Causes of chronic diseases – can we accurately measure them?

As highlighted in a recent debate paper, one assumption of epidemiology is the probabilistic causal paradigm, which allows researchers to infer relationships between risk factors and disease outcomes. However, as argued by Maziak (2015) it seems unrealistic to believe there is a unidirectional relationship between the two factors.

Take the following examples regarding obesity:arrows

1) Linear relationship: risk factor(s)–> obesity  –> negative health impact

2) One potential cyclical relationship*: risk factor(s) –> obesity –> negative health impact –> seek health care –> potential weight bias –> health care avoidance –> further unhealthy habits –> obesity

Although the first example is not incorrect per se, relevant contextual factors are largely ignored. It may seem obvious that the second example is more accurate and descriptive, yet despite this, a substantial amount of research that reflects such a linear relationship continues to be published. How can we mitigate the risk that future studies will produce similar outcomes… more rigorous methods? abandonment of subjective measures? As suggested by Maziak (2015), researchers should not start by understanding the relationship between risk factors and disease outcomes, but rather the complex interactions between risk factors themselves.

*Credit for this example given to Dr. Russell-Mayhew and & Dr. Alberga from the University of Calgary, which was presented at the 4th Canadian Obesity Summit.


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