Light pollution as a Risk Factor for Breast Cancer: A GISAssisted Case Study

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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
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• 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).

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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

• 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.

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Goal of the Study

• 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.

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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.

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Research Phases
The study was carried out in three separate
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.

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Phase 1 Analysis of Nightlight Map

• 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
  
• Radiance0.1DN(2/3) (nanowatts/cm2/sr)
• (The radiance in Israel ranges from 2.52 to 120
  nano-watts/cm2/sr).

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Breast Cancer Data

• Data source Israel Ministry of Health
• Resolution Small Statistical Areas (SSA)
• Number of observations ca. 214
• Time span 1998 2001.

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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).

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General Trends
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• 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.

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• 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.

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• 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
(R20.919) where higher light intensities
corresponded to higher rates of breast cancer
irrespective of the two group trends
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Regression Analysis

• Dependent variable per 100,000 breast cancer
  rates (CR)
• Main explanatory variable the logarithm of the
  average night light intensity in a locality (LI,
  nano-watts/cm2/sr).
• Controls
• Average per capita income (INC). The variable is
  added as a proxy for illumination inside peoples
  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.

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Regression results
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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 peoples homes, the income
  variable (INC) is a proxy for illumination
  intensity inside dwellings.

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Phase 2 Neighborhood Survey

• Study area four neighborhoods in the City of Tel
  Aviv.
• Selection criteria
• Breast cancer rates - highest and lowest rates
  across the entire city).
• Average incomes - above 5000 NIS and bellow 1500
  NIS.

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Haargaz a low-income neighborhood with low
breast cancer rates (40 cases per 100,000 women)
Afeka a high-income neighborhood with low
breast cancer rates (less than 30 cases per
100,000 women)
Tikva - a low-income neighborhood with extremely
high breast cancer rates (150 cases per 100,000
women)
Bavlei a high-income neighborhood with
extremely high cancer rates (160 cases per
100,000 per women)
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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).

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Results
Within each socio-economic group of
neighborhoods, a similar significant difference
is reveled (plt0.01, plt0.05) Neighborhoods with
high light intensity show significantly higher
breast cancer rates (plt0.01).
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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.

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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

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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.

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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.