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      Models for estimating projections for the prevalence and disease burden of chronic obstructive pulmonary disease (COPD): systematic review protocol

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          Abstract

          Introduction Policymakers and governments must decide their healthcare priorities on the basis of the best healthcare intelligence available to them. Recent interest has increasingly focused on the global implications of an increasing and elderly population with long-term conditions. 1–3 The most recent figures from the Global Burden of Disease Study 2010 show that the third top global cause of death was chronic obstructive pulmonary disease (COPD), 4 rising from fourth place in 1990. 5 It is predominantly caused by cigarette smoking and leads to lung airflow limitation, cough, excessive sputum production, and breathlessness. People with COPD can suffer from substantial disability as the condition progresses. 6 A pressing challenge for governments is how best to project the future trend in the prevalence and burden of COPD in order to plan adequate health and social care for those affected by this condition within the scope of limited resources. Governments should ideally be planning for COPD on two levels: (1) they should consider how to manage resources to care and treat people who are already affected by COPD; and (2) how to prevent a greater increase in the burden from COPD by minimising the continuing smoking epidemic. In order to make such calculations, governments and other healthcare providers need to draw on epidemiological models. Merriam-Webster's dictionary defines a ‘model’ as ‘a system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs’. This is a useful starting point when considering the role of models in epidemiology. Most models are explanatory in nature and describe the relationships between different parameters. The focus of this study is on models which help to project future epidemiological trends and patterns in populations with COPD. Governments and policymakers have access to many models, but a review is required to appraise the published COPD models to aid selection between them. Various features of COPD present a particular challenge to mathematical and epidemiological modelling, including the many different definitions of a COPD diagnosis and its overlap with a diagnosis of asthma. Although COPD is most clearly attributable to cigarette smoking, there is debate over how best to classify non-smokers who develop COPD with the immunological and pathological features of COPD as a result of exposure to occupational dusts and gases or recurrent chest infections. In addition, there is uncertainty as to the correct classification of older non-smoking adults who have evidence of lung cell remodelling including squamous metaplasia following chronic inflammation due to long-term asthma. Such older adults have often lost the reversibility in their airways obstruction and demonstrate spirometry which is consistent with the thresholds for COPD. 7–9 According to the Global Initiative for Chronic Obstructive Lung Disease (GOLD), the diagnosis of COPD is characterised by an obstructive lung defect with forced expiratory volume in one second to forced vital capacity (FEV1/FVC) ratio <0.7. 10 Controversy regarding this threshold also complicates decisions of precisely which population to include in modelling. Lung function decreases with age, so a proportion of elderly people (age 75+) who have never smoked still fit these criteria for COPD. Some doctors reasonably argue that such elderly people really have normal lung function for their age and that medicalisation of the elderly should be avoided. 7 An alternative threshold of the lower limit of normal for FEV1/FVC has been proposed with a decreasing threshold according to age by percentile. The bottom 5% of FEV1/FVC measurements for whichever total population being measured would be considered abnormal in the older age group. 9 However, no up-to-date large standardised population database currently exists to validate such a measure. The nearest is the use of the European Coal and Steel Workers Population to provide percent predicted FEV1 values; however, this population was standardised over 20 years ago and is based on a working white European population without ethnic minorities. 11–13 Similarly, younger people (age 30–40 years) with larger FVC values and greater respiratory reserve may already have sustained COPD-type damage to their lungs before they reach the <0.7 ratio threshold, so at this end of the age range there is a risk of under-diagnosis of COPD. 13 The debate regarding the diagnosis of COPD is more than just a debate over spirometry thresholds. As many developing countries do not have access to spirometry or even to a reliable power supply, the usefulness of such diagnostic thresholds is limited. It has been proposed that COPD may also be diagnosed on history and clinical features. However, studies have shown that using clinical indicators of pulmonary function to diagnose COPD missed many participants who had low lung function and airways obstruction, especially in current smokers. 14 Therefore, in many countries the current situation has evolved where COPD is diagnosed from physician opinion without corroborating evidence from spirometry, resulting in a significant overlap between a diagnosis of COPD and a diagnosis of asthma. It seems likely that classifications in the future will evolve as the role of host susceptibility is increasingly understood in terms of genetic and epigenetic features. Several candidate genes related to COPD have been identified. 15 In addition, the science of epigenetics helps to explain how DNA transcription has been activated or suppressed by DNA methylation, acetylation, or other mechanisms in response to predominantly prenatal and early life environmental influences. 16 The result of such switching on or off of DNA transcription is to determine the host's response to noxious stimuli including cigarette smoke. Increased understanding of these factors is helping to unravel the mysteries of why some life-long smokers are virtually unaffected by their habit while others have severe COPD. Estimates as to the prevalence of COPD among smokers aged >45 years vary from 15% to 50% according to the criteria used for diagnosis. 17,18 Modelling COPD is also challenged by the key feature of exacerbations. An exacerbation may be triggered by increased bacterial or viral load in the lungs which induce an aggressive immune response and associated clinical features. 19–21 Associated with a greater frequency of exacerbations is higher morbidity, due to faster disease progression in terms of loss of lung function, and also mortality. 21 An additional challenge is the level of mathematical sophistication within each model. Ideally, a researcher with considerable statistical skill would be available to check the algorithms that drive each model and so provide a full appraisal of the quality of each model. In the absence of this ideal, it was decided to appraise the quality of reporting of each model as a proxy for the model's mathematical quality. Taking these challenges into account, it will be necessary to describe a degree of context with each model in order that it can be applied in an appropriate setting. This will help subsequent researchers to understand the necessary caveats to include when describing the results from each model. Objectives To identify all available models for estimating projections of COPD prevalence and burden, and to assess the quality of reporting of each model in its key publication. Methods A search strategy has been developed using search terms to cover the three concepts of ‘modelling’, ‘disease burden’, and ‘chronic obstructive pulmonary disease’ (see Appendix 1 for full details). Searches will be conducted in the following electronic databases: MEDLINE, EMBASE, CAB Abstracts, World Health Organization (WHO) Library and Information Services (WHOLIS — library catalogue of books and reports), WHO Regional Indexes (AIM (AFRO), LILACS (AMRO/PAHO), IMEMR (EMRO), IMSEAR (SEARO), WPRIM (WPRO)), and a modified search strategy will be used to identify reports from the WHO home website and Google. Searches will be for both published and unpublished modelling studies from 1980 (when modelling methods first began to be widely used) to 2013. Two authors will independently review the studies against the inclusion criteria and make a decision as to whether the study is suitable. Disagreements will be resolved by discussion and, if this is not possible, a third reviewer will arbitrate. Inclusion criteria Any modelling study which uses demographic and epidemiological data to project the prevalence and disease burden will be included. The included projected outcomes which are of interest are one or more of: incidence, prevalence and mortality, and disease burden. With regard to ‘disease burden’, the outcomes of interest can be considered from the individual's point of view, from the point of view of the healthcare system, and from the point of view of broader society. For the purposes of this review, the focus is on the perspective of the healthcare system. Other perspectives are valid; however, different instruments are used to measure them and the purpose of this study is to guide policymakers who will focus on the healthcare system perspective. Quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) are often used to measure and quantify the burden to the individual of the morbidity they are suffering. Treatments are assigned a cost per restored QALY, and this is an important measure used in cost-effectiveness studies. However, the scope of this study is more limited in order to avoid confusion of perspectives. Some of the studies included may discuss QALYs and DALYs, but they have not been chosen as primary disease burden outcomes for this review. Instead, we will concentrate on primary care visits, emergency department visits, hospital admissions, and COPD treatment costs. Exclusion criteria There will be no exclusions on the basis of language of the report. Studies which are population-based surveys of prevalence without modelling will be excluded as there has recently been a systematic review of such studies. 22 ‘Models’ will be excluded if they describe animals, cell lines, clinical series, or estimates of individual risk (such as individual prognostic models). Decision analytical models or decision support models will be excluded where they refer to clinical decision-making for individuals rather than populations. Models that compare one intervention with another intervention will also be excluded, as the aim is accurately to project the baseline outcomes so it is premature to take into account the effect of interventions. Also excluded will be regression models which start with a COPD population and ‘back-calculate’ the prevalence or burden using regression to quantify risk factors, as this follows a different logic from that of projection modelling. Participants The source population for the model may be from anywhere in the world. The model will pertain to adult populations aged >40 years as it is usually not appropriate to diagnose COPD in younger people. 10 COPD may be diagnosed by physician, spirometry, or by questionnaire. Other assumptions regarding the diagnosis of COPD will be evaluated in the context of the model. Data extraction The data will be extracted by one author and checked by a second. Data will be extracted using a pre-piloted data extraction form. The following identification details will be extracted for each model: author and email address, year, institution, and funding source. These data will be followed by: the purpose of the model, model title, model type, model setting, time period, and population (age, sex and country). Also extracted will be: inputs to the model, source of input data, details of processing of the model, outcomes for COPD (incidence, prevalence, mortality, GP visits, emergency department visits, hospitalisations, treatment costs), model output/results, details of the model's availability, any comparisons with other studies, social and economic policy implications of model outcomes, and future research recommendations. In this way, the data extraction form aims to encompass a comprehensive picture of the model. Quality appraisal framework Ideally, a quality appraisal of the actual modelling process would be undertaken. However, this requires significant statistical technical expertise. A pragmatic decision has therefore been made to quality appraise the reporting of the models rather than the actual modelling process for those that have full published reports. In order to do this, a quality of reporting framework has been designed following review of key guidelines as to good practice in modelling. 23–26 A scoring mechanism was devised in collaboration with Simon Capewell of Liverpool University 27 to weight the importance of the different elements required to produce a relevant high-quality model (see Appendix 2). Strategy for data synthesis The study will be the unit of analysis. Models will be described and classified. A detailed critical narrative synthesis of the highest scoring models will be undertaken. Where the models are not available, we will write to the model authors for further clarification. No subgroup analysis is planned.

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          Most cited references 21

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          Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010.

          Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex. We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions. In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45-54% since 1990; ischaemic heart disease and stroke YLLs increased by 17-28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted. Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis. Bill & Melinda Gates Foundation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

            Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children. Bill & Melinda Gates Foundation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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              Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

              Measuring disease and injury burden in populations requires a composite metric that captures both premature mortality and the prevalence and severity of ill-health. The 1990 Global Burden of Disease study proposed disability-adjusted life years (DALYs) to measure disease burden. No comprehensive update of disease burden worldwide incorporating a systematic reassessment of disease and injury-specific epidemiology has been done since the 1990 study. We aimed to calculate disease burden worldwide and for 21 regions for 1990, 2005, and 2010 with methods to enable meaningful comparisons over time. We calculated DALYs as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were calculated for 291 causes, 20 age groups, both sexes, and for 187 countries, and aggregated to regional and global estimates of disease burden for three points in time with strictly comparable definitions and methods. YLLs were calculated from age-sex-country-time-specific estimates of mortality by cause, with death by standardised lost life expectancy at each age. YLDs were calculated as prevalence of 1160 disabling sequelae, by age, sex, and cause, and weighted by new disability weights for each health state. Neither YLLs nor YLDs were age-weighted or discounted. Uncertainty around cause-specific DALYs was calculated incorporating uncertainty in levels of all-cause mortality, cause-specific mortality, prevalence, and disability weights. Global DALYs remained stable from 1990 (2·503 billion) to 2010 (2·490 billion). Crude DALYs per 1000 decreased by 23% (472 per 1000 to 361 per 1000). An important shift has occurred in DALY composition with the contribution of deaths and disability among children (younger than 5 years of age) declining from 41% of global DALYs in 1990 to 25% in 2010. YLLs typically account for about half of disease burden in more developed regions (high-income Asia Pacific, western Europe, high-income North America, and Australasia), rising to over 80% of DALYs in sub-Saharan Africa. In 1990, 47% of DALYs worldwide were from communicable, maternal, neonatal, and nutritional disorders, 43% from non-communicable diseases, and 10% from injuries. By 2010, this had shifted to 35%, 54%, and 11%, respectively. Ischaemic heart disease was the leading cause of DALYs worldwide in 2010 (up from fourth rank in 1990, increasing by 29%), followed by lower respiratory infections (top rank in 1990; 44% decline in DALYs), stroke (fifth in 1990; 19% increase), diarrhoeal diseases (second in 1990; 51% decrease), and HIV/AIDS (33rd in 1990; 351% increase). Major depressive disorder increased from 15th to 11th rank (37% increase) and road injury from 12th to 10th rank (34% increase). Substantial heterogeneity exists in rankings of leading causes of disease burden among regions. Global disease burden has continued to shift away from communicable to non-communicable diseases and from premature death to years lived with disability. In sub-Saharan Africa, however, many communicable, maternal, neonatal, and nutritional disorders remain the dominant causes of disease burden. The rising burden from mental and behavioural disorders, musculoskeletal disorders, and diabetes will impose new challenges on health systems. Regional heterogeneity highlights the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account. Because of improved definitions, methods, and data, these results for 1990 and 2010 supersede all previously published Global Burden of Disease results. Bill & Melinda Gates Foundation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Affiliations
                [1 ]Allergy and Respiratory Research Group, Centre for Population Health Sciences, The University of Edinburgh , Edinburgh, UK
                [2 ]Centre for Population Health Sciences, The University of Edinburgh , Edinburgh, UK
                Author notes
                [* ]Allergy and Respiratory Research Group, Centre for Population Health Sciences, The University of Edinburgh , Edinburgh EH8 9DX, UK. Tel: +44 (0)131 650 9242 E-mail: Susannah.mclean@ 123456ed.ac.uk

                SM drafted the article with oversight from CS SW and AS. AS and SM conceived the project as part of SM's PhD.

                Journal
                Prim Care Respir J
                Prim Care Respir J
                Primary Care Respiratory Journal: Journal of the General Practice Airways Group
                Nature Publishing Group
                1471-4418
                1475-1534
                June 2013
                30 May 2013
                : 22
                : 2
                : PS8-PS21
                pcrj201348
                10.4104/pcrj.2013.00048
                6442795
                23732639
                Copyright © 2013 Primary Care Respiratory Society UK
                Categories
                Protocol Summary

                modelling, epidemiology, copd

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