As with many preventive exposures, causal associations between preventive exposures (e.g. pharmaceuticals) and incident HIV may be optimally assessed through randomized clinical trials (RCT).
Before discussing dietary data, let us first consider some of the more clearly delineated preventive exposures.
Estimating measures of interaction on an additive scale for preventive exposures.
Recoding preventive exposures to get valid measures of interaction on an additive scale.
Fortunately, there is an easy solution to get valid estimates for measures of interaction on an additive scale when having preventive exposures by appropriately recoding one or both of the exposures.
Strictly speaking, these baselines should be called 'recommended levels', as benefits may continue to accrue at higher (for preventive exposures) or lower (for carcinogenic exposures) levels, but the terminology of 'optimum' is retained for consistency.
Moreover, with preventive exposures, measures such as the relative excess risk due to interaction or the attributable proportion can give results that are in the opposite direction of other additive interaction measures such as the synergy index [2].
Consequently, in the absence of RCT data, establishing a causal association between a non-preventive exposure and incident HIV requires application of epidemiologic analysis methods that can control for time-varying behavioral confounding and accommodate small numbers of observed events.
Finally, for most causal, non-preventive exposures, a randomized trial is impossible.
However, causal associations between non-preventive exposures, such as high-risk sexual or substance use behaviors, and incident HIV cannot be ethically evaluated using an RCT.
Exposure to messages regarding the social determinants of health does not necessarily persuade people to endorse preventive policies; exposure can actually heighten resistance to redressing inequities [ 6].
