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      Lifetime burden of disease due to incident tuberculosis: a global reappraisal including post-tuberculosis sequelae

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          Summary

          Background

          Many individuals who survive tuberculosis disease face ongoing disability and elevated mortality risks. However, the impact of post-tuberculosis sequelae is generally omitted from policy analyses and disease burden estimates. We therefore estimated the global burden of tuberculosis, inclusive of post-tuberculosis morbidity and mortality.

          Methods

          We constructed a hypothetical cohort of individuals developing tuberculosis in 2019, including pulmonary and extrapulmonary disease. We simulated lifetime health outcomes for this cohort, stratified by country, age, sex, HIV status, and treatment status. We used disability-adjusted life-years (DALYs) to summarise fatal and non-fatal health losses attributable to tuberculosis, during the disease episode and afterwards. We estimated post-tuberculosis mortality and morbidity based on the decreased lung function caused by pulmonary tuberculosis disease.

          Findings

          Globally, we estimated 122 (95% uncertainty interval [UI] 98–151) million DALYs due to incident tuberculosis disease in 2019, with 58 (38–83) million DALYs attributed to post-tuberculosis sequelae, representing 47% (95% UI 37–57) of the total burden estimate. The increase in burden from post-tuberculosis varied substantially across countries and regions, driven largely by differences in estimated case fatality for the disease episode. We estimated 12·1 DALYs (95% UI 10·0–14·9) per incident tuberculosis case, of which 6·3 DALYs (5·6–7·0) were from the disease episode and 5·8 DALYs (3·8–8·3) were from post-tuberculosis. Per-case post-tuberculosis burden estimates were greater for younger individuals, and in countries with high incidence rates. The burden of post-tuberculosis was spread over the remaining lifetime of tuberculosis survivors, with almost a third of total DALYs (28%, 95% UI 23–34) accruing 15 or more years after incident tuberculosis.

          Interpretation

          Post-tuberculosis sequelae add substantially to the overall disease burden caused by tuberculosis. This hitherto unquantified burden has been omitted from most previous policy analyses. Future policy analyses and burden estimates should take better account of post-tuberculosis, to avoid the potential misallocation of funding, political attention, and research effort resulting from continued neglect of this issue.

          Funding

          National Institutes of Health.

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          Most cited references29

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          Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

          Summary Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation.
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            Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

            Summary Background Chronic obstructive pulmonary disease (COPD) and asthma are common diseases with a heterogeneous distribution worldwide. Here, we present methods and disease and risk estimates for COPD and asthma from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) 2015 study. The GBD study provides annual updates on estimates of deaths, prevalence, and disability-adjusted life years (DALYs), a summary measure of fatal and non-fatal disease outcomes, for over 300 diseases and injuries, for 188 countries from 1990 to the most recent year. Methods We estimated numbers of deaths due to COPD and asthma using the GBD Cause of Death Ensemble modelling (CODEm) tool. First, we analysed data from vital registration and verbal autopsy for the aggregate category of all chronic respiratory diseases. Subsequently, models were run for asthma and COPD relying on covariates to predict rates in countries that have incomplete or no vital registration data. Disease estimates for COPD and asthma were based on systematic reviews of published papers, unpublished reports, surveys, and health service encounter data from the USA. We used the Global Initiative of Chronic Obstructive Lung Disease spirometry-based definition as the reference for COPD and a reported diagnosis of asthma with current wheeze as the definition of asthma. We used a Bayesian meta-regression tool, DisMod-MR 2.1, to derive estimates of prevalence and incidence. We estimated population-attributable fractions for risk factors for COPD and asthma from exposure data, relative risks, and a theoretical minimum exposure level. Results were stratified by Socio-demographic Index (SDI), a composite measure of income per capita, mean years of education over the age of 15 years, and total fertility rate. Findings In 2015, 3·2 million people (95% uncertainty interval [UI] 3·1 million to 3·3 million) died from COPD worldwide, an increase of 11·6% (95% UI 5·3 to 19·8) compared with 1990. There was a decrease in age-standardised death rate of 41·9% (37·7 to 45·1) but this was counteracted by population growth and ageing of the global population. From 1990 to 2015, the prevalence of COPD increased by 44·2% (41·7 to 46·6), whereas age-standardised prevalence decreased by 14·7% (13·5 to 15·9). In 2015, 0·40 million people (0·36 million to 0·44 million) died from asthma, a decrease of 26·7% (−7·2 to 43·7) from 1990, and the age-standardised death rate decreased by 58·8% (39·0 to 69·0). The prevalence of asthma increased by 12·6% (9·0 to 16·4), whereas the age-standardised prevalence decreased by 17·7% (15·1 to 19·9). Age-standardised DALY rates due to COPD increased until the middle range of the SDI before reducing sharply. Age-standardised DALY rates due to asthma in both sexes decreased monotonically with rising SDI. The relation between with SDI and DALY rates due to asthma was attributed to variation in years of life lost (YLLs), whereas DALY rates due to COPD varied similarly for YLLs and years lived with disability across the SDI continuum. Smoking and ambient particulate matter were the main risk factors for COPD followed by household air pollution, occupational particulates, ozone, and secondhand smoke. Together, these risks explained 73·3% (95% UI 65·8 to 80·1) of DALYs due to COPD. Smoking and occupational asthmagens were the only risks quantified for asthma in GBD, accounting for 16·5% (14·6 to 18·7) of DALYs due to asthma. Interpretation Asthma was the most prevalent chronic respiratory disease worldwide in 2015, with twice the number of cases of COPD. Deaths from COPD were eight times more common than deaths from asthma. In 2015, COPD caused 2·6% of global DALYs and asthma 1·1% of global DALYs. Although there are laudable international collaborative efforts to make surveys of asthma and COPD more comparable, no consensus exists on case definitions and how to measure disease severity for population health measurements like GBD. Comparisons between countries and over time are important, as much of the chronic respiratory burden is either preventable or treatable with affordable interventions. Funding Bill & Melinda Gates Foundation.
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              Quantifying the burden of disease: the technical basis for disability-adjusted life years.

              C. Murray (1994)
              Detailed assumptions used in constructing a new indicator of the burden of disease, the disability-adjusted life year (DALY), are presented. Four key social choices in any indicator of the burden of disease are carefully reviewed. First, the advantages and disadvantages of various methods of calculating the duration of life lost due to a death at each age are discussed. DALYs use a standard expected-life lost based on model life-table West Level 26. Second, the value of time lived at different ages is captured in DALYs using an exponential function which reflects the dependence of the young and the elderly on adults. Third, the time lived with a disability is made comparable with the time lost due to premature mortality by defining six classes of disability severity. Assigned to each class is a severity weight between 0 and 1. Finally, a three percent discount rate is used in the calculation of DALYs. The formula for calculating DALYs based on these assumptions is provided.
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                Author and article information

                Contributors
                Journal
                Lancet Glob Health
                Lancet Glob Health
                The Lancet. Global Health
                Elsevier Ltd
                2214-109X
                16 November 2021
                December 2021
                16 November 2021
                : 9
                : 12
                : e1679-e1687
                Affiliations
                [a ]Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
                [b ]Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA
                [c ]TB Modelling Group, TB Centre, London School of Hygiene & Tropical Medicine, London, UK
                [d ]Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
                [e ]Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
                [f ]Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK
                [g ]Division of Pulmonology, Department of Medicine, Stellenbosch University, Stellenbosch, South Africa
                [h ]DSI-NRF South African Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
                [i ]Heart Lung Clinic, St Vincent's Hospital, Sydney, NSW, Australia
                [j ]Faculty of Medicine, St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
                [k ]Socios En Salud Sucursal Peru, Partners In Health, Lima, Peru
                [l ]Infectious Diseases and Immune Defence Division, The Walter & Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
                [m ]Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Observatory WC, South Africa
                [n ]Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
                [o ]International Union Against Tuberculosis and Lung Disease, Paris, France
                [p ]Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
                [q ]Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
                [r ]Department of Medicine, Stanford University, Palo Alto CA, USA
                [s ]Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
                [t ]The Aurum Institute, Parktown, Johannesburg, South Africa
                [u ]Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
                [v ]Department of Medicine, Case Western Reserve University, Cleveland, OH, USA
                [w ]Department of Medicine, Rutgers University, Newark, NJ, USA
                [x ]Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
                Author notes
                [* ]Correspondence to: Dr Nicolas A Menzies, Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA 02115, USA nmenzies@ 123456hsph.harvard.edu
                Article
                S2214-109X(21)00367-3
                10.1016/S2214-109X(21)00367-3
                8609280
                34798027
                9c6e49d0-a99a-4a62-830d-c171646a9a92
                © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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