Kim, Yun Jeong
(Department of Preventive Medicine, College of Medicine, Korea University)
,
Park, Man Sik
(Department of Statistics, College of Natural Sciences, Sungshin Women's University)
,
Lee, Eunil
(Department of Preventive Medicine, College of Medicine, Korea University)
,
Choi, Jae Wook
(Department of Preventive Medicine, College of Medicine, Korea University)
We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In ...
We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In this study, we applied a spatial regression method (an intrinsic conditional autoregressive [iCAR] model) to analyze the relationship between the incidence of breast cancer and artificial light at night (ALAN) levels in 25 regions including central city, urbanized, and rural areas. By Poisson regression analysis, there was a significant correlation between ALAN, alcohol consumption rates, and the incidence of breast cancer. We also found significant spatial effects between ALAN and the incidence of breast cancer, with an increase in the deviance information criterion (DIC) from 374.3 to 348.6 and an increase in $R^2$ from 0.574 to 0.667. Therefore, spatial analysis (an iCAR model) is more appropriate for assessing ALAN effects on breast cancer. To our knowledge, this study is the first to show spatial effects of light pollution on breast cancer, despite the limitations of an ecological study. We suggest that a decrease in ALAN could reduce breast cancer more than expected because of spatial effects.
We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In this study, we applied a spatial regression method (an intrinsic conditional autoregressive [iCAR] model) to analyze the relationship between the incidence of breast cancer and artificial light at night (ALAN) levels in 25 regions including central city, urbanized, and rural areas. By Poisson regression analysis, there was a significant correlation between ALAN, alcohol consumption rates, and the incidence of breast cancer. We also found significant spatial effects between ALAN and the incidence of breast cancer, with an increase in the deviance information criterion (DIC) from 374.3 to 348.6 and an increase in $R^2$ from 0.574 to 0.667. Therefore, spatial analysis (an iCAR model) is more appropriate for assessing ALAN effects on breast cancer. To our knowledge, this study is the first to show spatial effects of light pollution on breast cancer, despite the limitations of an ecological study. We suggest that a decrease in ALAN could reduce breast cancer more than expected because of spatial effects.
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
문제 정의
We identified a significant association between ALAN and breast cancer incidence with spatial effects. To our knowledge, this study is the first study to show spatial effects in an association between ALAN and breast cancer.
가설 설정
3) was used to apply the hierarchical modeling framework to the data. In order to estimate the parameters of our interest, we considered three different sets of initial values and assumed that the prior distributions of the parameters were non-informative. For stabilization of the posterior distributions, we first removed 50,000 chains from the 100,000 ones per initial set.
제안 방법
He first publicized his theory in an essay “On the Mode of Communication of Cholera” in 1849. He published a second edition with a much more elaborate investigation of the effect of the water supply in the Soho, London epidemic of 1854 by using map clusters.
When comparing the distribution of the incidence of breast cancer and ALAN intensities along with the observed distribution, the predicted distribution by the Poisson or iCAR models, the predicted distribution by the iCAR model showed greater similarity to the real observed distribution than did the predicted distribution by the Poisson model, especially in the peripheral regions (Figure 3). In addition, after calculating the regional average incidence of breast cancer according to the spatial effect with the Winbugs14 program (Supplementary Table 1), we plotted the regression of observed values and predicted values applied by the Poisson model and the iCAR model (Figure 4). The plot of the iCAR model is closer to the observed values.
In this study, we used statistical spatial analysis to analyze spatial effects between ALAN and breast cancer incidence in a province of Korea with possible confounding variables including obesity, alcohol consumption rate, and environmental pollution levels such as O3 and PM10. We identified a significant association between ALAN and breast cancer incidence with spatial effects.
Poisson regression analysis without considering spatial effects was performed to investigate the effect of risk factors on breast cancer incidence, including ALAN, smoking, alcohol consumption, obesity, O3, PM10, and urbanized parts of each region. A significant association was found between the incidence of breast cancer and ALAN (Table 2).
For stabilization of the posterior distributions, we first removed 50,000 chains from the 100,000 ones per initial set. The deviance information criterion, simply called DIC (Spiegelhalter et al. (2002); Gelman et al. (2004)) was used to find out which model explained the data more accurately: the Poisson regressions with or without spatial effects.
We analyzed the distribution of variables on the map using ArcGIS and performed a Poisson regression analysis and a spatial analysis, which is the Poisson regression analysis with spatial effects. The results of the Poisson regression analysis about the correlation between the incidence of breast cancer and light pollution were compared with the results of the spatial analysis.
대상 데이터
The target areas are located in the southwest of South Korea, in Jeollanam-do province and the adjacent Gwangju metropolitan city (Figure 1). The total population of the study area is 3,373,121 based on the 2010 census (Population Census, 1992-2010).
데이터처리
We analyzed the distribution of variables on the map using ArcGIS and performed a Poisson regression analysis and a spatial analysis, which is the Poisson regression analysis with spatial effects. The results of the Poisson regression analysis about the correlation between the incidence of breast cancer and light pollution were compared with the results of the spatial analysis. The following hierarchical spatial modeling framework, including the iCAR model, is commonly used in that it can assess the significance of potential factors as well as spatial relationships of areal data:
성능/효과
, 2005) and a Massachusetts study (DeChello and Sheehan, 2007). Both studies showed that the incidences of the cancers were not explained only by the covariates because the contribution by covariates became more pronounced when spatial effects were considered. These studies showed that a regression model with spatial effects could be a more suitable model to explain the environmental factors and cancer distributions because of the spatial proximity and correlation between disease and environmental factors among regions (Moore and Carpenter, 1999; Vieira et al.
667). When comparing the distribution of the incidence of breast cancer and ALAN intensities along with the observed distribution, the predicted distribution by the Poisson or iCAR models, the predicted distribution by the iCAR model showed greater similarity to the real observed distribution than did the predicted distribution by the Poisson model, especially in the peripheral regions (Figure 3). In addition, after calculating the regional average incidence of breast cancer according to the spatial effect with the Winbugs14 program (Supplementary Table 1), we plotted the regression of observed values and predicted values applied by the Poisson model and the iCAR model (Figure 4).
With the spatial effect, the value of the deviance information criterion (DIC) that showed the fitness of the model was decreased (374.332 to 348.564), and also R², that is the proportion of the variance explained by the model, was increased (0.574 to 0.667).
후속연구
However, our study could contribute to the knowledge of the spatial phenomena of breast cancer associated with ALAN. Therefore, we expect that the results of our study could be used as supportive data to establish anti-light pollution policies, considering regional characteristics with spatial effects.
참고문헌 (47)
Althuis MD, Fergenbaum JH, Garcia-Closas M, et al (2004). Etiology of hormone receptor-defined breast cancer: a systematic review of the literature. Cancer Epidemiol Biomarkers Prev, 13, 1558-68.
Anisimov VN (2006). Light pollution, reproductive function and cancer risk. Neuro Endocrinol Lett, 27, 35-52.
Blask DE, Brainard GC, Dauchy RT, et al (2005). Melatonindepleted blood from premenopausal women exposed to light at night stimulates growth of human breast cancer xenografts in nude rats. Cancer Res, 65, 11174-84.
Bonde JP, Hansen J, Kolstad HA, et al (2012). Work at night and breast cancer--report on evidence-based options for preventive actions. Scand J Work Environ Health, 38, 380-90.
Dauchy RT, Blask DE, Sauer LA, et al (1999). Dim light during darkness stimulates tumor progression by enhancing tumor fatty acid uptake and metabolism. Cancer Lett, 144, 131-6.
Dauchy RT, Sauer LA, Blask DE, et al (1997). Light contamination during the dark phase in photoperiodically controlled” animal rooms: effect on tumor growth and metabolism in rats. Lab Anim Sci, 47, 511-8.
Davis S, Mirick DK (2006). Circadian disruption, shift work and the risk of cancer: a summary of the evidence and studies in Seattle. Cancer Causes Control, 17, 539-45.
DeChello LM, Sheehan TJ (2007). Spatial analysis of colorectal cancer incidence and proportion of late-stage in Massachusetts residents: 1995-1998. Int J Health Geogr, 6, 20.
Earnest A, Hock Ong ME, Shahidah N, et al (2012). Spatial analysis of ambulance response times related to prehospital cardiac arrests in the city-state of Singapore. Prehosp Emerg Care, 16, 256-65.
Griffith DA (1996). Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying georeferrenced data. Canadian Geographer, 40, 351-67.
Haus E, Smolensky M (2006). Biological clocks and shift work: circadian dysregulation and potential long-term effects. Cancer Causes Control, 17, 489-500.
Hill SM, Belancio VP, Dauchy RT, et al (2015). Melatonin: an inhibitor of breast cancer. Endocr Relat Cancer.
Jia Y, Lu Y, Wu K, et al (2013). Does night work increase the risk of breast cancer? A systematic review and meta-analysis of epidemiological studies. Cancer Epidemiol, 37, 197-206.
Kim YJ, Lee E, Lee HS, et al (2015). High prevalence of breast cancer in light polluted areas in urban and rural regions of South Korea: An ecologic study on the treatment prevalence of female cancers based on National Health Insurance data. Chronobiol Int, 32, 657-67.
Kloog I, Haim A, Stevens RG, et al (2008). Light at night codistributes with incident breast but not lung cancer in the female population of Israel. Chronobiol Int, 25, 65-81.
Lim YR, Bae HJ, Lim YH, et al (2014). Spatial analysis of PM10 and cardiovascular mortality in the Seoul metropolitan area. Environ Health Toxicol, 29, 2014005.
Martinez-Campa C, Alonso-Gonzalez C, Mediavilla MD, et al (2006). Melatonin inhibits both ER alpha activation and breast cancer cell proliferation induced by a metalloestrogen, cadmium. J Pineal Res, 40, 291-6.
McElroy JA, Newcomb PA, Titus-Ernstoff L, et al (2006). Duration of sleep and breast cancer risk in a large populationbased case-control study. J Sleep Res, 15, 241-9.
Moore DA, Carpenter TE (1999). Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiol Rev, 21, 143-61.
Rivenbark AG, O'Connor SM, Coleman WB (2013). Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine. Am J Pathol, 183, 1113-24.
Sanchez-Barcelo EJ, Mediavilla MD, Alonso-Gonzalez C, et al (2012). Breast cancer therapy based on melatonin. Recent Pat Endocr Metab Immune Drug Discov, 6, 108-16.
Schernhammer ES, Berrino F, Krogh V, et al (2008). Urinary 6-sulfatoxymelatonin levels and risk of breast cancer in postmenopausal women. J Natl Cancer Inst, 100, 898-905.
Schernhammer ES, Hankinson SE (2009). Urinary melatonin levels and postmenopausal breast cancer risk in the Nurses' Health Study cohort. Cancer Epidemiol Biomarkers Prev, 18, 74-9.
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, 825-8.
Schoenfeld ER, O'Leary ES, Henderson K, et al (2003). Electromagnetic fields and breast cancer on Long Island: a case-control study. Am J Epidemiol, 158, 47-58.
Srinivasan V, Spence DW, Pandi-Perumal SR, et al (2008). Melatonin, environmental light, and breast cancer. Breast Cancer Res Treat, 108, 339-50.
Stevens RG, Blask DE, Brainard GC, et al (2007). Meeting report: the role of environmental lighting and circadian disruption in cancer and other diseases. Environ Health Perspect, 115, 1357-62.
Vieira V, Webster T, Weinberg J, et al (2005). Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: an application of generalized additive models to case-control data. Environ Health, 4, 11.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.