Risk Factor Transitions: Exposures and Comparative Risk Assessment
The third session of the workshop began with a presentation by Majid Ezzati from Imperial College, London. Since chronic diseases generally take years or often decades to develop, it is possible to project future trends for these diseases by looking at the current incidence of their risk factors. In the case of sub-Saharan Africa, an examination of the risk factors indicates that the trends for chronic diseases in the future will be a mixed bag. Data on diet patterns and obesity indicate that people in sub-Saharan Africa can expect to see rising levels of diabetes, cardiovascular disease, and certain types of cancer, and data on blood pressure paint a similarly discouraging picture for future cardiovascular health in sub-Saharan Africa. On the other hand, neither blood glucose levels nor cholesterol levels yet show the effects of the region’s nutrition transition, as they are among the lowest in the world. Furthermore, the rates of smoking remain low in sub-Saharan Africa, at least for the time being, which is another bit of good news.
Ezzati cautioned that while these broad statements may be true in terms of averages across the region, there are large variations in risk factors from country to country and, within individual countries, between rural areas and cities. For example, the risk factors tend to follow economic development, and such development can differ strikingly from place to place. Furthermore, just as is the case with data on morbidity and mortality, the availability of data on risk factors varies greatly from risk factor to risk factor and also from region to region.
The most comprehensive exploration of risk factor transitions on the global level has been the work done by various institutions under the rubric of the Global Burden of Disease (GBD). Ezzati reported on recent GBD work that sought to estimate trends in four important metabolic risk factors for chronic diseases—body mass index, systolic blood pressure, serum total cholesterol, and fasting plasma glucose—by age, sex, and country/region between 1980 and 2008.
The primary model used in the analysis was fitted using the Markov chain Monte Carlo (MCMC) algorithm, with specific emphasis placed on computational efficiency and convergence. Ezzati noted that sensitivity analyses and a validity assessment of this model found high predictive power and robust performance using various subgroups of data.
Results using this enhanced model showed that (1) there has been a worldwide rise in body weight and glycaemia, with only a few rare regions in which they have been stable, including among men in parts of sub-Saharan Africa; (2) average blood pressures have decreased in high-income countries and, increasingly, also in the countries of South America, while they have remained stable in East Asia and have risen in sub-Saharan Africa; (3) Western countries have successfully lowered serum total cholesterol, which has, by contrast, risen in East and Southeast Asia; and (4) significant heterogeneity exists
in trends among Africa’s subregions. The next steps in the evolution of the GBD and related projects will include the incorporation of trends in other risk factors, such as child and maternal undernutrition, tobacco use, poor water and sanitation, and household solid fuel use.
