Sensitivity analysis: UVR and skin cancer

The text on this page is taken from an equivalent page of the IEHIAS-project.

As part of the EU-funded INTARESE poject. which contributed to the development of this Toolbox, a case study was undertaken to assess the health impacts of exposures to UVR under different climate change scenarios.

As with all such studies, considerable uncertainties were inevitably inherant in the assessment. In order to explore these, sensitivity analysis was undertaken, during which key parameters in the model were incrementally adjusted and the changes in the predicted health effects assessed.

Explanation of the method

Approach

Uncertainties were explored by means of sensitivity analyses, applied to the modelled results for Helsinki, under SRES-B1 2030, model 2. In the sensitivity analyses we assessed the effect of changing the standard model settings with regard to the following factors (see also Table 1 below):

• Changes in ambient UVR due to other factors. In the standard model setting, we assumed that important (optical) properties of the surface and the atmosphere relevant for ambient UVR (e.g. albedo, cloud cover, aerosols etc) remain unchanged. In the sensitivity analyses we explore the effect of ±10% change in modelled ambient UVR (2030).
• Cloud cover. We explored the effect of a 10% increase in cloud cover in 2030, resulting in a 3.2% decrease in modelled ambient UVR compared to the standard model setting (which assumed no change in cloud cover). Based on current annual cloud cover (International Satellite Cloud Climatology Project (ISCCP)) and data from Josefsson and Landelius (2000).
• Population attributable fraction (PAF). We used the higher PAF estimates of the WHO (Lucas et al. 2006) in our standard model setting; the lower PAF is applied in the sensitivity analyses to account for uncertainty in the PAF.
• Biological Amplification Factor (BAF; CMM only). With regard to the BAF (Scotto and Fears 1987), the FHN/UE (face, head or neck, upper extremity) result is considered as the higher risk estimate and the TL (trunk & lower extremity) result as the lower estimate. The higher risk estimate represents the standard model setting in our calculations; the lower BAF is applied in the sensitivity analyses to account for uncertainty in the BAF.
• Additive effect of increasing summer temperatures: Somestudies indicate that the future increase surface temperature could also influence the relationship between ambient UV and skin cancer, by facilitating the induction of skin cancer and/or changing exposure behaviour (Ilyas 2007; van der Leun, Piacentini et al. 2008). We will take this into account using the epidemiological results by Van der Leun et al (2008): BCC incidence rates increase with 2.9% (SE1.4) per 1°C increase in daily summer max temperature; SCC incidence rates increase with 5.5% (SE 1.6) per 1°C increase in daily summer maximum temperature. In the sensitivity analyses we explored the effect of a 1 degrees Celsius increase in summer maximum temperature between 2000 and 2030, resulting in an increase in incidence rates between 2.9% (based on additive effect for BCC) and 5.5% (based on the additive effect for SCC).
• Incidence:mortality ratios. In the standard model setting, the estimation of age- and gender-specific incidence: mortality ratios (imr) isbased on the incidence and morality rates in the EURA region as presented by Lucas et al (2006). In the sensitivity analyses, we increase these standard imr with 10% in order to account for improved treatment and increased survival changes. As a second imr alternative, we apply age-specific imr based on the incidence and morality rates in the Helsinkiarea (Helsinki and Uusimaa) from the Finish Cancer registry. The third alternative combines the previous two by increasing the Helsinki imr with 10%.
• DALY calculations. Our standard model settings are based on uniform age weighing, no discounting and no adjustments for co-morbidity in the DALY calculations. In the sensitivity we apply non-uniform age-weighing (based on WHO BoD study), a 3% discount rate (based on WHO GBD study) and adjustment of disease weights for co-morbidity at higher ages (based on malignant melanoma spreadsheets Victorian BoD study). Please note that in WP3.7-UVR, the discounting for the 2030-scenarios calculates the net present value in 2030.

Table 1: Overview sensitivity analyses WP3.7-UVR, applied to model 2 for Helsinki SRES-B1-2030

Standard model setting	Alternative setting used in sensitivity analyses
Ambient UVR: as modelled based on SRES-B1 2030	UVR high alternative: 10% additional increase in the modelled UV exposure in 2030 UVR low alternative: 10 % decrease in the modelled UV exposure in 2030.
No change in cloud cover compared to baseline. Ambient UVR: as modelled based on SRES-B1 2030	Increasing cloud cover (10%): a 10 % increase in cloud cover in 2030, resulting in a 3.2% decrease in the modelled UV exposure in 2030.
High BAF estimate (8% for males, 10% for females)- CMM only	Low BAF estimate (6% for males, 5% for females)-CMM only
High PAF estimate (0.9)	Low PAF estimate (0.5)
Age- and gender-specific incidence: mortality ratios (imr) based on the incidence and morality rates in the EURA region as presented by Lucas et al (2006).	Improved treatment: standard imr increase with 10% due to improved survival changes compared to standard setting Helsinkiimr alternative: Age-specific incidence: mortality ratios based on the incidence and morality rates in the Helsinkiarea (Helsinki and Uusimaa) from the Finish Cancer registry. Improved treatment plus Helsinki imr alternative: Helinki imr increase with 10%
No additive effect of increasing summer temperature	Additive effect of a 1°C increase in summer maximum temperature in 2000-2030. Low estimate: 2.9% increase in incidence rates High estimate: 5.5% increase in incidence rates
DALY calculations standard setting: Uniform age weights; no discounting; no adjustment of disease weights for co-morbidity.	Alternative 1: Age weights, based on WHO GBD Alternative 2: Discount rate 3%, based on WHO GBD Alternative 3: adjustment for co-morbidity, based on Australian/Victorian BoD study. Alternative 4: WHO GBD age weights and 3% discount rate Alternative 5: WHO GBD age weights, WHO GBD 3% discount rate, and adjustment for co-morbidity based on Australian/Victorian BoD study.

References

• Ilyas, M. 2007 Climate augmentation of erythemal UV-B radiation dose damage in the tropics and global change. Current Science 93(11).
• Josefsson, W. and Landelius, T 2000 Effect of clouds on UV irradiance: as estimated from cloud amount, cloud type, precipitation, global radiation and sunshine duration. Journal of Geophysical Research 105, D4, doi:10.1029/1999JD900255.
• Lucas, R., McMichael, T., et al. 2006 Solar ultraviolet radiation: global burden of disease from solar ultraviolet radiation. Geneva: World Health Organization.
• Mathers, C.D., Vos, T., and Stevenson C. 1999 The burden of disease and injury in Australia. Canberra: Australian Institute of Health and Welfare.
• Scotto, J. and Fears, T. 1987 The association of solar ultraviolet and skin melanoma incidence among Caucasians In the United States. Cancer Investigation 5(4), 275-283.
• van der Leun, J., Piacentini, R. et al. 2008 Climate change and human skin cancer. Photochemical and Photobiological Science 6, 730-733.
• WHO 2004 The global burden of diseases: 2004 update. Geneva: World Health Organization.

See also

• Changing ambient UVR and future melanoma skin cancer
• Changing ambient UVR and future non-melanoma skin cancer