiction Addiction, 114, 150–
166
T
ab
le
3
.
(C
on
ti
n
u
ed
)
R
eg
io
n
an
d
co
un
tr
y
P
re
va
le
nc
e

300. of
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
am
on
g
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug

301. us
e
%
(U
I)
N
um
be
r
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in

302. g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h

303. H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
P
ro
po
rt
io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je

304. ct
in
g
dr
ug
us
e
am
on
g
th
e
to
ta
lp
op
ul
at
io
n
w
it
h
H
C
V
vi
ra
em
ic
in

305. fe
ct
io
n
%
(U
I)
R
w
an
da
N
K
N
K
N
K
N
K
Sa
o
To
m
e
an
d
P
ri

306. n
ci
pe
–
–
N
K
–
Se
n
eg
al
2
9
.5
(2
2
.9
,3
6
.3
)
6
5
0
0
(1
5

307. 0
0
,1
3
5
0
0
)
N
K
N
K
Se
yc
h
el
le
s
3
1
.5
(2
7
.2
,3
6
.0
)

308. 5
0
0
(5
0
0
,5
0
0
)
N
K
N
G
Si
er
ra
Le
on
e
N
K
N
K
N
K
N

309. K
So
m
al
ia
N
K
N
K
N
K
N
K
So
u
th
A
fr
ic
a
N
K
N
K
3
5

310. 6
0
0
0
(2
2
7
0
0
0
,4
4
1
0
0
0
)
N
K
Sw
az
ila
n
d
N
K
N
K
N
K

311. N
K
To
go
N
K
N
K
N
K
N
K
U
ga
n
da
N
K
N
K
N
K
N
K

312. U
n
it
ed
R
ep
u
bl
ic
of
T
an
za
n
ia
2
0
.8
(1
6
.4
,2
5
.4
)
7
1
5
0
0

313. (4
1
0
0
0
,1
0
8
0
0
0
)
N
K
N
G
Za
m
bi
a
N
K
N
K
N
K
N
K

314. Zi
m
ba
bw
e
N
K
N
K
N
K
N
K
M
id
dl
e
Ea
st
an
d
N
or
th
A
fr
ic
a

315. A
lg
er
ia
N
K
N
K
3
8
8
0
0
0
(1
4
0
0
0
0
,6
7
4
0
0
0
)
N
K

316. B
ah
ra
in
N
K
N
K
1
7
0
0
0
(1
1
0
0
0
,1
7
5
0
0
)
N
K
C
yp
ru

317. s
3
7
.3
(3
2
.9
,4
1
.8
)
<
5
0
0
(<
5
0
0
,<
5
0
0
)
N
K
N
G

318. Eg
yp
t
3
7
.1
(2
6
.7
,4
7
.5
)
N
K
5
6
2
5
0
0
0
(4
0
0
7
0
0
0

319. ,6
0
4
4
0
0
0
)
N
K
Ir
aq
N
K
N
K
8
5
5
0
0
(6
0
5
0
0
,9
6
5

320. 0
0
)
N
K
Is
ra
el
3
4
.0
(2
8
.3
,3
9
.9
)
N
K
1
0
0
0
0
0
(6
0
0

321. 0
0
,1
0
3
0
0
0
)
N
K
Jo
rd
an
N
K
N
K
2
4
5
0
0
(1
0
5
0
0
,2

322. 9
0
0
0
)
N
K
K
u
w
ai
t
N
K
N
K
N
K
N
K
Le
ba
n
on
1
7
.6

323. (1
0
.5
,2
5
.2
)
N
K
7
5
0
0
(3
0
0
0
,1
8
0
0
0
)
N
K
Li
by
an
A

324. ra
b
Ja
m
ah
ir
iy
a
7
0
.9
(6
6
.4
,7
5
.2
)
1
5
0
0
(5
0
0
,2
0
0
0
)

325. 4
1
5
0
0
(3
2
0
0
0
,4
3
0
0
0
)
3
.3
(1
.7
,5
.5
)
M
or
oc
co
4
0

326. .4
(2
5
.4
,5
5
.8
)
1
2
5
0
0
(5
5
0
0
,2
1
0
0
0
)
2
6
3
0
0
0
(1
9

327. 0
0
0
0
,3
2
8
0
0
0
)
4
.7
(1
.9
,8
.5
)
O
cc
.P
al
es
ti
n
ia
n
Te
rr
.

328. 3
1
.2
(2
6
.3
,3
6
.2
)
N
K
N
K
N
K
O
m
an
N
K
N
K
1
5
5

329. 0
0
(1
2
0
0
0
,1
7
5
0
0
)
N
K
Q
at
ar
N
K
N
K
3
7
5
0
0
(2
9
5

330. 0
0
,4
0
0
0
0
)
N
K
Sa
u
di
A
ra
bi
a
5
8
.3
(5
3
.7
,6
3
.0
)
N
K

331. 1
0
6
0
0
0
(7
8
5
0
0
,1
9
0
0
0
0
)
N
K
So
u
th
Su
da
n
–
–
N

332. K
–
Su
da
n
N
K
N
K
N
K
N
K
(C
on
ti
n
u
es
)
160 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166

333. UI = 0.4–1.7). The proportion of people with recent
injecting drug use among all people living with HCV
infection ranged from 1.5% (UI = 0.7–2.4) in the Middle
East and North Africa to 30.5% (UI = 11.7–56.7) in
North America (Table 2). Regions with people with
recent injecting drug use comprising > 10% of all people
living with HCV infection included Latin America
(22.0%, UI = 15.3–30.4), eastern Europe (17.9%,
UI = 8.2–30.9), Australasia (17.7%, UI = 12.1–25.2),
the Caribbean (16.7%, 8.9–30.6) and western Europe
(17.2%, UI = 9.9–30.4).
At the country level, there was very marked variation
in the estimates of HCV viraemic prevalence between
countries, ranging from 0.5% (UI = 0.0–1.4; Maldives) to
72.8% (UI = 68.8–76.7; Mauritius) (Fig. 1 and Table 3).
The HCV viraemic prevalence was 60–80% in 10
countries, 40–< 60% in 38 countries and < 40% in 50
countries. The largest populations of people with recent
injecting drug use living with HCV infection were in
Russia (969 500; UI = 463 000–1 570 500), the United
States (895000; UI = 353 500–1 601 500), China
(828 000; UI = 493 000–1 228 500) and Brazil
(461 000, UI = 336 500–596 500) (Fig. 2 and Table 3);
together, these countries accounted for 51% of people with
recent injecting drug use living with HCV infection. The
top 25 countries accounting for 82% of all people with
recent injecting drug use living with HCV infection globally
are shown in Fig. 3. The proportion of people with recent
injecting drug use among all people living with HCV infec-
tion varied between 0.9% (UI = 0.4–3.0; India) and 46.6%
(UI = 22.1–100.0; Commonwealth of Puerto Rico) (Fig. 4
and Table 3). The proportion of people with recent
injecting drug use among all people living with HCV
infection was < 10% in 21 countries, ≥ 10–< 20% in 11
countries and ≥ 20% in 23 countries.

334. DISCUSSION
This study estimated that there are 6.1 million
(UI = 3.4–9.2) people with recent injecting drug use
living with HCV infection world-wide, comprising 8.5%
(UI = 4.6–13.1) of all HCV infections globally. There was
considerable variation in the prevalence of HCV infection
among people with recent injecting drug use at regional
and country levels, and in the proportion of all HCV
infection among people with recent injecting drug use.
These findings highlight countries and regions where a
focus upon HCV prevention and treatment among people
with recent injecting drug use will be required if HCV
elimination targets are to be met.
The greatest numbers of people with recent injecting
drug use living with HCV infection are in eastern Europe,
East and Southeast Asia and North America. Half of all
people with recent injecting drug use living with HCVTa
bl
e
3
.
(C
on
ti
n
u
ed
)
R
eg

335. io
n
an
d
co
un
tr
y
P
re
va
le
nc
e
of
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
am
on
g
pe
op
le

336. w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
%
(U
I)
N
um
be
r
of
pe
op
le
w
it
h
re

337. ce
nt
in
je
ct
in
g
dr
ug
us
e
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)

338. To
ta
lp
op
ul
at
io
n
liv
in
g
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
(U
I)
P
ro
po
rt

339. io
n
of
pe
op
le
w
it
h
re
ce
nt
in
je
ct
in
g
dr
ug
us
e
am
on
g
th
e
to
ta
lp
op
ul
at

340. io
n
w
it
h
H
C
V
vi
ra
em
ic
in
fe
ct
io
n
%
(U
I)
Sy
ri
an
A
ra
b
R
ep
.
2

341. .5
(0
.9
,4
.3
)
N
K
5
5
4
0
0
0
(2
4
5
0
0
0
,6
5
3
0
0
0
)
N
K

342. T
u
n
is
ia
2
1
.8
(1
9
.0
,2
4
.7
)
N
K
1
0
8
0
0
0
(2
5
0
0
0
,1
2

343. 3
0
0
0
)
N
K
T
u
rk
ey
3
3
.7
(3
0
.7
,3
6
.7
)
N
K
4
9
2
0
0
0

344. (2
7
1
0
0
0
,7
6
3
0
0
0
)
N
K
U
n
it
ed
A
ra
b
Em
ir
at
es
N
K
N

345. K
1
3
1
0
0
0
(5
0
0
0
0
,1
5
9
0
0
0
)
N
K
Y
em
en
N
K
N
K
2

346. 1
1
0
0
0
(1
4
3
0
0
0
,2
5
8
0
0
0
)
N
K
N
K
=
n
o
es
ti
m
at
e
of
pr
ev

347. al
en
ce
of
th
at
H
C
V
w
as
lo
ca
te
d,
ye
t
ev
id
en
ce
of
in
je
ct
in
g
dr
u
g
u

348. se
oc
cu
rr
in
g
in
th
at
co
u
n
tr
y
w
as
id
en
ti
fi
ed
;–
=
n
o
ev
id
en
ce

349. lo
ca
te
d
th
at
in
je
ct
in
g
dr
u
g
u
se
w
as
oc
cu
rr
in
g
in
th
is
co
u
n
tr
y;
N

350. R
=
u
n
ce
rt
ai
n
ty
w
as
n
ot
es
ti
m
at
ed
ar
ou
n
d
th
e
es
ti
m
at
e;
N
G

351. =
n
o
es
ti
m
at
e
of
H
C
V
am
on
g
th
e
ge
n
er
al
po
pu
la
ti
on
w
as
av
ai
la
bl

352. e.
P
W
ID
=
pe
op
le
w
h
o
in
je
ct
dr
u
gs
;U
I
=
u
n
ce
rt
ai
n
ty
in
te
rv
al
(s

353. ee
m
et
h
od
s
fo
r
de
ta
ils
of
es
ti
m
at
io
n
).
161
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
infection are from just four countries: the Russian Federa-
tion, the United States, China and Brazil. Further, the top
25 countries account for 82% of all people with recent
injecting drug use living with HCV infection globally.
Although PWID are a critical population for HCV
elimination in many settings, concerted efforts to increase

354. access to prevention and treatment for people with recent
injecting drug use in these countries will be pivotal to the
success of global HCVelimination efforts. Key among these
will be harm reduction measures to prevent incident
Figure 1 Estimated prevalence of hepatitis C virus (HCV)
viraemic infection among people with recent injecting drug use,
by country [Colour figure
can be viewed at wileyonlinelibrary.com]
Figure 2 Estimated number of people with recent injecting drug
use living with hepatitis C virus (HCV) viraemic infection, by
country [Colour figure
can be viewed at wileyonlinelibrary.com]
162 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
http://wileyonlinelibrary.com
http://wileyonlinelibrary.com
infections [11] and increased testing, linkage to care and
uptake of directly acting anti-viral therapy among people
with recent injecting drug use [12,13].
Countries or territories where it is estimated that at
least one-third of people living with HCV infection are peo-
ple with recent injecting drug use include Georgia, Austria,
Finland, Malaysia and Puerto Rico. In a further 16 coun-
tries, at least one-quarter of people living with HCV infec-
tion are people with recent injecting drug use. However,
there are also 21 countries where the proportion of people
living with HCVare people with recent injecting drug use is

355. < 10%. Collectively, these data highlight the variation in
the proportion of overall viraemic HCV infection occurring
among people with recent injecting drug use globally,
reflecting the differing epidemiology of HCV in different
settings. As such, different types of prevention, testing
and treatment strategies will be needed to address HCV
elimination targets according to the epidemiology within
a given country. It should also be noted that there were
124 countries and territories where injecting drug use is
known to occur, but no data were available to assess the
proportion of people with HCV infection who are people
with recent injecting drug use.
This study was limited to estimates among people with
recent injecting drug use and will not include those who
have even ‘temporarily’ or permanently ceased injecting.
As such, this study underestimates the proportion of infec-
tions that occur among PWID within an overall epidemic,
given that some infections due to injecting drug use will be
among people with a history of injecting who have ceased
injecting. It is critical to consider people who have recently
injected drugs as well as those who have ceased injecting in
the design of strategies to address HCV.
There are several limitations to this study. The search
may have missed some literature (particularly grey
literature), despite our wide scope of online searchers and
requests for information from people across many coun-
tries. To address this possibility, we liaised with the WHO,
Global Fund, United Nations Office on Drugs and Crime
(UNODC) and Joint United Nations Programme on HIV
and AIDS (UNAIDS) staff to contact experts within coun-
tries and obtain reports that were not available online.
However, we doubt that any missed papers will alter these
findings in a meaningful fashion.

356. Errors may have been made in data extraction and
interpretation. To reduce such errors, all sources and data
Figure 3 Countries with the greatest total number of people with
hepatitis C virus (HCV) viraemic infection among people with
recent
injecting drug use globally. The size of the bubble represents
the total proportion of hepatitis C viraemic infections that
among people with
recent injecting drug use. X indicates that data were not
available to calculate the total proportion of viraemic HCV
infections among people
with recent injecting drug use [Colour figure can be viewed at
wileyonlinelibrary.com]
163
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
http://wileyonlinelibrary.com
from which the final estimates were derived were double-
checked by at least two reviewers prior to inclusion with
a further round prior to finalization with a third reviewer.
We have online interactive presentations of these data at
(https://ndarc.med.unsw.edu.au/resource/global-epidemiol-
ogy-injecting-drug-use-2017) to ensure full transparency
and to increase the potential for people to interact with the
estimates and results, and suggest additional data sources.
We encourage feedback at [email protected]
Although the review team searched for publications in
multiple languages, we may have missed documents in
languages in which we are not fluent. Those with access
to data or papers/reports in other languages should con-

357. tact us. It is also important to acknowledge a number of
features of our approach to synthesis and imputation of es-
timates, driven by the gaps in data available. Although
there has been a clear increase in efforts to quantify the ex-
tent of IDU and HCV among PWID, there are still major
gaps in data in some regions. A hierarchical grading sys-
tem was used to evaluate estimates based on geographical
generalizability (e.g. from multiple sites) and across various
populations of PWID (e.g. treatment and non-treatment
samples). Exclusion of estimates based on a study’s meth-
odology grade was applied only to estimates of IDU and
anti-HCV prevalence. Nonetheless, our recent approach,
which involved pooling estimates, and our more sophisti-
cated approach to estimating uncertainty around all our
estimates, including our method of estimating uncertainty
around imputed estimates, are both improvements upon
previous reviews.
A limitation is the lack of country-level data to estimate
the viraemic HCV prevalence (98 countries), numbers of
people living with HCV (76 countries) and the proportion
among the overall population living with HCV among
people with recent injecting drug use (55 countries). Data
were sparse in regions such as the Caribbean, Latin
America, Pacific Island States and Territories, sub-Saharan
Africa and the Middle East and North Africa. The estimates
for these regions should be interpreted with caution, and
highlights that further work is needed to improve estimates
in countries from these regions.
In this study, data on HCV antibody prevalence [multi-
plied by an estimate of the proportion of people with HCV
antibodies who would have active viraemia, 0.75 (95%
CI = 0.71, 0.79)] was used to estimate the viraemic HCV
prevalence, instead of actual data on HCV RNA prevalence.
We opted for this approach because the data on HCV anti-

358. body prevalence were of higher quality and coverage, and
there were few countries for which any data were available
for HCV RNA (Table 1). Instead, we used data on the
estimated viraemic prevalence from a well-defined series
of nine prospective cohorts of acute HCV infection among
people who inject drugs with well-characterized events of
spontaneous clearance [9]. Although this provides an ex-
tremely accurate estimate of the proportion who progress
to viraemic infection, the limitation is that this approach
may have either over- or underestimated the true preva-
lence of viraemic infection in people with recent injecting
drug use in various settings. In some regions, increased
re-infection risk and/or higher HIV prevalence may result
Figure 4 Estimated proportion of people with recent injecting
drug use among the total population with hepatitis C virus
(HCV) viraemic infection,
by country [Colour figure can be viewed at
wileyonlinelibrary.com]
164 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
https://ndarc.med.unsw.edu.au/resource/global-epidemiology-
injecting-drug-use-2017
https://ndarc.med.unsw.edu.au/resource/global-epidemiology-
injecting-drug-use-2017
http://wileyonlinelibrary.com
in a higher viraemic prevalence, and our approach may
have underestimated the viraemic prevalence [14].
Conversely, it is known that some factors (e.g. female sex)
increase spontaneous clearance and can reduce the

359. viraemic prevalence, which might have overestimated the
viraemic prevalence observed. Also, these analyses did
not take into consideration clearance due to HCV treat-
ment, which might have led to an overestimation of the
prevalence and numbers of people with recent injecting
drug use living with HCV infection. However, this is also
unlikely to have affected these estimates, as uptake of
HCV treatment among PWID was very low prior to 2015
[15–19]. This study clearly demonstrates the need to inte-
grate HCV RNA testing into future studies of HCV among
people with recent injecting drug use to enable the evalua-
tion of viraemic HCV RNA prevalence to improve national,
regional and global estimates, particularly given that larger
numbers of PWID are initiating HCV treatment (and will
be anti-HCV positive, but HCV RNA-negative).
Denominator data are also subject to limitations.
General population data may be in error for some countries
where accurate census data are lacking. Population sizes of
people with recent injecting drug use were based on the
best available empirical estimates for each country, but
there is often considerable uncertainty around estimates
of this population, which translates to uncertainty in esti-
mates of the number of PWID with HCV infection and
the proportion of HCV infections occurring among people
with recent injecting drug use. Estimates of HCV viraemia
in people with recent injecting drug use incorporated the
uncertainties in the IDU population size, anti-HCV preva-
lence estimate and viraemia multiplier. However, estimates
of the prevalence of recent IDU and of HCV prevalence both
in people with recent injecting drug use and in the general
population are subject to biases, which may be responsible
for some estimates that do not seem correct. Further, the
extracted data were often from a single year and changes
in injecting drug-user populations and HCV incidence
could not be measured. This highlights the importance of

360. continuing to improve country-level estimates of people
with recent injecting drug use and those with viraemic
HCV infection.
Irrespective of these limitations, this review advances
our understanding of HCV prevalence and disease burden
among people with recent injecting drug use. Accurate
estimates of the prevalence and burden of viraemic HCV
infection among people with recent injecting drug use
are crucial to guide policy and practice and guide the devel-
opment of strategies to enhance testing, linkage to care
and treatment in this population. This review highlights
that concerted efforts will be required in countries with
large numbers of people infected with HCV to achieve
global HCVelimination among PWID. Further, it highlights
that strategies to achieve a reduction in HCV burden will
need to be tailored to the individual country, based on the
HCV epidemiology and the proportion of overall infections
occurring in people with recent injecting drug use. Collec-
tively, these data will inform mathematical modelling to
identify strategies to increase diagnosis and treatment
and reduce the number of new infections to achieve HCV
elimination at a country level. Further work is needed to
understand more clearly the population size of people with
a history of injecting drug use and the prevalence of
viraemic HCV infection and burden in those with former,
but not recent, injecting drug use.
Declaration of interests
J.G. is a consultant/adviser and has received research
grants from AbbVie, Bristol-Myers Squibb, Cepheid, Gilead
Sciences and Merck/MSD. G.D. is a consultant/adviser and
has received research grants from Abbvie, Abbot
Diagnostics, Bristol Myers Squibb, Cepheid, Gilead,

361. GlaxoSmithKline, Merck, Janssen and Roche. S.B. and H.
R. have not received any remuneration. The CDA Founda-
tion and the Polaris Observatory has not received any
funding from commercial organizations. J.S. reports non-
financial support from Gilead Sciences. During the past
3 years, LD has received investigator-initiated untied
educational grants for studies of opioid medications in
Australia from Indivior, Mundipharma, and Seqirus. S.L.
has received investigator-initiated untied educational
grants from Indivior. A.P. has received investigator-initiated
untied educational grants from Mundipharma and Seqirus.
E.B.C. received PhD funding from the Canadian Network
on Hepatitis C. M.H. reports personal fees from Gilead,
Abbvie and MSD.
Acknowledgements
The Australian National Drug and Alcohol Research
Centre, UNSW Sydney, provided some funding towards
the costs of this systematic review. The Open Society Foun-
dation, World Health Organization, the Global Fund, and
UNAIDS provided funding towards the systematic review
to estimate the number of people with recent injecting
drug use and the HCV antibody prevalence among
people who have recently injected drugs. The John C
Martin Foundation provided funding towards the system-
atic review and modelling study to estimate the global
viraemic HCV prevalence L.D. and R.P.M. are supported
by Australian National Health and Medical Research
Council (NHMRC) Principal Research Fellowships. S.L. is
supported by an NHMRC Career Development Fellowship.
A.P. is supported by an NHMRC Early Career Fellowship.
J.L. acknowledges funding from the Bill & Melinda Gates
Foundation. The Kirby Institute is funded by the Australian
Government Department of Health and Ageing. The views
expressed in this publication do not necessarily represent

362. 165
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
the position of the Australian Government. J.G. is sup-
ported by an NHMRC Career Development Fellowship. C.
D. is supported by an NHMRC Practitioner Fellowship. J.S.
acknowledges funding from a PhD scholarship from the
Engineering and Physical Sciences Research Council
(EPSRC). E.B.C. acknowledges funding from Canadian Net-
work on Hepatitis C (CanHepC). A.T. has received PhD
funding from the National Institute for Health Research
(NIHR). M.H. and P.V. acknowledge support from NIHR
Health Protection Research Unit (HPRU) in Evaluation of
Interventions at the University of Bristol. P.V. acknowledges
support from the NIHR HPRU in Blood-Borne and Sexually
Transmitted Infections at University College London and
National Institute for Drug Abuse (grant number R01
DA037773–01A1). We thank the research assistants
who assisted with searches for and extraction of data from
the eligible papers in this review: Erin Yong, Gabrielle Gib-
son, Griselda Buckland, Harriet Townsend, Julia Stadum
and Laura Sergeant (NDARC, UNSW) and Diana
Sergiienko (Ukrainian Institute of Public Health Policy).
We also thank Mary Kumvaj, the librarian who provided
specialist advice on our search strategy and search strings
for the peer-reviewed literature searches. Finally, we thank
the individuals who provided encouragement and support
in various ways throughout the conduct of this study,
including circulating requests for data, provision of
in-country contacts and assistance with locating data:
Annette Verster (WHO), Daniel Wolfe (Open Society

363. Foundations), Andre Noor (EMCDDA), Eleni Kalamara
(EMCDDA), Mauro Guarinieri (Global Fund), Christoforos
Mallouris (UNAIDS), Susie McLean, Catherine Cook [Harm
Reduction International(HRI)], Maria Phelan (HRI), Katie
Stone (HRI), Riku Lehtovuori (UNODC), Keith Sabin
(UNAIDS), Jinkou Zhao (Global Fund), Vladimir Poznyak
(WHO) and Gilberto Gerra (UNODC). Assistance in sourc-
ing and verifying data was provided by many individuals
from government, non-government and research organiza-
tions around the world, for which we are thankful. These
individuals are listed in the Appendix (p. 154).
References
1. World Health Organization (WHO). Global Hepatitis Report
2017. Geneva: WHO; 2017.
2. Nelson P. K., Mathers B. M., Cowie B., Hagan H., Des
Jarlais D.,
Horyniak D. et al. Global epidemiology of hepatitis B and hep-
atitis C in people who inject drugs: results of systematic
reviews. Lancet 2011; 378: 571–83.
3. Morris M. D., Shiboski S., Bruneau J., Hahn J. A., Hellard
M.,
Prins M. et al. Geographic differences in temporal incidence
trends of hepatitis C virus infection among people who inject
drugs: the InC3 collaboration. Clin Infect Dis 2017; 64: 860–9.
4. Wiessing L., Ferri M., Grady B., Kantzanou M., Sperle I.,
Cullen K. J. et al. Hepatitis C virus infection epidemiology
among people who inject drugs in Europe: a systematic re-
view of data for scaling up treatment and prevention. PLoS
One 2014; 9: e103345.
5. Hagan H., Pouget E. R., Des Jarlais D. C., Lelutiu-

364. Weinberger
C. Meta-regression of hepatitis C virus infection in relation to
time since onset of illicit drug injection: the influence of time
and place. Am J Epidemiol 2008; 168: 1099–109.
6. Page K., Morris M. D., Hahn J. A., Maher L., Prins M.
Injection
drug use and hepatitis C virus infection in young adult injec-
tors: using evidence to inform comprehensive prevention.
Clin Infect Dis 2013; 57: S32–S38.
7. Degenhardt L., Peacock A., Colledge S., Leung J., Grebely J.,
Vickerman P. et al. Global prevalence of injecting drug use
and sociodemographic characteristics and prevalence of HIV,
HBV, and HCV in people who inject drugs: a multistage sys-
tematic review. Lancet Glob Health 2017; 5: e1192–e1207.
8. The Polaris Observatory HCV Collaborators. Global
prevalence
and genotype distribution of hepatitis C virus infection in 2015:
a modelling study. Lancet Gastroenterol Hepatol 2017; 2: 161–
76.
9. Grebely J., Page K., Sacks-Davis R., van der Loeff M. S.,
Rice T.
M., Bruneau J. et al. The effects of female sex, viral genotype,
and IL28B genotype on spontaneous clearance of acute hep-
atitis C virus infection. Hepatology 2014; 59: 109–20.
10. Mathers B. M., Degenhardt L., Phillips B., Wiessing L.,
Hickman M., Strathdee S. A. et al. Global epidemiology of
injecting drug use and HIV among people who inject drugs:
a systematic review. Lancet 2008; 372: 1733–45.
11. Platt L., Minozzi S., Reed J., Vickerman P., Hagan H.,
French C.

365. et al. Needle and syringe programmes and opioid substitution
therapy for preventing HCV transmission among people who
inject drugs: findings from a Cochrane review and meta-
analysis. Addiction 2017; https://doi.org/10.1111/add.14012.
12. Bajis S., Dore G. J., Hajarizadeh B., Cunningham E. B.,
Maher
L., Grebely J. Interventions to enhance testing, linkage to care
and treatment uptake for hepatitis C virus infection among
people who inject drugs: a systematic review. Int J Drug Policy
2017; 47: 34–46.
13. Grebely J., Dore G. J., Morin S., Rockstroh J. K., Klein M.
B.
Elimination of HCVas a public health concern among people
who inject drugs by 2030—what will it take to get there? J Int
AIDS Soc 2017; 20: 22146.
14. Smith D. J., Jordan A. E., Frank M., Hagan H. Spontaneous
vi-
ral clearance of hepatitis C virus (HCV) infection among
people who inject drugs (PWID) and HIV-positive men who
have sex with men (HIV+ MSM): a systematic review and
meta-analysis. BMC Infect Dis 2016; 16: 471.
15. Alavi M., Raffa J. D., Deans G. D., Lai C., Krajden M.,
Dore G. J.,
et al. Continued low uptake of treatment for hepatitis C virus
infection in a large community-based cohort of inner city res-
idents. Liver Int 2014; 34: 1198–206.
16. Iversen J., Grebely J., Topp L., Wand H., Dore G., Maher L.
Up-
take of hepatitis C treatment among people who inject drugs
attending needle and syringe programs in Australia, 1999–
2011. J Viral Hepatol 2014; 21: 198–207.

366. 17. Mehta S. H., Genberg B. L., Astemborski J., Kavasery R.,
Kirk
G. D., Vlahov D. et al. Limited uptake of hepatitis C treatment
among injection drug users. J Community Health 2008; 33:
126–33.
18. Grebely J., Raffa J. D., Lai C., Krajden M., Kerr T., Fischer
B.,
et al. Low uptake of treatment for hepatitis C virus infection
in a large community-based study of inner city residents. J Vi-
ral Hepatol 2009; 16: 352–8.
19. Strathdee S. A., Latka M., Campbell J., O’Driscoll P. T.,
Golub
E. T., Kapadia F. et al. Factors associated with interest in initi-
ating treatment for hepatitis C virus (HCV) infection among
young HCV-infected injection drug users. Clin Infect Dis
2005; 40: S304–S312.
166 Jason Grebely et al.
© 2018 Society for the Study of Addiction Addiction, 114, 150–
166
https://doi.org/10.1111/add.14012
This document is a scanned copy of a printed document. No
warranty is given about the
accuracy of the copy. Users should refer to the original
published version of the material.

367. Morbidity and Mortality Weekly Report
Weekly / Vol. 68 / No. 39 October 4, 2019
Continuing Education examination available at
https://www.cdc.gov/mmwr/cme/conted_info.html#weekly.
U.S. Department of Health and Human Services
Centers for Disease Control and Prevention
INSIDE
839 Flavored Tobacco Product Use Among Middle and
High School Students — United States, 2014–2018
845 Trends and Characteristics in Marijuana Use Among
Public School Students — King County, Washington,
2004–2016
851 Evaluation of Infection Prevention and Control
Readiness at Frontline Health Care Facilities in
High-Risk Districts Bordering Ebola Virus Disease–
Affected Areas in the Democratic Republic of the
Congo — Uganda, 2018
855 Progress Toward Rubella and Congenital Rubella
Syndrome Control and Elimination — Worldwide,
2000–2018
860 Characteristics of a Multistate Outbreak of Lung
Injury Associated with E-cigarette Use, or Vaping —
United States, 2019
865 E-cigarette Product Use, or Vaping, Among Persons
with Associated Lung Injury — Illinois and
Wisconsin, April–September 2019

368. 870 QuickStats
National Trends in Hepatitis C Infection by Opioid Use
Disorder Status Among
Pregnant Women at Delivery Hospitalization — United States,
2000–2015
Jean Y. Ko, PhD1; Sarah C. Haight, MPH1; Sarah F. Schillie,
MD2; Michele K. Bohm, MPH3; Patricia M. Dietz, DrPH2
Hepatitis C virus (HCV) is transmitted primarily through
parenteral exposures to infectious blood or body fluids that
contain blood (e.g., via injection drug use, needle stick inju-
ries) (1). In the last 10 years, increases in HCV infection in
the general U.S. population (1) and among pregnant women
(2) are attributed to a surge in injection drug use associated
with the opioid crisis. Opioid use disorders among pregnant
women have increased (3), and approximately 68% of pregnant
women with HCV infection have opioid use disorder (4).
National trends in HCV infection among pregnant women
by opioid use disorder status have not been reported to date.
CDC analyzed hospital discharge data from the 2000–2015
Healthcare Cost and Utilization Project (HCUP) to determine
whether HCV infection trends differ by opioid use disorder
status at delivery. During this period, the national rate of HCV
infection among women giving birth increased >400%, from
0.8 to 4.1 per 1,000 deliveries. Among women with opioid use
disorder, rates of HCV infection increased 148%, from 87.4 to
216.9 per 1,000 deliveries, and among those without opioid
use disorder, rates increased 271%, although the rates in this
group were much lower, increasing from 0.7 to 2.6 per 1,000
deliveries. These findings align with prior ecological data link-
ing hepatitis C increases with the opioid crisis (2). Treatment
of opioid use disorder should include screening and referral
for related conditions such as HCV infection.

369. To evaluate HCV infection prevalence at hospital delivery
among women with and without opioid use disorder, data
from HCUP’s National Inpatient Sample (NIS, 2000–2015)
(https://www.hcup-us.ahrq.gov/) were analyzed. The fourth
quarter of 2015 and more recent data were excluded because
of the transition to the International Classification of Diseases,
Tenth Revision, Clinical Modification (ICD-10-CM) during
that period. The NIS is the largest publicly available all-payer
inpatient health care database in the United States, yielding
national estimates representing approximately 35 million
hospitalizations. Discharges for in-hospital deliveries were
identified using International Classification of Diseases, Ninth
Revision, Clinical Modification (ICD-9-CM) diagnostic and
procedure codes pertaining to obstetric delivery (5).
HCV infection was identified from ICD-9-CM codes
070.41, 070.44, 070.51, 070.54, 070.70, 070.71, and V02.62;
https://www.cdc.gov/mmwr/cme/conted_info.html#weekly
https://www.hcup-us.ahrq.gov/
Morbidity and Mortality Weekly Report
834 MMWR / October 4, 2019 / Vol. 68 / No. 39 US
Department of Health and Human Services/Centers for Disease
Control and Prevention
The MMWR series of publications is published by the Center
for Surveillance, Epidemiology, and Laboratory Services,
Centers for Disease Control and Prevention (CDC),
U.S. Department of Health and Human Services, Atlanta, GA
30329-4027.
Suggested citation: [Author names; first three, then et al., if
more than six.] [Report title]. MMWR Morb Mortal Wkly Rep

370. 2019;68:[inclusive page numbers].
Centers for Disease Control and Prevention
Robert R. Redfield, MD, Director
Anne Schuchat, MD, Principal Deputy Director
Chesley L. Richards, MD, MPH, Deputy Director for Public
Health Science and Surveillance
Rebecca Bunnell, PhD, MEd, Director, Office of Science
Barbara Ellis, PhD, MS, Acting Director, Office of Science
Quality, Office of Science
Michael F. Iademarco, MD, MPH, Director, Center for
Surveillance, Epidemiology, and Laboratory Services
MMWR Editorial and Production Staff (Weekly)
Charlotte K. Kent, PhD, MPH, Editor in Chief
Jacqueline Gindler, MD, Editor
Mary Dott, MD, MPH, Online Editor
Terisa F. Rutledge, Managing Editor
Douglas W. Weatherwax, Lead Technical Writer-Editor
Glenn Damon, Soumya Dunworth, PhD, Teresa M. Hood, MS,
Technical Writer-Editors
Martha F. Boyd, Lead Visual Information Specialist
Maureen A. Leahy, Julia C. Martinroe,
Stephen R. Spriggs, Tong Yang,
Visual Information Specialists
Quang M. Doan, MBA, Phyllis H. King,
Terraye M. Starr, Moua Yang,

371. Information Technology Specialists
MMWR Editorial Board
Timothy F. Jones, MD, Chairman
Ileana Arias, PhD
Matthew L. Boulton, MD, MPH
Jay C. Butler, MD
Virginia A. Caine, MD
Katherine Lyon Daniel, PhD
Jonathan E. Fielding, MD, MPH, MBA
David W. Fleming, MD
William E. Halperin, MD, DrPH, MPH
Jewel Mullen, MD, MPH, MPA
Jeff Niederdeppe, PhD
Patricia Quinlisk, MD, MPH
Stephen C. Redd, MD
Patrick L. Remington, MD, MPH
Carlos Roig, MS, MA
William Schaffner, MD
Morgan Bobb Swanson, BS
and opioid use disorder was identified from codes for opi-
oid dependence and nondependent abuse (304.00–304.03,
304.70–304.73, and 305.50–305.53), aligning with Diagnostic
and Statistical Manual of Mental Disorders, 5th Edition
criteria*
(6). Deliveries were categorized by maternal diagnoses: HCV

372. infection only, opioid use disorder only, both HCV infection
and opioid use disorder, or neither. Demographic variables
of interest included age, payer source, race/ethnicity, median
income quartiles for residency ZIP code, and hospital geo-
graphic region.
Survey-specific analysis techniques accounted for clustering,
stratification, and weighting. National annual prevalence rates
of opioid use disorder and HCV infection per 1,000 delivery
hospitalizations during 2000–2015 and 95% confidence
intervals (CIs) were calculated using SAS (version 9.4; SAS
Institute). HCV infection rates were calculated by opioid use
disorder status. Joinpoint regression was used to model the
average percentage change in HCV infection and opioid use
disorder rates over time and their statistical significance. The
program identifies points (joinpoints) where the slope of the
trend significantly changes and calculates the average percent-
age change in the rate during the years between joinpoints.
Using 2015 data, distribution of diagnoses by payer source,
* ICD-9-CM codes related to opioid dependence and
nondependent abuse, in
remission, were included in this analysis because both early
remission and opioid
use disorder could have occurred during pregnancy.
race/ethnicity, median income for residency ZIP code, and
hospital region were calculated. Polytomous logistic regression
models were used to calculate unadjusted odds ratios (ORs)
and 95% CIs comparing the likelihood of each delivery hos-
pitalization having one or both diagnoses versus neither by
sociodemographic characteristics. Statistical significance was
set at p<0.05.
During 2000–2015, the rate of HCV infection increased
from 0.8 (95% CI = 0.7–0.9) to 4.1 (95% CI = 3.7–4.4)

373. per 1,000 deliveries. Rates significantly increased from 2000
to 2004 (15.7%; p<0.001), 2004 to 2010 (6.1%; p<0.001),
and 2010 to 2015 (14.9%; p<0.001). Among deliveries with
opioid use disorder diagnoses, the rate of maternal HCV
infection increased from 87.4 (95% CI = 56.3–118.5) to
216.9 (95% CI = 197.9–235.9) per 1,000 deliveries (Figure).
The rate significantly increased during 2000–2004 (17.2%;
p<0.001), remained statistically unchanged during 2004–2011
(-2.4%; p = 0.1), and significantly increased during 2011–2015
(7.9%; p<0.001). Among deliveries without opioid use disor-
der diagnoses, the rate of HCV infection increased from 0.7
(95% CI = 0.6–0.8) to 2.6 (95% CI = 2.4–2.9) per 1,000
deliveries during 2000–2015. The rate remained statistically
unchanged during 2000–2002 (21.1%; p = 0.1), and sig-
nificantly increased during 2002–2011 (5.5%; p<0.001) and
2011–2015 (15.0%; p<0.001).
In 2015, all three groups (those with HCV infection only,
opioid use disorder only, and both HCV infection and opioid
Morbidity and Mortality Weekly Report
MMWR / October 4, 2019 / Vol. 68 / No. 39 835US Department
of Health and Human Services/Centers for Disease Control and
Prevention
FIGURE. National prevalence* of maternal hepatitis C virus
(HCV) infection per 1,000 delivery hospitalizations, by opioid
use disorder (OUD)
status, 2000–2015†
0
2

374. 4
6
8
10
12
14
16
18
20
0
50
100
150
200
250
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2011 2012 2013 2014 2015
D
iagnoses per 1,000 deliveries, w

375. ithout O
U
DD
ia
gn
os
es
p
er
1
,0
00
d
el
iv
er
ie
s,
w
ith
O
U
D

376. Year
With OUD
Without OUD
* Prevalence numerator consisted of HCV infection
International Classification of Diseases, Ninth Revision,
Clinical Modification (ICD-9-CM) codes (070.41, 070.44,
070.51,
070.54, 070.70, 070.71, and V02.62), and denominator
consisted of delivery hospitalizations discharges with and
without opioid type dependence and nondependent
opioid abuse based on ICD-9-CM codes (304.00–304.03,
304.70–304.73, and 305.50–305.53).
† Rates are for 2000 through the third quarter of 2015.
use disorder) shared similar risk factors (Table 1). Compared
with women aged ≥35 years, those aged 25–34 years were
more likely to have a diagnosis of HCV infection (OR = 1.2,
95% CI = 1.0–1.4), opioid use disorder (OR = 1.8, 95%
CI = 1.6–2.0), or both (OR = 1.8, 95% CI: 1.4–2.3) at delivery
(Table 2). Women with publicly billed deliveries (Medicaid or
Medicare) were the most likely to have a diagnosis of HCV
infection (OR = 5.5, 95% CI = 4.7–6.4), opioid use disorder
(OR = 6.4, 95% CI = 5.8–7.2), or both (OR = 9.9, 95%
CI = 7.8–12.6) at delivery, compared with privately billed
deliveries. Compared with non-Hispanic black women, Native
American women were the most likely to have a diagnosis
of HCV infection (OR = 5.0, 95% CI = 2.9–8.7) or opioid
use disorder (OR = 5.9, 95% CI = 4.0–8.8) at delivery, and
non-Hispanic white women were the most likely to have a
diagnosis of both (OR = 10.9, 95% CI = 6.3–18.6) at deliv-
ery. Women from areas with median income of <$42,000
were the most likely to receive a diagnosis of HCV infection

377. (OR = 2.5, 95% CI = 2.0–3.0), opioid use disorder (OR = 2.0,
95% CI = 1.7–2.3), or both (OR = 2.5, 95% CI = 1.8–3.4) at
delivery, compared with those from areas with median income
≥$68,000. Compared with U.S. residents of the Western census
region (the referent group), residents of the South were the
most likely to receive a diagnosis of HCV infection (OR = 1.9,
95% CI = 1.5–2.3) at delivery. Women living in the Northeast
were the most likely to receive a diagnosis of opioid use
disorder
(OR = 2.0, 95% CI = 1.6–2.4) or both HCV infection and
opioid use disorder (OR = 4.8, 95% CI = 3.1–7.5) at delivery.
Discussion
In the United States, the 2015 rate of HCV infection at
delivery hospitalization (4.1 per 1,000) was approximately five
times higher than it was in 2000 (0.8 per 1,000). Rates were
substantially higher among women with opioid use disorder,
suggesting a link between the opioid crisis and increases in
HCV infection. Results from this analysis are consistent with
previously reported findings. For example, these estimates using
hospital discharge data are similar to those from an analysis of
birth certificate data, which found that maternal HCV infec-
tion almost doubled during 2009–2014 from 1.8 to 3.4 per
1,000 live births (2). Increased likelihood of HCV infection,
opioid use disorder diagnosis, or both among women with pub-
licly billed deliveries is similar to previous findings that women
with HCV infection were more likely to be Medicaid-insured
(4). In this analysis, Native American women were significantly
more likely to have an HCV infection or opioid use disorder
diagnosis at delivery than were non-Hispanic black women.
High rates of overdose deaths and HCV infection in American
Indian and Alaska Native persons have been previously noted
in the general adult population (7,8). Lower HCV infection
rates at delivery among women in the West reflect distribution
of HCV infection in the general population (1).

378. Current U.S. Preventive Service Task Force and CDC guide-
lines recommend hepatitis C testing for persons at high risk
(e.g., persons who inject drugs†,§); however, epidemiologic
†
https://www.uspreventiveservicestaskforce.org/Page/Document/
UpdateSummaryFinal/hepatitis-c-screening.
§ https://www.cdc.gov/hepatitis/hcv/guidelinesc.htm.
https://www.uspreventiveservicestaskforce.org/Page/Document/
UpdateSummaryFinal/hepatitis-c-screening
https://www.uspreventiveservicestaskforce.org/Page/Document/
UpdateSummaryFinal/hepatitis-c-screening
https://www.cdc.gov/hepatitis/hcv/guidelinesc.htm
Morbidity and Mortality Weekly Report
836 MMWR / October 4, 2019 / Vol. 68 / No. 39 US
Department of Health and Human Services/Centers for Disease
Control and Prevention
TABLE 1. Prevalence of hepatitis C virus (HCV) infection and
opioid use disorder* at delivery hospitalization, by demographic
characteristic
(N = 2,860,130) — United States, 2015†
Characteristic
Total§ HCV infection only Opioid use disorder only
HCV infection and
opioid use disorder
No.

379. (95% CI)
No.
(95% CI)
Prevalence
% (95% CI)
No.
(95% CI)
Prevalence
% (95% CI)
No.
(95% CI)
Prevalence
% (95% CI)
Age group (yrs)
<25 784,830
(759,112–810,548)
1,820
(1,563–2,077)
0.2 (0.2–0.3) 4,000
(3,640–4,360)
0.5 (0.5–0.6) 1,005
(821–1,189)
0.1 (0.1–0.2)
25–34 1,616,900

380. (1,560,018–1,673,782)
4,560
(4,161–4,959)
0.3 (0.3–0.3) 9,380
(8,686–10,074)
0.6 (0.5–0.6) 2,695
(2,313–3,077)
0.2 (0.1–0.2)
≥35 458,380
(437,269–479,491)
1,115
(962–1,268)
0.2 (0.2–0.3) 1,495
(1,310–1,680)
0.3 (0.3–0.4) 420
(322–518)
0.1 (0.1–0.1)
Payer source
Public¶ 1,240,210
(1,193,733–1,286,686)
5,885
(5,344–6,426)
0.5 (0.4–0.5) 12,025

381. (11,147–12,903)
1.0 (0.9–1.0) 3,565
(3,067–4,063)
0.3 (0.2–0.3)
Private** 1,466,650
(1,401,828–1,531,472)
1,290
(1,115–1,465)
0.1 (0.1–0.1) 2,245
(1,999–2,491)
0.2 (0.1–0.2) 430
(327–533)
0.0 (0.0–0.0)
Other/Self pay†† 148,680
(138,378–158,982)
310
(231–389)
0.2 (0.2–0.3) 575
(463–687)
0.4 (0.3–0.5) 115
(64–166)
0.1 (0.0–0.1)
Race/Ethnicity§§
White 1,418,351

382. (1,362,897–1,473,804)
5,705
(5,158–6,252)
0.4 (0.4–0.4) 11,565
(10,700–12,430)
0.8 (0.8–0.9) 3,470
(2,985–3,955)
0.2 (0.2–0.3)
Black 395,535
(371,201–419,868)
450
(351–549)
0.1 (0.1–0.1) 885
(726–1,044)
0.2 (0.2–0.3) 90
(40–140)
0.0 (0.0–0.0)
Hispanic 552,715
(516,126–589,304)
470
(375–565)
0.1 (0.1–0.1) 925
(757–1,093)

383. 0.2 (0.1–0.2) 220
(115–325)
0.0 (0.0–0.1)
Native American 19,555
(16,288–22,822)
110
(47–173)
0.6 (0.3–0.8) 255
(157–353)
1.3 (0.8–1.8) 35
(0–70)
0.2 (0.0–0.3)
Asian-Pacific
Islander/Other
274,615
(252,818–296,412)
300
(206–394)
0.1 (0.1–0.1) 350
(250–450)
0.1 (0.1–0.2) 65 (1–129) 0.0 (0.0–0.0)
Median income for ZIP code¶¶ ($)
1–41,999 822,850

384. (783,465–862,234)
2,935
(2,552–3,318)
0.4 (0.3–0.4) 5,225
(4,697–5,753)
0.6 (0.6–0.7) 1,630
(1,352–1,908)
0.2 (0.2–0.2)
42,000–51,999 671,335
(643,392–699,278)
2,010
(1,780–2,240)
0.3 (0.3–0.3) 3,925
(3,538–4,312)
0.6 (0.5–0.6) 1,045
(845–1,245)
0.2 (0.1–0.2)
52,000–67,999 700,610
(669,764–731,456)
1,420
(1,229–1,611)
0.2 (0.2–0.2) 3,395
(3,043–3,747)
0.5 (0.4–0.5) 840

385. (686–994)
0.1 (0.1–0.1)
≥68,000 628,510
(581,576–675,444)
920
(770–1,070)
0.1 (0.1–0.2) 2,050
(1,766–2,334)
0.3 (0.3–0.4) 505
(370–640)
0.1 (0.1–0.1)
Region***
Northeast 457,160
(418,652–495,668)
1,110
(927–1,293)
0.2 (0.2–0.3) 3,390
(2,902–3,878)
0.7 (0.6–0.8) 1,190
900–1,480)
0.3 (0.2–0.3)
Midwest 608,746
(570,546–646,947)

386. 1,375
(1,152–1,598)
0.2 (0.2–0.3) 3,300
(2,849–3,751)
0.5 (0.5–0.6) 895
(630–1,160)
0.1 (0.1–0.2)
South 1,111,188
(1,046,643–1,175,733)
3,760
(3,265–4,255)
0.3 (0.3–0.4) 5,600
(4,941–6,259)
0.5 (0.4–0.6) 1,665
(1,313–2,017)
0.1 (0.1–0.2)
West 683,036
(637,875–728,198
1,250
(1,063–1,437)
0.2 (0.2–0.2) 2,585
(2,199–2,971)
0.4 (0.3–0.4) 370
(232–508)

387. 0.1 (0.0–0.1)
Abbreviation: CI = confidence interval.
* Includes International Classification of Diseases, Ninth
Revision, Clinical Modification codes for HCV infection
(070.41, 070.44, 070.51, 070.54, 070.70–070.71, and
V02.62) and opioid use disorder (304.00–304.03, 304.70–
304.73, and 305.50–305.53).
† Only representative of the first three quarters of 2015.
§ Includes deliveries with HCV infection only, opioid use
disorder only, HCV infection and opioid use disorder, and
neither HCV or opioid use disorder diagnoses.
¶ Includes Medicare and Medicaid.
** Includes Blue Cross, commercial carriers, private health
maintenance organizations, and preferred provider
organizations.
†† Includes worker’s compensation, Civilian Health and
Medical Program of the Uniformed Services, Civilian Health
and Medical Program of the Department of
Veteran’s Affairs, Title V, and other government programs.
§§ Whites, blacks, Native Americans, and Asian-Pacific
Islanders/Others were non-Hispanic; Hispanic persons could be
of any race.
¶¶ Estimated median household income of residents in the
patient’s ZIP code derived from ZIP code demographic data
obtained from Claritas (https://www.hcup-us.
ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp).
*** Northeast: Connecticut, Maine, Massachusetts, New
Hampshire, New Jersey, New York, Pennsylvania, Rhode
Island, and Vermont. Midwest: Illinois, Indiana, Iowa,
Kansas, Michigan, Minnesota, Missouri, Nebraska, North

388. Dakota, Ohio, South Dakota, and Wisconsin. South: Alabama,
Arkansas, Delaware, District of Columbia,
Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi,
North Carolina, Oklahoma, South Carolina, Tennessee, Texas,
Virginia, and West Virginia. West: Alaska,
Arizona, California, Colorado, Hawaii, Idaho, Montana,
Nevada, New Mexico, Oregon, Utah, Washington, and
Wyoming.
https://www.hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp
https://www.hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp
Morbidity and Mortality Weekly Report
MMWR / October 4, 2019 / Vol. 68 / No. 39 837US Department
of Health and Human Services/Centers for Disease Control and
Prevention
TABLE 2. Association of hepatitis C virus (HCV) infection and
opioid use disorder* at delivery hospitalization with
demographic characteristics
(N = 2,860,130) — United States, 2015†
Characteristic
OR (95% CI)
HCV infection only Opioid use disorder only HCV infection and
opioid use disorder
Age group (yrs)
<25 1.0 (0.8–1.1) 1.6 (1.4–1.8)§ 1.4 (1.1–1.8)§
25–34 1.2 (1.0–1.4)§ 1.8 (1.6–2.0)§ 1.8 (1.4–2.3)§
≥35 Ref. Ref. Ref.
Payer source

389. Public¶ 5.5 (4.7–6.4)§ 6.4 (5.8–7.2)§ 9.9 (7.8–12.6)§
Private** Ref. Ref. Ref.
Other/Self pay†† 2.4 (1.8–3.2)§ 2.5 (2.0–3.1)§ 2.6 (1.6–4.3)§
Race/Ethnicity§§
White 3.6 (2.9–4.5)§ 3.7 (3.1–4.4)§ 10.9 (6.3–18.6)§
Black Ref. Ref. Ref.
Hispanic 0.7 (0.6–1.0) 0.7 (0.6–1.0) 1.7 (0.8–3.6)
Native American 5.0 (2.9–8.7)§ 5.9 (4.0–8.8)§ 8.0 (2.7–23.5)§
Asian-Pacific Islander/Other 1.0 (0.7–1.4) 0.6 (0.4–0.8)§ 1.0
(0.4–2.9)
Median income for ZIP code¶¶ ($)
1–41,999 2.5 (2.0–3.0)§ 2.0 (1.7–2.3)§ 2.5 (1.8–3.4)§
42,000–51,999 2.1 (1.7–2.5)§ 1.8 (1.5–2.1)§ 1.9 (1.5–2.6)§
52,000–67,999 1.4 (1.1–1.7)§ 1.5 (1.3–1.7)§ 1.5 (1.1–2.0)§
≥68,000 Ref. Ref. Ref.
Region***
Northeast 1.3 (1.1–1.7)§ 2.0 (1.6–2.4)§ 4.8 (3.1–7.5)§
Midwest 1.2 (1.0–1.5) 1.4 (1.2–1.8)§ 2.7 (1.7–4.4)§
South 1.9 (1.5–2.3)§ 1.3 (1.1–1.6)§ 2.8 (1.8–4.3)§
West Ref. Ref. Ref.
Abbreviations: CI = confidence interval; Ref. = referent;
OR = odds ratio.
* Includes International Classification of Diseases, Ninth
Revision, Clinical Modification codes for HCV infection
(070.41, 070.44, 070.51, 070.54, 070.70–070.71, and
V02.62) and opioid use disorder (304.00–304.03, 304.70–
304.73, and 305.50–305.53).
† Only representative of the first three quarters of 2015.
§ p<0.05.
¶ Includes Medicare and Medicaid.
** Includes Blue Cross, commercial carriers, private health
maintenance organizations, and preferred provider
organizations.

390. †† Includes worker’s compensation, Civilian Health and
Medical Program of the Uniformed Services, Civilian Health
and Medical Program of the Department of
Veteran’s Affairs, Title V, and other government programs.
§§ Whites, blacks, Native Americans, and Asian-Pacific
Islanders/Others were non-Hispanic; Hispanic persons could be
of any race.
¶¶ Estimated median household income of residents in the
patient’s ZIP code derived from ZIP code demographic data
obtained from Claritas (https://www.hcup-us.
ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp).
*** Northeast: Connecticut, Maine, Massachusetts, New
Hampshire, New Jersey, New York, Pennsylvania, Rhode
Island, and Vermont. Midwest: Illinois, Indiana, Iowa,
Kansas, Michigan, Minnesota, Missouri, Nebraska, North
Dakota, Ohio, South Dakota, and Wisconsin. South: Alabama,
Arkansas, Delaware, District of Columbia,
Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi,
North Carolina, Oklahoma, South Carolina, Tennessee, Texas,
Virginia, and West Virginia. West: Alaska,
Arizona, California, Colorado, Hawaii, Idaho, Montana,
Nevada, New Mexico, Oregon, Utah, Washington, and
Wyoming.
changes in HCV infection in the United States have prompted
a review of the evidence informing HCV testing by the U.S.
Preventive Services Task Force and CDC. The American
Association for the Study of Liver Diseases and the Infectious
Diseases Society of America recommend hepatitis C screen-
ing for all pregnant women (9). Hepatitis C treatment for
adults with direct-acting antiviral agents consists of an oral
regimen of ≤12 weeks, resulting in a virologic cure in >90% of
infected persons (10). Although treatment of HCV infection