Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

GBD 2016 Lower Respiratory Infecti, Christopher Troeger, Brigette F. Blacker, Ibrahim A. Khalil, Puja C. Rao, Shujin Cao, Stephanie R. M. Zimsen, Sam Albertson, Jeffrey D. Stanaway, Aniruddha Deshpande, Tamer Farag, Mohammad H. Forouzanfar, Zegeye Abebe, Ifedayo Morayo O. Adetifa, Tara Ballav Adhikari, Mohammed Akibu, Faris Hasan Al Lami, Ayman Al-Eyadhy, Nelson Alvis-Guzman, Azmeraw T. AmareYaw Ampem Amoako, Carl Abelardo T. Antonio, Olatunde Aremu, Ephrem Tsegay Asfaw, Solomon Weldegebreal Asgedom, Tesfay Mehari Atey, Engi Farouk Attia, Euripide Frinel G. Arthur Avokpaho, Henok Tadesse Ayele, Tambe Betrand Ayuk, Kalpana Balakrishnan, Aleksandra Barac, Quique Bassat, Masoud Behzadifar, Meysam Behzadifar, Soumyadeep Bhaumik, Zulfiqar A. Bhutta, Ali Bijani, Michael Brauer, Alexandria Brown, Paulo A. M. Camargos, Carlos A. Castaneda-Orjuela, Danny Colombara, Sara Conti, Abel Fekadu Dadi, Lalit Dandona, Rakhi Dandona, Huyen Phuc Do, Eleonora Dubljanin, Dumessa Edessa, Maarten J. Postma

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Background Lower respiratory infections are a leading cause of morbidity and mortality around the world. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, provides an up-to-date analysis of the burden of lower respiratory infections in 195 countries. This study assesses cases, deaths, and aetiologies spanning the past 26 years and shows how the burden of lower respiratory infection has changed in people of all ages.

Methods We used three separate modelling strategies for lower respiratory infections in GBD 2016: a Bayesian hierarchical ensemble modelling platform (Cause of Death Ensemble model), which uses vital registration, verbal autopsy data, and surveillance system data to predict mortality due to lower respiratory infections; a compartmental meta-regression tool (DisMod-MR), which uses scientific literature, population representative surveys, and healthcare data to predict incidence, prevalence, and mortality; and modelling of counterfactual estimates of the population attributable fraction of lower respiratory infection episodes due to Streptococcus pneumoniae, Haemophilus influenzae type b, influenza, and respiratory syncytial virus. We calculated each modelled estimate for each age, sex, year, and location. We modelled the exposure level in a population for a given risk factor using DisMod-MR and a spatiotemporal Gaussian process regression, and assessed the effectiveness of targeted interventions for each risk factor in children younger than 5 years. We also did a decomposition analysis of the change in LRI deaths from 2000-16 using the risk factors associated with LRI in GBD 2016.

Findings In 2016, lower respiratory infections caused 652 572 deaths (95% uncertainty interval [UI] 586 475-720 612) in children younger than 5 years (under-5s), 1 080 958 deaths (943 749-1 170 638) in adults older than 70 years, and 2 377 697 deaths (2145 584-2 512 809) in people of all ages, worldwide. Streptococcus pneumoniae was the leading cause of lower respiratory infection morbidity and mortality globally, contributing to more deaths than all other aetiologies combined in 2016 (1189 937 deaths, 95% UI 690 445-1 770 660). Childhood wasting remains the leading risk factor for lower respiratory infection mortality among children younger than 5 years, responsible for 61.4% of lower respiratory infection deaths in 2016 (95% UI 45.7-69.6). Interventions to improve wasting, household air pollution, ambient particulate matter pollution, and expanded antibiotic use could avert one under-5 death due to lower respiratory infection for every 4000 children treated in the countries with the highest lower respiratory infection burden.

Interpretation Our findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults. By highlighting regions and populations with the highest burden, and the risk factors that could have the greatest effect, funders, policy makers, and programme implementers can more effectively reduce lower respiratory infections among the world's most susceptible populations. Copyright (C) The Author(s). Published by Elsevier Ltd.

Original languageEnglish
Pages (from-to)1191-1210
Number of pages20
JournalLancet Infectious Diseases
Issue number11
Early online date19-Sep-2018
Publication statusPublished - Nov-2018


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