INTRODUCTION: Bladder cancer (BC) is the fifth most prevalent cancer in Canada, with 9000 Canadians diagnosed each year. While smoking is the most important risk factor, environmental and occupational carcinogens have been found to significantly contribute to BC rates. As Canada is highly reliant on natural resource industries, this study seeks to identify geographical and industry-related trends of BC rates in Ontario. METHODS: The 1991 and 2001 Canadian Census Health and Environment Cohort (CanCHEC; Statistics Canada) was used, along with individual years of census data. Maps identifying hot and cold spots for BC within Ontario were generated, and the former were assessed for industry patterns between location and BC rates. Cox proportional hazards models were run for each age cohort to predict the likelihood of developing BC by industry of work. RESULTS: Significant geographical and industrial trends in BC rates were identified. For 1991-2001, hot spots included the Cochrane, Manitoulin, Parry Sound, and Sudbury (90% confidence interval [CI]), and Nipissing and Temiskaming (95% CI) regions. Toronto and York were cold spots. Concurrently, metal (p=0.039), paper and publishing (p=0.0062), and wood and furniture (p<0.0001) industries had increased rates of BC. Notably, these industries had high employment density in our hot spot areas and low density in our cold spots. CONCLUSIONS: Significant geographical and industrial BC trends were found in Northern Ontario regions reliant on heavy employment in natural resource-based industries, such as forestry, agriculture, and wood/paper. These findings may inform future screening guidelines and aid in identifying individuals at risk of BC development.
INTRODUCTION: Bladder cancer (BC) is the fifth most prevalent cancer in Canada, with 9000 Canadians diagnosed each year. While smoking is the most important risk factor, environmental and occupational carcinogens have been found to significantly contribute to BC rates. As Canada is highly reliant on natural resource industries, this study seeks to identify geographical and industry-related trends of BC rates in Ontario. METHODS: The 1991 and 2001 Canadian Census Health and Environment Cohort (CanCHEC; Statistics Canada) was used, along with individual years of census data. Maps identifying hot and cold spots for BC within Ontario were generated, and the former were assessed for industry patterns between location and BC rates. Cox proportional hazards models were run for each age cohort to predict the likelihood of developing BC by industry of work. RESULTS: Significant geographical and industrial trends in BC rates were identified. For 1991-2001, hot spots included the Cochrane, Manitoulin, Parry Sound, and Sudbury (90% confidence interval [CI]), and Nipissing and Temiskaming (95% CI) regions. Toronto and York were cold spots. Concurrently, metal (p=0.039), paper and publishing (p=0.0062), and wood and furniture (p<0.0001) industries had increased rates of BC. Notably, these industries had high employment density in our hot spot areas and low density in our cold spots. CONCLUSIONS: Significant geographical and industrial BC trends were found in Northern Ontario regions reliant on heavy employment in natural resource-based industries, such as forestry, agriculture, and wood/paper. These findings may inform future screening guidelines and aid in identifying individuals at risk of BC development.
Authors: Marcus George Kwesi Cumberbatch; Ibrahim Jubber; Peter C Black; Francesco Esperto; Jonine D Figueroa; Ashish M Kamat; Lambertus Kiemeney; Yair Lotan; Karl Pang; Debra T Silverman; Ariana Znaor; James W F Catto Journal: Eur Urol Date: 2018-09-26 Impact factor: 20.096
Authors: Young Long Jung; Emile Tompa; Christopher Longo; Christina Kalcevich; Joanne Kim; Chaojie Song; Paul Demers Journal: J Occup Environ Med Date: 2018-03 Impact factor: 2.162
Authors: Norm D Smith; Sandip M Prasad; Amit R Patel; Adam B Weiner; Joseph J Pariser; Aria Razmaria; Chieko Maene; Todd Schuble; Brandon Pierce; Gary D Steinberg Journal: J Urol Date: 2015-07-30 Impact factor: 7.450
Authors: Martin C S Wong; Franklin D H Fung; Colette Leung; Wilson W L Cheung; William B Goggins; C F Ng Journal: Sci Rep Date: 2018-01-18 Impact factor: 4.379