Details

Autor(en) / Beteiligte

• Eitan, Ori
• Yuval
• Barchana, Micha
• Dubnov, Jonathan
• Linn, Shai
• Carmel, Yohay
• Broday, David M.

Titel

Spatial analysis of air pollution and cancer incidence rates in Haifa Bay, Israel

Ist Teil von

• The Science of the total environment, 2010-09, Vol.408 (20), p.4429-4439

Ort / Verlag

Kidlington: Elsevier B.V

Erscheinungsjahr

2010

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM 10, emitted both by industry and traffic, and to SO 2, a marker of industrial emissions, was developed. Ward-based maps of standardized incidence rates of three prevalent cancers: Non-Hodgkin's lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO 2-based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM 10, with the relative risk associated with an increase of 1 μg/m 3 of PM 10 being 12%. Special consideration of wards with expected rates < 1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk.