The lower the better: does simplicity lead to absurdity?

摘要

With the growing availability of results from large clinical trials, the effectiveness of cardiovascular prevention strategies is becoming better defined and the strategies are being used more widely. However, when the effect of a treatment strategy has been demonstrated, such as lowering blood pressure with antihypertensive drugs, many questions remain unanswered. What is the best blood pressure target to aim for? What is the best specific treatment? What is the impact of the treatment for individuals and different populations? Health policy decision-makers need answers to these questions to improve the health system and make it more cost efficient. One approach aiming to answer these questions is to model the impact of a treatment strategy for a specific national population. Such modelling requires a certain amount of information: (i) a reliable measure of the treatment effect of the strategy from randomised, controlled clinical trials and (ii) an accurate description of the target population, including all characteristics that are relevant for the impact prediction. In the case of the impact of blood pressure-lowering drugs, the relevant characteristics are the joint distributions of risk factors of cardiovascular events, including, amongst others, blood pressure, the structure of the cardiovascular risk in the target population, and the link between that risk structure and the joint distributions of risk factors.These elements are only partially known in a given national population. Transversal cohort studies ideally inform on the joint distributions of risk factors within the cohorts. Longitudinal studies of these cohorts inform on the link between the joint distributions of risk factors and the incidence of cardiovascular events, fatal or not: the most typical example is the Framingham study . These prospective studies can be analysed together at the individual level, which increases the power of theanalyses (e.g. allowing specific exploration in important strata such as by age and sex) . Death statistics comprise core information that is most frequently available at the nation level, and can be used to extrapolate other information sources at that level .