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      Respiratory-related death in individuals with incident asthma and COPD: a competing risk analysis

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      1 , 2 , 3 , , 1 , 2 , 1 , 2
      BMC Pulmonary Medicine
      BioMed Central
      COPD, Asthma, ACO, Mortality

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          Abstract

          Background

          Distinguishing between mortality attributed to respiratory causes and other causes among people with asthma, COPD, and asthma-COPD overlap (ACO) is important. This study used electronic health records in England to estimate excess risk of death from respiratory-related causes after accounting for other causes of death.

          Methods

          We used linked Clinical Practice Research Datalink (CPRD) primary care and Office for National Statistics mortality data to identify adults with asthma and COPD from 2005 to 2015. Causes of death were ascertained using death certificates. Hazard ratios (HR) and excess risk of death were estimated using Fine-Gray competing risk models and adjusting for age, sex, smoking status, body mass index and socioeconomic status.

          Results

          65,021 people with asthma and 45,649 with COPD in the CPRD dataset were frequency matched 5:1 with people without the disease on age, sex and general practice. Only 14 in 100,000 people with asthma are predicted to experience a respiratory-related death up to 10 years post-diagnosis, whereas in COPD this is 98 in 100,000. Asthma is associated with an 0.01% excess incidence of respiratory related mortality whereas COPD is associated with an 0.07% excess. Among people with asthma-COPD overlap (N = 22,145) we observed an increased risk of respiratory-related death compared to those with asthma alone (HR = 1.30; 95% CI 1.21–1.40) but not COPD alone (HR = 0.89; 95% CI 0.83–0.94).

          Conclusions

          Asthma and COPD are associated with an increased risk of respiratory-related death after accounting for other causes; however, diagnosis of COPD carries a much higher probability. ACO is associated with a lower risk compared to COPD alone but higher risk compared to asthma alone.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12890-022-01823-4.

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          Most cited references40

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          Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries—Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5–23·9), representing an additional 7·61 million (7·20–8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0–8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0–24·0) and the death rate by 31·8% (30·1–33·3). Total deaths from injuries increased by 2·3% (0·5–4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2–15·1) to 57·9 deaths (55·9–59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000–289 000) globally in 2007 to 352 000 (334 000–363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8–148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2–40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2–36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990—neonatal disorders, lower respiratory infections, and diarrhoeal diseases—were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Funding Bill & Melinda Gates Foundation.
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            A Proportional Hazards Model for the Subdistribution of a Competing Risk

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              Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

              The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                alicia.gayle14@imperial.ac.uk
                Journal
                BMC Pulm Med
                BMC Pulm Med
                BMC Pulmonary Medicine
                BioMed Central (London )
                1471-2466
                8 January 2022
                8 January 2022
                2022
                : 22
                : 28
                Affiliations
                [1 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, National Heart and Lung Institute, , Imperial College London, ; G08 Emmanuel Kaye Building, Manresa Road, London, SW3 6LR UK
                [2 ]GRID grid.451056.3, ISNI 0000 0001 2116 3923, Imperial Biomedical Research Center, , National Institute for Health Research, ; London, UK
                [3 ]GRID grid.417815.e, ISNI 0000 0004 5929 4381, AstraZeneca PLC, ; Cambridge, UK
                Article
                1823
                10.1186/s12890-022-01823-4
                8742941
                4f15eb08-cd2c-4f25-8a3d-9137db43998b
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 4 October 2021
                : 23 December 2021
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Respiratory medicine
                copd,asthma,aco,mortality
                Respiratory medicine
                copd, asthma, aco, mortality

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