Fast track — ArticlesSelected major risk factors and global and regional burden of disease
Introduction
Detailed descriptions of the magnitude and distribution of diseases and injuries, and their causes are important inputs to strategies for improving population health. Much work has focused on the quantification of mortality patterns and, more recently, on burden of disease.1, 2 Data on disease or injury outcomes alone, such as death or admission to hospital, tend to focus on the need for palliative or curative services. Reliable and comparable analysis of risks to health, however, is key for preventing disease and injury. Analysis of morbidity and mortality due to risk factors has frequently been done in the context of methodological traditions of individual risk factors and in a limited number of settings.3, 4, 5, 6, 7, 8, 9, 10 As a result, most such estimates have been made relative to an arbitrary, constant level of population exposure, without standardisation of the baseline exposure across risk factors. For example, the implicit baseline for much of the estimates of occupational disease and injuries has been “no work”. Furthermore, the criteria for assessment of scientific evidence on prevalence, causality, and hazard size have varied greatly across risk factors, resulting in lack of comparability of estimated population health effects. Finally, the outcome of such estimates has been morbidity or mortality due to specific disease(s), making comparison among different risk factors difficult.
To assess risk factors in a unified framework, while acknowledging risk-factor specific characteristics, the Comparative Risk Assessment module of the global burden of disease (GBD) 2000 study has been set up as a systematic assessment of the changes in population health, which would result from modifying the population distribution of exposure to a risk factor or a group of risk factors.11 This unified framework for describing population exposure to risk factors and their consequences for population health is an important step in linking the growing interest in the causal determinants of health across various public-health disciplines from natural, physical, and medical sciences to the social sciences and humanities.
In addition to the above disciplinary obstacles and divisions, analysis of population health in a risk-based approach requires a framework for selection of risk factors among distal-eg, poverty or inequality-proximal and environmental-eg, air pollution or diet-and physiological-eg, blood pressure, HIV-1 as risk factor for tuberculosis-determinants of health.11, 12 Our aim was to develop such a framework by selecting risk factors in various levels of causality. Although gaps in epidemiological research on multiple layers of causality and risk-factor interactions would not allow inclusion of all inherently inter-related risk factors of interest, this selected group serves to emphasise the potential for disease prevention as a public-health tool.
The results of this work and additional background material are also presented inWorld Health Report 2002: Reducing Risk, Promoting Healthy Life (available online at http://www.who.int/whr). World Health Report 2002 further addresses interventions and policies to reduce risks.
Section snippets
Methods
Mathers and colleagues12 describe two models for causal attribution of health outcomes or states: categorical attribution and counterfactual analysis. In categorical attribution, an event such as death is attributed to a single cause (such as a disease or risk factor) or group of causes, according to a defined set of rules-eg, the International Classification of Disease (ICD) system for attribution of causes of death.13 In counterfactual analysis, the contribution of one or a group of risk
Results
The mortality and burden of disease for men and women attributable to risk factors included in the Comparative Risk Assessment project in the 14 GBD subregions are presented in Table 2, Table 3. Figure 1 shows the contribution of the 20 leading global risk factors to mortality and burden of disease for the world and for three broad combinations of regions—demographically and economically developed, lower-mortality developing, and high-mortality developing. Figure 2 presents the burden of
Discussion
Quantitative risk assessment is always affected by uncertainty of exposure, and of both the existence and magnitude of hazard. In one classification, risk assessment uncertainty can be divided into parameter uncertainty and model uncertainty.33 Parameter uncertainty is often quantifiable with random-variable methods— eg, uncertainty due to sample size or measurement error. Model uncertainty is due to gaps in scientific theory, measurement technology, and data. It includes uncertainty in causal
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