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ORIGINAL PAPER
Nighttime light level co-distributes with breast cancer incidence
worldwide
Itai Kloog •Richard G. Stevens •
Abraham Haim •Boris A. Portnov
Received: 28 April 2010 / Accepted: 20 July 2010 / Published online: 3 August 2010
Springer Science+Business Media B.V. 2010
Abstract 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 con-
sumption. 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.
Keywords Breast cancer Epidemiology Geography
Light at night (LAN)
Introduction
There is evidence that excessive exposure to light at night
(LAN) may increase the risk of breast cancer (reviewed in
[1,2]). Possible mechanisms include the suppression of
melatonin (MLT) secretion by the pineal gland leading to
increased tumor growth [3,4]; the adverse effects of LAN
on thermoregulatory and immune functions [5,6], and the
direct disruption of circadian gene function in the supra-
chaismatic nuclei by LAN, leading to alterations of cell
cycle regulation in the breast tissue [7,8]. The original
hypothesis was based on a suppression of melatonin. If this
is a primary mechanism, then we predicted that LAN
would be associated with hormone-dependent cancers (e.g.,
breast and prostate cancers), and not, or to a lesser extent,
with non-hormone-dependent cancers (e.g., lung, colorec-
tal, and larynx).
The ‘‘LAN–breast cancer’’ theory has been supported by
the observation that shift-working women are at higher risk
of developing breast cancer [9–12], while blind women
[13–15] and long sleepers [16–19] seem to be at a lower
risk. The studies in shift workers resulted in the classifi-
cation of ‘‘shift work’’ as a 2A ‘‘probable human carcino-
gen’’ by the International Agency For Research on Cancer
(IARC) [20].
I. Kloog B. A. Portnov (&)
Department of Natural Resources & Environmental
Management, University of Haifa, 31905 Mount Carmel, Haifa,
Israel
e-mail: portnov@nrem.haifa.ac.il
R. G. Stevens
University of Connecticut Health Center, Farmington,
CT 06030-6325, USA
e-mail: bugs@uchc.edu
A. Haim
The Israeli Center for Interdisciplinary Research
in Chronobiology, University of Haifa, 31905 Mount Carmel,
Haifa, Israel
123
Cancer Causes Control (2010) 21:2059–2068
DOI 10.1007/s10552-010-9624-4
Breast cancer is the second leading cause of cancer
death in women (after lung cancer) and is the most com-
mon cancer among women worldwide, excluding non-
melanoma skin cancers. According to the World Health
Organization (WHO), 1,300,000 women are diagnosed
with breast cancer annually and about 465,000 die from
this disease every year [21].
Parkin and colleagues [22–24] conducted several com-
prehensive studies of worldwide differences in cancer
rates. During this ongoing research, the estimates of
prevalence, mortality, and incidence of 26 most common
cancers were collected for 20 geographic regions of the
world. The rates of breast cancer in women appear to differ
widely across the globe, being generally higher in devel-
oped countries than in the developing ones. Thus, in North
America age-standardized rates (ASR) of breast cancer
incidence are 92.7 per 100,000, compared to Africa where
breast cancer ASR are 21.5 per 100,000 (see Fig. 1).
Global variation of lung cancer is also large with ASR
reaching 33.85 per 100,000 in North America vs. 1.61 per
100,000 in Africa.
However, to the best of our knowledge, no studies car-
ried out to date have attempted to investigate the possibility
that the above disparities in female cancer incidence rates
are associated with country-specific LAN emissions (light
pollution), thus testing a prediction of the LAN–cancer
theory.
In the present analysis, we investigate whether exposure
to LAN is associated with cancers in women, using ASR of
common cancers in women available for 164 world coun-
tries obtained from the GLOBOCAN 2002 database. This
analysis is a continuation of our previous studies showing a
significant co-distribution of LAN and prostate cancer
worldwide but not with lung and colorectal cancer in men
[25] and an extension of previous studies which detected a
positive association between LAN and breast cancer within
Israel [26].
If LAN is significantly associated with hormone-
dependent cancers, then an elevated incidence of breast
cancer with elevated levels of LAN can be expected, but
not elevated risk of colorectal, larynx, liver, and lung
cancer, the cancers which are not hormone dependent and
thus included in the present analysis as negative controls.
The limitations of population level, or ecological,
studies are well known and include exposure misclassifi-
cation and missing confounder bias. However, such studies
are important in providing context for research among
subpopulations of people using case–control and cohort
designs. If no association is found at the population level in
a study with good statistical power, then that would be
evidence against a strong effect of a putative risk factor. A
positive association is thus a necessary, but not sufficient
condition for there to be a large effect of a common
exposure on risk in society at large.
Methods
Cancer data
Data on cancer ASR in women for the present analysis
were obtained from the GLOBOCAN 2002 database,
maintained by the IARC [23]. The IARC cancer data are
reported for individual countries of the world for the period
of 1998–2002 [22]. These data have been previously used
widely in epidemiological research (e.g., [27,28]).
Fig. 1 Worldwide variation of
breast and lung cancers
incidence by continent Note:
Age-standardized incident rates
per 100,000 Source: Calculated
using data from Ferlay et al.
(2004)
2060 Cancer Causes Control (2010) 21:2059–2068
123
The data were obtained for breast, lung, and colorectal
cancers (three of the most common cancers in women) as
well as for larynx cancer and liver cancer the main risk
factors for which are well known (smoking for larynx
cancer and hepatitis B virus (HBV) or hepatitis C virus
(HCV) for liver cancer).
Explanatory variables
Several development indicators of the world countries were
included in the present analysis as potential predictors of
country-specific cancer incidence rates and perhaps as
confounders of any possible LAN effect.
GDP per capita ($US) is a commonly used measure of
population welfare that reflects differences in the diet and
lifestyles of different socio-economic strata [29,30]. Risk
of breast cancer tends to be higher among high-income
groups than across low-income strata and is significantly
higher in the developed countries than in the developing
ones [31].
Percent urban population
Living in cities is often associated with a considerable
amount of physiological stress associated with high resi-
dential densities, traffic congestion, and air pollution,
which may increase cancer risk [32]. In addition, residents
of urban areas are exposed to more environmental smok-
ing, due to high residential densities thus also creating
passive smokers under these conditions, which is another
cause of cancer [33]. Dietary differences and reduced
physical activities associated with urban living may also
play a role in the development of cancer.
Electricity consumption (kWh per capita)
Electricity consumption may be an indicator of socio-
economic development and industrial emission of gaseous
substances associated with electricity production [34,35].
Fertility rates (average number of births per woman)
Fertility is negatively associated with breast cancer risk
[36]. Fertility rates used in the analysis, to account for this
effect, are total fertility rate (TFR), which is a more
accurate measure of fertility than crude birth rates, since
they refer to the average number of births per woman,
rather than to average natural growth for population as a
whole [37].
In addition, LAN exposure was measured in the analysis
using satellite image data, as further detailed in the ‘‘Data
Sources’’ and ‘‘GIS Analysis’’ sections. In particular, the
worldwide satellite image for 1996/97, used in the analysis,
was to compare with 2002 cancer incidence rates, the latest
available, thus helping to account, at least to a some extent,
for the latency period between exposure and the onset of
cancer.
Descriptive statistics of the research variables used in
the analysis are presented in Appendix 1.
Data sources
Data for the present analysis were obtained from the fol-
lowing two main sources:
•Country-level data on per capita gross domestic product
(GDP), percent of urban population, and per capita
electricity consumption for 1998–1999 were obtained
from the ESRI ArcGIS
TM
database, and country-
specific fertility rates were obtained from the CIA
World Fact Book [37,38].
•Data on nighttime illumination (LAN) were obtained
from the U.S. Defense Meteorological Satellite Pro-
gram (DMSP) [39]. The DMSP satellite provides
continuous reading of the entire Earth surface during
nighttime as it cycles around the globe. The satellite
image for 1996/97, used in our analysis, was con-
structed by the DMSP by averaging daily readings of
the satellite sensors and removing cloud cover.
[Reported in nanowatts per centimeter squared per
steradian.]
Geographic information systems (GIS) analysis
GIS has been used extensively in recent years as an
important research tool for cancer-related studies [26,40–
44].
In the present study, GIS technology was used for
matching country-specific cancer incidence rates with the
LAN levels obtained from satellite images. The task was
performed using the ‘‘spatial join’’ tool in the ArcGIS
9.x
TM
software, which joins data from two geographic
layers by appending attributes from one layer to another,
based on the relative location of features in the layers [45].
The ‘‘spatial join’’ between two data sources was per-
formed as follows: At the beginning, a worldwide radiance-
calibrated satellite image of nighttime illumination, com-
prised of average nightlight intensity in 1996/97 and
measured in light radiance units (i.e., nanowatts/cm2/sr),
was imported to the ArcGIS 9
TM
software. The image
reflects the fraction of light escaped into space and detected
by the satellite’s sensors.
Although these satellite measurements are a magnitude
lower than actual LAN levels detected on the ground, they
represent accurately the relative levels of nightlight inten-
sity observed in different localities [26], thus reflecting the
Cancer Causes Control (2010) 21:2059–2068 2061
123
levels of nighttime illumination from various outdoor
sources to which local residents are exposed.
The original nighttime illumination image was con-
verted into a vector map using ArcGIS 9.x
TM
‘‘raster-to-
feature’’ conversion tool. The conversion resulted in a
polygon layer containing approximately 3,800,000 poly-
gons characterized by various LAN intensities (with a
minimum LAN value of 0 (no illumination) and the
maximum value of 255 nanowatts/cm
2
/sr (maximum
illumination).
Using the average LAN exposure in a country may
result in a bias caused by a country’s differences in
geography and population structure. For example, countries
with large unpopulated areas (such as e.g., Canada or
Sweden) are likely to exhibit disproportionately low aver-
age LAN estimates. To minimize this bias, we used a
previously developed novel method of adjusting LAN
exposure, which takes into account both a country’s geo-
graphic distribution of population and its local LAN
intensities [46]. To perform this adjustment, the map of
LAN intensity polygons was overlapped with another map
containing places worldwide with a population greater than
1,000 residents. Each city was mapped using its ‘‘central
reference’’ point, normally represented by the location of
the city hall or the central post office. Average LAN values
were then calculated for each populated place by obtaining
LAN values from the LAN intensity polygon into which
the populated place falls. Representing big cities by their
‘‘central reference points’’ may potentially lead to a certain
overestimation of the calculated average LAN exposures
due to the fact that cities’ central areas are normally more
lighted up than their peripheral neighborhoods. However, it
is unlikely to cause a substantial bias in the comparative
analysis since the same procedure was applied to all
localities in all countries under study. Moreover, this
approach may be considered compensatory for the exclu-
sion of populated places with less than 1,000 residents,
omitted from the analysis due to restrictions on data
availability.
The difference between simply averaged and popula-
tion-adjusted LAN estimates can be considerable. For
example, calculating the LAN exposure for Canada by
simple averaging values of LAN polygons (that is, without
accounting for the skewed geographic patterns of the
country’s population), results in relatively low LAN esti-
mates of 6.57 nanowatts/cm
2
/sr, giving the country rank of
133 (out of 164 countries in our sample). Concurrently, the
population-weighted LAN estimate for Canada is 122.84
nanowatts/cm
2
/sr which gives it the second rank among
164 countries in our sample, which reflects better the
country’s high development status and the elevated per
capita LAN exposure of its residents. Another example is
Sweden, whose unadjusted LAN estimate is 6.08
nanowatts/cm
2
/sr (rank 128), while population-adjusted
LAN is 94.23 nanowatts/cm
2
/sr, giving it the rank of 5
among 164 countries in the sample.
The locality-specific LAN values obtained were then
multiplied by the population size of localities and summed
up for each country under study and later divided by the
total population size of the country’s populated places. This
resulted in the average LAN exposure estimate per person
in each country under study. Several countries are reported
in Appendix 2.
Statistical analysis
To identify and measure the significance of factors
affecting the selected cancer rates, several statistical tech-
niques were used. We started with an ordinary least squares
(OLS) model. During the analysis, multicollinearity and
normality were tested, and their results were found satis-
factory (Tolerance [0.27). The tolerance statistic esti-
mates the degree of inter-collinearity between independent
variables, with values approaching zero, indicating that a
strong multicollinearity may be present. In econometric
studies, tolerance values greater than 0.1 are considered to
be satisfactory [47]. The tolerance value of 0.27 we
obtained is considerably higher than 0.1, thus indicating
that the multicollinearity between the explanatory variables
is well within acceptable limits.
The analysis was performed separately for each cancer
type using the following linear model:
Cancerincidence rate ¼B0 constantðÞ
þB1Electricity consumptionðÞ
þB2GDP per capitaðÞ
þB3LANðÞ
þB4Percent of Urban populationðÞ
þB5fertility rateðÞ
þerandom error termðÞ
where B0, ...…,B5 are regression coefficients.
The residuals of the OLS model were tested for the
presence of spatial autocorrelation using the Moran’s Itest
statistic. The test showed significant clustering of residuals
(Moran’s indicator: (0.366–2.461, p\0.001) which
necessitated the use of spatial dependency (SD) models, to
take the spatial dependency of residuals into account and
improve the robustness of regression estimates [48]. The
spatial dependency (SD) regression modeling was per-
formed in the GeoDa
TM
spatial analysis software [49]. [It
should be noted that the results of the SD modeling were
found to be essentially similar to the OLS estimates and are
not reported in the following discussion, for brevity’s sake,
and can be obtained from the authors upon request. In
2062 Cancer Causes Control (2010) 21:2059–2068
123
addition, a weighted analysis using the country population
was also conducted and made no meaningful difference in
the parameter estimates].
Results
Table 1shows factors associated with cancer incidence
rates. The multicollinearity of all variables was tested and
found within tolerable limits (Tolerance [0.27). All
models in Table 1are OLS, estimated separately for the
following five cancer types: breast, colorectal, larynx, liver,
and lung. Two regression models (1 and 2) are reported
separately for breast cancer incidence rates. These models
differ in that Model 2 omits the five ‘‘outlier’’ Gulf States.
The models for breast, lung, and colorectal cancers
provide good fit (R
2
=0.571–0.648) and have a high
degree of generality (F=41.975–59.893, p\0.01), while
the liver and larynx models present poor fits (0.018–0.125),
thus implying that predictors included in these models do
not explain well the variability of these cancer types across
the globe.
Among all the cancer types analyzed, only breast cancer
exhibited a significant positive association with LAN
exposure (b=0.150, t=2.365; p\0.05). For all other
cancer types, LAN exposure was found not to be statisti-
cally significant.
Per capita GDP (ln) is also positively associated with
ASRs of breast, lung, and colorectal cancer (p\0.01),
while it is inversely associated with liver cancer, albeit the
association is not significant (p[0.05).
Fertility rates are negatively associated with breast
cancer as well as lung and colorectal cancer (p\0.01), but
not with larynx and liver cancer (p[0.3), as could be
expected.
To investigate whether the LAN–breast cancer associ-
ation differs by countries with different reproductive pat-
terns, the ANOVA analysis was run. For the analysis, the
countries in our sample were grouped into low, medium,
and high fertility based on Jenk’s ‘natural breaks’ method
[50]. This method determines the best arrangement of
values into classes by comparing the sum of squared dif-
ferences of values from the means of their classes and thus
identifies ‘‘break points’’ in the data values by picking the
class breaks that best group similar values and maximize
the differences between classes. The fertility cut-points
were less than 2.67 children per woman (low-fertility
group), 2.67–4.58 children per woman (medium-fertility
group), and greater than 4.58 children per woman (high-
fertility group). Notably, the LAN/breast cancer connec-
tion is much stronger in the low-fertility group (F=16.91,
p\0.001), to which most developed countries of the
world belong, than in high-fertility group (F=1.24, n.s).
Table 1 Factors affecting most common cancer incidence rates in women worldwide (method: ordinary least square (OLS) regression)
Variable Breast(1)
a
Breast(2)
a
Lung
a
Colon
a
Larynx
a
Liver
a
Tolerance
b
(Constant) -25.038 (-1.394) -46.559 (-2.721)*** -2.613 (-0.509) -16.896 (-2.426)** 1.735 (2.392)** 11.415 (1.604)
Light at night (LAN) (nanowatts/cm
2
/sr) 0.150 (2.365)** 0.277 (4.330)*** 0.032 (1.772) 0.026 (1.061) -7.55E–006 (-0.003) -0.006 (-0.227) 0.702
Electricity consumption (kWh per capita) 0.006 (1.317) 0.002 (0.603) 0.006 (5.351)*** 0.002 (1.420) 8.50E–005 (0.496) 0.619 (0.703) 0.911
Urban population (%) 0.115 (1.541) 0.187 (2.689)*** 0.016 (0.743) 0.032 (1.126) 0.003 (0.951) -0.003 (-0.099) 0.475
GDP per capita (ln), $US 7.882 (3.817)*** 9.314 (4.824)*** 1.465 (2.483)** 3.981 (4.974)*** -0.127 (-1.517) -1.016 (-1.242) 0.271
Fertility rates (per 1,000) -2.939 (-2.759)*** -1.080 (-1.035) -1.233 (-4.052)*** -1.757 (-4.255)*** -0.042 (-0.971) 0.585 (1.386) 0.424
Number of obs.
c
164 159 164 164 164 164
R
2
0.571 0.648 0.575 0.655 0.018 0.105
F 41.975*** 56.219*** 42.777*** 59.893*** 0.574 3.689***
Moran’s I
d
3.775*** 5.130*** 3.031*** 2.461** 0.336 -1.101
a
Regression coefficient (t-statistic in the parenthesis)
b
Tolerance (multicollinearity diagnostic)
c
Number of valid observations list-wise
d
Moran’s Iindex of spatial association of regression residuals
** Indicates a 0.05 significance level; *** indicates a 0.01 significance level
Breast(1): Breast cancer OLS model; all countries in the sample
Breast(2): Breast cancer OLS model; five ‘‘outlier’’ Gulf States are omitted
Cancer Causes Control (2010) 21:2059–2068 2063
123
However, in the high-fertility group, five countries had
relatively high LAN exposure, and these were all oil pro-
ducing Gulf states–Saudi Arabia, Oman, United Arab
Emirates, Qatar, and Kuwait (see Appendix 1-B). When
these are removed from the analysis, the strength of asso-
ciation between LAN and breast cancer of all countries
combined (n=159) considerably increased (from
t=2.365; p\0.05 (Breast(2) Model) to t=4.330;
p\0.001 (Breast(3) Model; see Table 1).
Sensitivity test
To estimate the relative contribution of LAN to breast
cancer ASRs, we split all the countries in our sample into
three groups—countries with minimal LAN exposure (less
than 15 nanowatts/cm
2
/sr); countries with average LAN
exposure (15–57 nanowatts/cm
2
/sr), and countries with the
highest LAN exposure (greater than 57 nanowatts/cm
2
/sr).
The Jenks ‘‘natural breaks’’ method was used to classify
countries into the groups. Next, the values of all other
variables from the second model (apart from LAN) were
set constant to the average values observed in each group,
and a sensitivity test of breast cancer ASRs to changes in
LAN values was run, using the ‘‘breast cancer’’ model
reported in Table 1. The results of the sensitivity test are
reported in Table 2.
As Table 2shows, when the values of all other variables
are fixed, the increase of LAN from 8.60 nanowatts/cm2/sr
(the average LAN value in the group of countries with
minimal LAN exposure) to 28.95 nanowatts/cm2/sr
(countries with average LAN exposure) corresponds to an
increase of 7.2% in breast cancer ASR. A further increase
in LAN value to 99.21 (the maximum LAN exposure)
corresponds to an increase of 23.25% in breast cancer
ASR. There were five countries that had high fertility but
also very high LAN exposure; these were all five Persian
Gulf States (Saudi Arabia, Oman, United Arab Emirates,
Qatar, and Kuwait). When these five ‘‘outlier’’ Gulf States
are omitted (Breast (2) model), the estimated breast cancer
ASRs rise by about 50% from the highest to the lowest
LAN countries.
We also fitted the model to the 80 countries with a per
capita GDP of [$3,000 in order to partially control for a
possible bias in the quality of the registries in the
GLOBOCAN database. Parameter estimates were virtually
unchanged compared to the full analysis of all 164
countries.
Discussion
The results of the present study are consistent with those of
previous studies of LAN and risk [1], and those obtained on
a national scale of breast cancer incidence in Israel [26].
Similar results were also obtained for another hormone-
dependent (prostate) cancer on a worldwide scale for men
[25].
We found a significant positive association between
country LAN level and breast cancer incidence, yet no such
association was found for the other cancer types (colorec-
tal, larynx, liver, and lung) which were used as negative
controls. The results of our analysis also revealed a sig-
nificant association between breast, colorectal, and lung
cancer and per capita GDP, which is consistent with the
fact that the relative risk of contracting cancer is positively
associated with average income of local residents [29,30].
Part, but not all, of this excess is probably due to better
access to medical and diagnostic procedures in the ‘‘high-
resource’’ societies [51–53]. Ecological studies have well-
documented limitations, but they also have strengths. The
analysis included 164 countries of the world and data on
several potentially important co-variables. Due to limita-
tions on data availability, other risk factors, including
occupation, alcohol consumption, and specific reproductive
factors such as age-at-first birth, were not available for the
analyses though the per capita income variable may capture
some of their effects; in addition, fertility rate may also
more specifically capture some of the inter-country vari-
ability in reproductive factors. Smoking may be partly
covered by the percent urban variable and the per capita
income variable, although for breast cancer, if smoking
increases risk, it probably has a very modest effect. Studies
have shown that greater urbanization increases smoking
Table 2 Sensitivity test of breast cancer ASR to plausible changes in
the ground LAN intensity
LAN level Average LAN
value (nanowatts/
cm2/sr)
Estimated
ASR (per 100,000
residents)
Percent
change
(%)
Breast (1) model (see Table 1)
Low 8.60 40.47 –
Medium 28.95 43.39 7.20
High 99.21 53.43 23.25
Breast (2) model (see Table 1)
Low 8.60 44.45 –
Medium 28.95 50.08 12.70
High 99.21 69.54 38.85
ASR-Age-standardized rates per 100,000 residents
The values of the fixed variables were set constant as follows: GDP
per capita =$US 9,000 (the average value for the ‘‘high-resource’’
countries under study); Urban population =65.3%, Electricity con-
sumption per capita =131.870 kWh, fertility rate =3.4 per 1,000
births
2064 Cancer Causes Control (2010) 21:2059–2068
123
[33] and that smoking is strongly linked with socio-eco-
nomic status [54,55]. It should, however, be noted that
dynamics in population movement as well as behavioral
patterns that limit exposure to LAN were also not assessed
by this study. Such information can be obtained by studies
carried out on a smaller scale such as localities within an
urban space, but not on a global level. Another limitation is
in the completeness of cancer registration in the developing
world where LAN exposure is low and national incidence
is extrapolated from data obtained from small incidence
registries within the country. Parkin et al. [56] conducted a
detailed analysis of cancer registration in Kampala, Uganda
over the period 1994–1996 and concluded that ‘‘…it gives
reassurance that published incidence rates are reasonably
accurate.’’ However, Curado et al. [57] caution that the
cancer registries in low- and medium-resource countries
are more susceptible to underreporting than those in high-
resource countries. We addressed this limitation in several
ways. First, we also analyzed four other cancer types which
should also suffer from this possible bias, yet only breast
cancer showed a strong association with LAN. Second, we
restricted analysis to the 80 countries with per capita GDP
greater than $3,000 and found that the change in parameter
estimates was negligible. We also restricted analysis to the
73 lowest fertility countries and again found a significant
association of LAN with breast cancer incidence.
When low-fertility countries are analyzed separately as a
group, there is a stronger association of LAN with breast
cancer incidence than among the high-fertility countries.
Two aspects of this analysis are important to note. First, the
variability of both LAN and breast cancer incidence is
greater in the low-fertility group of countries (n=73) than
in the high-fertility group of countries (n=48), perhaps
providing a better opportunity to observe an effect should
one exist. Second, given the legitimate concern of lower
quality registries in the high-fertility countries, the strong
finding in the low-fertility group lessens the concern about
reporting bias accounting for our results.
The lack of a strong association of LAN and incidence
of colorectal cancer (CRC) is interesting in light of the fact
that one strong epidemiological study found a significant
association of shift work with CRC [58]. This study was
based on evidence that melatonin influences risk in
experimental and clinical models. Another consideration is
that timing of feeding has been shown to have a strong
synchronizing effect in the gut independent of light;
whereas daytime feeding in nocturnal mice reset circadian
gene expression in the gut, the SCN was unaffected and
remained synchronized to the light cycle [59]. The relation
of circadian disruption, from lighting and from meal tim-
ing, and CRC deserves more attention.
LAN is increasing rapidly in developing countries but
also in developed countries. In order to save on energy
consumption on the one hand and lower CO
2
production on
the other, there is a global trend in industrialized countries
like the EU to shift to low-energy consuming non-incan-
descent lamps. This shift while indeed saving energy may
cause an increase in nighttime light exposure since these
new lamps produce more light per watt of electricity,
particularly in the blue range of the spectrum. This issue
should be addressed by the policy makers in each country.
To the best of our knowledge, the present analysis is the
first study to investigate the relationship between LAN and
the incidence of several common cancers in women
worldwide. No single study, ecological or otherwise, can
‘prove’ an association is causal. We view this analysis as
an important piece of the evidence based on whether and to
what extent LAN explains the global breast cancer burden.
As per the IARC Classification paradigm, causal inference
requires many different kinds of studies from different
designs and perspectives, all evaluated together by the
scientific community that may or may not eventually come
to consensus.
Appendix 1
See Table 3.
Table 3 Descriptive statistics
of the research variables Variable Measurement unit Minimum Maximum Mean SD
A. All Countries in the sample*
Dependent variables
Breast cancer ASR
a
per 100,000 3.9 101.1 37.527 22.928
Colorectal cancer ASR
a
per 100,000 0.9 42.2 11.935 9.911
Larynx cancer ASR
a
per 100,000 0.0 4.1 0.721 0.612
Liver cancer ASR
a
per 100,000 0.2 57.3 4.949 6.291
Lung cancer ASR
a
per 100,000 0.1 36.1 6.824 6.589
Cancer Causes Control (2010) 21:2059–2068 2065
123
Appendix 2
See Table 4.
References
1. Stevens RG (2009) Light-at-night, circadian disruption and breast
cancer: assessment of existing evidence. Int J Epidemiol
38:963–970
2. Kolstad HA (2008) Nightshift work and risk of breast cancer and
other cancers–a critical review of the epidemiologic evidence.
Scand J Work Environ Health 34:5–22
3. Blask DE, Brainard GC, Dauchy RT, Hanifin JP, Davidson LK,
Krause JA, Sauer LA, Rivera-Bermudez MA, Dubocovich ML,
Jasser SA, Lynch DT, Rollag MD, Zalatan F (2005) Melatonin-
depleted blood from premenopausal women exposed to light at
night stimulates growth of human breast cancer xenografts in
nude rats. Cancer Res 65:11174–11184
4. Srinivasan V, Spence DW, Pandi-Perumal SR, Trakht I, Esquif-
ino AI, Cardinali DP, Maestroni GJ (2008) Melatonin, environ-
mental light, and breast cancer. Breast Cancer Res Treat
108:339–350
5. Haim A, Shanas U, Zubidad AS, Scantelbry M (2005) Season-
ality and seasons out of time-The thermoregulatory effects of
light interference. Chronobiol Int 22:57–64
6. Nelson RJ (2004) Seasonal immune function and sickness
responses. Trends Immunol 25:187–192
7. Stevens RG, Blask DE, Brainard GC, Hansen J, Lockley SW,
Provencio I, Rea MS, Reinlib L (2007) Meeting report: the role of
environmental lighting and circadian disruption in cancer and
other diseases. Environ Health Perspect 115:1357–1362
8. Stevens RG, Rea MS (2001) Light in the built environment:
potential role of circadian disruption in endocrine disruption and
breast cancer. Cancer Causes Control 12:279–287
9. Davis S, Mirick DK, Stevens RG (2001) Night shift work, light
at night, and risk of breast cancer. J Natl Cancer Inst 93:
1557–1562
10. Hansen J (2001) Increased breast cancer risk among women who
work predominantly at night. Epidemiology 12:74–77
11. Lie JA, Roessink J, Kjaerheim K (2006) Breast cancer and night
work among Norwegian nurses. Cancer Causes Control 17:39–44
12. Schernhammer ES, Kroenke CH, Laden F, Hankinson SE (2006)
Night work and risk of breast cancer. Epidemiology 17:108–111
Table 4 Average LAN
exposure in selected countries Country Average
LAN exposure
per person
(nanowatts/
cm
2
/sr)
Bhutan 0.001
Senegal 0.022
India 0.059
Peru 0.554
Egypt 2.028
Argentina 4.501
Israel 10.707
United States 57.540
Table 3 continued
*Total number of countries–164
**Number of countries–5
a
Age-standardized rates per
100,000
Variable Measurement unit Minimum Maximum Mean SD
Explanatory variables
Electricity consumption
per capita
kWh per capita 0.01 3,367.42 75.66 295.29
Fertility rates average number of
births per woman
1.14 7.41 3.403 1.722
GDP per capita US$ 463 32,021 6,545.73 7,315.17
Light at Night nanowatts/cm
2
/sr 0.00 143.34 8.23 22.43
Urban population % of residents living
in urban areas
6.16 100.00 55.16 23.31
B. Subsample of the Gulf States**
Dependent variables
Breast cancer ASR
a
per 100,000 13.20 33.30 25.420 7.974
Colorectal cancer ASR
a
per 100,000 3.1 17.50 10.100 5.201
Larynx cancer ASR
a
per 100,000 0.0 0.90 0.460 0.336
Liver cancer ASR
a
per 100,000 0.60 8.90 3.940 3.349
Lung cancer ASR
a
per 100,000 2.30 6.0 4.180 1.578
Explanatory variables
Electricity consumption
per capita
kWh per capita 6.24 102.42 31.862 40.248
Fertility rates average number of
births per woman
2.79 5.72 4.216 1.365
GDP per capita US$ 7,535 22,123 14,185.21 6,134.46
Light at Night nanowatts/cm
2
/sr 24.26 115.39 57.210 37.305
Urban population % of residents living
in urban areas
72.51 97.57 86.460 9.50
2066 Cancer Causes Control (2010) 21:2059–2068
123
13. Hahn RA (1991) Profound bilateral blindness and the incidence
of breast cancer. Epidemiology 2:208–210
14. Kliukiene J, Tynes T, Andersen A (2001) Risk of breast cancer
among Norwegian women with visual impairment. Br J Cancer
84:397–399
15. Verkasalo PK, Pukkala E, Stevens RG, Ojamo M, Rudanko SL
(1999) Inverse association between breast cancer incidence and
degree of visual impairment in Finland. Br J Cancer
80:1459–1460
16. Pinheiro SP, Schernhammer ES, Tworoger SS, Michels KB
(2006) A prospective study on habitual duration of sleep and
incidence of breast cancer in a large cohort of women. Cancer
Res 66:5521–5525
17. Verkasalo PK, Lillberg K, Stevens RG, Hublin C, Partinen M,
Koskenvuo M, Kaprio J (2005) Sleep duration and breast cancer:
a prospective cohort study. Cancer Res 65:9595–9600
18. Kakizaki M, Kuriyama S, Sone T, Ohmori-Matsuda K, Hozawa
A, Nakaya N, Fukudo S, Tsuji I (2008) Sleep duration and the
risk of breast cancer: the Ohsaki Cohort Study. Br J Cancer
99:1502–1505
19. Wu AH, Wang R, Koh W-P, Stanczyk FZ, Lee H-P, Yu MC
(2008) Sleep duration, melatonin and breast cancer among Chi-
nese women in Singapore. Carcinogenesis 29:1244–1248
20. Straif K, Baan R, Grosse Y, Secretan B, El Ghissassi F, Bouvard
V, Altieri A, Benbrahim-Tallaa L, Cogliano V (2007) Carcino-
genicity of shift-work, painting, and fire-fighting. Lancet Oncol
12:1065–1066
21. ACS (2007) Global cancer facts and figures
22. Parkin DM, Bray F, Ferlay J, Pisani P (2001) Estimating the
world cancer burden: Globocan 2000. Int J Cancer 94:153–156
23. Parkin DM, Bray F, Ferlay J, Pisani P (2005) Global cancer
statistics, 2002. CA Cancer J Clin 55:74–108
24. Parkin DM, Bray FI, Devesa SS (2001) Cancer burden in the year
2000. The global picture. Eur J Cancer 37(Suppl 8):S4–S66
25. Kloog I, Haim A, Stevens RG, Portnov BA (2009) Global co-
distribution of light at night (LAN) and cancers of prostate, colon,
and lung in men. Chronobiol Int 26:108–125
26. Kloog I, Haim A, Stevens RG, Barchana M, Portnov BA (2008)
Light at Night Co-distributes with Incident Breast but not Lung
Cancer in the Female Population of Israel. Chronobiol Int
25:65–81
27. de Sanjose
´S, Diaz M, Castellsague
´X, Clifford G, Bruni L,
Mun
˜oz N, Bosch FX (2007) Worldwide prevalence and genotype
distribution of cervical human papillomavirus DNA in women
with normal cytology: a meta-analysis. Lancet Infect Dis
7:453–459
28. Boyle P, Ferlay J (2005) Cancer incidence and mortality in
Europe, 2004. Ann Oncol 16:481
29. Hulshof KF, Brussaard JH, Kruizinga AG, Telman J, Lowik MR
(2003) Socio-economic status, dietary intake and 10 y trends: the
Dutch National Food Consumption Survey. Eur J Clin Nutr
57:128–137
30. Hulshof KF, Lowik MR, Kok FJ, Wedel M, Brants HA, Hermus
RJ, ten Hoor F (1991) Diet and other life-style factors in high and
low socio-economic groups (Dutch Nutrition Surveillance Sys-
tem). Eur J Clin Nutr 45:441–450
31. Bray F, McCarron P, Parkin DM (2004) The changing global
patterns of female breast cancer incidence and mortality. Breast
Cancer Res 6:229–239
32. Han X, Naeher LP (2006) A review of traffic-related air pollution
exposure assessment studies in the developing world. Environ Int
32:106–120
33. Volzke H, Neuhauser H, Moebus S, Baumert J, Berger K, Stang
A, Ellert U, Werner A, Do
¨ring A (2006) Urban-rural disparities in
smoking behaviour in Germany. BMC Public Health 6:146
34. Gram-Hansenn K, Petersen NK. Diffrenet everday lives-Differnt
patterns of electrical use. In: ACEEE Summer Study on Energy
Efficiency in Buildings 2004. Pacific Grove, California
35. Jumbe BLC (2004) Cointegration and causality between elec-
tricity consumption and GDP: empirical evidence from Malawi.
Energy Economics 26:61–68
36. Kelsey JL, Gammon MD (1990) The epidemiology of breast
cancer. CA: Cancer J Clin 41:146–165
37. CIA (2006) ‘‘CIA World Factbook.’’ Retrieved 2006, 2006, from
http://www.cia.gov/index.html
38. ESRI (2007) ARCGIS. In. 9.2 ed: ESRI
39. DMSP (2004) DMSP Nighttime lights data download
40. Banerjee S, Wall MM, Carlin BP (2003) Frailty modeling for
spatially correlated survival data, with application to infant
mortality in Minnesota. Biostatistics 4:123–142
41. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian
SV, Carson R (2002) Geocoding and monitoring of US socio-
economic inequalities in mortality and cancer incidence: does the
choice of area-based measure and geographic level matter?: the
Public Health Disparities Geocoding Project. Am J Epidemiol
156:471–482
42. Maheswaran R, Strachan DP, Dodgeon B, Best NG (2002) A
population-based case-control study for examining early life
influences on geographical variation in adult mortality in England
and Wales using stomach cancer and stroke as examples. Int J
Epidemiol 31:375–382
43. O’Leary ES, Vena JE, Freudenheim JL, Brasure J (2004) Pesti-
cide exposure and risk of breast cancer: a nested case-control
study of residentially stable women living on Long Island.
Environ Res 94:134–144
44. Scott D, Curtis B, Twumasi FO (2002) Towards the creation of a
health information system for cancer in KwaZulu-Natal, South
Africa. Health Place 8:237–249
45. Minami M (2000) ESRI. Using ArcMap: GIS. ESRI, Redlands,
California
46. Kloog I, Haim A, Portnov BA (2009) Using kernel density
function as an urban analysis tool: Investigating the association
between nightlight exposure and the incidence of breast cancer in
Haifa, Israel. Comput Environ Urban Syst 33:55–63
47. Kinnear PR, Gray CD (2007) SPSS 15 Made Simple. Psychology
Press, Philadelphia, PA
48. Anselin L (1999) Spatial Econometrics. Bruton Center, School of
Social Sciences,University of Texas at Dallas, Dallas
49. Anselin L, Syabri I, Kho Y (2005) GeoDa: An Introduction to
Spatial Data Analysis. Geogr Ana006C 38:5–22
50. Jenks G (1967) The data model concept in statistical mapping. Int
Yearb Cartogr 7:186–190
51. Bradley CJ, Given CW, Roberts C (2002) Race, socioeconomic
status, and breast cancer treatment and survival. J Natl Cancer
Inst 94:490–496
52. Madison T, Schottenfeld D, James SA, Schwartz AG, Gruber SB
(2004) Endometrial cancer: socioeconomic status and racial/eth-
nic differences in stage at diagnosis, treatment, and survival. Am
J Public Health 94:2104–2111
53. Wells BL, Horm JW (1992) Stage at diagnosis in breast cancer:
race and socioeconomic factors. Am J Public Health 82:
1383–1385
54. Adler N, Boyce T, Chesney M, Cohen S, Folkman S, Kahn RL,
Syme SL (1994) Socioeconomic status and health: The challenge
of the gradient. Am Psychol 49:15–24
55. Jha P, Peto R, Zatonski W, Boreham J, Jarvis MJ, Lopez AD
(2006) Social inequalities in male mortality, and in male mor-
tality from smoking: indirect estimation from national death rates
in England and Wales, Poland, and North America. Lancet
368:367–370
Cancer Causes Control (2010) 21:2059–2068 2067
123
56. Parkin DM, Wabinga H, Nambooze S (2001) Completeness in an
African cancer registry. Cancer Causes Control 12:147–152
57. Curado MP, Voti L, Sortino-Rachou AM (2009) Cancer regis-
tration data and quality indicators in low and middle income
countries: their interpretation and potential use for the improve-
ment of cancer care. Cancer Causes Control 20:751–756
58. Schernhammer ES, Laden F, Speizer FE et al (2003) Night-Shift
work and risk of colorectal cancer in the Nurses’ Health Study.
J Natl Cancer Inst 95:825–828
59. Hoogerwerf WA, Hellmich HL, Corne
´lisson G et al (2007) Clock
gene expression in the murine gastrointestinal tract: endogenous
rhythmicity and effects of a feeding regimen. Gastroenterology
133:1250–1260
2068 Cancer Causes Control (2010) 21:2059–2068
123
