The impact of image resolution on power, bias, and confounding: A simulation study of ambient light at night exposure

Background: Studies of the impact of environmental pollutants on health outcomes can be compromised by mismeasured exposures or unmeasured confounding with other environmental exposures. Both problems can be exacerbated by measuring exposure from data sources with low spatial resolution. Artificial light at night, for example, is often estimated from low-resolution satellite images, which may result in substantial measurement error and increased correlation with air or noise pollution. Methods: Light at night exposure was considered in simulated epidemiologic studies in Vancouver, British Columbia. First, we assessed statistical power and bias for hypothetical studies that replaced true light exposure with estimates from sources with low resolution. Next, health status was simulated based on pollutants other than light exposure, and we assessed the frequency with which studies might incorrectly attribute negative health impacts to light exposure as a result of unmeasured confounding by the other environmental exposures. Results: When light was simulated to be the causal agent, studies relying on low-resolution data suffered from lower statistical power and biased estimates. Additionally, correlations between light and other pollutants increased as the spatial resolution of the light exposure map decreased, so studies estimating light exposure from images with lower spatial resolution were more prone to confounding. Conclusions: Studies estimating exposure to pollutants from data with lower spatial resolution are prone to increased bias, increased confounding, and reduced power. Studies examining effects of light at night should avoid using exposure estimates based on low-resolution maps, and should consider potential confounding with other environmental pollutants.