34 research outputs found
Spatial, temporal, and demographic patterns in prevalence of chewing tobacco use in 204 countries and territories, 1990-2019 : a systematic analysis from the Global Burden of Disease Study 2019
Interpretation Chewing tobacco remains a substantial public health problem in several regions of the world, and predominantly in south Asia. We found little change in the prevalence of chewing tobacco use between 1990 and 2019, and that control efforts have had much larger effects on the prevalence of smoking tobacco use than on chewing tobacco use in some countries. Mitigating the health effects of chewing tobacco requires stronger regulations and policies that specifically target use of chewing tobacco, especially in countries with high prevalence. Findings In 2019, 273 center dot 9 million (95% uncertainty interval 258 center dot 5 to 290 center dot 9) people aged 15 years and older used chewing tobacco, and the global age-standardised prevalence of chewing tobacco use was 4 center dot 72% (4 center dot 46 to 5 center dot 01). 228 center dot 2 million (213 center dot 6 to 244 center dot 7; 83 center dot 29% [82 center dot 15 to 84 center dot 42]) chewing tobacco users lived in the south Asia region. Prevalence among young people aged 15-19 years was over 10% in seven locations in 2019. Although global agestandardised prevalence of smoking tobacco use decreased significantly between 1990 and 2019 (annualised rate of change: -1 center dot 21% [-1 center dot 26 to -1 center dot 16]), similar progress was not observed for chewing tobacco (0 center dot 46% [0 center dot 13 to 0 center dot 79]). Among the 12 highest prevalence countries (Bangladesh, Bhutan, Cambodia, India, Madagascar, Marshall Islands, Myanmar, Nepal, Pakistan, Palau, Sri Lanka, and Yemen), only Yemen had a significant decrease in the prevalence of chewing tobacco use, which was among males between 1990 and 2019 (-0 center dot 94% [-1 center dot 72 to -0 center dot 14]), compared with nine of 12 countries that had significant decreases in the prevalence of smoking tobacco. Among females, none of these 12 countries had significant decreases in prevalence of chewing tobacco use, whereas seven of 12 countries had a significant decrease in the prevalence of tobacco smoking use for the period. Summary Background Chewing tobacco and other types of smokeless tobacco use have had less attention from the global health community than smoked tobacco use. However, the practice is popular in many parts of the world and has been linked to several adverse health outcomes. Understanding trends in prevalence with age, over time, and by location and sex is important for policy setting and in relation to monitoring and assessing commitment to the WHO Framework Convention on Tobacco Control. Methods We estimated prevalence of chewing tobacco use as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 using a modelling strategy that used information on multiple types of smokeless tobacco products. We generated a time series of prevalence of chewing tobacco use among individuals aged 15 years and older from 1990 to 2019 in 204 countries and territories, including age-sex specific estimates. We also compared these trends to those of smoked tobacco over the same time period. Findings In 2019, 273 & middot;9 million (95% uncertainty interval 258 & middot;5 to 290 & middot;9) people aged 15 years and older used chewing tobacco, and the global age-standardised prevalence of chewing tobacco use was 4 & middot;72% (4 & middot;46 to 5 & middot;01). 228 & middot;2 million (213 & middot;6 to 244 & middot;7; 83 & middot;29% [82 & middot;15 to 84 & middot;42]) chewing tobacco users lived in the south Asia region. Prevalence among young people aged 15-19 years was over 10% in seven locations in 2019. Although global age standardised prevalence of smoking tobacco use decreased significantly between 1990 and 2019 (annualised rate of change: -1 & middot;21% [-1 & middot;26 to -1 & middot;16]), similar progress was not observed for chewing tobacco (0 & middot;46% [0 & middot;13 to 0 & middot;79]). Among the 12 highest prevalence countries (Bangladesh, Bhutan, Cambodia, India, Madagascar, Marshall Islands, Myanmar, Nepal, Pakistan, Palau, Sri Lanka, and Yemen), only Yemen had a significant decrease in the prevalence of chewing tobacco use, which was among males between 1990 and 2019 (-0 & middot;94% [-1 & middot;72 to -0 & middot;14]), compared with nine of 12 countries that had significant decreases in the prevalence of smoking tobacco. Among females, none of these 12 countries had significant decreases in prevalence of chewing tobacco use, whereas seven of 12 countries had a significant decrease in the prevalence of tobacco smoking use for the period. Interpretation Chewing tobacco remains a substantial public health problem in several regions of the world, and predominantly in south Asia. We found little change in the prevalence of chewing tobacco use between 1990 and 2019, and that control efforts have had much larger effects on the prevalence of smoking tobacco use than on chewing tobacco use in some countries. Mitigating the health effects of chewing tobacco requires stronger regulations and policies that specifically target use of chewing tobacco, especially in countries with high prevalence. Copyright (c) 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe
Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019 : a systematic analysis from the Global Burden of Disease Study 2019
Background Ending the global tobacco epidemic is a defining challenge in global health. Timely and comprehensive estimates of the prevalence of smoking tobacco use and attributable disease burden are needed to guide tobacco control efforts nationally and globally. Methods We estimated the prevalence of smoking tobacco use and attributable disease burden for 204 countries and territories, by age and sex, from 1990 to 2019 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. We modelled multiple smoking-related indicators from 3625 nationally representative surveys. We completed systematic reviews and did Bayesian meta-regressions for 36 causally linked health outcomes to estimate non-linear dose-response risk curves for current and former smokers. We used a direct estimation approach to estimate attributable burden, providing more comprehensive estimates of the health effects of smoking than previously available. Findings Globally in 2019, 1.14 billion (95% uncertainty interval 1.13-1.16) individuals were current smokers, who consumed 7.41 trillion (7.11-7.74) cigarette-equivalents of tobacco in 2019. Although prevalence of smoking had decreased significantly since 1990 among both males (27.5% [26. 5-28.5] reduction) and females (37.7% [35.4-39.9] reduction) aged 15 years and older, population growth has led to a significant increase in the total number of smokers from 0.99 billion (0.98-1.00) in 1990. Globally in 2019, smoking tobacco use accounted for 7.69 million (7.16-8.20) deaths and 200 million (185-214) disability-adjusted life-years, and was the leading risk factor for death among males (20.2% [19.3-21.1] of male deaths). 6.68 million [86.9%] of 7.69 million deaths attributable to smoking tobacco use were among current smokers. Interpretation In the absence of intervention, the annual toll of 7.69 million deaths and 200 million disability-adjusted life-years attributable to smoking will increase over the coming decades. Substantial progress in reducing the prevalence of smoking tobacco use has been observed in countries from all regions and at all stages of development, but a large implementation gap remains for tobacco control. Countries have a dear and urgent opportunity to pass strong, evidence-based policies to accelerate reductions in the prevalence of smoking and reap massive health benefits for their citizens. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe
Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019
Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019.
Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019
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
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 developm nt 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. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens
The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019
Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe
Global, regional, and national burden of respiratory tract cancers and associated risk factors from 1990 to 2019 a systematic analysis for the Global Burden of Disease Study 2019
Background Prevention, control, and treatment of respiratory tract cancers are important steps towards achieving target 3.4 of the UN Sustainable Development Goals (SDGs)-a one-third reduction in premature mortality due to non-communicable diseases by 2030. We aimed to provide global, regional, and national estimates of the burden of tracheal, bronchus, and lung cancer and larynx cancer and their attributable risks from 1990 to 2019. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 methodology, we evaluated the incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs) of respiratory tract cancers (ie, tracheal, bronchus, and lung cancer and larynx cancer). Deaths from tracheal, bronchus, and lung cancer and larynx cancer attributable to each risk factor were estimated on the basis of risk exposure, relative risks, and the theoretical minimum risk exposure level input from 204 countries and territories, stratified by sex and Socio-demographic Index (SDI). Trends were estimated from 1990 to 2019, with an emphasis on the 2010-19 period. Findings Globally, there were 2.26 million (95% uncertainty interval 2.07 to 2.45) new cases of tracheal, bronchus, and lung cancer, and 2.04 million (1.88 to 2.19) deaths and 45.9 million (42.3 to 49.3) DALYs due to tracheal, bronchus, and lung cancer in 2019. There were 209 000 (194 000 to 225 000) new cases of larynx cancer, and 123 000 (115 000 to 133 000) deaths and 3.26 million (3.03 to 3.51) DALYs due to larynx cancer globally in 2019. From 2010 to 2019, the number of new tracheal, bronchus, and lung cancer cases increased by 23.3% (12.9 to 33.6) globally and the number of larynx cancer cases increased by 24.7% (16.0 to 34.1) globally. Global age-standardised incidence rates of tracheal, bronchus, and lung cancer decreased by 7.4% (-16.8 to 1.6) and age-standardised incidence rates of larynx cancer decreased by 3.0% (-10.5 to 5.0) in males over the past decade; however, during the same period, age-standardised incidence rates in females increased by 0.9% (-8.2 to 10.2) for tracheal, bronchus, and lung cancer and decreased by 0.5% (-8.4 to 8.1) for larynx cancer. Furthermore, although age-standardised incidence and death rates declined in both sexes combined from 2010 to 2019 at the global level for tracheal, bronchus, lung and larynx cancers, some locations had rising rates, particularly those on the lower end of the SDI range. Smoking contributed to an estimated 64.2% (61.9-66.4) of all deaths from tracheal, bronchus, and lung cancer and 63.4% (56.3-69.3) of all deaths from larynx cancer in 2019. For males and for both sexes combined, smoking was the leading specific risk factor for age-standardised deaths from tracheal, bronchus, and lung cancer per 100 000 in all SDI quintiles and GBD regions in 2019. However, among females, household air pollution from solid fuels was the leading specific risk factor in the low SDI quintile and in three GBD regions (central, eastern, and western sub-Saharan Africa) in 2019. Interpretation The numbers of incident cases and deaths from tracheal, bronchus, and lung cancer and larynx cancer increased globally during the past decade. Even more concerning, age-standardised incidence and death rates due to tracheal, bronchus, lung cancer and larynx cancer increased in some populations-namely, in the lower SDI quintiles and among females. Preventive measures such as smoking control interventions, air quality management programmes focused on major air pollution sources, and widespread access to clean energy should be prioritised in these settings. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd
Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019.
The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.Funding/Support: The Institute for Health Metrics and Evaluation received funding from the Bill & Melinda Gates Foundation and the American Lebanese Syrian Associated Charities. Dr Aljunid acknowledges the Department of Health Policy and Management of Kuwait University and the International Centre for Casemix and Clinical Coding, National University of Malaysia for the approval and support to participate in this research project. Dr Bhaskar acknowledges institutional support from the NSW Ministry of Health and NSW Health Pathology. Dr Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, which is funded by the German Federal Ministry of Education and Research. Dr Braithwaite acknowledges funding from the National Institutes of Health/ National Cancer Institute. Dr Conde acknowledges financial support from the European Research Council ERC Starting Grant agreement No 848325. Dr Costa acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia, IP under the Norma Transitória grant DL57/2016/CP1334/CT0006. Dr Ghith acknowledges support from a grant from Novo Nordisk Foundation (NNF16OC0021856). Dr Glasbey is supported by a National Institute of Health Research Doctoral Research Fellowship. Dr Vivek Kumar Gupta acknowledges funding support from National Health and Medical Research Council Australia. Dr Haque thanks Jazan University, Saudi Arabia for providing access to the Saudi Digital Library for this research study. Drs Herteliu, Pana, and Ausloos are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Dr Hugo received support from the Higher Education Improvement Coordination of the Brazilian Ministry of Education for a sabbatical period at the Institute for Health Metrics and Evaluation, between September 2019 and August 2020. Dr Sheikh Mohammed Shariful Islam acknowledges funding by a National Heart Foundation of Australia Fellowship and National Health and Medical Research Council Emerging Leadership Fellowship. Dr Jakovljevic acknowledges support through grant OI 175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Dr Katikireddi acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). Dr Md Nuruzzaman Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Dr Yun Jin Kim was supported by the Research Management Centre, Xiamen University Malaysia (XMUMRF/2020-C6/ITCM/0004). Dr Koulmane Laxminarayana acknowledges institutional support from Manipal Academy of Higher Education. Dr Landires is a member of the Sistema Nacional de Investigación, which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación. Dr Loureiro was supported by national funds through Fundação para a Ciência e Tecnologia under the Scientific Employment Stimulus–Institutional Call (CEECINST/00049/2018). Dr Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. Dr Moosavi appreciates NIGEB's support. Dr Pati acknowledges support from the SIAN Institute, Association for Biodiversity Conservation & Research. Dr Rakovac acknowledges a grant from the government of the Russian Federation in the context of World Health Organization Noncommunicable Diseases Office. Dr Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. Dr Sheikh acknowledges support from Health Data Research UK. Drs Adithi Shetty and Unnikrishnan acknowledge support given by Kasturba Medical College, Mangalore, Manipal Academy of Higher Education. Dr Pavanchand H. Shetty acknowledges Manipal Academy of Higher Education for their research support. Dr Diego Augusto Santos Silva was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil Finance Code 001 and is supported in part by CNPq (302028/2018-8). Dr Zhu acknowledges the Cancer Prevention and Research Institute of Texas grant RP210042
Hospital mortality and thirty day readmission among patients with non-acute myocardial infarction related cardiogenic shock.
BACKGROUND: Cardiogenic shock (CS) in absence of acute myocardial infarction (AMI) has significant morbidity and mortality. This population of patients has been excluded from prior major randomized trials and observational studies.
METHODS: We included patients with CS in absence of AMI from the 2013-14 HCUP\u27s National Readmission Database. 30-day readmissions were studied and etiology for readmission was identified by using ICD-9CM codes in primary diagnosis field. Multivariable mixed effect logistic regression models were created to identify predictors of 30-day readmission and in-hospital mortality, respectively.
RESULTS: We studied 38,198 index admissions with non-AMI CS, with an in-hospital mortality of 35.4%. Mean age, length and cost of stay were 63.6 years, 16.9 days and 69,947$, respectively among survivors of index admission. Among those discharged, 22.6% were readmitted within 30 days with \u3e50% readmissions occurring within 11-days. Cardiovascular etiologies (42.3%), especially heart failure (24.0%) comprised the commonest reason for readmission. Among non-cardiac causes were infectious (11.7%) and respiratory (9.2%) etiologies. Older age (50-64 years odds ratio:1.29, 65-79 years, OR:1.59, ≥80 years OR:2.69), ventilator use (OR:4.25), sepsis (OR:1.12), use of short term devices (intra-aortic balloon pump OR:2.67, Impella/TandemHeart OR:4.84, extracorporeal membrane oxygenation OR:3.68) and non-ischemic cardiomyopathy(OR:0.65) were among the predictors of in-hospital mortality. Older age (65-79 years, OR:1.25, ≥80 years OR:1.41), male sex (OR:1.08), and ventilator use (OR:1.21) predicted higher 30-day readmission.
CONCLUSION: Both, in-hospital mortality and 30-day readmission among those admitted for non-AMI CS were significantly elevated. The majority of readmissions were due to non-cardiovascular causes. Identifying high-risk factors may help devise strategies to improve quality of care and reduce adverse outcome rates
Etiologies and predictors of readmission among obese and morbidly obese patients admitted with heart failure
The relationship between severity of obesity and outcomes in heart failure (HF) has long been under debate. We studied index HF admissions from the 2013-14 National Readmission Database. Admissions were separated into three weight-based categories: non-obese (Non-Ob), obese (Ob), and morbidly obese (Morbid-Ob) to analyze hospital mortality and readmission at 30 days and 6 months. We investigated etiologies and predictors of 30-day readmission among these weight categories. We studied a total of 578,213 patients of whom 3.0% died during index hospitalization (Non-Ob 3.3% vs. Ob 1.9% vs. Morbid-Ob 1.9%; p \u3c 0.01). Non-Ob comprised 79.5%, Ob 9.9%, and Morbid-Ob 10.6% of patients. Morbid-Ob patients were the youngest among age categories and more likely to be female. In-hospital mortality during readmission at 30 days and 6 months was significantly lower among Morbid-Ob and Ob compared with Non-Ob patients (all p \u3c 0.01). Thirty-day readmission among Morbid-Ob was lower than Non-Ob and higher than Ob patients (19.6% vs. 20.5% vs. 18.6%, respectively; p \u3c 0.01). Morbid-Ob patients were less likely to be readmitted for cardiovascular etiologies compared with both Ob and Non-Ob (45.0% vs. 50.3% vs. 50.6%; p \u3c 0.01). Multivariable regression analysis revealed that Ob (adjusted odds ratio 0.84, 95% confidence intervals 0.82-0.86) and Morbid-Ob (aOR 0.83, 95% CI 0.81-0.85) were independently associated with lower 30-day readmission. Readmission at 6 months was highest among Morbid-Ob followed by Non-Ob and Ob (51.1% vs. 50.2% vs. 49.1%, p \u3c 0.01). Morbid-Ob and Ob patients experience lower in-hospital mortality during index HF admission and during readmission with 30 days or 6 months compared with Non-Ob. Morbid-Ob patients experience greater readmission at 6 months despite the lower rate at 30 days post discharge. Morbid-Ob patients are most likely to be readmitted for non-cardiovascular causes
Etiologies, Predictors, and Economic Impact of 30-Day Readmissions Among Patients With Peripartum Cardiomyopathy.
Peripartum cardiomyopathy (PPCM) is a pregnancy-associated cause of heart failure. Given the significant impact of heart failure on healthcare, we sought to identify etiologies and predictive factors for readmission in PPCM. We queried the 2013 to 2014 National Readmissions Database to identify patients admitted with a diagnosis of PPCM. Patients who were readmitted within 30 days were evaluated to identify etiologies and predictors of readmission. We identified 6,977 index admissions with PPCM. Of the 6,880 (98.6%) patients who survived the index hospitalization, 30-day readmission rate was 13%. Seventy-six percent of readmitted patients were admitted once, and the other 24% were readmitted at least twice within 30 days of discharge. Length of stay was ≥8 days (adjusted odds ratio [aOR] 2.80, 95% confidence interval [CI] 2.08 to 3.77), multiparity (aOR 2.07, 95% CI 1.09 to 3.92), coronary artery disease (aOR 2.28, 95% CI 1.42 to 3.67), and long-term anticoagulation use (aOR 2.51, 95% CI 1.73 to 3.64) were independently associated with increased risk of 30-day readmission. Among the readmissions, 48% were due to cardiac causes, where PPCM and related complications (24%) were the most common cardiac cause followed by heart failure (16%). The annual cost of stay for index admissions was 16,892). The annual charges attributed to readmission within 30 days were ≈$9 million. Cardiac etiologies were the most common cause for 30-day readmissions in PPCM patients, with a readmission rate of 13%. Long-term anticoagulation use, multiparity, coronary disease and length of stay predicted higher 30-day readmission