Willingness to Pay for Cancer Prevention

Cancer inflicts great pain, burden and cost upon American society, and preventing cancer is important but not costless. The aim of this review was to explore the upper limits that American society is paying and appears willing to pay to prevent cancer, by enforced environmental regulations and implemented clinical practice guidelines. Cost-effectiveness studies of clinical and environmental cancer-prevention policies and programmes were identified through a comprehensive literature review and confirmed to be officially sanctioned and implemented, enforced or funded. Data were collected in 2005-6 and analysed in 2007. The incremental cost-effectiveness ratios (ICERs) for clinical prevention policies ranged from under $US2000 to over $US6 000 000 per life-year saved (LYS), exceeding $US100 000 per LYS for only 11 of 101 guidelines. Median ICERs for tobacco-related ($US3978/LYS), colorectal ($US22 694/LYS) and breast ($US25 687/LYS) cancer prevention were within generally accepted ranges and tended not to vary greatly, whereas those for prostate ($US73 603/LYS) and cervical ($US125 157/LYS) cancer-prevention policies were considerably higher and varied substantially more. In contrast, both the median and range of the environmental policies were enormous, with 90% exceeding $US100 000 per LYS, and ICERs ranging from $US61 004 to over $US24 billion per LYS. Notwithstanding a relatively large and accessible literature evaluating the cost effectiveness of clinical and environmental cancer-prevention policies as well as the availability of ICERs for the policies identified in this study, the apparent willingness to pay to prevent cancer in the US still varies greatly and can be extremely high, particularly for many of the environmental cancer-prevention policies.

Cyanobacterial Bloom Phenology in Green Bay Using MERIS Satellite Data and Comparisons with Western Lake Erie and Saginaw Bay

Cyanobacteria blooms have been reported to be increasing worldwide. In addition to potentially causing major economic and ecological damage, these blooms can threaten human health. Furthermore, these blooms can be exacerbated by a warming climate. One approach to monitoring and modeling cyanobacterial biomass is to use processed satellite imagery to obtain long-term data sets. In this paper, an existing algorithm for estimating cyanobacterial biomass previously developed for MERIS is validated for Green Bay using cyanobacteria biovolume estimates obtained from field samples. Once the algorithm was validated, the existing MERIS imagery was used to determine the bloom phenology of the cyanobacterial biomass in Green Bay. Modeled datasets of heat flux (as a proxy for stratification), wind speed, water temperature, and gelbstoff absorption along with in situ river discharge data were used to separate bloom seasons in Green Bay from bloom seasons in nearby cyanobacteria bloom hotspots including western Lake Erie and Saginaw Bay. Of the ten-year MERIS dataset used here, the highest five years were considered “high bloom” years, and the lowest five years from biomass were considered “low bloom” years and these definitions were used to separate Green Bay. Green Bay had a strong relationship with gelbstoff absorption making it unique among the water bodies, while western Lake Erie responded strongly with river discharge as previously reported. Saginaw Bay, which has low interannual bloom variability, did not exhibit a largely influential single parameter

Cyanobacterial Bloom Phenology in Green Bay Using MERIS Satellite Data and Comparisons with Western Lake Erie and Saginaw Bay

Cyanobacteria blooms have been reported to be increasing worldwide. In addition to potentially causing major economic and ecological damage, these blooms can threaten human health. Furthermore, these blooms can be exacerbated by a warming climate. One approach to monitoring and modeling cyanobacterial biomass is to use processed satellite imagery to obtain long-term data sets. In this paper, an existing algorithm for estimating cyanobacterial biomass previously developed for MERIS is validated for Green Bay using cyanobacteria biovolume estimates obtained from field samples. Once the algorithm was validated, the existing MERIS imagery was used to determine the bloom phenology of the cyanobacterial biomass in Green Bay. Modeled datasets of heat flux (as a proxy for stratification), wind speed, water temperature, and gelbstoff absorption along with in situ river discharge data were used to separate bloom seasons in Green Bay from bloom seasons in nearby cyanobacteria bloom hotspots including western Lake Erie and Saginaw Bay. Of the ten-year MERIS dataset used here, the highest five years were considered “high bloom” years, and the lowest five years from biomass were considered “low bloom” years and these definitions were used to separate Green Bay. Green Bay had a strong relationship with gelbstoff absorption making it unique among the water bodies, while western Lake Erie responded strongly with river discharge as previously reported. Saginaw Bay, which has low interannual bloom variability, did not exhibit a largely influential single parameter