Boris A. Portnov

Light at Night Co‐distributes with Incident Breast but not Lung Cancer in the Female Population of Israel

Recent studies of shift‐working women have reported that excessive exposure to light at night (LAN) may be a risk factor for breast cancer. However, no studies have yet attempted to examine the co‐distribution of LAN and breast cancer incidence on a population level with the goal to assess the coherence of these earlier findings with population trends. Coherence is one of Hills “criteria” (actually, viewpoints) for an inference of causality. Nighttime satellite images were used to estimate LAN levels in 147 communities in Israel. Multiple regression analysis was performed to investigate the association between LAN and breast cancer incidence rates and, as a test of the specificity of our method, lung cancer incidence rates in women across localities under the prediction of a link with breast cancer but not lung cancer. After adjusting for several variables available on a population level, such as ethnic makeup, birth rate, population density, and local income level, a strong positive association between LAN intensity and breast cancer rate was revealed (p<0.05), and this association strengthened (p<0.01) when only statistically significant factors were filtered out by stepwise regression analysis. Concurrently, no association was found between LAN intensity and lung cancer rate. These results provide coherence of the previously reported case‐control and cohort studies with the co‐distribution of LAN and breast cancer on a population basis. The analysis yielded an estimated 73% higher breast cancer incidence in the highest LAN exposed communities compared to the lowest LAN exposed communities.

The new world atlas of artificial night sky brightness

The world atlas of zenith artificial night sky brightness is modelled with VIIRS DNB data and calibrated with more than 35,000 observations. Artificial lights raise night sky luminance, creating the most visible effect of light pollution—artificial skyglow. Despite the increasing interest among scientists in fields such as ecology, astronomy, health care, and land-use planning, light pollution lacks a current quantification of its magnitude on a global scale. To overcome this, we present the world atlas of artificial sky luminance, computed with our light pollution propagation software using new high-resolution satellite data and new precision sky brightness measurements. This atlas shows that more than 80% of the world and more than 99% of the U.S. and European populations live under light-polluted skies. The Milky Way is hidden from more than one-third of humanity, including 60% of Europeans and nearly 80% of North Americans. Moreover, 23% of the world’s land surfaces between 75°N and 60°S, 88% of Europe, and almost half of the United States experience light-polluted nights.

GLOBAL CO-DISTRIBUTION OF LIGHT AT NIGHT (LAN) AND CANCERS OF PROSTATE, COLON, AND LUNG IN MEN

The incidence rates of cancers in men differ by countries of the world. We compared the incidence rates of three of the most common cancers (prostate, lung, and colon) in men residing in 164 different countries with the population‐weighted light at night (LAN) exposure and with several developmental and environmental indicators, including per capita income, percent urban population, and electricity consumption. The estimate of per capita LAN exposure was a novel aspect of this study. Both ordinary least squares (OLS) and spatial error (SE) regression models were used in the analysis. We found a significant positive association between population exposure to LAN and incidence rates of prostate cancer, but no such association with lung cancer or colon cancer. The prostate cancer result is consistent with a biological theory and a limited number of previous studies of circadian disruption and risk. The LAN‐prostate cancer connection is postulated to be due to suppression of melatonin and/or disruption of clock gene function. An analysis holding other variables at average values across the 164 countries yielded a risk of prostate cancer in the highest LAN‐exposed countries 110% higher than in the lowest LAN exposed countries. This observed association is a necessary condition for a potentially large effect of LAN on risk of prostate cancer. However, it is not sufficient due to potential confounding by factors that increase the risk of prostate cancer and are also associated with LAN among the studied countries.

Nighttime light level co-distributes with breast cancer incidence worldwide

Breast cancer incidence varies widely among countries of the world for largely unknown reasons. We investigated whether country-level light at night (LAN) is associated with incidence. We compared incidence rates of five common cancers in women (breast, lung, colorectal, larynx, and liver), observed in 164 countries of the world from the GLOBOCAN database, with population-weighted country-level LAN, and with several developmental and environmental indicators, including fertility rate, per capita income, percent of urban population, and electricity consumption. Two types of regression models were used in the analysis: Ordinary Least Squares and Spatial Errors. We found a significant positive association between population LAN level and incidence rates of breast cancer. There was no such an association between LAN level and colorectal, larynx, liver, and lung cancers. A sensitivity test, holding other variables at their average values, yielded a 30–50% higher risk of breast cancer in the highest LAN exposed countries compared to the lowest LAN exposed countries. The possibility that under-reporting from the registries in the low-resource, and also low-LAN, countries created a spurious association was evaluated in several ways and shown not to account for the results. These findings provide coherence of the previously reported case–control and cohort studies with the co-distribution of LAN and breast cancer in entire populations.

Using kernel density function as an urban analysis tool: Investigating the association between nightlight exposure and the incidence of breast cancer in Haifa, Israel

Abstract The kernel density (KD) function estimates the intensity of events across a surface by calculating the overall number of cases situated within a given search radius from a target point. To form a continuous surface from individual observations, the KD technique does not require the presence of a parameter’s value in a given location (e.g., the incidence rate of a disease). This feature of KD smoothing is especially beneficial for empirical studies in which individual observations are represented by geographic coordinates only and have no other attributes, required by more commonly used smoothing techniques, such as spline and kriging. In the present study, we illustrate the use of KD technique for a study of association between the geographical distributions of breast cancer cases and exposure to artificial illumination during nighttime (light-at-night or LAN) in the city of Haifa, Israel.

On ecological fallacy, assessment errors stemming from misguided variable selection, and the effect of aggregation on the outcome of epidemiological study.

In social and environmental sciences, ecological fallacy is an incorrect assumption about an individual based on aggregate data for a group. In the present study, the validity of this assumption was tested using both individual estimates of exposure to air pollution and aggregate data for 1,492 schoolchildren living in the in vicinity of a major coal-fired power station in the Hadera region of Israel. In 1996 and 1999, the children underwent subsequent pulmonary function tests (PFT), and their parents completed a detailed questionnaire on their health status and housing conditions. The association between childrens PFT results and their exposure to air pollution was investigated in two phases. During the first phase, PFT averages were compared with average levels of air pollution detected in townships, and small census areas in which the children reside. During the second phase, individual pollution estimates were compared with individual PFT results, and pattern detection techniques (Getis-Ord statistic) were used to investigate the spatial data structure. While different levels of areal data aggregation changed the results only marginally, the choice of indices measuring the childrens PFT performance had a significant influence on the outcome of the analysis. As argued, differences between individual-level and group-level effects of exposure (i.e., ecological or cross-level bias) are not necessary outcomes of data aggregation, and that seemingly unexpected results may often stem from a misguided selection of variables chosen to measure health effects. The implications of the results of the analysis for epidemiological studies are discussed, and recommendations for public health policy are formulated.

Exploring Spatial Data Analysis Techniques Using R: The Case of Observations with No Neighbors

It is widely acknowledged that one of the impediments to a broader acceptance of techniques for spatial data analysis is that handling spatial data involves more insight and possibly the use of additional applications than other forms of data (Anselin, 2000, p. 217). We are perhaps more familiar with the potential difficulties caused by the inadequate mapping of data into temporal reference frameworks, such as the predicted complications attributed to the year 2000 problem, when a circular measure (99 + 1 = 0) was treated as linear. Spatial data come with many assumptions about their reference frameworks, including projection metadata, and are often derived from geographical information systems or other archives of spatial position data. Some of these are also time-specific, where boundary segments are introduced to or removed from maps of polygon representations of spatial objects.

Residential proximity to petroleum storage tanks and associated cancer risks: Double Kernel Density approach vs. zonal estimates.

BACKGROUND AND AIMS The relationship between exposure to petroleum products and cancer is well-established in occupational studies carried out among employees of transportation and oil-producing industries. However, question remains whether living near petroleum storage facilities may represent a cancer risk. In the present study, we examined cancer incidence rates associated with residential proximity to the Kiryat Haim industrial zone in Northern Israel, using different analytical techniques and adjusting for several potential confounders, such as road proximity, population density, smoking rates and socio-demographic attributes. METHODS Both traditional zonal approaches and more recently developed Double Kernel Density (DKD) tools were used to estimate relative risks of lung and NHL cancers attributed to residential proximity to the petroleum storage site. RESULTS Zonal approaches based on comparing ASRs across small census areas (SCAs) did not detect any significant association between residential proximity to the industrial zone and the two types of cancers under study (P>0.2). In contrast, the DKD approach revealed that the relative density of both lung and NHL cancers declined in line with distances from the industrial zone, especially among the elderly (Lung: t>-12.0; P<0.01; NHL: t>-9.0; P<0.01), adjusted for proximity to main roads, population density, smoking rate, average income, and several other potential confounders. CONCLUSIONS Living near petroleum storage sites may represent significant cancer risk which cannot always be detected by traditional zonal approaches commonly used in epidemiological studies, especially if the number of census areas available for the analysis is small.

High prevalence of childhood asthma in Northern Israel is linked to air pollution by particulate matter: evidence from GIS analysis and Bayesian Model Averaging.

The medical records of 3922 school children residing in the Greater Haifa Metropolitan Area in Northern Israel were analyzed. Individual exposure to ambient air pollution (SO2 and PM10) for each child was estimated using Geographic Information Systems tools. Factors affecting childhood asthma risk were then investigated using logistic regression and the more recently developed Bayesian Model Averaging (BMA) tools. The analysis reveals that childhood asthma in the study area appears to be significantly associated with particulate matter of less than 10 μm in aerodynamic diameter (PM10) (Odds Ratio (OR) = 1.11; P < 0.001). However, no significant association with asthma prevalence was found for SO2 (P > 0.2), when PM10 and SO2 were introduced into the models simultaneously. When considering a change in PM10 between the least and the most polluted parts of the study area (9.4 μg/m3), the corresponding OR, calculated using the BMA analysis, is 2.58 (with 95% posterior probability limits of OR ranging from 1.52 to 4.41), controlled for gender, age, proximity to main roads, the town of a childs residence, and familys socio-economic status. Thus, it is concluded that exposure to airborne particular matter, even at relatively low concentrations (40–50 μg/m3), generally below international air pollution standards (55–70 μg/m3), appears to be a considerable risk factor for childhood asthma in urban areas. This should be a cause of concern for public health authorities and environmental decision-makers.

A Mathematical Model of Segregation Patterns in Residential Neighbourhoods

A mathematical model is proposed which describes the dynamics and the spatial distributions of two population groups, where migration is driven by considerations of socioeconomic status. The model associates segregation with instabilities of spatially uniform mixed population states. These instabilities lead to a wide range of segregation forms including: (a) variable (weak) segregation where the population is everywhere mixed and the spatial variability is controlled by a ‘status-gap’ parameter, (b) strong segregation, where nearby neighbourhoods consists of pure (unmixed) population groups, and (c) intermediate forms involving enclaves of a pure population group in neighbourhoods of mixed population. The model associates tipping-point phenomena with the existence of an unstable mixed population state which introduces a threshold for population inversion. The model predicts that uneven invasions of one population group into another may result from interface instabilities rather than from urban heterogeneities.