Global Burden of Liver Cancer Estimation

General methods for the GBD 2015 study have been published previously. Herein, we present methods pertaining to the liver cancer estimation. Descriptions of the estimation process are available in the eAppendix in Supplement 1 (link, no link found) (eFigure 1, eFigure 2, and eTable 1).
The estimation process starts with liver cancer mortality, which we estimated using vital registration system data and cancer registry incidence data that were transformed to mortality estimates using separately modeled mortality-to-incidence ratios. Data were processed to adjust for aggregated causes, age groups, or uninformative causes of death. Liver cancer mortality was modeled by developing a large set of plausible models using different model types and combinations of covariates, that were tested using out-of-sample predictive validity (eTable 3 and eTable 4 in Supplement 1 (link, link, no link found)). The 2.5% and 97.5% quantiles from 1000 draws of the posterior distribution were used to generate 95% uncertainty intervals (UI). Liver cancer mortality was scaled with other causes of death to sum to 100% of the demographic estimates of all-cause mortality. Years of life lost were calculated by multiplying each death by the standard life expectancy. To generate mortality estimates for 4 liver cancer etiologies, proportions of liver cancer due to different causes were identified in a systematic review (eTable 5 in Supplement 1 (link, link, link, no link found)). Cases were attributed to HBV, HCV, alcohol, and other causes, which include remaining etiologies like liver flukes, nonalcoholic steatohepatitis, and aflatoxins. To estimate proportions for all locations, by sex, and over time, models were generated using DisMod-MR 2.1, a Bayesian meta-regression model (eAppendix in Supplement 1). Liver cancer mortality estimates were split into etiologies using the modeled proportions. Liver cancer incidence was estimated by dividing mortality by mortality-to-incidence ratios. Survival was estimated based on a theoretical best and worst liver cancer survival and a scaling factor derived from age-standardized mortality-to-incidence ratios. Prevalence was calculated using incidence and survival estimates and divided into 4 phases reflecting changing disability during: (1) diagnosis and treatment; (2) remission; (3) disseminated; and (4) terminal phase. Prevalence for each phase was multiplied by distinct disability weights to generate years lived with disability (eTable 6 in Supplement 1 (no link found, link, no link found)). The sum of years of life lost and years lived with disability represents DALYs. One DALY can be interpreted as 1 lost year of “healthy life.”
To group countries with similar development status, a Sociodemographic Index (SDI) was used, which combines total fertility rate, average educational attainment in the population over age 15, and measures of income per capita (eFigure 3 and eTable 7 in the Supplement 1).To assess the contribution of demographic vs epidemiological changes, we decomposed trends into 3 components—population aging, growth, and change in age-specific rates. Rates are reported as mean per 100 000 person-years with 95% UI in parentheses. Age-standardized rates were computed using the GBD population standard.

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Akinyemiju T., Abera S., Ahmed M., Alam N., Alemayohu M.A., Allen C., Al-Raddadi R., Alvis-Guzman N., Amoako Y., Artaman A., Ayele T.A., Barac A., Bensenor I., Berhane A., Bhutta Z., Castillo-Rivas J., Chitheer A., Choi J.Y., Cowie B., Dandona L., Dandona R., Dey S., Dicker D., Phuc H., Ekwueme D.U., Zaki M.E., Fischer F., Fürst T., Hancock J., Hay S.I., Hotez P., Jee S.H., Kasaeian A., Khader Y., Khang Y.H., Kumar G.A., Kutz M., Larson H., Lopez A., Lunevicius R., Malekzadeh R., McAlinden C., Meier T., Mendoza W., Mokdad A., Moradi-Lakeh M., Nagel G., Nguyen Q., Nguyen G., Ogbo F., Patton G., Pereira D.M., Pourmalek F., Qorbani M., Radfar A., Roshandel G., Salomon J.A., Sanabria J., Sartorius B., Satpathy M., Sawhney M., Sepanlou S., Shackelford K., Shore H., Sun J., Mengistu D.T., Topór-Mądry R., Tran B., Ukwaja K.N., Vlassov V., Vollset S.E., Vos T., Wakayo T., Weiderpass E., Werdecker A., Yonemoto N., Younis M., Yu C., Zaidi Z., Zhu L., Murray C.J., Naghavi M, & Fitzmaurice C. (2017). The Burden of Primary Liver Cancer and Underlying Etiologies From 1990 to 2015 at the Global, Regional, and National Level: Results From the Global Burden of Disease Study 2015. JAMA Oncology, 3(12), 1683-1691.

Publication 2017

Aflatoxins Age groups Alcohol Cancer Capita Diagnosis Disability Liver cancer Liver flukes Nonalcoholic steatohepatitis Supplement Worst

Corresponding Organization : University of Washington

Other organizations : University of Alabama at Birmingham, Mekelle University, University of Hohenheim, Jimma University, Queensland Government, University of Queensland, Ministry of Health, University of Cartagena, Komfo Anokye Teaching Hospital, University of Manitoba, University of Gondar, Center for Health, Exercise and Sport Sciences, Hospital Universitário da Universidade de São Paulo, Debre Berhan University, Aga Khan University, Hospital for Sick Children, SickKids Foundation, Costa Rican Department of Social Security, Ministry of Health, Seoul National University, Peter Doherty Institute, Public Health Foundation of India, Indian Institute of Public Health Gandhinagar, Duy Tan University, Centers for Disease Control and Prevention, Mansoura University, Bielefeld University, Swiss Tropical and Public Health Institute, University of Basel, Imperial College London, Baylor College of Medicine, Texas Children's Hospital, Sabin Vaccine Institute, Yonsei University, Tehran University of Medical Sciences, Jordan University of Science and Technology, London School of Hygiene & Tropical Medicine, Melbourne Health, University of Melbourne, Aintree University Hospitals NHS Foundation Trust, University of Liverpool, Bristol Hospital, Martin Luther University Halle-Wittenberg, Iran University of Medical Sciences, Universität Ulm, Zimmer Biomet (Germany), Western Sydney University, Ingham Institute, Rede de Química e Tecnologia, Universidade do Porto, University of British Columbia, Jahrom University of Medical Sciences, A.T. Still University, Golestan University, Golestan University of Medical Sciences, Harvard Global Health Institute, Marshall University, Case Western Reserve University, University of KwaZulu-Natal, Utkal University, All India Institute of Medical Sciences, Haramaya University, Queensland University of Technology, Jagiellonian University, Wroclaw Medical University, Hanoi Medical University, Johns Hopkins University, Federal Teaching Hospital Abakaliki, National Research University Higher School of Economics, Norwegian Institute of Public Health, University of Bergen, Cancer Registry of Norway, UiT The Arctic University of Norway, Karolinska Institutet, Federal Institute for Population Research, Kyoto University, Jackson State University, Harvard University, Wuhan University, Centre Hospito Universitaire de Sétif, University Ferhat Abbas of Setif, Jiangsu Provincial Center for Disease Control and Prevention

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Variable analysis

independent variables

Different model types and combinations of covariates used to model liver cancer mortality

dependent variables

Liver cancer mortality
Years of life lost
Proportions of liver cancer due to different causes (HBV, HCV, alcohol, other causes)
Liver cancer incidence
Liver cancer survival
Liver cancer prevalence
Years lived with disability
Disability-adjusted life years (DALYs)

control variables

Demographic estimates of all-cause mortality used to scale liver cancer mortality
Standard life expectancy used to calculate years of life lost
Theoretical best and worst liver cancer survival used to estimate survival
Age-standardized mortality-to-incidence ratios used to derive scaling factor for survival estimation
Disability weights used to calculate years lived with disability
Sociodemographic Index (SDI) used to group countries with similar development status

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