Gateway effects: why the cited evidence does not support their existence for low-risk tobacco products (and what evidence would)

It is often claimed that low-risk drugs still create harm because of “gateway effects”, in which they cause the use of a high-risk alternative. Such claims are popular among opponents of tobacco harm reduction, claiming that low-risk tobacco products (e.g.,ecigarettes, smokeless tobacco) cause people to start smoking, sometimes backed by empirical studies that ostensibly support the claim. However, these studies consistently ignore the obvious alternative causal pathways, particularly that observed associations may represent causation in the opposite direction (smoking causes  people to seek low risk alternatives) or confounding (the same individual factors increase the chance of using any tobacco product).

Published: 9 March 2015

Positive: Yes

Link to publication: http://antithrlies.com/2015/03/12/new-phillips-working-paper-on-thr-related-gateway-claims/

Author:

Carl V. Philips (CASAA)


Summary

Abstract

It is often claimed that low-risk drugs still create harm because of “gateway effects”, in which they cause the use of a high-risk alternative. Such claims are popular among opponents of tobacco harm reduction, claiming that low-risk tobacco products (e.g., ecigarettes, smokeless tobacco) cause people to start smoking, sometimes backed by empirical studies that ostensibly support the claim. However, these studies consistently ignore the obvious alternative causal pathways, particularly that observed associations may represent causation in the opposite direction (smoking causes people to seek low risk alternatives) or confounding (the same individual factors increase the chance of using any tobacco product). Due to these complications, any useful analysis must to deal with simultaneity and confounding by common case. In practice, existing analyses provide almost cartoon examples of drawing naïve causal conclusions from observed associations. The present analysis examines what evidence and research strategies would be needed to empirically detect such a gateway effect, if there were one, explaining key methodological concepts including causation and confounding, examining the logic of the claim, identifying potentially useful data, and debunking common fallacies on both side of the argument, as well as presenting an extended example of proper empirical testing. The analysis demonstrates that none of the empirical studies to date that purport to show a gateway effect from tobacco harm reduction products actually does so. The observations and approaches can be generalized to other cases where observed association of individual characteristics in cross-sectional data can result from one or several causal relationships.


Conclusion

Searching for some signal of a gateway effect amidst overwhelming confounding requires more rigorous methods than are typical in public health epidemiology. This generalizes to any attempt to use cross-sectional data to sort out causation in a particular direction from confounding or reverse causation. When seeking epidemiologic associations where confounding is minimal or relatively simple in its causes, the typical methods used in the field are still far from optimal, but the empirical results might still be basically useful. That is not the case in this context. While it might never be possible to convincingly demonstrate a gateway effect given the challenges, and statistical analyses have no hope of detecting a tiny effect, there are clearly better and worse ways to pursue the question.

Full text: phillips-how-to-detect-gateway-effects1