Table 1.

Summary of selected epidemiological approaches that could be triangulated to improve causal inference in environmental epidemiology (Note: This is illustrative rather than exhaustive)

Approach	Assumptions	Examples
Traditional methods
Cross population comparisons[12 48]	Populations being compared have different confounding structures; Beyond confounding, the effect of the exposure is the same in populations being compared	Findings for truck traffic air pollution and asthma are similar in high-income countries and low-and-middle income countries[28]
Occupational (homogeneous) cohorts[29]	Different jobs result in different environmental exposures Distributions of confounders are similar in groups doing different jobs	There is little or no confounding by smoking in studies of occupational causes of lung cancer[29], many of which may also be considered as environmental exposures
Extensions of traditional approaches
Instrumental variable (IV) analyses	IV robustly relates to exposure of interest IV is not related to confounders of exposure outcome association IV is not related to other (independent of the exposure of interest) risk factors of the outcome	Use of wind speed and height of the planetary boundary layer as IVs to test the effects of local air pollution on death.[31]
Gene–environment interactions (as an extension of Mendelian randomization)	Genetic variants would only be associated with the outcome in those who have the environmental exposure Groups can be accurately stratified into those exposed and unexposed	Active Glutathione S-transferase theta-1 (GSTT1) genotype is associated with renal cancer risk in those exposed to trichloroethylene (TCE), but not in those not exposed to TCE [36]
Negative control outcome (also known as pre-specified falsification)	There is no plausible causal effect of the real exposure on the negative control outcome Confounding structures are similar for the real and negative control outcome	Similar patterns of associations of social networks with acne, height and headaches (negative control outcomes) to those seen for, e.g., obesity and smoking, suggest that the assumed mechanisms of developing ‘new norms’ for obesity and smoking, and behaviours related to these, are not causal mechanisms. [39]
Regression discontinuity	Exposure is assigned on the basis of a threshold of a continuous variable Exposure assignment is judged to be essentially random close to the threshold	Smog alerts cause individuals to take substantial action to reduce exposure, thus reducing the risk of asthma hospitalizations[44]
Difference in differences	Baseline differences in outcome reflect confounding Rates of change in outcome are similar before exposure occurs Differences in differences are due to the exposure and no new confounding was introduced at the time of exposure	Greening vacant urban spaces (in comparison with urban spaces that have not been greened) reduces criminal behavior but has limited effects on health outcomes[45]
Triangulation of epidemiological evidence
Comparison and integration of evidence from different epidemiological methods which have differing key sources of bias	Bias is in different directions in the populations and/or methods that are being compared Thus, if the findings are similar in different populations, or using different methods, this indicates that bias is not a major problem	Researchers have used this spontaneously in some epidemiological fields for some decades, though we could not find examples in environmental epidemiology. We recommend that it should be used and formalized more in environmental epidemiology.