NR503. Week 5 Infectious diseases paper

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Submission Requirements

1 Application: Use Microsoft Word™ to create the written assessment.

2 Length: The paper (excluding the title page and reference page) should be limited to a maximum of four (4) pages. Papers not adhering to the page length may be returned to you for editing to meet the length guidelines.

3 A minimum of three (3) scholarly research/literature references must be used. CDC or other web sources may be utilized but are not counted towards the three minimum references required. Your course text may be used as an additional resource but is not included in the three minimum scholarly references.

4 APA format 6th edition.

5 Include scholarly in-text references and a reference list.

6 Adhere to the Chamberlain College of Nursing academic policy on integrity as it pertains to the submission of student created original work for assignments.

7 Do not write in the first person (such as “me” “I”)

Best Practices in Preparing the Project

The following are best practices in preparing this project:

1 Review directions and rubric thoroughly.

2 Follow submission requirements.

3 Make sure all elements on the grading rubric are included. Organize the paper using the rubric sections and appropriate headings to match the sections.

4 Rules of grammar, spelling, word usage, and punctuation are followed and consistent with formal, scientific writing.

5 Title page, running head, body of paper, and reference page must follow APA guidelines as found in the 6th edition of the manual. This includes the use of headings for each section of the paper except for the introduction where no heading is used.

6 Ideas and information that come from scholarly literature must be cited and referenced correctly.

7 A minimum of three (3) scholarly literature references must be used. **See above section on “Preparing the Paper”.

8 Abide by Chamberlain College of Nursing academic integrity policy.

This assignment will be run through Turn It In. Your second submission will be considered final and subject to grading.

Category

Points

Description

ASSIGNMENT CONTENT
	Category		Points		%	Description
Introduction	1 5	12%	Comprehensive description of the infectious disease (causes, symptoms, mode of transmission, complications, treatment) and the demographic of interest (mortality, morbidity, incidence, and prevalence). Integrate at risk aggregate populations with related descriptive epidemiology.
Determinants of Health	40	32%	Robust identification and description of the determinants of health with explanation of how those factors contribute to the development of this disease. Evidence supports background.
Epidemiological Triad	30	2 4%	Comprehensive review of the epidemiological triad (host factors, agent factors (presence or absence), and environmental factors). Uses example/s, resources, to fully describe the triad.
Role of the NP	25	20%	Succinctly defines the role of the nurse practitioner according to a national nurse practitioner organization (Board of Nursing, AANP, for example) and synthesize the role to the management of infectious diseases (surveillance, primary/secondary/tertiary interventions, reporting, data collecting, data analysis, and follow-up). This includes the integration of a model of practice which supports the implementation of an evidence-based practice.
			1 10	8 8%	Total CONTENT Points=110 pts
ASSIGNMENT FORMAT
APA	10	8%	All elements of the paper utilize APA Format 6th Ed.
Spelling/grammar/ voice	5	4%	All elements of the paper correctly utilize spelling, grammar and a scholarly voice.
15	12%	Total FORMAT Points=15 pts
125	100%	ASSIGNMENT TOTAL=125 points

Rubric Requirements

NR503_Week 5 Infectious Diseases Paper_Sept19

Criteria

Ratings

Pts

This criterion is linked to a Learning OutcomeAssignment Content Possible Points =110 Points
Introduction of Infectious Disease

15.0 pts

Excellent
Comprehensive description of the communicable disease (causes, symptoms, mode of transmission, complications, treatment) and the demographic of interest (mortality, morbidity, incidence, and prevalence). Integrate at risk aggregate populations.

14.0 pts

V. Good
Adequately identifies the communicable disease (causes, symptoms, mode of transmission, complications, treatment) and the demographic of interest (mortality, morbidity, incidence, and prevalence). Integration of at-risk aggregate populations.

12.0 pts

Satisfactory
Limited description of the communicable disease (causes, symptoms, mode of transmission, complications, treatment) and the demographic of interest (mortality, morbidity, incidence, and prevalence). Integration of at-risk aggregate populations scantly present.

8.0 pts

Needs Improvement
Unclear description of the communicable disease (causes, symptoms, mode of transmission, complications, treatment) and the demographic of interest (mortality, morbidity, incidence, and prevalence). Integration of at-risk aggregate populations inaccurately discussed/not related.

0.0 pts

Unsatisfactory
Data is minimal or absent.

15.0 pts

This criterion is linked to a Learning OutcomeDeterminants of Health

40.0 pts

Excellent
Robust identification and description of the determinants of health with explanation of how those factors contribute to the development of this disease. Evidence supports background.

36.0 pts

V. Good
Identification of determinants is complete but lack depth in an area or overall, presents risk factors, disease impact and at least one set of incidence and prevalence statistics which are presented and supported by evidence.

33.0 pts

Satisfactory
Description of determinants is missing one or more key points. Limited presentation of the contributing factors. Lack of evidence to support writing may be present or evidence may be inconsistent throughout.

20.0 pts

Needs Improvement
Determinants missing depth in general and more than one key point and lack of contributing factors. There is may be no evidence or evidence may be present inconsistently or without relationship to writing.

0.0 pts

Unsatisfactory
Content is minimal if present or not included in writing at all.

40.0 pts

This criterion is linked to a Learning OutcomeEpidemiological Triad

30.0 pts

Excellent
Comprehensive review of the epidemiological triad (host factors, agent factors (presence or absence), and environmental factors). Writing includes examples that expand content beyond a definition into application. Full integration of evidence, sources.

27.0 pts

V. Good
Adequate review of the epidemiological triad (host factors, agent factors (presence or absence), and environmental factors.) Examples may be omitted. There is integration of evidence in the majority of the writing.

25.0 pts

Satisfactory
Limited review of the epidemiological triad (host factors, agent factors (presence or absence), and environmental factors.) Examples may be omitted. There is integration of evidence in the writing, which may be inconsistent.

15.0 pts

Needs Improvement
Minimal or unclear review of the epidemiological triad (the host factors, agent factors (presence or absence), and environmental factors.) There are no examples. Evidence is present but may not be consistent throughout.

0.0 pts

Unsatisfactory
Review of the epidemiological triad (host factors, agent factors (presence or absence), and environmental factors) scant, unclear, or not provided.

30.0 pts

This criterion is linked to a Learning OutcomeRole of the NP

25.0 pts

Excellent
Succinctly defines the role of the nurse practitioner according to a national nurse practitioner organization (Board of Nursing, AANP, for example) and synthesizes the role to the management of infectious diseases (surveillance, primary/secondary/tertiary interventions, reporting, data collecting, data analysis, and follow-up). Writing includes integration of a model of practice which supports the implementation of an evidence-based practice. References support all writing.

23.0 pts

V. Good
An adequate, but not fully comprehensive review

21.0 pts

Satisfactory
A limited review

13.0 pts

Needs Improvement
Minimal or unclear

0.0 pts

Unsatisfactory
Not provided

25.0 pts

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, 2014–201

845 Trends and Characteristics in Marijuana Use Among

Public School Students — King County, Washington,
2004–201

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, 20

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

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, DrPH

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.

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 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,

Information Technology Specialists
MMWR Editorial Board

Timothy F. Jones, MD, Chairman
Ileana Arias, Ph

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
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)
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†

6
8

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

ithout

O
U

DD
ia

gn
os

es
p

er
1

,0
00

d
el

iv
er

ie
s,

w
ith

O
U
D

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 (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).

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.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.
(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
(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

(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

(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)

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

(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
(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)

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)

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

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.

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 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. †† 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,

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
with direct-acting antiviral agents during pregnancy is not
approved (10), testing remains important to identify infections,
engage infected women in postpartum treatment, and identify
infants who might have been exposed. Left untreated, HCV
infection might lead to cirrhosis and pose continued risk to

others through parenteral exposures (e.g., injection drug use
or transmission via subsequent pregnancies) (1).

The findings in this report are subject to at least five limi-
tations. First, this study likely produced underestimates of
opioid use disorder and HCV infection. Although universal
screening for substance use is the standard of care during preg-
nancy, it is not universally implemented. Further, stigma and
associated fear of reporting opioid use disorder likely reduces
self-disclosure. Risk-based hepatitis C testing is the current
care standard but might not be adequately implemented.
Second, increases in observed rates might reflect changes in
screening practices and protocols for opioid use disorder and
HCV in addition to actual increases in these conditions. Third,
ICD-9-CM does not differentiate between chronic or incident

https://www.hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp

Morbidity and Mortality Weekly Report

838 MMWR / October 4, 2019 / Vol. 68 / No. 39 US Department of Health and Human Services/Centers for Disease Control and Prevention

Summary
What is already known about this topic?

Ecological studies link increases in hepatitis C virus (HCV)
infection to the U.S. opioid crisis. Opioid use disorder among
pregnant women has increased; the majority of those with HCV
infection have opioid use disorder.

What is added by this report?

The U.S. rate of HCV infection at delivery increased from 0.8 per
1,000 live births in 2000 to 4.1 in 2015, including increases from
87.4 to 216.9 and from 0.7 to 2.6 among women with and
without opioid use disorder, respectively.

What are the implications for public health practice?

Treatment of opioid use disorder should include screening and
referral for related conditions such as HCV infection.

acute HCV infection. Fourth, these analyses might not rep-
resent most recent trends because data were only analyzed up
to the third quarter of 2015. Finally, results of this analysis
are only generalizable to hospital births; however, fewer than
2% of U.S births occur outside of the hospital.¶

Opioid use disorder (3) and HCV infection rates significantly
increased during 2000–2015 among women delivering in hos-
pitals in the United States. HCV infection rates at delivery were
significantly higher among women with opioid use disorder than
among those who did not have opioid use disorder. Treatment
of opioid use disorder should include screening and referral for
related conditions such as HCV infection.

Acknowledgments

Katelyn Chiang, MPH; Agency for Healthcare Research and
Quality, Healthcare Cost and Utilization Project and its data partners
that contribute to the National Inpatient Sample.

Corresponding author: Jean Y. Ko, JeanKo@cdc.gov, 770-488-5200.

¶ https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01 .

1Division of Reproductive Health, National Center for Chronic Disease
Prevention and Health Promotion, CDC; 2National Center for HIV/AIDS,
Viral Hepatitis, STD, and TB Prevention, CDC; 3Division of Unintentional
Injury Prevention, National Center for Injury Prevention and Control, CDC.

All authors have completed and submitted the International
Committee of Medical Journal Editors form for disclosure of potential
conflicts of interest. No potential conflicts of interest were disclosed.

References
1. CDC. Surveillance for viral hepatitis—United States, 2016. Atlanta, GA:

US Department of Health and Human Services, CDC; 2018. https://
www.cdc.gov/hepatitis/statistics/2016surveillance/commentary.htm

2. Patrick SW, Bauer AM, Warren MD, Jones TF, Wester C. Hepatitis C
virus infection among women giving birth—Tennessee and United
States, 2009–2014. MMWR Morb Mortal Wkly Rep 2017;66:470–3.
https://doi.org/10.15585/mmwr.mm6618a3

3. Haight SC, Ko JY, Tong VT, Bohm MK, Callaghan WM. Opioid use
disorder documented at delivery hospitalization—United States,
1999–2014. MMWR Morb Mortal Wkly Rep 2018;67:845–9. https://
doi.org/10.15585/mmwr.mm6731a1

4. Chappell CA, Hillier SL, Crowe D, Meyn LA, Bogen DL, Krans EE.
Hepatitis C virus screening among children exposed during pregnancy.
Pediatrics 2018;141:e20173273. https://doi.org/10.1542/peds.2017-3273

5. Kuklina EV, Whiteman MK, Hillis SD, et al. An enhanced method for
identifying obstetric deliveries: implications for estimating maternal
morbidity. Matern Child Health J 2008;12:469–77. https://doi.
org/10.1007/s10995-007-0256-6

6. American Psychiatric Association. Diagnostic and statistical manual of
mental disorders (5th ed.). Arlington, VA: American Psychiatric
Association; 2013.

7. Rempel JD, Uhanova J. Hepatitis C virus in American Indian/Alaskan
Native and aboriginal peoples of North America. Viruses 2012;4:3912–31.
https://doi.org/10.3390/v4123912

8. Seth P, Scholl L, Rudd RA, Bacon S. Overdose deaths involving opioids,
cocaine, and psychostimulants—United States, 2015–2016. MMWR
Morb Mortal Wkly Rep 2018;67:349–58. https://doi.org/10.15585/
mmwr.mm6712a1

9. American Association for the Study of Liver Diseases; Infectious Diseases
Society of America. HCV in pregnancy. Alexandra, VA: American
Association for the Study of Liver Diseases; Arlington, VA: Infectious
Diseases Society of America; 2018. https://www.hcvguidelines.org/
unique-populations/pregnancy

10. Hughes BL, Page CM, Kuller JA; Society for Maternal-Fetal Medicine.
Hepatitis C in pregnancy: screening, treatment, and management.
Am J Obstet Gynecol 2017;217:B2–12. https://doi.org/10.1016/j.
ajog.2017.07.039

mailto:JeanKo@cdc.gov

https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01

https://www.cdc.gov/hepatitis/statistics/2016surveillance/commentary.htm

https://doi.org/10.15585/mmwr.mm6618a3

https://doi.org/10.15585/mmwr.mm6731a1

https://doi.org/10.1542/peds.2017-3273

https://doi.org/10.1007/s10995-007-0256-6

https://doi.org/10.3390/v4123912

https://doi.org/10.15585/mmwr.mm6712a1

https://www.hcvguidelines.org/unique-populations/pregnancy

https://doi.org/10.1016/j.ajog.2017.07.039

This content is in the Public Domain.

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: jgrebely@kirby.unsw.edu.au
Submitted 8 January 2018; initial review completed 29 March 2018; final version accepted 12 July 2018

INTRODUCTION

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

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

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
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,
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
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

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
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

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).

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%) –
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.

152 Jason Grebely et al.

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fe
ct
io
n
an

d
th
e

pr
op
or
ti
on

of
pe
op
le
w
it
h
re
ce
n
t
in
je
ct
in
g
dr
u
g
u
se

m
on

g
th
e
to
ta
lp
op
u
la
ti
on

w
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
n
am
on
g
pe
op
le
w
it
h

re
ce
nt

in
je
ct
in
g
dr
ug

us
e

%
(U

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
C
V

vi
ra
em

ic
in
fe
ct
io
n
(U

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
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
io
n
w
it
h
H
C
V

vi
ra
em

ic
in
fe
ct
io
n
%
(U

Ea
st
er
n
Eu

ro
pe

4
8
.6

(4
2
.0
,5

.2
)

1
4
6
6
5
0
0
(6
9
9

5
0
0
,2

3
7

0
0
)

8
1
8
1
0
0
0
(6

3
0
4
0
0
0
,8

2
5

0
0
0
)

7
.9

(8
.2
,3

.9
)

W
es
te
rn

Eu
ro
pe

9
.9

(3
5
.7
,4

.1
)

4
0
2
5
0
0
(2
6
4

5
0
0
,5

7
0
0
0
)

2
3
4
7
0
0
0
(1

9
6
9
0
0
0
,3

2
8

9
0
0
0
)

1
7
.2

(9
.9
,3

.4
)

Ea
st
an

So
u
th

ea
st

A
si
a

3
7
.7

(

2
8
.2

.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
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

.8
)

So
u
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

8
0
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
,3

.6
)

C
en
tr
al
A
si
a

0
.5

(3
6
.5
,4

4
.5

)

1
1
4
0
0
0
(6
9
0
0
0
,1

5
0
0
0
)

2
5
1
6
0
0
0
(2

0
1
0
0
0
0
,2

4
9
0
0
0
)

4
.5

(2
.6
,6

.9
)

C
ar
ib
be
an

4
7
.6

(

4
0
.2

5
.1
)

3
7
5
0
0
(2
2
5
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
)

6
.7

(8
.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
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
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

.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
2
9
0
0
0
,4

0
3

4
0
0
0
)

3
0
.5

(

1
1
.7

6
.7
)

P
ac
ifi
c
Is
la
n
d
st
at
es

an
d
Te
rr
it
or
ie
sa

1
.4

(3
2
.4
,5

0
.5
)

9
0
0
0
(5
5
0
0
,1

4
0
0
0
)

1
1
7
5
0
0
(1
0
1
0
0
0
,3

6
0
0
0
)

7
.9

(1
.9
,1

1
.1

)

A
u
st
ra
la
si
a

2
.8

(3
8
.9
,4

6
.8
)

4
9
5
0
0
(3
5
5
0
0
,6

5
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
.2
)

Su
b-
Sa
h
ar
an

A
fr
ic
a

1
6
.3

2
.7

0
.1
)

2
2
5
0
0
0
(4
5
5
0
0
,4

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
)

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
.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

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

1
.6

.0
)

6
0
6
3
5
0
0
(3

4
3
4
5
0
0
,9

4
6
0
0
0
)

7
1
1
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
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
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
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
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
.a
N
ot
e

th
at

n
o
es
ti
m
at
es

of
th
e
pr
ev
al
en

ce
of

ti
-H
C
V
am

on
g
pe
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
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
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
es
.

153

T
ab
le
3

C
ou

n
tr
y-
le
ve
le
st
im

at
es

of
th
e
pr
ev
al
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
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
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
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
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
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
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

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
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
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
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
ct
io
n
%
(U
I)
Ea
st
er
n
Eu
ro
pe

A
rm

en
ia

3
2
.0

(2
2
.0
,4

.3
)

4
0
0
0
(1
5
0
0
,8

5
0
0
)

N
K

N
G

A
ze
rb
ai
ja
n

6
.6

(3
4
.9
,5

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

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
.1
)

1
8
0
0
0
(7
0
0
0
,3

1
5
0
0
)

N
K
N
G

B
os
n
i

a
an

d
H
er
ze
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

(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
,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

0
.9

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
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
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

(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
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
.4

6
.9
)

H
u
n
ga
ry

3
5
.0

(

2
2
.9

7
.2
)

1
5
0
0
(5
0
0
,2

5
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

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
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
.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
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
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

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
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
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

(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
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

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
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
.3

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
rn
Eu
ro
pe

A
lb
an

ia
2
5
.5

(

2
0
.1

1
.1
)

1
5
0
0
(1
0
0
0
,2

5
0
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

(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

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

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
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
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

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
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
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
8
0
0
0
(9
1
0
0
0
,2

1
0
0
0
)

2
8
.9

(1
8
.8
,5

1
.8
)

Fi
n
la
n
d

5
5
.2

1
.2

9
.4
)

9
5
0
0
(7
0
0
0
,1

2
5
0
0
)

2
2
5
0
0
(1
6
0
0
0
,2

6
0
0
0
)

4
1
.6

(

2
7
.4

2
.8
)

Fr
an

ce
4
8
.0

(4
4
.5
,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

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
u
bl
ic
)
M
ac
ed
on

4
6
.6

(

4
3
.4

9
.9
)

2
5
0
0
(1

5
0
0
,3

0
0
0
)
N
K
N
G

(C
on

ti
n
u
es
)

154 Jason Grebely et al.

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
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
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
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
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
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
I)

G
er
m
an

y
4
8
.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
(9
0
0
0
0
,3

3
0
0
0
)

3
1
.3

(6
.2
,8

0
.6
)

G
re
ec
e

4
9
.2

(4
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

6
9
0
0
0
)
1
.9

(1
.2
,3

.1
)

G
re
en
la
n
d

–
–

N
K
–

Ic
el
an

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
,1

0
0
0
)

N
R

Ir
el
an

d
5
6
.0

2
.5

9
.4
)

5
0
0
0
(3
5
0
0
,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
)

It
al
y

4
3
.4

(

3
8
.8

8
.1
)

1
4
8
5
0
0
(9
8
5
0
0
,2

0
5
0
0
0
)

6
8
0
0
0
0
(4
5
5
0
0
0
,1

4
1
0
0
0
)

2
1
.8

(7
.6
,3

3
.9
)

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
)

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

(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
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
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
)
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
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
)

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
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
)

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
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

(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
.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

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
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
5
0
0
,7

0
0
0
)

2
1
.3

(

3
.3

3
.5
)

Sp
ai
n

5
3
.3

(5
0
.2
,5

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
0
0
)
1
.4

(0
.5
,3

.6
)

Sw
ed
en

6
1
.3

(5
7
.6
,6

4
.9
)

5
0
0
0
(<

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
,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

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
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
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
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

(

2
0
.8

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
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
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
)

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
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
)

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
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
)

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
5
0
0
0
)

3
7
.1

(2
5
.2
,5

9
.2
)

(C
on
ti
n
u
es
)

155

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
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
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
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
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
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
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
)

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
0
0
)
N
K
N
G
N
or
th

K
or
ea

–
–
N
K
–

P
h
ili
pp
in
es

2
6
.4

(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
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
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
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

(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

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
)

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

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
)

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
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
.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
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
.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
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
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
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
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
6
.2

(1
3
.2
,4

7
.0
)

M
al
di
ve
s

0
.5

(0
.0
,1

.4
)
< 5 0 0 (<

5
0
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
(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
)

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

4
8
7
0
0
0
)

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

Sr
iL
an

ka
N
K

N
K
N
K
N
K
C
en
tr
al
A
si
a

K
az
ak
h
st
an

4
4
.1

(

3
9
.8

8
.4
)

4
9
5
0
0
(3
0
0
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
,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

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
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
N
K

U
zb
ek
is
ta
n

3
8
.8

4
.7

3
.0
)

3
6
5
0
0
(2
2
5
0
0
,5

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

.6
)
C
ar
ib
be
an

A
n
ti
gu

a
an

d
B
ar
bu

da
–

–
N
K

–

B
ah

am
as

N
K
N
K
N
K
N
K

B
ar
ba
do
s

–
–
N
K
–

B
er
m
u
da

N
K
N
K
N
K
N
K
(C
on
ti
n
u
es
)

156 Jason Grebely et al.

C
om

m
on

w
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
,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
0
.0
)

C
u
ba

–
–

3
5
0
0
0
(1
3
5
0
0
,7

7
0
0
0
)
–

D
om

in
ic
a

–
–
N
K
–
D
om

in
ic
an

R
ep
u
bl
ic
N
K
N
K

6
8
0
0
0
(4
1
5
0
0
,1

0
8
0
0
0
)

N
K

G
re
n

ad
a

–
–
N
K
–

H
ai
ti

N
K
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
ev
is

–
–
N
K
–

Sa
in
t
Lu

ci
a

–
–
N
K
–

St
V
in
ce
n
t
an

d
th
e

G
re
n
ad
in
es

–
–
N
K
–

T
ri
n
id
ad

an
d
To
ba
go

–
–
N
K
–

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
(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
.1

(6
.3
,2

1
.0
)

B
el
iz
e

–
–
N
K
–

B
ol
iv
ia

N
K
N
K
N
K
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

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

(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
)
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
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
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
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

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
)

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
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
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
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
0
0
0
(9
9
0
0
0
,1

8
2
0
0
0
)
N
K
(C
on
ti
n
u
es
)

157

Su
ri
n
am

e
N
K

N
K
N
K
N
K

U
ru
gu

ay
1
6
.4

(1
4
.1
,1

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
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

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
)

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
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
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

(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

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
N
K
N
K

Fi
ji

N
K
N
K
5
0
0
(< 5 0 0 ,3 0 0 0 ) N K

Fr
en
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

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
N
K

N
au

ru
–

–
N
K
–

N
ew

C
al
ed
on

ia
N
K

N
K
N
K
N
K

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
K
N
K
N
K

P
ap
u
a
N
ew

u
in
ea

N
K
N
K

9
4
5
0
0
(7
0
5
0
0
,3

2
8
0
0
0
)

N
K
Sa
m
oa
N
K
N
K
< 5 0 0 (< 5 0 0 ,5 0 0 ) 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
N
K
N
K

T
u
va
lu

–
–
N
K
–

V
an

u
at
u

N
K
N
K
N
K
N
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
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

(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
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
,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
en
in

N
K
N
K
N
K
N
K

B
ot
sw

an
a

–
–
N
K
–
(C
on
ti
n
u
es
)

158 Jason Grebely et al.

B
u
rk
in
a
Fa
so

N
K
N
K

2
4
7
0
0
0
(1
8
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
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
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
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
)
–

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
om

or
os

–
–
N
K
–
C
on

go
(K
in
sh
as
a)

N
K
N
K
N
K
N
K

C
ot
e
d’
Iv
oi
re

1
.3

(0
.0
,7

.1
)
< 5 0 0 (< 5 0 0 ,< 5 0 0 ) N K N G

D
jib
ou

i
N
K

N
K
N
K
N
K

Eq
u
at
or
ia
lG

u
in
ea
–
–
N
K
–

Er
it
re
a

–
–
N
K
–

Et
h
io
pi
a

N
K
N
K

6
4
7
0
0
0
(4
1
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
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

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
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

G
u
in
ea
-B
is
sa
u

–
–
N
K
–

K
en
ya

1
2
.3

(7
.4
,1

7
.7
)

4
0
0
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
.7
,9

.7
)

Le
so
th
o

–
–
N
K
–

Li
be
ri
a

N
K
N
K
N
K
N
K

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
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
K
N
K

M
al
i

N
K
N
K
N
K
N
K

M
au

ri
ta
n
ia

–
–
N
K
–
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
)
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
0
0
,3
1
0
0
0
)
N
K
N
G
N
am

ib
ia

–
–
N
K
–

N
ig
er

N
K
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

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
,1
.1
)

R
ep
u
bl
ic
of
th
e
C
on

go
–

–
N
K
–
(C
on
ti
n
u
es
)

159

R
w
an

da
N
K

N
K
N
K
N
K

Sa
o
To
m
e
an

d
P
ri
n
ci
pe

–
–
N
K
–

Se
n
eg
al

2
9
.5

(2
2
.9
,3

6
.3
)

6
5
0
0
(1
5
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
)
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
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
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
N
K

To
go

N
K
N
K
N
K
N
K

U
ga
n
da

N
K
N
K
N
K
N
K
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
(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

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

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
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
s

3
7
.3

(3
2
.9
,4

1
.8
)
< 5 0 0 (< 5 0 0 ,< 5 0 0 ) N K N G

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
,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
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
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

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

(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
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
)

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
.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
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
.

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
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
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

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
K
–
Su
da
n
N
K
N
K
N
K
N
K
(C
on
ti
n
u
es
)

160 Jason Grebely et al.

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.

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
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
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
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)
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
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
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
.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

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
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
(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
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
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
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
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

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
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
=
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
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
ee

m
et
h
od
s
fo
r
de
ta
ils

of
es
ti
m
at
io
n
).

161

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
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.

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
< 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.

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

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 global.reviews@unsw.edu.au.

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-
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-
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.

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
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
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,
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

165

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
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).

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166 Jason Grebely et al.

https://doi.org/10.1111/add.14012

This document is a scanned copy of a printed document. No warranty is given about the
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