Dr Catherine O. Johnson
Is Cardiovascular Disease Slowing Improvements in Life Expectancy?
OECD and The King's Fund Workshop Proceedings
4. Global trends in cardiovascular disease – an update from the Global Burden of Disease Study
Abstract
The results of the Global Burden Disease study suggest that the decrease in cardiovascular mortality seen in most high-income countries over the past few decades is levelling off in countries like South Korea, France, Australia, Germany, Austria, Japan, and the United States among those aged 70 years, and is increasing in Latvia, Estonia, Greece and Portugal. Similar patterns were observed among those aged 50‑ 69, with some exceptions – for example, rates in South Korea and Austria among younger ages continue to decrease even in more recent years. Evidence suggests that the prevalence of common risk factors for cardiovascular disease, including elevated low-density lipoprotein cholesterol and systolic blood pressure, is increasing, leading to expected increases in mortality burden. Locally targeted population interventions are needed in order to appropriately assess and intervene with groups at risk; together with timely monitoring of both cardiovascular mortality and common risk factors over time.
History of the GBD
The Global Burden of Disease Study (GBD) generates global estimates of death and disability from over 300 causes. The list of causes is based largely on the International Classification of Diseases (ICD) reporting system, now in its 11th iteration (WHO, 2018[1]). The results produced are comprehensive, using methods that are consistent from country to country to enable comparisons across locations, between different types of health loss, and over time. The GBD began in the early 1990s, in collaboration with the World Bank. Today, it is an annual process, generating consistent, reliable results used by health departments, policy makers, and other stakeholders worldwide to guide decision-making around health policy and spending. The core estimation processes are run by researchers at the Institute for Health Metrics and Evaluation at the University of Washington in Seattle, Washington, working in concert with a collaborator network of over 3 600 investigators from around the world. The process is overseen by a management team at IHME and an Independent Advisory Council. Full details of the history of the GBD, the protocols and processes, and persons involved can be found at http://www.healthdata.org/gbd/about.
Methods and metrics
Four key components of disease burden are estimated as part of each GBD cycle: 1) estimates of all-cause mortality, population, and fertility; 2) estimates of cause-specific mortality; 3) estimates of prevalence and incidence of nonfatal disease processes; and 4) estimates of attributable burden for a variety of risk factors. The full time series is re-estimated with each GBD cycle to reflect updates to methods, data, and data processing. Details of the various estimation processes can be found in the capstones and associated appendices (Murray et al., 2018[2]; Dicker et al., 2018[3]; James et al., 2018[4]; Stanaway et al., 2018[5]; Roth et al., 2018[6])
Using these estimates, the GBD computes a number of summary measures of disease burden. These include years of life lost due to premature mortality (YLL), which is calculated from the sum of each death multiplied by the standard life expectancy for that age group. The standard life expectancy is determined from the lowest observed risk of death for each age group in all populations with more than 5 million people. Years lived with disability are calculated by taking the sum of the prevalence of each condition multiplied by the associated disability weight after accounting for the results of the comorbidity simulation process. Disability-adjusted life years (DALYs) are calculated as the sum of YLLs and YLDs for a specific age/sex/location/year combination.
Input data
All input data used in the GBD can be accessed via the Global Health Data Exchange website (IHME, 2017[7]). Data can be queried by component, location, and cause or risk.
Cardiovascular diseases
Cause-specific mortality is estimated for a number of cardiovascular diseases, including ischemic heart disease and stroke. Causes are estimated at a number of different levels. For example, we estimate deaths due to stroke as a level 3 cause, while also estimating the subtypes of ischaemic stroke, intracerebral haemorrhage and subarachnoid haemorrhage separately. In order to not double-count the deaths; we scale the three subtypes such that the sum equals the total number deaths for stroke for each age/sex/location/year combination. The total number of cardiovascular deaths is thus the scaled total of all causes at the lowest level of the hierarchy (Table 4.1).
Table 4.1. Cardiovascular disease cause list
Source Roth et al: (2018[6]), “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’, http://dx.doi.org/10.1016/S0140-6 736(18)32203-7.
Results
All results for the current cycle of the GBD can be viewed via the GBD Compare website (IHME, 2017[7]), including a custom grouping for member countries of the Organisation for Economic Co‑operation and Development. The estimates can also be downloaded from the GBD results tool: http://ghdx.healthdata.org/gbd-results-tool. Information on additional visualisations and commonly used terms and abbreviations can be found here: https://www.healthdata.org/sites/default/files/files/Data_viz/GBD_2017_Tools_Overview.pdf.
Figure 4.1. GBD Compare visualisation, both sexes, ischaemic heart disease mortality rates among those 50 to 69 years of age
Source: Institute for Health Metrics and Evaluation (IHME). GBD Compare. Seattle, WA: IHME, University of Washington, 2015. Available from http://vizhub.healthdata.org/gbd-compare (accessed 27 February 2020).
Figure 4.2. GBD Compare visualisation, both sexes, ischaemic heart disease mortality rates among those 70 or more years of age
Source: Institute for Health Metrics and Evaluation (IHME). GBD Compare. Seattle, WA: IHME, University of Washington, 2015. Available from http://vizhub.healthdata.org/gbd-compare (accessed 27 February 2020).
With regard to cardiovascular mortality, ischaemic heart disease and stroke are the main drivers; many of the other causes included in the cardiovascular grouping contribute relatively few deaths. The detailed cause list also allows for exploration of atherosclerotic vs non-atherosclerotic causes (e.g. ischemic heart disease vs. endocarditis).
Interpretation and conclusions
The results of the GBD estimation process are consistent with other findings that the decrease in cardiovascular mortality seen in most high-income countries over the past few decades is levelling off, and may be increasing in certain countries (Shah, Roberts and Shah, 2013[8]; Menotti et al., 2019[9]; Regidor et al., 2019[10]; Shah et al., 2019[11]). Countries where results indicate that cardiovascular mortality may be increasing among those aged 70 years or more include Latvia, Estonia, Greece and Portugal, while rates in many countries including South Korea, France, Australia, Germany, Austria, Japan, and the United States appear to be levelling off. Similar patterns were observed among those aged 50 to 69 years of age, with some exceptions – for example, rates in South Korea and Austria among younger ages continue to decrease even in more recent years.
Our assessment of common risk factors for cardiovascular disease, including elevated low-density lipoprotein cholesterol, systolic blood pressure, and fasting plasma glucose indicates that while many countries (e.g. United Kingdom and Denmark) have seen improvements in their cardiometabolic risk profile, prevalence of these risks is increasing in a number of countries (e.g. Netherlands, Belgium, Chile, Mexico), resulting in the changes seen and pointing to increased mortality burden if these increases continue.
Local interventions specific to the target population are needed in order to appropriately assess and intervene with groups at risk; however, the comprehensive, consistent, and comparable methods of burden estimation employed by the GBD can be used to monitor trends for both cardiovascular mortality and common cardiovascular risk factors over time.
References
[3] Dicker, D. et al. (2018), “Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: A systematic analysis for the Global Burden of Disease Study 2017”, The Lancet, Vol. 392/10159, pp. 1684-1735, http://dx.doi.org/10.1016/S0140-6736(18)31891-9.
[7] IHME (2017), GBD Compare | IHME Viz Hub, https://vizhub.healthdata.org/gbd-compare/# (accessed on 6 March 2020).
[4] James, S. et al. (2018), “Global, regional, and national incidence, prevalence, and years lived with disability for 354 Diseases and Injuries for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017”, The Lancet, Vol. 392/10159, pp. 1789-1858, http://dx.doi.org/10.1016/S0140-6736(18)32279-7.
[9] Menotti, A. et al. (2019), “Coronary heart disease mortality trends during 50 years as explained by risk factor changes: The European cohorts of the Seven Countries Study”, European Journal of Preventive Cardiology, http://dx.doi.org/10.1177/2047487318821250.
[2] Murray, C. et al. (2018), “Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017”, The Lancet, Vol. 392/10159, pp. 1995-2051, http://dx.doi.org/10.1016/S0140-6736(18)32278-5.
[10] Regidor, E. et al. (2019), “Mortality in Spain in the Context of the Economic Crisis and Austerity Policies.”, American Journal of Public Health, Vol. 109/7, pp. 1043-1049, http://dx.doi.org/10.2105/AJPH.2019.305075.
[6] Roth, G. et al. (2018), “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”, The Lancet, Vol. 392/10159, pp. 1736-1788, http://dx.doi.org/10.1016/s0140-6736(18)32203-7.
[11] Shah, N. et al. (2019), “Trends in Cardiometabolic Mortality in the United States, 1999-2017”, JAMA, Vol. 322/8, p. 780, http://dx.doi.org/10.1001/jama.2019.9161.
[8] Shah, R., S. Roberts and D. Shah (2013), “A fresh perspective on comparing the FDA and the CHMP/EMA: approval of antineoplastic tyrosine kinase inhibitors”, British Journal of Clinical Pharmacology, Vol. 76/3, pp. 396-411, http://dx.doi.org/10.1111/bcp.12085.
[5] Stanaway, J. et al. (2018), “Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017”, The Lancet, Vol. 392/10159, pp. 1923-1994, http://dx.doi.org/10.1016/S0140-6736(18)32225-6.
[1] WHO (2018), WHO | International Classification of Diseases, 11th Revision (ICD-11), World Health Organization, https://www.who.int/classifications/icd/en/ (accessed on 6 February 2020).