A discussion paper looks at approaches to public health communication and challenges posed by J-shaped associations between health effects and major modifiable risk factors.
Interventions that alter population-level risk exposure have yielded a number of improvements in public health, the authors state. Tobacco taxes are an example of such population-based approaches to disease prevention. In the case of tobacco, the harms of shifting total population exposure through taxation are minimal, because there is no safe level of consumption. However, other risk factors do not exhibit the same linear relationship between exposure and mortality—and therefore may introduce new complexities in communicating with individuals and the public. In particular, many risk factors, such as alcohol consumption, exhibit a J-shaped association when plotting health effects like mortality on the vertical axis against the magnitude of the risk factor on the horizontal axis.
The authors suggest that three public health strategies may help make the challenges surrounding J-shaped curves more soluble. First, health communication should emphasise the nadir of a J-shaped curve as a healthy range for the general population. Presentation of the risk curve could be paired with information about what proportion of the population lies an unhealthy distance away from the nadir. Then conversations might focus more on what is epidemiologically important, such as curbing excessive intake, rather than on theoretical risks to small subpopulations. Secondly, ‘linearising’ elements of a given J-shaped curve enables less controversial application of traditional population-based approaches. Linearisation refers to the idea that certain subcomponents of complex risk factors like BMI may be characterised by a more straightforward relationship between exposures and health effects. For example, powdered alcohol, or very sugary alcoholic beverages appealing to adolescents,are cases for which regulation would address a specific health harm that does not have a countervailing benefit.
Thirdly, when a population is variably distributed across a J-shaped risk curve, ‘funneling’ subpopulations from either side toward a curve’s nadir could in some cases help focus on a shared objective of lower risk.
Ultimately, the authors argue, successful approaches will depend on more robust and precise mapping of the inflections of epidemiologically important J-shaped relationships as well as understanding how many people actually are distributed along different points on the curve. Further characterisation should include the causal mechanisms underlying the J-shaped trajectory.
Source: J-Shaped Curves and Public Health. Dave A. Chokshi; Abdulrahman M. El-Sayed; Nicholas W. Stine. JAMA October 6, 2015, Vol 314, No. 13. Free Access.