Supplementary appendixThis appendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors.
Supplement to: The Polaris Observatory Collaborators. Global prevalence, treatment, and prevention of hepatitis B virus infection in 2016: a modelling study. Lancet Gastroenterol Hepatol 2018; published online March 26. http://dx.doi.org/10.1016/S2468-1253(18)30056-6.
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The Global Prevalence, Cascade of Care, and Prophylaxes Coverage of Hepatitis B in 2016:
A Modeling Study – APPENDIX
Contents
Section 1. Contributors ........................................................................................................................................... 2
Section 2. Description of the PRoGReSs model ...................................................................................................... 7
Section 3. Model inputs ........................................................................................................................................ 19
Section 4. Delphi Process ..................................................................................................................................... 22
Section 5. Country inputs...................................................................................................................................... 23
Section 6. Model validation .................................................................................................................................. 37
Section 7. Countries within the Global Burden of Disease (GBD) regions ............................................................. 40
Section 8. Countries within the World Health Organization (WHO) regions .......................................................... 41
Section 9. Countries within the World Bank Income Level .................................................................................... 42
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Section 1. Contributors
The following individuals contributed to the content of this publication and we are grateful for their efforts:
Contributor Affiliation
Faisal Aba Alkhail Department of Liver and Small Bowel Transplantation, King Faisal Specialist Hospital and Research
Center, Alfaisal University, Riyadh, Saudi Arabia
Oyemakinde Akin Federal Ministry of Health, Abuja, Nigeria
Imad Al ghazzawi GI and Hepatology Department, Jordan Royal Medical Services, Amman, Jordan
Abdullah Alghamdi Gastroenterology Unit, Medical Department, King Fahad Hospital, Jeddah, Saudi Arabia
Khalid Alswat Department of Medicine, King Saud University Liver Disease Research Center, College of Medicine,
King Saud University, Riyadh, Saudi Arabia
Krasimir Antonov University Hospital “St. Ivan Rilski”, Sofia, Bulgaria
Vic Arendt Centre Hospitalier de Luxembourg, Luxembourg; Luxembourg Institute of Health, Esch sur Alzette,
Luxembourg
GK Arykbaeva Deputy Chief Physician of the Republican Clinical Infectious Diseases Hospital, Bishkek, Kyrgyzstan
Jamshidbek Ashurov Research Institute of Virology, Tashkent, Uzbekistan
MA Atabekova Department of Gastroenterology, NG, Bishkek, Kyrgyzstan
Oidov Baatarkhuu Department of Infectious Diseases, Mongolian National University of Medical Sciences, Ulaanbaatar,
Mongolia
Ismail Balik Infectious Diseases, Ankara University, Ankara, Turkey
Jovan Basho University of Rosario School of Medicine, Rosario, Argentina
Geoff Beckett CDC Division of Viral Hepatitis, Atlanta, USA
Adele S. Benzaken Department of STI, AIDS and Viral Hepatitis, Ministry of Health, Brazil
Fernando Bessone University of Rosario School of Medicine, Rosario, Argentina
Asmamaw Bezabeh Non-communicable Diseases case team Technical Assistant to the FMOH, FMOH/WHO Ethiopia
Mohammed Musa Borodo Aminu Kano Teaching Hospital, Kano, Nigeria; Bayero University, Kano, Nigeria
Wornei Silva Miranda Braga Ministry of Health, Brazil
Cheryl Brunton Canterbury District Health Board, Christchurch, New Zealand
Ivan Mauricio Cardenas Cañon Communicable Diseases Division, Ministry of Health and Social Protection, Bogota, Colombia
Gourdas Choudhuri Department of Gastroenterology and Hepato-Biliary Sciences, Fortis Memorial Research Institute,
Gurgaon, Haryana, India
Matthew Cramp Plymouth University Peninsula Schools of Medicine and Dentistry, Plymouth, UK
Robert J. de Knegt Department of Gastroenterology and Hepatology, Erasmus MC University Medical Center Rotterdam,
the Netherlands
Fernando De la Hoz National University of Colombia, Bogota, Colombia
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Moutaz Derbala Division of Gastroenterology, Department of Medicine, Hamad Medical Corporation, Doha, Qatar
Gregory J. Dore Kirby Institute, University of New South Wales, Sydney, NSW, Australia
Wahid Doss Cairo University, Cairo, Egypt
Anara Dzhumagulova Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan
Intizor Egamova Research Institute of Virology, Tashkent, Uzbekistan
Aiman Aly Elbardiny Ministry of Public Health Qatar, Doha, Qatar
Gul Ergör Public Health and Epidemiology, Dokuz Eylul University, Izmir Turkey
Layli Eslami Digestive Disease Research Institute, Tehran, Iran
Gamal Esmat Cairo University, Cairo, Egypt
Javed Iqbal Farooqi Postgraduate Medical Institute, Khyber Medical University, Peshawar, Pakistan; Government Lady
Reading Hospital, Peshawar, Pakistan
Ed Gane Auckland Hospital Clinical Studies Unit, Auckland, New Zealand
Rino Gani Division of Hepatobiliary, Department of Internal Medicine, Faculty of Medicine, University of
Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
Amir Ghafoor Khan Department of Gastroenterology & Hepatology, Lady Reading Hospital, Peshawar, Pakistan
Liana Gheorghe Center of Gastroenterology & Hepatology, Fundeni Clinical Institute, Bucharest, Romania
Massimo Ghidinelli Pan American Health Organization, Washington D.C., USA
Bertha Gomez Pan American Health Organization, Bogota, Colombia
Monica Alonso Gonzalez Pan American Health Organization, Washington, D.C., USA
Sergeja Gregorčič Clinic for Infectious Diseases and Febrile Illnesses, University Medical Centre, Ljubljana, Slovenia
Jessie Gunter CDA Foundation, Lafayette, CO, US
Waldemar Halota Department of Infectious Diseases and Hepatology, CMUMK Bydgoszcz, Bydgoszcz, Poland
Saeed Hamid The Aga Khan University, Karachi, Pakistan
Waseem Hamoudi Department of Gastroenterology & Hepatology, Al Bashir Hospital, Amman, Jordan; Jordan Ministry of
Health, Amman, Jordan
Aaron Harris CDC Division of Viral Hepatitis, Atlanta, USA
Irsan Hasan Division of Hepatobiliary, Department of Internal Medicine, Faculty of Medicine, University of
Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
Stefan Hrysovsky 1st Department of Internal Medicine SZU, Bratislava, Slovak Republic; Clinic of Infectious Diseases,
Medical Faculty, Kosice, Slovak Republic
Wasim Jafri Aga Khan University, Karachi, Pakistan
Deian Jelev University Hospital “St. Ivan Rilski”, Sofia, Bulgaria
G.I. Jumagulova Scientific Production Association Preventive Medicine, Bishkek, Kyrgyzstan
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KA Nogoibaeva Associate Professor of the Department of Infectious Diseases, Kyrgyz State Medical Institute of
Retraining and Improvement of Qualification, Bishkek, Kyrgyzstan
Kulpash S. Kaliaskarova Ministry of Healthcare and Social Development of the Republic of Kazakhstan, Astana, Kazakhstan;
Republican Coordination Center for Hepatology and Gastroenterology, Astana, Kazakhstan
Shakhida Karamatova Center for Disease Control and Prevention (CDC) Office in Uzbekistan
Kaliya Kasymbekova World Health Organization, Bishkek, Kyrgyzstan
Omor Tilegenovich Kasymov
Preventive Medicine of the Ministry of Health of the Kyrgyz Republic, Bishkek, Kyrgyzstan; Professor
of the Department of Hygiene of the Kyrgyz-Russian Slavic University. B.N. Yeltsin, Bishkek,
Kyrgyzstan
Masaya Kato World Health Organization, Viet Nam Country Office
Ainara Keshikbaeva Mandatory Health Insurance Fund, Bishkek, Kyrgyzstan
Jawad Khamis Salmaniya Medical Complex, Manama, Bahrain
Aziza Khikmatullaeva Research Institute of Virology, Tashkent, Uzbekistan
Ibtissam Khoudri Department of Epidemiology and Disease Control, Ministry of Health, Rabat, Morocco
Do Young Kim Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
Young Seok Kim Department of Internal Medicine, Soon Chun Hyang University Bucheon Hospital, Bucheon, Korea
Iskren Kotzev University Hospital "St. Marina", Varna, Bulgaria
Ainura Kutmanova MWMS Head of Department Infectious and Tropical Diseases, Bishkek, Kyrgyzstan
Martin Lagging Dept. of Infectious Medicine/Virology, Institute of Biomedicine, Sahlgrenska Academy, University of
Gothenburg, Sweden
Moon-sing Lai Department of Medicine, North District Hospital, Hong Kong, SAR China
Linh-Vi Le World Health Organization, Regional Office for the Western Pacific, Manila, Phillippine
Olufunmilayo Lesi University of Lagos, Lagos, Nigeria; Lagos University Teaching Hospital, Lagos, Nigeria
Laurentius Lesmana
Division of Hepatobiliary, Department of Internal Medicine, Faculty of Medicine, University of
Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia; Digestive Disease and GI Oncology
Center, Medistra Hospital, Jakarta, Indonesia
Angeline Lo Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, SAR China
Abderrahmane Maaroufi Department of Epidemiology and Disease Control, Ministry of Health, Rabat, Morocco
Matti Maimets University of Tartu, Tartu University Hospital, Tartu, Estonia
Maya Makhmudova Republican Blood Center, Tashkent, Uzbekistan
Simten Malhan Department of Health Care Management, Başkent University Ankara, Turkey
Lyudmila Mateva University Hospital “St. Ivan Rilski”, Sofia, Bulgaria
Brian McMahon Alaska Native Health Consortium, Anchorage, Alaska
Rumyana Mitova University Hospital “Queen Joanna”, Sofia, Bulgaria
Antons Mozalevskis World Health Organization Regional Office for Europe, Copenhagen, Denmark
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David Handojo Muljono Eijkman Institute for Molecular Biology; Jakarta, Indonesia; University of Sydney, Department of
Hepatitis & Emerging Infectious Diseases, Sydney, Australia
Benazzouz Mustapha IBN SINA Hospital, Rabat, Morocco
Noel Nelson CDC Division of Viral Hepatitis, Atlanta, USA
Vratislav Nemecek National Reference Laboratory for Hepatitis, National Institute of Public Health, Prague, Czech Republic
Oudou Njoya Research Laboratory on Viral Hepatitis & Health Communication, Faculty of Medicine, University of
Yaoundé, Yaoundé, Cameroon
Anne Øvrehus Department of Infectious Diseases, Odense University Hospital, Odense, Denmark
Olga Plotnikova Republican Center for Immunoprofilaxis, Bishkek, Kyrgyzstan
Maria Prins
Infectious Diseases Cluster, Department of Infectious Disease Research and Prevention, Public Health
Service of Amsterdam, the Netherlands; Department of Internal Medicine, Division of Infectious
Diseases, Tropical Medicine and AIDS, Center for Infection and Immunity Amsterdam (CINIMA),
Academic Medical Center (University of Amsterdam), Amsterdam, the Netherlands
Yuriy Nicolaevich Prokopenko Republican Coordination Center for Hepatology and Gastroenterology, Astana, Kazakhstan
Pankaj Puri Department of Internal Medicine, Armed Forces Medical College, Pune, India
Sarah Radke Institute of Environmental Science and Research, Wallaceville, New Zealand; The University of
Auckland, Auckland, New Zealand
Ezequiel Ridruejo Hepatology Section, Department of Medicine. Centro de Educación Médica e Investigaciones Clínicas
Norberto Quirno (CEMIC), Buenos Aires, Argentina
Françoise Roudot-Thoraval Département Santé Publique, Hôpital Henri Mondor, Créteil, France
Masood Siddiq Asian Institute of Medical Science (AIMS), Hyderabad, Sindh, Pakistan
Andri Sanityoso Division of Hepatobiliary, Department of Internal Medicine, Faculty of Medicine, University of
Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia
AB Satybaldieva General Director of the Republican Blood Center, Bishkek, Kyrgyzstan
Sarah Schillie CDC Division of Viral Hepatitis, Atlanta, USA
Makhmudkhan Sharapov Center for Disease Control and Prevention (CDC) Office in Uzbekistan
Daniel Shouval Liver Unit, Hadassah University Hospital, Jerusalem, Israel
Masood Siddiq Jinnah Memorial Hospital, Rawalpindi, Pakistan; Yusra Medical College, Rawalpindi, Pakistan
Marietta Simonova Clinic of Gastroenterology, Military Medical Academy, Sofia, Bulgaria
Ali Sulaiman
Division of Hepatobiliary, Department of Internal Medicine, Faculty of Medicine, University of
Indonesia, Cipto Mangunkusumo Hospital, Jakarta, Indonesia; Klinik Hati Ali Sulaiman, Jl. Cilamaya 46,
Jakarta, Indonesia
Sultan Suranbaev Department for Disease Prevention and State Sanitary and Epidemiological Surveillance, Bishkek,
Kyrgyzstan
Gulmira Syibildaevna Suranbaeva Preventive Medicine of the Ministry of Health of the Kyrgyz Republic, Bishkek, Kyrgyzstan; Chief
research officer of the Republican Scientific and Practical Center for the Control of Viral Infections
Konstantin Tchernev “Sofiamed” Hospital, Sofia, Bulgaria
Vera Toygombaeva Deparment of General and Clinical Epidemiology of Kyrgyz-Russian Slavic University, B.N. Yeltsin,
Bishkek, Kyrgyzstan
Page 6 of 62
Owen Tak-Yin Tsang Department of Medicine and Geriatrics, Princess Margaret Hospital Authority, Hong Kong, SAR China
Steven Tsang Department of Medicine, Tseung Kwan O Hospital, Hong Kong, SAR China
Nazira Usmanova Center for Disease Control and Prevention (CDC) Office in Kyrgyzstan
Jonas Valantinas Centre of Hepatology, Gastroenterology, and Dietetics, Faculty of Medicine, Vilnius University, Vilnius,
Lithuania
Roman Vi International HepatoTransplant Group, Astana, Kazakhstan; Republican Coordination Center for
Hepatology and Gastroenterology, Astana, Kazakhstan
Jerneja Videčnik Zorman Clinic for Infectious Diseases and Febrile Illnesses, University Medical Centre, Ljubljana, Slovenia
Gabriela Vidiella Coordinadora del Programa Nacional de Control de Hepatitis Virales, Ministerio de Salud de la Nación,
CABA, Argentina
Hans Heiner Wedemeyer Dept. of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School Germany; German
Liver Foundation, Germany
Mohammed Youbi Department of Epidemiology and Disease Control, Ministry of Health, Rabat, Morocco
Eli Zuckerman Liver Unit, Carmel University Medical Center, Bruce Rappaport Faculty of Medicine, Technion, Israeli
Institute of Technology, Haifa, Israel
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Section 2. Description of the PRoGReSs model
The PRoGReSs model was named after the modelers who developed it – Ken Pasini, Homie Razavi, Ivane
Gamkrelidze, and Devin Razavi-Shearer. It is a compartmental, deterministic, dynamic Markov disease progression
model developed in Microsoft Excel and Microsoft Visual Basic (Microsoft Corporation, Redmond, WA, United
States) to quantify the annual HBV-infected population by disease stage, sex, and age in a country. Excel was selected due to its transparency, flexibility, and widespread availability.
The disease stages considered in the PRoGReSs model were chronic hepatitis B, compensated cirrhosis,
decompensated cirrhosis, hepatocellular carcinoma, and liver transplant. Populations with decompensated cirrhosis
and hepatocellular carcinoma were considered liver transplant-eligible.
HBV-infected population in each disease stage was further divided into high-viral load (HBsAg-positive with HBV
DNA of 20,000 IU/mL or more), low-viral load (HBsAg-positive with HBV DNA of less than 20,000 IU/mL), and
treatment responder subpopulations. The population susceptible to HBV was also tracked by age and sex, consisting
of uninfected individuals who had never been exposed to HBV and had not been successfully immunized. The
scheme of the modeled disease progression of HBV is presented in Figure 1.
Newly infected cases entered the model through the incidence calculation described below. Those developing a
chronic hepatitis B infection were split into low- and high-viral load cases using reported data on respective
proportions of high-viral load cases among HBeAg-negative and HBeAg-positive populations. Since the risk for chronic hepatitis B infection largely depends on the age of acquisition of infection, the model began in 1900 to allow
for full flexibility of age of infection of the currently infected population and to estimate the current susceptible
population.
Figure 1. Flow of disease progression of HBV
Legend: CHB — chronic hepatitis B; Cirr — compensated cirrhosis; DCC — decompensated cirrhosis; HCC — hepatocellular carcinoma; LT — liver transplant; LVL — low-viral load; HVL — high-viral load; U —
untreated/non-responder; T — treatment responder; black arrows — disease progression; orange arrows — non-
liver-related death; red arrows — liver-related death; blue arrows — treatment response; purple arrows — treatment
discontinuation; green arrows — liver transplantation
Disease progression, aging, and mortality
The number of cases transitioning annually from one disease stage in a given year, sex, and age to (1) death, (2)
another disease stage, (3) treatment responder stage, (4) liver transplant stage, or (5) back to untreated stage to the
following year and following age was calculated by multiplying the annual (1) mortality, (2) disease progression,
(3) treatment response, (4) liver transplant, and (5) treatment discontinuation rates, respectively, by the prevalent
population in the given disease stage, year, sex, and age (Equation 1). The remaining population moved to the next year and age, while staying in the same disease stage, to simulate aging. Population aged 85 and above was
considered a single cohort.
Acute hepatitis
B
CHB LVL (U)
CHB HVL (U)
CHB LVL (T)
Cirr LVL (U)
Cirr HVL (U)
Cirr LVL (T)
DCC LVL (U)
DCC HVL (U)
DCC LVL (T)
HCC LVL (U)
HCC HVL (U)
HCC LVL (T)
LT LVL (U)
LT HVL (U)
LT LVL (T)
Clearance
Non-liver-related death Liver-related death
Fulminant hepatitis B death
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The annual background (all-cause) mortality rate by sex and age group was applied to all populations. An additional
liver-related mortality rate was applied to populations with decompensated cirrhosis, hepatocellular carcinoma, and
liver transplant recipients (Tables 1–2).
Different progression rates by sex and age group were used for low- and high-viral load cases (Table 1). A
discontinuation variable was used to estimate the duration of treatment. The default input was indefinite. At the
population level, the annual number of treated patients was estimated where discontinuation by one patient and start
up by another patient had the same effect as one patient continuing treatment. It was also assumed that those on
treatment would stay at the same liver disease stage. Clinical trials have shown regression of fibrosis and cirrhosis
with time but the model does not allow regression as to remain conservative in regard to the effect of current
treatment.(1) There is evidence that individuals under treatment can still progress to HCC, but the annual rate is
estimated to be 0.74% and this is not included in the model.(2)
Equation 1. Prevalent cases in stage , at time , of sex , and age re a en a e
revalent ases
e ases where:
is annual background mortality rate at time , for sex , at age is annual liver-related mortality rate for stage , at time , for sex , at age , , …, are annual progression rates from stage to , stage to , …, stage
to , respectively, for sex , at age is annual treatment response rate for stage , at time , defined as
Total initiating treatment
Treatment response rate
Total treatment eligi le ases
is annual liver transplantation rate for stage , at time , defined as
Total liver transplantations
Total liver transplant eligi le ases
is treatment discontinuation rate at time e a e is the number of cases incident or progressing to stage , at time , for sex , at age
Page 9 of 62
Table 1. Annual disease progression rates of HBsAg infection (%)
Progression rates — Male
Age group 0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85+
Fulm hepatitis B to Death (3, 4) 67.0 67.0 67.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0
Low (5, 6) 62.8 62.8 62.8 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4
High (5, 6) 75.4 75.4 75.4 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
CHB to Cirr (LVL)*
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.7 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
Low* 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.4 0.1 0.6 0.7 0.7 0.8 0.8 0.8 0.4 0.4 0.4
High* 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.7 0.8 0.8 0.8 0.9 0.8 1.7 1.7 1.7 1.7
CHB to Cirr (HVL)* 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.1 1.5 2.2 3.0 3.4 3.5 3.6 3.6 3.6 3.6 3.6
Low* 0.3 0.3 0.3 0.3 0.3 0.3 0.3 1.1 0.1 1.2 1.3 2.3 2.6 1.8 1.9 3.1 3.1 3.1
High* 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.5 1.5 2.3 4.2 3.9 3.5 3.6 3.6 3.6 3.6 3.6
CHB to HCC (LVL)* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
Low* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.3 0.3 0.3 0.3 0.3
High* 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.4 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5
CHB to HCC (HVL)* 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.7 0.7 0.7 0.7 0.7 0.7 0.7
Low* 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.2 0.7 0.5 0.5 0.5 0.5 0.5
High* 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.4 0.4 0.7 1.4 1.4 1.4 1.4 1.4 1.4
Cirr to DCC (LVL) (7) 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
Low – – – – – – – – – – – – – – – – – –
High – – – – – – – – – – – – – – – – – –
Cirr to DCC (HVL) (7) 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3
Low(7) 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8
High(7) 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9
Cirr to HCC (LVL)* 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 2.0 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1
Low* 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.2 1.2 1.1 2.1 1.7 1.7 1.7 1.7 1.7 1.7
High* 1.0 1.0 1.0 1.0 1.0 1.0 1.9 1.9 2.0 2.1 4.3 2.1 2.1 2.1 2.1 2.1 2.1 2.1
Cirr to HCC (HVL)* 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.5 2.2 3.8 6.9 9.6 10.7 11.1 11.1 11.2 11.2 11.2
Low* 1.4 1.4 1.4 1.4 1.4 1.4 0.4 1.0 1.5 1.1 1.0 9.5 8.9 8.9 8.9 1.0 1.0 1.0
High* 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.5 4.0 4.8 6.9 12.7 13.2 13.2 13.2 13.2 13.2 13.2
DCC to LR Death (8) 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0
Low(9) 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0
High(10) 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1
HCC to LR Death, first-year (11) 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3
Low (12) 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0
High (13) 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6
HCC to LR Death, subseq-yr (11) 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9
Low (11) (weighted average) 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2
High (14) 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4
Page 10 of 62
Progression rates — Female
Age group 0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85+
Fulm hepatitis B to Death (3, 4) 67.0 67.0 67.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0
Low (5, 6) 62.8 62.8 62.8 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4 84.4
High (5, 6) 75.4 75.4 75.4 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
CHB to Cirr (LVL)*
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.4 0.4 0.4 0.4 0.4 0.4
Low* 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.4 0.4 0.4 0.4 0.4 0.4
High* 0.3 0.3 0.3 0.3 0.3 0.3 0.3 1.3 0.3 0.4 0.2 0.8 0.5 0.5 2.8 0.8 0.8 0.8
CHB to Cirr (HVL)* 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.9 1.4 2.1 2.5 2.6 2.6 2.6 2.6 2.6 2.6
Low* 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.9 0.2 1.4 1.8 0.7 1.5 1.5 0.9 0.9 0.9
High* 0.6 0.6 0.6 0.6 0.6 0.6 0.6 1.2 1.5 1.4 2.3 2.6 2.6 2.6 2.6 2.6 2.6 2.6
CHB to HCC (LVL)* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Low* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
High* 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.1 0.5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
CHB to HCC (HVL)* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.4 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6
Low* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.4 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5
High* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.4 0.5 0.6 0.6 1.5 1.5 1.5 1.5 1.5 1.5
Cirr to DCC (LVL) (7) 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
Low – – – – – – – – – – – – – – – – – –
High – – – – – – – – – – – – – – – – – –
Cirr to DCC (HVL) (7) 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3
Low (7) 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8
High (7) 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9 3.9
Cirr to HCC (LVL)* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.5 1.6 1.6 1.6 1.7 1.7 1.7
Low* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.5 1.6 1.6 1.6 1.6 1.6 1.6
High* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 3.4 3.4 3.4 3.4 3.4 3.4 3.4
Cirr to HCC (HVL)* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 6.7 6.7 6.7 6.7 6.7 6.7 6.7
Low* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 6.7 4.5 4.5 4.5 2.7 2.7 2.7
High* 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 10.0 6.7 6.7 6.7 6.7 6.7 6.7
DCC to LR Death (8) 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0 16.0
Low (9) 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0
High (10) 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1
HCC to LR Death, first-year (11) 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3 61.3
Low (12) 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0 19.0
High (13) 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6 92.6
HCC to LR Death, subseq-yr (11) 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9
Low (11) (weighted average) 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2
High (14) 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4 21.4
Low and High progression rates form the estimate range used in calculating uncertainty intervals of the outputs. HBV — hepatitis B virus; Fulm — fulminant; CHB — chronic hepatitis B; Cirr — compensated cirrhosis; DCC — decompensated cirrhosis; HCC — hepatocellular carcinoma; LR Death — liver-related death; LVL — low-viral load; HVL — high-viral load; subseq-yr — subsequent-year; – low and high estimates not available; * A root mean square was used using polynomial functions to find the best fit between the data points and forecasts that were provided by Dr. Mindie Nguyen and her team at Stanford University. The data utilized was the raw data from the REVEAL Study as ell as Dr. guyen’s ohort in the United States
Page 11 of 62
Table 2. Annual progression rates of liver transplant recipients to liver-related death (%) (15)
Year First-year Subsequent-year
1986 and earlier 33.1 3.9
1987 33.1 3.9
1988 24.2 3.6
1989 24.2 3.9
1990 21.3 3.8
1991 20.8 4.1
1992 19.5 4.3
1993 17.8 4.2
1994 15.6 3.8
1995 15.8 3.9
1996 15.9 4.1
1997 14.1 4.0
1998 14.1 4.0
1999 14.7 4.2
2000 13.3 3.9
2001 14.1 4.1
2002 13.0 4.0
2003 13.6 4.0
2004 12.9 5.2
2005 13.4 4.9
2006 12.5 4.9
2007 10.7 4.9
2008 and later 10.7 4.9
Incidence
Annual incident cases of HBsAg infections by sex and age were calculated separately for perinatally and
horizontally acquired infection (Figure 2). Incident cases developing a chronic HBsAg infection were added to the
disease progression model annually, and the resulting prevalence of HBsAg was used to calculate incident cases in
the following year, generating a dynamic model (Figure 3). Among those not developing a chronic infection, risk for
fulminant hepatitis B was 0.5% (0.1%–1.0%).(5)
Figure 2. Incidence determination scheme in the model
Incidence among 0–15-year-olds
Incidence
Incidence
Back-calculation
Serologic and
treatment status of
mothers,
vaccination status
of infants
Ho
rizo
nta
lP
erin
atal
1900 to year of known prevalence After year of known prevalence
Prevalence
among 0–35
Incidence among >15-year-oldsPrevalence
among peers
Page 12 of 62
Figure 3. Flow between populations in the model
Perinatal incidence
To calculate incident cases of perinatally acquired HBsAg, the annual modeled prevalence of HBsAg among women
of childbearing age is subdivided into those that are estimated to be HBeAg-positive and HBeAg-negative. The
proportion among these two groups that have a high viral load and low viral load are then combined into low-viral
and high-viral load groupings.(16, 17) The high-viral load group, in conjunction with the reported proportion of
high-viral load women receiving peripartum antiviral treatment, was used to segment the HBsAg-infected women of
childbearing age into the following serologic and treatment statuses: (1) HBsAg-positive with high viral load untreated, (2) HBsAg-positive with low viral load untreated, (3) HBsAg-positive with high viral load treated, and (4)
HBsAg-negative.
The reported num er of annual irths y mother’s age group as then re-indexed to annual births by serologic and
treatment status of mothers, estimated by Equation 2.
Equation 2. Total births in year to mothers of serologic and treatment status
Total irths Total irths
where:
is the age group of mother (15–19, 20–24, …, 45–49) is the proportion of -year old women of childbearing age at time with serologic and treatment
status
Afterwards, the births were segmented by vaccination status: (1) no vaccination, (2) birth dose of HBsAg vaccine
only, (3) complete HBsAg vaccine series with birth dose without HBIG, (4) complete vaccine series with birth dose
with HBIG, and (5) complete vaccine series without birth dose.
Finally, using the transmission rates y the mothers’ serologi and treatment status and the infants’ va ination
status (Table 3), the number of perinatally acquired cases of HBsAg infection was calculated (Equation 3).
Decompensated
cirrhosis
Hepatocellular
carcinoma
Susceptible
infants (
Page 13 of 62
Equation 3. New perinatally acquired cases of HBsAg
e perinatally a uired ases of HBsAg Total irths
where:
ranges over the serologic and treatment status of mothers ranges over the vaccination status of infants is the proportion of infants with vaccination status born to mothers with serologic and treatment status
is the transmission rate from a mother with serologic and treatment status to an infant with vaccination
status Uninfected infants that did not receive complete HBsAg vaccine series entered the susceptible population.
Chronically infected infants were added to the disease progression model.
Page 14 of 62
Table 3. Mother-to-child transmission rates of HBsAg (%)
HBV — hepatitis B virus; HBsAg — hepatitis B surface antigen; HBIG — hepatitis B immune glo ulin; † Assumed
a 95% (response rate to treatment with tenofovir(1)) reduction relative to transmission rates for HBsAg-positive with
high viral load, untreated peripartum mothers
Horizontal incidence Horizontally acquired incident cases of HBsAg infection were calculated separately (1) up to the year of known
prevalence y sex and age group in a ountry/region (“year of kno n prevalen e”), and (2) after the year of kno n
prevalence (Figure 2). Cases incident up to the year of kno n prevalen e are referred to as “histori al,” hile ases
in ident after ards are referred to as “for ard.”
Horizontal incidence — historical
Total annual historical horizontally acquired incident cases of HBsAg were calculated by first defining a curve
des ri ing relative sizes of these in ident ases (“relative in iden e”). Then, a ali ration pro edure mat hing modeled prevalence to reported prevalence was used to transform relative incidence to annual incident cases by sex
and age.
Relative incidence
Relative incidence was built by back-calculating arrays of quinquennial estimated incident cases for both sexes
(Table 4) that satisfied the conditions in Equations 4–5. These were then converted to annual estimated incident
cases, linearly interpolated over 1900–year of known prevalence, and passed through a 5-year-average filter. The
resulting annual incident cases were divided by peak incident cases to generate the relative incidence curve with a
peak of one. An example of a relative incidence curve is presented in Figure 4.
Table 4. Array of incident (horizontal) cases at time t, of sex , of age group
Age group / Year
,
,
YKP — year of known prevalence; , — total prevalent population in YKP of sex in age group ; is
age group 0, is age group 1–4, is age group 5–9, …, is age group 85+
Equation 4. Condition 1 for back-calculated array of incident (horizontal) cases
For each sex and age group ,
Vaccination status of infant
Serologic status of mother
HBsAg-positive with high viral
load, untreated peripartum
HBsAg-positive with low
viral load, untreated
peripartum
HBsAg-positive with high
viral load, treated peripartum
No vaccination 100.0 (90.3–100.0)
(16, 18-25)
0.0 (0.0–0.0)
(16, 26) 5.6 (4.5–5.6)†
Birth dose of HBV vaccine only 90.0 (81.3–100.0)
(18, 19, 27)
0.0 (0.0–0.0)
(16, 26) 5.1 (4.1–5.6)†
Complete HBV vaccine series with
birth dose without HBIG
13.8 (9.6–41.3)
(27-29)
0.0 (0.0–0.0)
(16, 26) 0.7 (0.5–2.1)†
Complete HBV vaccine series with
birth dose with HBIG
7.7 (2.5–20.7)
(26, 27, 30)
0.0 (0.0–0.0)
(16, 26) 0.4 (0.1–1.0)†
Complete HBV vaccine series
without birth dose
32.7 (29.0–35.0)
(23)
0.0 (0.0–0.0)
(16, 26) 1.6 (1.5–1.8)†
Page 15 of 62
H L H L H L L L ,
where:
YKP is year of known prevalence
is incident (horizontal) cases at time , of sex , at age group
is age group 0, is age group 1–4, is age group 5–9, …, is age group 85+ is average risk for chronic infection for age group
H L , L L are survival functions from background death, liver-related death
among high-viral load population, and liver-related death among low-viral load population,
respectively, from time to year of known prevalence for sex , and age group HVL is proportion of high-viral load cases among incident cases of HBsAg
, is prevalent cases of HBsAg, of sex and age group in year of known prevalence
Equation 5. Condition 2 for back-calculated array of incident (horizontal) cases
For each time point , sex , and age group ,
Unva inated population
Unva inated population
where:
YKP is year of known prevalence
is incident (horizontal) cases at time , sex , and age group
is average shape parameter for age group
Unva inated population
at time , of sex , at age group was estimated using reported history of
vaccinations in the country
Figure 4. Relative incidence curve of HBsAg infections — Poland
HBsAg — hepatitis B surface antigen
Incidence calibration
A (1) scalar multiplier of relative incidence and (2) quinquennial sex and age group distributions of historical
horizontal incident cases were calculated using the secant method(31) to match (1) modeled total prevalent cases
(Equation 6) and (2) modeled prevalence of HBsAg by sex and age group in the year of the known prevalence to
reported prevalence. An example of annual incident cases calculated using this procedure is presented in Figure 5.
An example of modeled prevalence by sex and age group resulting from the incidence calibration, along with the
target prevalence, is presented in Figure 6.
Equation 6. Total HBsAg-infected population in year of known prevalence
0.0
0.2
0.4
0.6
0.8
1.0
1.2
190
0
191
0
192
0
193
0
194
0
195
0
196
0
197
0
198
0
199
0
200
0
201
0
202
0
203
0
204
0
205
0
Rel
ati
ve
Inci
den
ce (
Ho
rizo
nta
l)
Page 16 of 62
revalent ases of HBsAg
In ident ases of hroni HBsAg Deaths among HBsAg infe ted population
where: YKP is year of known prevalence
Figure 5. Annual acute incident cases (historical) of HBsAg infections — Poland
HBsAg — hepatitis B surface antigen
Figure 6. Target and modeled prevalence of HBsAg by sex and age group before and after calibration —
Poland
(a) Prevalence of HBsAg, Uncalibrated
(b) Prevalence of HBsAg, one round of calibration
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
190
0
191
0
192
0
193
0
194
0
195
0
196
0
197
0
198
0
199
0
200
0
201
0
202
0
203
0
204
0
205
0
Acu
te I
nci
den
t C
ase
s
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Ma
les
Target Prevalence Model Prevalence
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Fem
ale
s
Target Prevalence Model Prevalence
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Ma
les
Target Prevalence Model Prevalence
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Fem
ale
s
Target Prevalence Model Prevalence
Page 17 of 62
(c) Prevalence of HBsAg, two rounds of calibration
(d) Prevalence of HBsAg, three rounds of calibration
HBsAg — hepatitis B surface antigen
Horizontal incidence — forward
Susceptible population
To calculate the number of new horizontally acquired HBsAg infections after the year of known prevalence, we first
estimated the susceptible population in the year of known prevalence (Equation 7). Susceptible infants were
calculated as described above.
Equation 7. Susceptible population in year of known prevalence, of sex , at age
In ident HBsAg ases
Imm
where:
YKP is year of known prevalence
is background mortality rate at time , of sex , at age is population at time , of sex , at age Imm is an estimate of immunization coverage of population at time at age
In all years following the year of known prevalence, we calculated the annual susceptible population by sex and age
by (1) adding infants susceptible to infection to the existing 0-year old susceptible population, as described above,
and (2) subtracting deaths due to background mortality, new infections, and new catch-up immunizations from the
existing susceptible population (Equation 8).
Equation 8. Susceptible population after year of known prevalence, of sex , at age e HBsAg Cases Cat h up immunizations
where:
is background mortality rate at time , of sex , at age
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Ma
les
Target Prevalence Model Prevalence
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Fem
ale
s
Target Prevalence Model Prevalence
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Ma
les
Target Prevalence Model Prevalence
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
0–
4
5–
9
10–
14
15–
19
20–
24
25–
29
30–
34
35–
39
40–
44
45–
49
50–
54
55–
59
60–
64
65–
69
70–
74
75–
79
80–
84
85+
Pre
va
len
ce, 2
01
0 —
Fem
ale
s
Target Prevalence Model Prevalence
Page 18 of 62
Incidence function
Incidence of horizontally acquired HBsAg infection was assumed to be a linear function of HBsAg prevalence with
high viral load (Equation 9). For those younger than 15, incidence was determined by prevalence of HBsAg with
high viral load among 0–35-year-olds to simulate household infection from siblings, peers, parents, and other adults.
For those 15 or older, incidence was a function of prevalence with high viral load among peers of the same age.
Equation 9. Incident (horizontal) cases in year , of sex , and age
0– 5 if
if
where:
is prevalence of HBsAg with high viral load at time , at age (group) is the susceptible population at time , of sex , at age is background mortality rate at time , of sex , at age is the shape parameter for age is scale parameter for sex (see below)
Scale parameter for both sexes was inferred through a calibration procedure that matched the number of cases incident in the year of known prevalence to those incident in the following year, thereby tying the forward incident
cases to historical incident cases. An example of calibration of incident cases (forward) is presented in Figure 7.
Shape parameter for each age was assumed to be constant across all countries.
Figure 7. Annual acute incident cases of HBsAg infection — Poland
(a) Annual acute incident cases, uncalibrated
(b) Annual acute incident cases, calibrated
HBsAg — hepatitis B surface antigen
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
190
0
191
0
192
0
193
0
194
0
195
0
196
0
197
0
198
0
199
0
200
0
201
0
202
0
203
0
204
0
205
0
Acu
te I
nci
den
t C
ase
s
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
190
0
191
0
192
0
193
0
194
0
195
0
196
0
197
0
198
0
199
0
200
0
201
0
202
0
203
0
204
0
205
0
Acu
te I
nci
den
t C
ase
s
Page 19 of 62
Section 3. Model inputs
Geographic scope
All countries with a total population of 1.0 million people or more were considered with three exceptions. Due to
collaborations with the World Health Organization (WHO) Regional Office for the Western Pacific and the Pan
American Health Organization (PAHO), Fiji, Kiribati, and Belize were included as well.
Literature review and scoring The following data were extracted from relevant studies: HBsAg prevalence, sex, and age distribution of the
infected study population (if available), time period(s) of the estimate, the population that was studied (general
population, pregnant women, students, etc.), setting of the study (urban, rural, urban/rural), scope of the study
(single center, multi-center, city, multi-city, region, national, etc.), type of analysis (surveillance, meta-analysis,
review article, modeling, other/unknown), and the sample size.
HBsAg prevalence studies were scored on a scale of 0–10, following the approach described previously for
HCV.(32) This system was based on three metrics, which accounted for 60% (generalizability), 20% (sample size)
and 20% (year of analysis) of the overall score, respectively:
verall s ore eneraliza ility s ore Sample size s ore ear of analysis s ore
Sample size score: The log of sample size was scaled to 0–10 whereby all studies with a sample size greater than
10,000 received a score of 10.
Year of analysis score: Study year was assessed so that analyses conducted after 2010 received a score of 10;
2004–2010, a score of 8; 2000–2003, a score of 6; and analyses conducted prior to 2000 received a score of zero. All studies that did not report the year of study were assumed to have been conducted two years prior to the year of
publication.
For simplicity, the 0-10 scores were converted to a data quality scale of 1-3, where an overall score of 0.0 < 4.0
received a data quality score of 1, 4.0 < 8.0 received a score of 2 and 8.0 < 10.0 received a score of 3. Modeling
studies were automatically given a data quality score of 2. Studies without a formal assessment, but deemed to be of
quality for inclusion, were given a score of 1.
Generalizability score: The table below indicates the criteria used to score articles on their ability to be generalized
to the total population.
Page 20 of 62
Geographical
scope ↓ Scale, 0–10
National
10†
0 3 4 6 9
Meta-analysis: 4 Model: 6
Meta-analysis: 5
Large region
Multi-region
Multi-city
Large city
0 1 2–3‡ 4–5
‡ 6–8
‡
Small
Region/Town
Village
Tribe
Hospital
0 0 1 1 2
Population →
High-risk,
any sampling
method
- IVDUs - HIV - Surgical patients
Healthy adults,
self-selected
- Blood donors
Healthy adults,
self-selected
- Health check-up
patients
- Screening
Healthy adults,
randomly selected
- Healthcare workers
- Pregnant women
- Soldiers
General
population,
randomly selected
† 10 reserved for a nationally representative sample ith a stratified, multistage, and random sampling design that do uments the study design and demographi s of su je ts thoroughly (e.g. HA ES); ‡ aria ility su je t to
author’s dis retion ased on quality of study design, as well as the geographic scope of the respective country.
Expert consensus was assigned a default score of 1, unless supportive data were available. Expert consensus
estimates based on supporting data were scored as follows: 2 = expert input based on published or unpublished data;
3 = expert consensus based on well conducted studies ahead of print and/or large national databases.
While blood donor studies were excluded from use as base estimates, they were utilized as the low prevalence
interval when available.
When sex and age distributions were unavailable for a country, the distribution from another country with a similar
vaccination history was calibrated to match the reported prevalence in the year of known prevalence. For countries
in which no prevalence data was available, then the weighted average of countries within the same region as defined
by the Global Burden of Diseases, Injuries and Risk Factors were utilized.
Page 21 of 62
Required inputs
The following inputs were required to build and calibrate each country model:
Model input Definition Source
Population, annual, by sex and age Number of people in the country (33)
Background mortality rate, annual, by
sex and 5-year age group, and among
infants
Proportion of deaths among the total population (33)
Births, annual, by 5-year age group of
mother Number of births (33)
Sex ratio at birth, quinquennial Ratio of male to female births (33)
Newly diagnosed population, annual Number of newly diagnosed HBsAg-infected cases Table 6
Treated population, annual Number of HBsAg-infected cases who have received
antiviral treatment Table 6
Peripartum treatment coverage of
mothers, annual
Proportion of HBsAg-positive mothers who have received
peripartum antiviral treatment Table 7
On-treatment response rate Proportion of HBsAg-infected cases achieving
undetectable HBV DNA following antiviral treatment (1)
Treatment discontinuation rate Proportion of HBsAg-infected population on treatment
who discontinue treatment annually Country-specific data
Infant vaccination coverage, annual, by
serologic and treatment status of mothers
Proportion of infants who have received a birth dose,
complete, or partial HBsAg vaccine series, or HBIG (of
those having received birth dose)
Table 7
Catch-up vaccination coverage, annual,
by 5-year age group
Proportion of non-infants who have received complete
HBsAg vaccine series Country-specific data
Liver transplants, annual Number of liver transplantations attributed to HBV
infection
International(34) or national
registry adjusted for proportion
attributed to HBV infection
HBeAg-positive among HBsAg-positive
women of childbearing age, in a given
year
Proportion of HBsAg-positive women of childbearing age
who are HBeAg-positive Country-specific data
HBeAg-negative with high viral load, in a
given year
Proportion of HBeAg-negative population with HBV DNA
≥20,000 IU/mL (16, 17)
HBeAg-positive with high viral load, in a
given year
Proportion of HBeAg-positive population with HBV DNA
≥20,000 IU/mL (16, 17)
Target HBsAg prevalence by sex and 5-
year age group in a given year
Reported proportion of total population that is HBsAg-
positive Table 5
Target total diagnosed, in a given year Reported number of diagnosed HBsAg-infected cases National registry or
extrapolated
Progression rates, by start and end health
state, year, sex, and 5-year age group
Proportion of HBsAg-infected population progressing from
start health state to end health state annually See Tables 1–2
Perinatal transmission rates of HBsAg by
vaccination status of infants, and serologic
and treatment status of mothers
Probability of mother-to-child transmission of HBsAg See Table 3
Risk for chronic infection, for perinatally
acquired HBsAg, erinatal; and for horizontally acquired HBsAg at age ,
Proportion of incident cases HBsAg who remain
chronically infected with HBsAg
erinatal
if
if if
(35), Assumption
Relative incidence, annual Relative number of new horizontally acquired cases of
HBsAg Back-calculation
Incidence multiplier Scaling factor for relative incidence Calibration
Shape parameter for age
Function of age determining the linear relationship
between the prevalence and incidence of horizontally
acquired HBsAg, defined as
if
if
if
Assumption
Scale parameter for sex Scaling factor applied to the shape parameter Calibration
Proportion diagnosed, by disease stage in
a given year
Proportion of diagnosed cases among HBsAg-infected
population Calibration
HBV — hepatitis B virus; HBsAg — hepatitis B surface antigen; HBeAg — hepatitis B e antigen; HBIG — hepatitis B immune globulin
Page 22 of 62
Section 4. Delphi Process
Activities
Ph
ase
1 –
Da
ta G
ath
erin
g
1a
Identify country experts who are willing to collaborate
Experts were identified through HBV-related scientific contributions, or through referrals and recommendations from leading researchers. Panels consisted of hepatologists, gastroenterologists, virologists, infectious disease specialists,
epidemiologists, health economists, health scientists and Ministry of Health representatives
1b
Literature Search
Review the internal database for previously identified sources
Review online sources (MOH, WHO, etc.) to capture non-indexed sources
Run a literature search from 2013 forward to identify recent publications
Summarize input data available through the literature
Gather empirical data for new HCC cases, liver transplants (LT), percent of HCC and LT due to HBV, annual newly
diagnosed, annual treated
Build draft model based on published data or extrapolate inputs from countries with data when data are missing (as a
placeholder)
Schedule meeting with experts
Ph
ase
2 –
Co
un
try
Meeti
ng
s a
nd
Mo
deli
ng
2a
Expert Meeting 1 (2-3 hours)
Provide a background on the project, model and methodology
Review data identified in Phase 1b and highlight gaps in data
Request data in local non-indexed journals, unpublished data and any other available data (e.g., hospital-level data) that
can be used to fill the gaps
Gain agreement on countries that can used as for extrapolation when no local data are available
2b
Follow up with Experts Post Meeting 1
Send minutes of the meeting and list of remaining action items to experts
Follow up with experts to collect missing data and get copies of publications in the local journals, unpublished data,
relevant Ph.D. theses, government reports and raw hospital or registry-level data
Analyze raw data and send to experts for approval
2c
Disease Burden Modeling
Populate disease burden model with inputs and calibrate model to empirical data
Develop 2-3 scenarios to prepare for meeting 2, including a WHO target scenario (elimination by 2030)
Schedule second meeting
Develop a slide deck summarizing all inputs and associated data sources
Perform a final check of the model and slide deck and approve internally
2d
Expert Meeting 2 (2-3 hours)
Review all inputs as well as data provided by experts since meeting 1 and results of analyses of any raw data provided
Gain agreement on all inputs to be used in the model
Update the model using any updated inputs
Run scenarios requested by experts (e.g., slow increase in the number of treated patients, disease control, WHO target)
and review results and insights
Agree on final strategies that would be considered as part of a national strategy
Ph
ase
3 –
Fo
llo
w-u
p A
na
lyse
s
3a
Follow-up Analyses
Update model as necessary and send results to experts
Provide support to address follow-up questions
Lock down inputs and outputs as approved
Run additional scenarios to support the development of a national strategy (e.g., economic impact, birth cohort
screening and sources of transmission)
Report results to Polaris Observatory
Update analysis as new information becomes available (e.g., new national studies, updated treatment data)
Page 23 of 62
Section 5. Country inputs
HBsAg prevalence by sex and age group
The highest scoring HBsAg prevalence estimates, the uncertainty range and the prevalence distribution by sex and
age are shown in Table 5. HBsAg prevalence was not available by sex or for all age groups in all countries. When
only prevalence by age group was available, the published male-to-female ratio was applied uniformly to all reported prevalence by age groups to determine the prevalence by sex. When prevalence from oldest age groups was
missing (typically after age 65), it was assumed that prevalence in each of these age groups was 0.9 times prevalence
in the previous age group. When prevalence from the youngest age groups was missing, it was assumed that
prevalence was constant in younger age cohorts prior to vaccination. A back-calculation procedure was employed to
estimate prevalence in these age groups. This back-calculation method took into account birth rates, prevalence of
mothers, as well as perinatal prophylaxes measures.
Alternatively, country registry or diagnosis data were used to develop estimated prevalence by age group. In this method, the annual number of newly diagnosed cases was collected and adjusted for mortality. Birth year was used
to calculate age and to consolidate data from multiple years in the last year of available data. The number of
diagnosed cases in ea h age group as divided y the ountry’s population in that age group (in the last year of
available data). A weighting factor was applied to get the sum product of the rough prevalence by age and general
population by age to equal the estimated total infections in the country. This weighting factor times the rough
prevalence was used to estimate the actual prevalence by age group.
Treated patients
Annual HBsAg-infected population on antiviral therapy for HBsAg was estimated through (1) national databases, (2) audit drug sales data, (3) government reports, (4) reports from major treatment centers (and extrapolated to the
whole country), and (5) drug suppliers. In the absence of country-specific data, estimates from approved countries
with a similar healthcare system were used for extrapolation (Table 6). Unless otherwise specified, it was assumed
that successfully treated patients remained on treatment indefinitely.
Total diagnosed
In countries where HBsAg was a notifiable infection and a reliable annual number of newly diagnosed cases was
reported, total diagnosed cases was calculated by summing data from all available years, after accounting for
mortality among the diagnosed. In countries where the number of total and newly diagnosed cases was not available, expert panel input was used (Table 6). Lastly, secant method was used to solve for the proportion of diagnosed
HBsAg-infected population to match the reported number of diagnosed cases to modeled in a given year.(31) This
method assumes that at base, those in later stages of the disease are more likely to be diagnosed than those in earlier
stages.
Perinatal prophylaxes
The perinatal prophylaxes in 2016 are shown in Table 7. This table includes the coverage rates of one year olds that
have received the full HBV va ination s hedule (≥ Dose) and the overage of infants that re eive the first dose of vaccination within the first 24 hours of life (Birth Dose). Additionally, the coverage of HBIG is included, which is
assumed to be only applied to infants born to HBsAg+ mothers, unless otherwise noted, and only covers those that
also receive birth dose. Lastly, the percentage of mothers with a high viral load that receive peripartum anti-viral
treatment is also included.
Page 24 of 62
Table 5. HBsAg published prevalence
Country
Prev.
Est.
Status
HBsAg
Prevalence
Base
Study Year
Data
Quality
Score
HBsAg
Prevalence
Low
HBsAg
Prevalence
High
HBsAg
Prevalence
Source
HBsAg
Age
Source
HBeAg
Prevalence
WoCBA
HBeAg
Prevalence
Source
Afghanistan E 1.90% 2003-2011 2 -- -- (36) --
Albania I 7.11% 2010-2015 2 4.56% 11.80% EC,
(37, 38) (39) 17.39% (40)
Algeria E 2.15% 1998 2 1.40% 3.23% (41) EX1
Angola E 13.00% 2002 2 9.30% 15.10% (42-44) EX2
Argentina I 0.26% 2013-2014 2 0.10% 0.42% (45) (46)
Armenia E 2.00% 2014* 2 1.81% 2.38% (47), EX EX3
Australia I 0.98% 2016 2 0.80% 1.19% (48) (48) 29.0% (16)
Azerbaijan E 2.70% 2010* 1 2.00% 3.14% (49) EX4
Bahrain I 1.00% 2016 1 0.58% 1.16% EC,(50),
EX (51)
Bangladesh E 5.50% 2008* 3 3.14% 7.7% (52-54) (54) 30.2% (55)
Belarus E 4.80% 2006* 2 4.22% 5.57% (56), EX EX5
Belgium I 0.66% 2003 2 0.51% 0.84% (57) (57)
Belize E 4.00% 1992 1 0.25% 4.64% (58, 59),
EX EX
6
Brazil I 0.6% 2004-2009 3 0.21% 0.80% (60-63) (64)
Bulgaria I 3.86% 1999-2000 2 1.70% 7.70% (65-67) (68)
Burkina Faso I 8.80% 2010 3 7.74% 10.20% (69), EX (70) 18.2% (71)
Burundi I 4.60% 2002 3 4.30% 5.90% (72) (72) 21.7% EC
Cambodia I 4.60% 2010-2012 2 4.05% 7.7% (73, 74),
EX (74)
Cameroon I 11.90% 2011 3 10.90% 12.80% (75, 76) (75) 28.0% (77)
Canada I 0.67% 2015 2 0.20% 0.80% EC,
(78, 79) (79) 22.3% (80)
Central African
Republic E 11.60% 2010 3 10.20% 13.50% (81), EX (81)
Chad E 12.20% 2012 3 10.1% 14.20% (82, 83),
EX (82)
Chile I 0.15% 2009-2010 3 0.01% 0.30% (84-86) (87)
China E 7.20% 2006 3 6.70% 7.70% (88) (88) 30.0% (88)
Colombia I 0.47% 2014 2 0.15% 5.66% (58, 89, 90) (89)
Costa Rica I 0.20% 1994 1 0.12% 0.23% (58, 91),
EX
EX6,
EC
Côte d'Ivoire I 13.00% 2015-2016 2 5.10% 15.10% (83, 92),
EX (92) 24.32% (93)
Croatia I 0.70% 2010-2011 2 0.5% 1.5% (94-96) EX7
Cuba E 1.05% 1995 1 0.58% 1.22% (58, 97),
EX EX
8
Czech Republic I 0.56% 2001 3 0.05% 0.66% (98), EC,
(99)
EX7,
EC 9.1% (100)
Democratic
People's Republic
of Korea
E 6.50% 2005* 1 -- -- (101) --
Denmark I 0.24% 2007 3 0.23% 0.37% (102) (102) 11% EC
Dominican
Republic E 3.20% 1993 2 0.95% 3.68%
(58, 103),
EX (103)
Egypt I 1.00% 2015 3 1.70% 1.16% (104, 105),
EX (104) 10% EC
El Salvador I 1.06% 2014 1 0.12% 1.20% EC(58),
EX
EX6,
EC
Eritrea E 9.18% 1995 1 -- -- (106) --
Estonia I 2.60% 1995-1997 1 2.29% 3.02% (107), EX EX
9,
EC 30% EC
Ethiopia I 9.40% 2015 3 8.90% 12.00% EC EC 5% EC
Fiji I 2.00% 2009 1 1.80% 2.30% (108), EC (109) 70.6% (110)
Page 25 of 62
Country
Prev.
Est.
Status
HBsAg
Prevalence
Base
Study Year
Data
Quality
Score
HBsAg
Prevalence
Low
HBsAg
Prevalence
High
HBsAg
Prevalence
Source
HBsAg
Age
Source
HBeAg
Prevalence
WoCBA
HBeAg
Prevalence
Source
Finland I 0.23% 2005-2007 1 0.20% 0.27% (111), EX EX10
France I 0.65% 2004 2 0.45% 0.93% (112) (112) 14.4% (113)
Gabon E 9.20% 2007 3 3.1% 10.70% (83, 114),
EX (114) 10.1% (115)
Gambia, The E 5.97% 2007-2008 3 5.30% 6.90% (116), EX (116) 2% (117)
Georgia E 2.9% 2015 3 3.10% 3.51% (118-120) (119)
Germany I 0.30% 2008-2011 3 0.20% 0.80% (121, 122) (121,
123)
Ghana I 12.3% 1995-2015 1 5.1% 10.00% (83, 124,
125) (124) 18.2% (126)
Greece I 2.16% 2014 3 1.36% 2.58% (127, 128) (127) 4.45% (129)
Guatemala I 1.80% 1990 1 0.38% 2.07% (58, 130),
EX
EX6,
EC
Haiti E 2.5% 2012 2 2.8% 5.0% (58, 131-
133) (133)
Hong Kong I 7.80% 2015 3 5.50% 9.05% (134, 135),
EX (134) 27.0% (136)
Hungary E 0.60% 2015* 1 0.50% 0.70% (137) EX7
India I 2.97% 2000-2001 2 2.59% 3.35% (138) (138) 16.1% (139)
Indonesia I 7.10% 2013 3 6.44% 9.40% (140) EX,
(141) (140) 28.0% (142)
Iran I 1.79% 2016 3 1.67% 1.91% (143) (144) 11.0% (145)
Iraq I 3.50% 2015 2 1.60% 4.06% EC, (146),
EX (146)
Ireland E 0.10% 2003 3 0.09% 0.12% (147), EX (147) 9.8% (148)
Israel I 1.75% 2016 3 0.98% 2.70% (149) (149) 9.1% EC
Italy I 0.60% 2010 1 0.26% 0.90% (150) EX (151) 5.0% (152)
Jamaica I 5.30% 1990-1991 1 0.64% 3.20% (58, 153,
154)
EX8,
EC
Japan I 0.65% 2005 2 0.63% 0.67% (155) (155) 19.0% (156)
Jordan I 3.00% 2006-2013 1 0.96% 3.48% (157, 158),
EX
EX11
,
EC 2.33% (159)
Kazakhstan I 3.54% 2016* 3 2.00% 5.20% (160, 161) (161)
Kenya E 2.10% 2007 3 1.40% 3.10% (162) (162) 8.8% (163)
Kiribati I 15.00% 2015 1 7.00% 17.00% (164) (110) 47.8% (110)
Korea, Republic
of I 2.98% 2010 3 2.50% 3.50% (165) (165) 31.8% (166)
Kuwait I 4.80% 2003-2004 2 1.90% 5.57% (167, 168),
EX (168) 7.3% (169)
Kyrgyzstan I 4.70% 2015 2 3.60% 10.30% EC(170,
171) EC 14.4% EC
Laos I 4.10% 2011 2 2.6% 5.5% (172) (172)
Lebanon I 1.69% 2011-2012 3 1.60% 1.89% (173) (174) 6.3% (175)
Libya E 2.20% 2005 3 1.90% 2.60% (176), EX (176) 21.7% (177)
Lithuania I 0.80% 2016 1 -- -- EC --
Madagascar E 6.90% 2011-2013 3 5.60% 8.60% (178) (178) 5.0% (179)
Malawi E 8.20% 1989-2008 1 5.60% 9.40% (180) (180)
Malaysia I 0.62% 2005-2011 1 0.25% 0.72% (158, 181),
EX (181)
Mali E 8.70% 2014 2 7.70% 10.10% (182), EX EX12
6.0% (183)
Mauritania E 15.70% 1983* 1 13.80% 18.20% (184), EX EX12
Mexico I 0.21% 1999-2000 3 0.10% 0.40% (185) (186)
Mongolia I 10.60% 2014* 3 4.25% 12.30% (158, 187),
EX (188)
Morocco I 1.81% 2005-2011 3 0.95% 2.10% (189), EX (189) 6% EC
Mozambique E 11.60% 2009-2011 1 8.40% 14.00% (190, 191) EX2
Page 26 of 62
Country
Prev.
Est.
Status
HBsAg
Prevalence
Base
Study Year
Data
Quality
Score
HBsAg
Prevalence
Low
HBsAg
Prevalence
High
HBsAg
Prevalence
Source
HBsAg
Age
Source
HBeAg
Prevalence
WoCBA
HBeAg
Prevalence
Source
Myanmar E 9.70% 2010* 1 2.42% 10.65% (158, 192),
EX EX
13
Nepal E 0.63% 2005-2014 2 0.63% 0.90% (193, 194) --
Netherlands I 0.20% 2007 3 0.10% 0.50% (195, 196) (195) 8.9% (197)
New Zealand I 2.60% 1999-2002 3 1.71% 5.70% (198), EC (198)
Nicaragua E 1.50% 1990-1992 1 0.18% 1.73% (58, 199),
EX EX
6
Nigeria I 12.20% 2013 2 10.30% 14.50% (200) (200) 6.5% (201)
Norway E 0.50% 1992-2009 2 0.44% 0.61% (202), EX (202)
Oman I 2.50% 2016 1 2.00% 3.00% EC (203) 5% EC
Pakistan I 2.50% 2007-2008 3 1.10% 11.90% (204, 205) (204) 22.0% (206)
Palestine E 4.00% 2012 1 2.00% 6.00% (207) --
Papua New
Guinea E 11.90% 1989 1 10.47% 13.80% (208), EX EX
13 54.55% (209)
Peru I 0.42% 2009* 3 0.33% 0.50% (210-212) EC
Philippines I 16.70% 2003 3 14.3% 19.1% (213) (213) 15.9% (214)
Poland I 1.00% 2010 2 0.63% 1.30% EC, (215)
(216-
222) ,
EC
5.0% (215)
Portugal I 1.45% 2014 1 0.90% 2.00% (223) (223)
Qatar I 1.20% 2016 1 1.06% 1.39% EC, EX EC
Romania I 4.40% 2006-2008 3 4.00% 4.80% (224) (224) 8.9% (225)
Russia I 2.0% 2005-2009 3 0.21% 1.96% (158, 226),
EX EX
5 16% (227)
Rwanda E 5.70% 2009-2011 1 2.90% 7.00% (228-230) (229)
Saudi Arabia I 1.76% 2016 2 1.31% 2.04% EC, (231),
EX
(232-
234),
EC
6.2% (235)
Senegal E 11.00% 1973* 1 9.70% 12.80% (236), EX (236) 9.5% (237)
Singapore E 3.60% 2010 3 2.90% 4.20% (238) (238)
Slovakia I 1.74% 2011 2 2.0% 0.01% (158, 239),
EX EC
Slovenia I 1.00% 2015 1 1.2% 0.02% EC, (158),
EX (240)
Somalia E 18.99% 1992* 2 -- -- (241) --
Spain I 0.70% 2016 3 0.40% 1.00% (242, 243),
EC
(243),
EC 6% (244)
Sudan E 6.93% 2000 1 4.89% 8.98% (245, 246),
EX (245)
Sweden I 0.20% 2015 2 0.06% 0.25% EC, (247),
EX (248) 16.4% (249)
Switzerland I 0.44% 2016 2 0.18% 1.11% (250) (250)
Syria E 5.60% 2004 3 1.69% 6.50% (251, 252),
EX (251)
Taiwan I 13.70% 2002-2007 2 12.90% 17.30% (253, 254) (253) 29.0% (255)
Tajikistan E 8.00% 2015 2 2.90% 5.30% (256) (256)
Tanzania E 6.00% 1992 1 3.90% 8.70% (257) (257)
Thailand E 4.00% 2004 2 0.32% 4.22% (158, 258),
EX (258) 30.0% (259)
Tunisia E 5.30% 1996 2 4.80% 5.80% (260) (260) 4.3% (261)
Turkey I 4.00% 2010 3 3.49% 6.11% (262-264) (262) 12.5% (265)
Turkmenistan E 15.60% 1995 1 12.44% 17.66% (266), EX (267)
Uganda I 10.30% 2005 3 8.33% 11.10% (268), EX (268) 16.9% (269, 270)
Ukraine E 1.80% 2006-2008 2 0.87% 1.80% (271, 272) --
United Arab
Emirates I 3.70% 1997-1998 2 0.23% 4.29%
(273, 274),
EX EX
14
Page 27 of 62
Country
Prev.
Est.
Status
HBsAg
Prevalence
Base
Study Year
Data
Quality
Score
HBsAg
Prevalence
Low
HBsAg
Prevalence
High
HBsAg
Prevalence
Source
HBsAg
Age
Source
HBeAg
Prevalence
WoCBA
HBeAg
Prevalence
Source
United Kingdom I 0.69% 2013 2 0.4% 1.27% (275, 276),
EC (275) 15.0% (277)
United States of
America I 0.30% 2007-2012 3 0.20% 0.40% (278), EC (278) 21.2% (30)
Uzbekistan I 8.10% 2016 2 3.05% 13.30% (267, 279) (279) 13.9% (280)
Venezuela E 1.80% 1993 1 1.62% 4.00% (281-283) EX15
Viet Nam I 10.0% 2012 3 7.8% 12.5% (284-288),
EC (284) 36.8% (287, 289)
Yemen E 4.20% 2008 1 1.49% 10.80% (158, 290,
291) (290)
Zambia E 5.90% 2008* 2 5.19% 6.84% (292), EX EX2 16.1% (293)
Zimbabwe E 15.40% 1989-1991 2 13.55% 17.86% (294), EX (294) 25.0% (294)
* Study year unavailable. Used publication year minus two; ** Estimate adjusted for total population; 1 Extrapolated from Libya; 2 Extrapolated from Madagascar; 3 Extrapolated from Georgia; 4 Extrapolated from Uzbekistan; 5 Extrapolated from Poland; 6
Extrapolated from Mexico; 7 Extrapolated from Slovak Republic; 8 Extrapolated from Dominican Republic; 9 Extrapolated from
Sweden; 10 Extrapolated from Netherlands; 11 Extrapolated from Lebanon; 12 Extrapolated from Senegal; 13 Extrapolated from
Indonesia; 14
Extrapolated from Saudi Arabia; 15
Extrapolated from Brazil I = Expert Input: received feedback on inputs and
model outputs from country experts; E = Estimated: prevalence modeled & estimated using published data; EC = Expert
Consensus; HBeAg = Hepatitis B e Antigen; WoCBA = Women of Child Bearing Age
Page 28 of 62
Table 6. Treated and diagnosed, 2016
Country
On
Antiviral
Treatment
2016
Treated
Source
Type
Treated
Source
Total
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Newly
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Albania 186 PR*
(295),
CDA
Estimate
7,149 PS, PR (38, 295) 315 PR (295)
Algeria 900 PS (296) 39,146 NS, PR (296, 297) 1,200 PS (296)
Angola 829 EX CDA
Estimate 1,089 EX
CDA
Estimate 101 EX
CDA
Estimate
Argentina 76 EC EC 16,322 EC EC 1,000 EC EC
Armenia 20 GSD (298) 5,270 WHO (299) 58 WHO (299)
Australia 15,059 NS (48) 144,231 NS (48) 6,640 NS (300)
Azerbaijan 61 GSD (298) 11,827 WHO (299) 200 WHO (299)
Bahrain 0 EX CDA
Estimate 448 NS (51) 15 NS (51)
Bangladesh 536 GSD (298) 2,433 PS (301) 453 PS (301)
Belarus 5 GSD (298) 18,983 WHO (299) 69 WHO (299)
Belgium 478 PS* (302) 29,323 PS (302) 97 NS (299)
Belize 5 GSD (298) 421 NS (62, 85,
303-306) 12 NS
(62, 85,
303-306)
Brazil 22,456 NS (307) 212,031 NS (308) 16,319 NS (308)
Bulgaria 1,600 EC EC 29,230 PR, WHO (309) (299) 322 (299) (299)
Burkina Faso 637 EX CDA
Estimate 4,362 NS (310) 238 NS (310)
Burundi 3,689 EC EC 8,226 NS (311-313) 950 NS (311)
Cambodia 1,500 EC,
GSD (298) 25,900 EC EC 640 NS, EC EC
Cameroon 964 GSD (298) 23,428 PS (314-321) 1,495 NS, PS (314, 316-
323)
Canada 18,000 EC EC 134,001 EC EC 5,341 NS (324)
Central
African
Republic
149 EX CDA
Estimate 213 EX
CDA
Estimate 17 EX
CDA
Estimate
Chad 535 EX CDA
Estimate 4,039 EX
CDA
Estimate 311 EX
CDA
Estimate
Chile 46 EX CDA
Estimate 22,729 NS
(62, 85,
303-306) 29 NS
(62, 85,
303-306)
China 3,500,000 PR (325) 16,085,456 NS, WHO (325, 326) 1,330,654 NS (325)
Colombia 292 CDA
Estimate
CDA
Estimate 57,088 NS
(62, 85,
303-306,
327, 328)
2,000 NS
(62, 85,
303-306,
327, 328)
Costa Rica 22 EX CDA
Estimate 1,347 NS
(62, 85,
303-306) 89 NS
(62, 85,
303-306)
Côte d'Ivoire 1,205 GSD (298) 73,882 PS (329-331) 3,675 NS (332)
Croatia 400 EC EC 9,000 NS (333) 129 NS (333)
Cuba 178 EX CDA
Estimate 36,581 NS
(62, 85,
303-306) 2,099 NS
(62, 85,
303-306)
Czech
Republic 1,500 EC EC 9,523 EC EC 5 WHO (299)
Denmark 750 EC EC 8,200 PS (334) 302 PS (334)
Page 29 of 62
Country
On
Antiviral
Treatment
2016
Treated
Source
Type
Treated
Source
Total
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Newly
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Dominican
Republic 603 GSD (298) 15,466 NS
(62, 85,
303-306) 1,141 NS
(62, 85,
303-306)
Egypt 2,250 EC EC 25,000 EC EC 3,500 EC EC
El Salvador 143 EC EC 3,182 PS
(62, 85,
303-306,
335)
193 PS
(62, 85,
303-306,
336)
Estonia 54 NS (337) 323 NS (338) 17 NS (338)
Ethiopia 840 EC EC 778,000 EC* EC 400 EC EC
Fiji 3 GSD (298) 382 EC EC 20 EX CDA
Estimate
Finland 2,743 EC EC 7,000 EC EC 390 NS (339)
France 19,000 EC EC 70,033 PS* (340) 3,700 PS (341)
Gabon 150 EX CDA
Estimate 1,961 PS* (342) 485 EX
CDA
Estimate
Gambia, The 20 EX CDA
Estimate 319 EX
CDA
Estimate 17 EX
CDA
Estimate
Georgia 50 GSD (298) 16,038 WHO (299) 1,013 WHO (299)
Germany 20,000 EC EC 67,001 EC EC 500 EC EC
Ghana 800 EX CDA
Estimate 14,465 PS (343-350) 1,600 PS (343, 345)
Greece 23,249 PS* (127, 128) 83,635 PS* (127, 128) 1,800 EC EC
Guatemala 1,273 GSD (298) 9,195 PS (62, 85,
303-306) 220 PS
(62, 85,
303-306)
Haiti 120 GSD (298) 10,244 PS (62, 85,
303-306) 967 PS
(62, 85,
303-306)
Hong Kong 36,644 PS*, EC (134), EC 124,852 EC EC 5,433 EC EC
Hungary 242 EX CDA
Estimate 1,582 WHO (299) 62 NS (351)
India 4,671 EC EC 518,988 NS (352) 42,611 EX CDA
Estimate
Indonesia 21,468 GSD (298) 413,949 NS, EC (353) 42,198 NS, EC (353)
Iran 18,000 EC EC 339,058 EC EC 12,085 EC EC
Iraq 8 EX CDA
Estimate 6,385 PS (354-358) 524 EX
CDA
Estimate
Ireland 57 EX CDA
Estimate 2,406 WHO (299) 36 NS (359)
Israel 1,598 PR (149, 360) 20,077 PR (149, 360) 1,400 PR (149, 360)
Italy 35,000 EC EC 97,086 EC EC 342 NS (361)
Jamaica 89 EX EX 316 PR (362) 16 PR (362)
Japan 70,000 NS EC 516,171 NS EC 2,000 EC EC
Jordan 680 EC EC 2,143 PS, NS (363-366) 200 EC EC
Kazakhstan 127 GSD (298) 58,486 WHO (299) 172 WHO (299)
Kenya 60 EX CDA
Estimate 361 EX
CDA
Estimate 33 EX
CDA
Estimate
Kiribati 0 EC EC 73 WHO (326) 5 WHO (326)
Page 30 of 62
Country
On
Antiviral
Treatment
2016
Treated
Source
Type
Treated
Source
Total
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Newly
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Republic of
Korea 230,000 EC EC 1,027,923 EC EC 94,254 EX
CDA
Estimate
Kuwait 1 EX CDA
Estimate 5,548 EX
CDA
Estimate 517 EX
CDA
Estimate
Kyrgyzstan 1,117 EC EC 49,462 EC EC 2,700 EC EC
Laos 173 EX Analog -
Singapore 4,062 PS (367) 1,377 PS (367)
Lebanon 935 NS* (368) 2,610 NS* (368) 560 NS* (368)
Libya 585 EX Analog -
Algeria 14,951 EX
CDA
Estimate 627 EX
CDA
Estimate
Lithuania 200 EC EC - - - 50 EC EC
Madagascar 144 EX CDA
Estimate 2,699 PS, NS* (369) 181 EX
CDA
Estimate
Malawi 57 EX CDA
Estimate 378 EX
CDA
Estimate 35 EX
CDA
Estimate
Malaysia 5,298 EC EC 25,868 EC EC 5,000 NS (370)
Mali 354 EX CDA
Estimate 121,849 NS, PS (371, 372) 5,495 NS, PS (371, 372)
Mauritania 143 EX CDA
Estimate 2,277 PS* (373) 117 EX
CDA
Estimate
Mexico 317 EX CDA
Estimate 12,528 NS*
(62, 85,
303-306),
CDA
Adjustment
760 NS* (62, 85,
303-306)
Mongolia 1,240 GSD (298) 16,880 NS, WHO (326, 374)
747 WHO (326)
Morocco 14,400 EC EC 150,000 EC EC 462 PS (375)
Mozambique 231 EX CDA
Estimate 1,392 EX
CDA
Estimate 129 EX
CDA
Estimate
Myanmar 175 GSD (298) 5,869 PS (376) 503 PS (376)
Netherlands 5,500 EC EC 22,522 NS (377) 1,074 NS (378)
New Zealand 10,000 EC EC 67,000 EC EC 1,000 EC EC
Nicaragua 191 GSD (298) 3,159 NS (62, 85,
303-306) 153 NS
(62, 85,
303-306)
Nigeria 5,140 EX CDA
Estimate 43,472 PS (379-389) 4,211 EX
CDA
Estimate
Norway 215 EX CDA
Estimate 3,237 WHO (299) 30 WHO (299)
Oman 700 EC EC 6,690 NS, PS (390-392) 409 EX CDA
Estimate
Pakistan 26,405 DS (393) 126,329 EC EC 10,652 EC EC
Papua New
Guinea 3 EX
CDA
Estimate 428,684 WHO (326) 39,450 EX
CDA
Estimate
Peru 305 EC* EC 12,362 NS (62, 85,
303-306) 1,191 EC EC
Philippines 59 EC EC 342,034 EC EC 58,000 EC EC
Poland 7,478 EC EC 200,000 EC EC 1,650 EC EC
Portugal 1,800 EC EC 7,865 NS (394) 47 NS (394)
Qatar 846 EC EC 5,470 NS,PS (395-397) 405 EC, PR (395, 398),
EC
Romania 7 EX CDA
Estimate 66,346 NS (299) 284 NS (299)
Page 31 of 62
Country
On
Antiviral
Treatment
2016
Treated
Source
Type
Treated
Source
Total
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Newly
Diagnosed
2016
Diagnosed
Source
Type
Diagnosed
Source
Russia 3,000 NS EC 547,000 PS (399) 1,637 PS (399)
Rwanda 43 EX CDA
Estimate 784 NS (400) 61 EX
CDA
Estimate
Saudi Arabia 2 EX CDA
Estimate 30,000 EC EC 3,000 EC EC
Senegal 961 GSD (298) 50,486 PS (401, 402) 2,325 PS (401)
Singapore 711 EX CDA
Estimate 1,523 NS, WHO (326, 403) 48 NS (403)
Slovakia 660 EC EC 5,989 NS (404) 370 NS (404)
Slovenia 878 EC EC 1,084 PS (405) 12 PS (405)
Spain 12,494 DS (393) 42,999 EX CDA
Estimate 520 EX
CDA
Estimate
Sudan 13 EX CDA
Estimate 112,775 EX
CDA
Estimate 10,428 EX
CDA
Estimate
Sweden 1,695 NS, EC EC (406) 16,760 NS, EC (407) 1,957 NS* (407)
Switzerland 2,206 NS (408) 28,740 NS* (408) 1,360 NS (408)
Syria 5 EX CDA
Estimate 49,348 EX
CDA
Estimate 4,555 EX
CDA
Estimate
Taiwan 228,499 EC EC 1,395,678 EC EC 31,200 EX CDA
Estimate
Tajikistan
