Changing ambient UVR and future melanoma skin cancer

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

It is widely accepted that ambient UVR is carcinogenic (Lucas et al 2006). This assessment will focus on the impact on malignant melanoma skin cancer (CMM).

The international mechanism for protecting the ozone layer is the “Montreal Protocol on Substances That Deplete the Ozone Layer” that came into force in 1989 and its subsequent amendments (UNEP 2006). The production of the most harmful ozone depleting substances (ODS) are now phased out worldwide. The atmospheric concentrations of these gases is anticipated to decline over the next decades (IPCC 2005). As a result stratospheric ozone is expected to increase and, subsequently, ambient UVR is anticipated to decrease. Hence, a decrease in melanoma skin cancer is expected, but other factors may play an important role as well such as the ageing of the population.

Tip: Please take a look at the 'See Also/References' section for an overview of other case summary reports, worked examples of single applied methods, worked example of complete assessment, key literature and other useful (data)sources.



We explore the effects of changing exposure to ambient ultraviolet radiation (UVR) on melanoma skin cancer, accounting for the recovery of the ozone layer due to decreasing emission of ODS and future emissions of CH4 and N20 (IPCC 2005). We also account for future demographic change.

Summary report fig 1 0.jpg

Scenario(s) and type of assessment

Type of assessment: prognostic.

UVR scenarios: IPCC (2000) SRES A2 and B1: future population & emissions/concentrations of ODS, CH4 and N2O.

SRES population scenarios were downscaled from the regional to the city-level.

IPCC scenario SRES-A2 SRES-B1
ODS emission decreasing trend

same as B1

decreasing trend

same as A2

CH4 emissions higher increase lower increase
N2O -emissions higher increase lower increase
Population higher growth lower growth
Age structure slower ageing faster ageing

Geographical and temporal scope

Study area(s): City of Helsinki, Greater London, City of Rome

Populations: 5-year age groups up to 85+; males/females

Timeframe: 2001, 2030, 2050

Environmental and health factors

Source: emissions of ODS (decreasing), N2O (increasing) and CH4 (increasing), affecting ozone fields

Environmental hazard: ambient UVR (erythemal dose).

Other risk factor: age.

Health outcomes: melanoma skin cancer (CMM) incidence/mortality (rates)


Stakeholder Interest Role
Health authorities, policy/decision makers and health promotion agencies Healthy population Issue recommendations on solar exposure
Health care providers Disease prevention, treatment Treatment and advice particularly to groups at risk
Societies (cancer, osteoporosis, etc) Support member interests Issue public information
Advocacy groups Protecting vulnerable groups Reduce health impacts related to UVR.
Researchers Research Further knowledge of UVR-related health effects

Assessment methods

Exposure assessment

Modelled current and future exposure

Based on modelled future percental change in exposure (SRES-A2 and SRES-B1 storylines and associated emission modelling). UVR exposures were calculated by applying ozone fields (estimated from SRES data on atmospheric concentrations of ODS, N2O and CH4) from the Oslo Chemical Transport Model (Rummukainen et al., 1999) complemented by atmospheric fields from the European Centre for Midrange Weather Forecasting (ECMWF). These model outcomes have been made available to INTARESE by Bjorg Rognerud, University of Oslo. These ozone fields were translated to ambient UVR assuming otherwise current atmospheric conditions.

Health effect assessment

Exposure-response function (ERF):

The ERF is based on Scotto and Fears’ (1987) estimation of the biological amplification factor (BAF= relative change in disease risk due to a 10% increase in UVR exposure), adjusted for age.

Face, head or neck (FHN), upper extremity (UE) * Trunk & lower extremity (TL) **
Male 8 % 6 %
Female 10 % 5 %

* higher risk estimate; standard model setting; ** lower risk estimate

Health effect modelling:

  • In the baseline model baseline incidence rates and baseline population estimates are applied.
  • In model 1, baseline incidence rates and future population estimates are applied.
  • In model 2, future incidence rates are calculated by applying the BAF and the future percental change in modelled ambient UVR to the baseline incidence rates. Results are combined with future population estimates.

Mortality (rates) (baseline/future) are calculated using the age-specific incidence:mortality ratios (imr), based on the incidence and morality rates in the EURA region as presented by Lucas et al (2006).

Attributable incidence and mortality are calculated using the upper estimate of population attributable fractions (PAF=0.9) as estimated by the WHO (Lucas et al. 2006).

WP3.7-UVR: Model runs
Health model Demographic change Decreasing ambient UVR, due to future stratospheric ozone recovery
baseline - -*
model 1** X -*
model 2** X X
* using baseline incidence rates per age group and gender
** Selected scenarios: SRES-B1 2030, SRES-B1 2050, SRES-A2 2030, SRES-A2 2050

Impact Assessment

Disability Adjusted Life Years (DALY’s)

Our DALY worksheets/spreadsheet builds on the following templates/studies:


Main assumptions:

  • Recovery of the ozone layer is influenced by two factors: 1) future changes in the emissions of ozone depleting substances (ODS) and 2) future atmospheric levels of relevant GHGs (CH4, N2O).
  • The erythemal dose is a proper proxy exposure indicator for skin cancer effects.
  • Everybody will experience the same decreasing UVR exposure.
  • No changes in exposure behaviour ormedical treatment.
  • If data is only available for larger age groups, an even distribution over the corresponding 5-year age groups is assumed.
  • Several assumptions were made in the downscaling of the SRES-population scenarios.
  • BAF and PAF for lightly pigmented populations apply to the whole population.
  • We did not account for lag-time between exposure and health effect.
  • Several assumptions were made in estimating baseline incidence rates.
  • Incidence: mortality ratios based on data from Lucas et al.(Lucas, McMichael et al. 2006)are assumed to be representative for our cases as well; no future change in these incidence:mortality ratios.
  • Standard DALY calculations are based on the WHO standard life table, uniform age weighing, no discounting and no adjustments for co-morbidity. Selected disease models apply to the case-study population.

UVR scenarios: In order to deal with uncertainties in future emissions and demographic trends, we use two different SRES scenarios from the IPCC: SRES-A2 and SRES-B1.

UVR sensitivity analyses: Standard model setting versus alternative model setting for ambient UVR, cloud cover, BAF, PAF, incidence:mortality ratios, additive effect of increasing summer temperature, and DALY calculations (age weights, discounting; adjustment for co-morbidity). The sensitivity analysis is applied to model 2 for Helsinki, SRES B1-2030.


Main findings:

UVR exposure: UVR exposures are higher (and ozone recovery slower) in SRES-B1 compared to SRES-A2. The larger increase in ozone column in SRES-A2 can probably be explained by the higher abundance of CH4 in this scenario.

Health impact model 1 only accounts for future change in population size and structure, without accounting for future changes in incidence and mortality rates. The growing and ageing population results in a future increase in skin cancer incidence/mortality in both scenarios. As population growth is highest in SRES-A2, the total number of cases and deaths is also higher in this scenario compared to SRES-B1. For Helsinki and London, skin cancer incidence and mortality rates in the total population, however, are higher in the SRES-B1 scenario compared to the results for SRES-A2; this can be explained by the faster ageing of the population in SRES-B1.

Health impact model 2 also accounts for the future recovery of the ozone layer. The modelling shows a decrease in age-specific skin cancer incidence rates due to the recovery of the ozone layer. As expected, skin cancer incidence/mortality (rates) are lower in model 2 compared to model 1. The SRES-B1 scenario (i.e. the alternative with the lower recovery of the ozone layer) has somewhat higher UVR levels and, consequently, higher age-specific incidence rates than SRES-A2.

Similar conclusions for model 1 and model 2 can be drawn for the DALY calculations.

The sensitivity analyses shows that the choices regarding the PAF(high estimate is 0.9; low estimate is 0.5) had a large influence (circa 44%) on the health outcomes. Hence for policy purposes, it might be recommended to present all results using both the high and low PAF estimates. Applying age weights and discounting simultaneously to the DALY calculations decreased the number of DALY’s with more than 46%. The choice regarding the imr showed a relative large effect on the outcomes (25% decrease in DALYs compared to standard setting). The sensitivity analyses also shows that the additive effect of an 1°C increase in summer temperature (Van der Leun et al 2008) in 2030 (compared to baseline) could outweigh the effect of the decreasing UVR levels on age-specific incidence and mortality rates.



  • Decreasing UVR levels result in lower age-specific incidence and mortality rates (model 1 versus model 2).
  • For the population as a whole, this positive effect of decreasing ambient UVR is offset by increasing population size and further ageing of the population. Hence, policies to prevent or treat melanoma will still be relevant in the future, despite the recovery of the ozone layer.
  • Based on the sensitivity analyses, it can be concluded that the positive effect of the recovery of the ozone layer could be outweighed by the possible additive effect of 1°C increase in summer temperature between 2000 and 2030, which is not very unlikely given the IPCC climate change scenarios (the national projections for Finland by Jylhä et al. (2004) indicated a temperature rise of 1-3˚C in 2010-2039 compared to 1960-1990).

Lessons learned

  • The inclusive full-chain model stimulated discussion about the risk assessment context. The exclusive framework stimulated discussion about excluded system factors.
  • You often have to work with what is available. This facilitated discussion on choices/assumptions made, improving the (transparency of) the assessments. The sensitivity analyses explored the impact of varying relevant input assumptions, in order to determine how ‘sensitive’ a model is to changes in the value of the parameters of the model. Further discussion with stakeholder might add to this as well, but will be very time-consuming.
  • One of the biggest challenges was the downscaling of the SRES-population scenarios from the OECD region to the city level (downscaling).
  • There is no one-size fits all integrated assessment approach. General principles, methods and frameworks have to be made meaningful with the specific context of the case study. This requires: creativity, transparency, open attitude, and learning by doing.
  • Our inventory of important literature, supporting materials and other useful (data) sources might be useful in future assessments.

See also

Case study summary reports

Worked examples - complete assessments

Worked examples of single applied methods

Other useful (data) sources


  • Curado, M., B. Edwards, et al., Eds. (2007). Cancer Incidence in Five Continents, Vol. IX Lyon, IARC Scientific Publications No. 160.
  • DHS (2005) [ Victorian Burden of Disease study: mortality and morbidity in 2001.] Melbourne: State of Victoria, Department of Human Services.
  • Ilyas, M. (2007). "Climate augmentation of erythemal UV-B radiation dose damage in the tropics and global change." Current Science 93(11).
  • IPCC (2000) Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge University Press.
  • IPCC (2005). Special Report on Safeguarding the Ozone Layer and the Global Climate System: Issues related to hydrofluorocarbons and perfluorocarbons. Cambridge, . Cambridge University Press.
  • Jylhä et al. (2004). Climate change projections for Finland during the 21st century. Boreal environment research, 9, 127-152.
  • Lucas, R., T. McMichael, 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.
  • Rummukainen, M., I. Isaksen, et al. (1999). "A global model tool for three-dimensional multiyear stratospheric chemistry simulations: Model description and first results." J. Geophys. Res. 104(D21): 26437-26456.
  • Scotto, J. and T. Fears (1987). "The association of solar ultraviolet and skin melanoma incidence among Caucasians In the United States." Cancer Investigation 5(4): 275-283.
  • Stouthard M, Essink-Bot, M., et al (1997). Disability weights for diseases in the Netherlands. Rotterdam, Department of Public Health, Erasmus University.
  • UNEP (2006). Handbook for the Montreal Protocol- 7th edition. Nairobi, United Nations Environmental Programme, Ozone Secretariat.
  • van der Leun, J., R. Piacentini, et al. (2008). "Climate change and human skin cancer." Photochem. Photobiol. Sci 6: 730-733.
  • WHO (2004). The global burden of diseases: 2004 update. Geneva: World Health Organization.

See also

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