BACKGROUND: Prostate cancer is the second most common type of cancer among men but its aetiology is still largely unknown. Different studies have proposed several risk factors such as ethnic origin, age, genetic factors, hormonal factors, diet and insulin-like growth factor, but the spatial distribution of the disease suggests that other environmental factors are involved. This paper studies the spatial distribution of prostate cancer mortality in an industrialized area using distances from each of a number of industrial facilities as indirect measures of exposure to industrial pollution. MATERIALS AND METHODS: We studied the Gran Bilbao area (Spain) with a population of 791,519 inhabitants distributed in 657 census tracts. There were 20 industrial facilities within the area, 8 of them in the central axis of the region. We analysed prostate cancer mortality during the period 1996-2003. There were 883 deaths giving a crude rate of 14 per 100,000 inhabitants. We extended the standard Poisson regression model by the inclusion of a multiplicative non-linear function to model the effect of distance from an industrial facility. The function's shape combined an elevated risk close to the source with a neutral effect at large distance. We also included socio-demographic covariates in the model to control potential confounding. RESULTS: We aggregated the industrial facilities by sector: metal, mineral, chemical and other activities. Results relating to metal industries showed a significantly elevated risk by a factor of approximately 1.4 in the immediate vicinity, decaying with distance to a value of 1.08 at 12km. The remaining sectors did not show a statistically significant excess of risk at the source. DISCUSSION: Notwithstanding the limitations of this kind of study, we found evidence of association between the spatial distribution of prostate cancer mortality aggregated by census tracts and proximity to metal industrial facilities located within the area, after adjusting for socio-demographic characteristics at municipality level.
BACKGROUND:Prostate cancer is the second most common type of cancer among men but its aetiology is still largely unknown. Different studies have proposed several risk factors such as ethnic origin, age, genetic factors, hormonal factors, diet and insulin-like growth factor, but the spatial distribution of the disease suggests that other environmental factors are involved. This paper studies the spatial distribution of prostate cancer mortality in an industrialized area using distances from each of a number of industrial facilities as indirect measures of exposure to industrial pollution. MATERIALS AND METHODS: We studied the Gran Bilbao area (Spain) with a population of 791,519 inhabitants distributed in 657 census tracts. There were 20 industrial facilities within the area, 8 of them in the central axis of the region. We analysed prostate cancer mortality during the period 1996-2003. There were 883 deaths giving a crude rate of 14 per 100,000 inhabitants. We extended the standard Poisson regression model by the inclusion of a multiplicative non-linear function to model the effect of distance from an industrial facility. The function's shape combined an elevated risk close to the source with a neutral effect at large distance. We also included socio-demographic covariates in the model to control potential confounding. RESULTS: We aggregated the industrial facilities by sector: metal, mineral, chemical and other activities. Results relating to metal industries showed a significantly elevated risk by a factor of approximately 1.4 in the immediate vicinity, decaying with distance to a value of 1.08 at 12km. The remaining sectors did not show a statistically significant excess of risk at the source. DISCUSSION: Notwithstanding the limitations of this kind of study, we found evidence of association between the spatial distribution of prostate cancer mortality aggregated by census tracts and proximity to metal industrial facilities located within the area, after adjusting for socio-demographic characteristics at municipality level.
Authors: Juan A Ortega-García; Fernando A López-Hernández; Alberto Cárceles-Álvarez; José L Fuster-Soler; Diana I Sotomayor; Rebeca Ramis Journal: Environ Res Date: 2017-03-19 Impact factor: 6.498
Authors: Vanessa Santos-Sánchez; Juan Antonio Córdoba-Doña; Javier García-Pérez; Antonio Escolar-Pujolar; Lucia Pozzi; Rebeca Ramis Journal: Int J Environ Res Public Health Date: 2020-03-13 Impact factor: 3.390