Comparative quantification of health risks conceptual framework and methodological issues

Abstract

Reliable and comparable analysis of risks to health is key for preventing disease and injury. Causal attribution of morbidity and mortality to risk factors has traditionally been conducted in the context of methodological traditions of individual risk factors, often in a limited number of settings, restricting comparability.In this paper, we discuss the conceptual and methodological issues for quantifying the population health effects of individual or groups of risk factors in various levels of causality using knowledge from different scientific disciplines. The issues include: comparing the burden of disease due to the observed exposure distribution in a population with the burden from a hypothetical distribution or series of distributions, rather than a single reference level such as non-exposed; considering the multiple stages in the causal network of interactions among risk factor(s) and disease outcome to allow making inferences about some combinations of risk factors for which epidemiological studies have not been conducted, including the joint effects of multiple risk factors; calculating the health loss due to risk factor(s) as a time-indexed "stream" of disease burden due to a time-indexed "stream" of exposure, including consideration of discounting; and the sources of uncertainty.

Figure 1

A causal-web illustrating various levels of disease causality. Feedbacks from outcomes to preceding layers may also exist. For example, individuals or societies may modify their risk behavior based on health outcomes. The "driving force, pressure, state, exposure, effect" (DPSEE) model of Corvalan et al. [56] does consider the multiple layers of causality. These layers however focus the risk evolution process which is less suitable for multi-risk factor interaction within and between layers. More complete discussions of causality and multiple causes are provided by Yerushalmy and Palmer [55], Evans [57,58], and Rothman and Greenland [26,59].

Figure 2

A possible causal diagram based on established relationships for estimating the incidence of coronary heart disease (CHD). Other interactions may also be possible.

Figure 3

A (three-dimensional) representation of a time-indexed distributional transition of population exposure to a risk factor, with a decreasing central tendency.

Figure 4

Attributable and avoidable burden. a = disease burden at T₀ attributable to prior exposure. The burden not attributable to risk factor of interest (light area) may be decreasing, constant, or increasing over time. The middle case is shown in the figure. b = disease burden at T₀ not attributable to the risk factor of interest (caused by other factors only). Dashed arrows represent the path of burden after a reduction at T₀. c = disease burden avoidable at T_x with a 50% exposure reduction at T₀. d = remaining disease burden at T_x after a 50% reduction in risk factor.