ArticlesEstimates of global and regional potential health gains from reducing multiple major risk factors
Introduction
Reliable and comparable analysis of risks to health is key for prevention of disease and injury. Estimates of the burden of disease from individual health risks have been made for selected risk factors.1 Diseases and injuries are, however, almost always caused by multiple risk factors,2, 3 motivating the analysis of the health benefits of simultaneous reductions in multiple risks. Assessment of the combined effects of multiple distant risks is especially important because many factors act through intermediate factors,4, 5 or in combination with others. For example, education, occupation, and income can affect smoking, physical activity, and diet, which are risk factors for vascular diseases, both directly and through intermediate factors such as body-mass index (BMI), blood pressure, and cholesterol. Multicausality also means that various interventions can be used for disease prevention. The specific choice of interventions is determined by cost, technology availability, infrastructure, and preferences.
Previous research has estimated the joint effects of multiple risk factors in specific cohorts,6, 7, 8, 9 or for specific groups of diseases and risks.10, 11 Innovative methods have also been developed to quantify the effects of multiple risk factors, especially those that interact over time.12, 13 Effects of multiple risk factors beyond specific diseases or cohorts, however, remain almost unexplored in epidemiology and population health. We used comprehensive reviews of data for selected major risk factors to examine the joint health effects of these risks on healthy life expectancy (HALE). Because these risks make varying contributions to disease burden in different regions, we could also estimate how much of the crosspopulation health differentials-eg, differences in HALE—result from these risks. Through estimation of gains in HALE based on causes of disease, this work also contributes systematically to the continued debate on the potential limits to life expectancy.14
Section snippets
Estimation of joint population attributable fractions
Methods and data sources for estimation of the burden of disease attributable to individual risk factors have been described elsewhere.1 The contribution of a risk factor to disease or mortality, relative to some alternative exposure distribution (ie, population attributable fraction [PAF], defined as the proportional reduction in population disease or mortality that would occur if exposure to the risk factor were reduced to an alternative exposure distribution15, 16), is defined by the
Role of the funding source
The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results
Table 2, Table 3, Table 4, Table 5 show the individual and joint contributions of the selected risk factors for the ten leading diseases in the world and for three broad groups of subregions: high-mortality developing regions (38% of global population), lower-mortality developing regions (40% of global population), and demographically and economically developed regions (22% of global population). For most diseases, the joint effects of these risk factors were substantially less than the crude
Discussion
The estimates of the total effect of the 20 selected leading global risk factors showed that these risks jointly contributed to considerable loss of healthy life in different regions of the world. In particular, some leading global diseases—lower respiratory infection, diarrhoea, lung cancer, ischaemic heart disease, and stroke—were substantially a result of exposure to these risk factors. Removal of these risks would have not only increased global healthy life expectancy by 9·3 years (17%),
Comparative Risk Assessment Collaborating group
Members listed in previous report: M Ezzati, A D Lopez, A Rodgers et al. Selected major risk factors and global and regional burden of disease. Lancet 2002; 360: 1347–60.
Contributors
M Ezzati, S Vander Hoorn, A Rodgers, C J L Murray, and A D Lopez developed framework and methods for joint-effect analyses, and C D Mathers for HALE. S Vander Hoorn designed and conducted statistical analysis. A rodgers designed figures. Risk factor working groups reviewed scientific evidence and data sources for
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