Population-level study links short-wavelength nighttime illumination with breast cancer incidence in a major metropolitan area

ABSTRACT

Several population-level studies revealed a positive association between breast cancer (BC) incidence and artificial light at night (ALAN) exposure. However, the effect of short-wavelength illumination, implicated by laboratory research and small-scale cohort studies as the main driving force behind BC–ALAN association, has not been supported by any population-level study carried out to date. We investigated a possible link between BC and ALAN of different subspectra using a multi-spectral year-2011 satellite image, taken from the International Space Station, and superimposing it with year-2013 BC incidence data available for the Great Haifa Metropolitan Area in Israel. The analysis was performed using both ordinary least square (OLS) and spatial dependency models, controlling for socioeconomic and locational attributes of the study area. The study revealed strong associations between BC and blue and green light subspectra (B = 0.336 ± 0.001 and B = 0.335 ± 0.002, respectively; p < 0.01), compared to a somewhat weaker effect for the red subspectrum (B = 0.056 ± 0.001; p < 0.01). However, spatial dependency models, controlling for spatial autocorrelation of regression residuals, confirmed only a positive association between BC incidence and short-wavelength (blue) ALAN subspectrum (z = 2.462, p < 0.05) while reporting insignificant associations between BC and either green (z = 1.425, p > 0.1) or red (z = −0.604, p > 0.1) subspectra. The obtained result is in line with the results of laboratory- and small-scale cohort studies linking short-wavelength nighttime illumination with circadian disruption and melatonin suppression. The detected effect of blue lights on BC incidence may help to develop informed illumination policies aimed at minimizing the adverse health effects of ALAN exposure on human health.

KEYWORDS:

• Artificial light at night
• short-wavelength illumination
• melatonin
• satellite images
• breast cancer incidence

Declaration of interest

The authors report no conflicts of interest.

Supplemental data for this article can be accessed on the publisher’s website.

Notes

¹ Descriptive statistics of the research variables for selected percentile groups, ranked according to BC incidence densities (number of BC cases per km²), are available in Supplementary materials (see Table S1).

² SE models include spatially autocorrelated error terms as an additional explanatory variable. The values of this variable are calculated as a linear function of neighboring error terms, thus helping to capture much of the local variation and obtain less biased regression estimates (Anselin 2005; Jetz et al. 2005). When the values of the SE variable are calculated for smoothed surfaces with small distances between neighboring observations, the inclusion of this variable into the model may increase the model fit considerably (Jetz et al. 2005). In fact, R² reported for SE models is a pseudo-R², based on weighted predicted values and residuals (Anselin 2013), which is not fully comparable with R² in traditional OLS models. Therefore, empirical studies consider log-likelihood, Akaike and Schwarz information criteria as more proper measures of fit for spatial dependence models (Anselin 2005).

Additional information

Funding

The authors are grateful to the Israel Ministry of Science, Technology and Space for supporting this study [the Ilan Ramon scholarship].