Light pollution as a Risk Factor for Breast Cancer: A GIS-Assisted

Case Study I. Kloog, 1 B. Portnov 1, and A. Haim 2

1Department of Natural Resource and Environmental Management,University of Haifa 2Department of Biology, University of

Haifa-Oranim

Presented 21 June 2005

Definition of Light Pollution :

Light pollution is “environmental pollution consisting of harmful or annoying light from cities and outdoor lighting, which prevents

the observation of faint objects” (http://www.darksky.org).

• Brainard G. C. et al. (2001) show that there is a strong correlation between the exposure to the prolonged photoperiod and melatonin levels in blood.

• Davis et al. (2001) point up at a direct link between the lack of melatonin caused by an exposure to a prolonged period of artificial illumination and an increase in the breast cancer rate. In particular, studies of night shift workers indicate higher rates of breast cancer by 36-60% compared to the general population;

• Verkasalo et al. (1999) pointed out that the rate of breast cancer in visually impaired women decreases as the degree of impairment increases.

Previous studies

Very few studies have been done to date to determine a possible negative impact of artificial light on human health. However, some indirect evidence in this direction is nevertheless available:

• Although previous studies indicates that the exposure to light pollution may be a risk factor in breast cancer development, the empirical evidence in this direction is rather fragmented and largely inconsistent.

• The goal of the present analysis is to Investigate the relationship between exposure to artificial illumination (light pollution) and breast cancer rates, using Israel as a case study.

Goal of the Study:

Novelty of the study

• To the best of our knowledge this is the first study that uses macro-level remote sensing data to link light pollution with the incidence of breast cancer.

Research Phases

• A GIS assisted analysis of light intensity using satellite maps - a low-resolution scale that covers the whole country;

• Light intensity and breast cancer rates in residential neighborhoods - a high resolution scale covering four residential neighborhoods in Tel-Aviv, where light intensity was measured in-situ.

• Questionnaires: which were distributed among breast cancer patients and among a control group of healthy women from the same area.

The study was carried out in three separate phases:

• Data source: 1996-1997 satellite image of radiance-calibrated night light intensity supplied by the U.S. Defense Meteorological Satellite Program (DMSP).

• Data range: The light intensity is measured in digital numbers (DN) ranging from 8 to 113, which are converted into nano-watts/cm2/sr as follows:

Radiance=0.1*DN(2/3) (nanowatts/cm2/sr)

(The radiance in Israel ranges from 2.52 to 120 nano-watts/cm2/sr).

Phase 1: Analysis of Nightlight Map

Breast Cancer Data

• Data source: Israel Ministry of Health• Resolution: Small Statistical Areas

(SSA)• Number of observations: ca. 214• Time span: 1998 – 2001.

Data Matching

•The rates of breast cancer and nightlight intensity were merged using the Spatial Join tool in ArcGIS.

•The result is the mean values, standard deviation of light intensity against cancer rates for each locality (or SSA).

The scatter plot reveals three distinct groups of towns – localities with abnormally high, average and low cancer rates.

For each group of localities, the trend lines show a positive association between night light intensity and breast cancer rates (R2=0.019-0.675).

General Trends

• The localities with abnormally high cancer rates consist mainly of towns that are located on the seam line between the Palestinian autonomous areas and the State of Israel (Tzoran, Tzor yigal, Meitar).

• In all these towns and their surrounding areas we found extensive and large scale illumination systems (with very high light intensity ranging between 0.29 and 0.47 micro-lux), which were built by the state for security reasons.

• The localities with abnormally low cancer rates consist mainly of towns with predominantly minority population : Arab, Druze or Circassian (Kusife, Shfram, Umm Al-Fahm, Baqa Al-gharbiyye etc.).

• These towns are characterized by relatively low average incomes - low-income households and municipalities try to minimize their outlays, by using (inter alia) less illumination at both private homes and public domains.

• These localities are also characterized by the relatively low rates of labor force participation by the minority population (ca. 37%, as opposed to 52% among Jews), and specifically by minority women, reduces their exposure to artificial light at the work place.

• we then plotted the cancer rates in selected localities of each of these groups as a function of the in-situ measured light intensity

As shown, there is a positive correlation (R2=0.919) where higher light intensities corresponded to higher rates of breast cancer irrespective of the two group trends

R2 = 0.9199

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1. Dependent variable: per 100,000 breast cancer rates (CR)

2. Main explanatory variable: the logarithm of the average night light intensity in a locality (LI, nano-watts/cm2/sr).

3. Controls: Average per capita income (INC). The variable is added

as a proxy for illumination inside people’s houses: As the average income increases, so does the electricity consumption within the home, since wealthier households can afford more illumination.

Population The variable is added since it adds missing data, not captured by the satellite imaginary (artificial illumination in public transport, shopping centers etc.), that contributes to light pollution in a locality. The bigger the population of a town, the more vehicles and public lighted facilities it has.

G1: localities with abnormally high cancer rates. G2: localities with abnormally low cancer rates.

Regression Analysis:

Regression resultsExplanatory B B VIF

OLS Model SL model(Constant) -113.25 -143.23

(-4.17)* (-5.21)*Mean light pollution 6.46 7.65 1.746

(4.24)* (4.38)*Per Capita Income 21.02 24.39 1.454

(6.62)* (7.71)*Localities with abnormally high cancer rates 41.05 40.73 1.089

(10.96)* (11.21)*Localities with abnormally low cancer rates -35.35 -32.33 1.256

(-13.43)** (-12.48)*Population size 2.96 2.93 1.586

(2.87)* (2.99)*Number of observations 209 209Residual standard error 12.08 11.64Log-likelihood - -1075R2 0.778 -R2 adjusted 0.772 -F 142.64 -

Explanation of the model • More affluent and more illuminated localities tend to exhibit

(ceteris paribus) higher rates of breast cancer.

• While the average night light intensity (LI) is an indication of artificial illumination of spaces outside people’s homes, the income variable (INC) is a proxy for illumination intensity inside dwellings.

Phase 2: Neighborhood Survey

• Study area: four neighborhoods in the City of Tel Aviv.

• Selection criteria: 1. Breast cancer rates - highest and lowest rates across

the entire city).

2. Average incomes - above 5000 NIS and bellow 1500 NIS.

Tikva - a low-income neighborhood with extremely high breast cancer rates (150 cases per 100,000 women)

Haargaz – a low-income neighborhood with low breast cancer rates (40 cases per 100,000 women)

Bavlei – a high-income neighborhood with extremely high cancer rates (160 cases per 100,000 per women)

Afeka – a high-income neighborhood with low breast cancer rates (less than 30 cases per 100,000 women)

in-situ Light Measurements

• Using a light-meter (LI-COR, LI-189), night light intensity was measured at 40 randomly selected points in each neighborhood.

• The light intensity was measured in micro-Lux at the average height of woman eyesight (1.7 m above the ground).

Within each socio-economic group of neighborhoods, a similar significant difference is reveled (p<0.01, p<0.05):

Neighborhoods with high light intensity show significantly higher breast cancer rates (p<0.01).

Results:

Phase 3: Questioners• A specially designed questionnaire was distributed among

breast cancer patients in the Sheba Medical Center in the Tel Aviv metropolis. The control group consisted of healthy women.

• The data were collected using questioners (Helsinki committee approved) that were filled anonymously by the subjects with the care of the Department of Oncology of the Sheba Medical Center.

• Data were collected between the dates of 1.1.2003 to 1.3.2005 from 100 breast cancer patients and 100 healthy women.

Questioners Results• The answers to each question in the questioner were

compared between the two groups, averaged and subjected to a t-test.

• The analysis of answers of the two questioner groups showed clear and highly significant differences between the group of breast cancer patients and healthy women in several important categories

Category Group mean std t sig.

Breast cancer occurrence in the family with cancer 0.376 0.487 4.114 0.000

  without cancer 0.115 0.321    

Colorectal cancer occurrence in the family with cancer 0.178 0.385 1.846 0.067

  without cancer 0.082 0.277    

Exposure to light through the bedroom window at night with cancer 0.297 0.459 2.013 0.046

  without cancer 0.164 0.373    

Walking distance from shopping centers with cancer 0.653 0.478 3.315 0.001

  without cancer 0.393 0.493    

Walking distance from cultural centers with cancer 0.6 0.492 1.749 0.082

  without cancer 0.459 0.502    

Directions for future research

• This study has been a preliminary analysis which included a limited set of case studies. Future research needs to be carried out in both higher resolution (a worldwide scale and other countries) and low resolution scale (e.g., in-depth analysis of individual urban localities such as Tel Aviv and Haifa), to strengthen our results.

• In addition the relationship between light pollution and other hormonal cancers (such as prostate and colon cancers) needs to be also investigated.

Conclusions and Directions of Future Research

• The survey thus reveals a strong association between the exposure to high nightlight intensity and the incidence of breast cancer.

• We thus suggest that municipalities should adopt a smart policy of illumination. Such a policy should reduce illumination when and where not absolutely necessary, to both save energy (and money) and prevent excessive light pollution which appears to be a general environmental hazard to public health.