Difference between revisions of "Health impact modelling: ultraviolet radiation and melanoma skin cancer"

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{{assessment|moderator=Jouni Tuomisto}}
 
{{assessment|moderator=Jouni Tuomisto}}
  
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:''This case study was carried out as part (work package 3.7) of the EU-funded INTARESE project. The contents were obtained from [http://www.integrated-assessment.eu/resource_centre/health_impact_modelling_ultraviolet_radiation_and_non_melanoma_skin_cancer IEHIAS toolbox].
  
 
{{summary box
 
{{summary box
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==Explanation==
 
==Explanation==
  
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'''Explanation of the Method'''
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INTARESE PROJECT-WP3.7-UVR
  
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The assessment model used Excel as a platform, in order to calculate the number of incident cases and the number of deaths at baseline. Calculations were performed for 5-year age groups (up to 85+), both for males and females, in three study areas (London, Rome and Helsinki).
 
  
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In each case, future changes in ambient UVR (annual erythemal dose) were combined with exposure-response functions (ERF) for melanoma (incidence) and (future) population estimates.  The ERFwas derived from 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 (Table 1).
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Several health impact model runs were carried out.  The baseline model and model 1 used the baseline incidence rates per age group and gender. For model 2, future incidence rate per age group and gender were calculated by applying the BAF and the future percental change in modelled ambient UVR to the baseline incidence rates. The (future) incidence rates were combined with the baseline and future population estimates in order to calculate skin cancer incidence (number of cases).
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{|{{prettytable}}
 
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!
 
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! Face, head or neck (FHN), upper extremity (UE) *
 
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! Trunk & lower extremity (TL) **
 
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|-----
 
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|Male
 
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|8%
 
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|6%
 
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|-----
 
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|Female
 
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|10%
 
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|5%
 
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|}
 
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<sup>*</sup>higher risk estimate; standard model setting;<sup>*</sup><sup>*</sup> lower risk estimate
 
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Several health impact model runs were carried out (Table 2).  The baseline model and model 1 used the baseline incidence rates per age group and gender. For model 2, future incidence rate per age group and gender were calculated by applying the BAF and the future percental change in modelled ambient UVR to the baseline incidence rates. The (future) incidence rates were combined with the baseline and future population estimates in order to calculate skin cancer incidence (number of cases).
 
  
 
The results were then combined with the baseline and future population estimates in order to calculate skin cancer incidence (number of cases).  Thus:
 
The results were then combined with the baseline and future population estimates in order to calculate skin cancer incidence (number of cases).  Thus:
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* Model 1: baseline incidence rates and future population estimates are applied.
 
* Model 1: baseline incidence rates and future population estimates are applied.
  
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* Model 2: estimated future incidence rates and future population estimates are applied.  
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* Model 2: estimated future incidence rates and future population estimates are applied.
  
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    * Purpose defines the specific information need of the decision-making and the research question that is asked.
 
  
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===Boundaries===
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The input data sheet includes the population (in thousands) data for males and females developed in baseline; 2030 and 2050 B1, and 2030 and 2050 A2. The data also includes the baseline incidence rate per 10000 per year and the incidence mortality ratio for both males and females in all the scenarios. The calculations were carried by dividing the age range starting from 0 – 85+ into a 5-year age group for both the sexes.
  
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    * Boundaries define which parts of the reality are taken into the assessment and which are excluded within
 
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      spatial, temporal and other dimensions.
 
  
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===Scenarios===
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The summery tables include the data for both health impact model 1 and 2. The data derived from the calculation of attributable incident cases, attributable deaths and attributable DALY were presented both individually and together for males and females. The attributable incident cases, mortality rate and DALY were also calculated per million combining both males and females. The data were explored for 2001 and 2030 B1 and A2, 2050 B1 and A2, except for the attributable DALY data which was for 2001 and 2030 B1 and A2.   
  
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    * Scenarios define particular conditions that are of interest irrespective whether they describe
 
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      reality or not (e.g. what-if scenarios).
 
  
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===Intended users===
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The summery figures contain data for health impact model 1 and 2, including the total incidence, mortality and DALY for males and females together. The total incidence, mortality and DALY rate per million for both males and females were also calculated. The calculations was for 2001, 2030 B1 and A2 and 2050 B1 and A2, except for the DALYs which was for 2001 and 2030 B1 and A2. Data were illustrated graphically and also presented in tables. 
  
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    * Intended users are those for whom the assessment is made.
 
  
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===Participants===
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The health impact model 2 incidence rate per 100000 includes data for males and females; dividing the 0-85+ age range into 5-year age groups individually for both sexes. The calculation was performed for baseline, B1 (2030, 2050) and A2 (2030, 2050).
  
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    * Participants are those who may participate in the making of the assessment.
 
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      The minimum group of people for a successful assessment is always described.
 
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      If some groups must be excluded, this must be explicitly motivated.
 
  
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==Definition==
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The attributable (based on PAF) and the total incidence cases, number of deaths and incidence and mortality rates per 100000 per year were calculated individually for baseline, model 1 B1 2030, model 1 B1 2050, model 1 A2 2030, model 1 A2 2050, model 2 B1 2030, model 2 A2 2030 and model 2 A2 2050. The age range was 0-85+ for both males and females which was categorised into 5-year age group for both attributable and total data in all the respective scenarios.
  
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Upload a causal diagram and change the right name here.
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==See Also==
  
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[[image:Causal diagram.PNG|thumb|Add a legend for you diagram.]]
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*[[Changing ambient UVR and future skin cancer in London, Rome and Helsinki: melanoma skin cancer (CMM)]]
  
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===Decision variables===
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*[[Melanoma health impact model for Helsinki.xls]]
  
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    * Decision variables: decisions that are considered.
 
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===Indicators===
 
  
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    * Indicators: outcome variables of interest.
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==References==
  
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===Value variables===
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*[[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.]]
  
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    * Value variables: value judgements (usually about indicators).
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*[[Lucas, R., T. McMichael, et al. 2006 Solar ultraviolet radiation: Global burden of disease from solar ultraviolet radiation. Geneva:, World Health Organization.]]
  
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===Other variables===
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<references/>
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    * Other variables: any variables that link to the causal network and are within the boundaries of the assessment.
 
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===Analyses===
 
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    * Analyses: statistical and other analyses that contain two or more variables, e.g. optimizing.
 
  
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===Indices===
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==Related files==
  
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    * Indices: lists of particular locations along spatial, temporal, or other dimensions.
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{{mfiles}}
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==Result==
 
  
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    * Results of indicators and assessment-specific analyses.
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{{eracedu}}
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===Results===
 
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===Conclusions===
 
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    * Conclusions are based on the results, given the scope.
 
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==See also==
 
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==References==
 
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<references/>
 

Latest revision as of 11:59, 14 February 2012



This case study was carried out as part (work package 3.7) of the EU-funded INTARESE project. The contents were obtained from IEHIAS toolbox.
Main message:
Question:

What was the overall health impact of exposures to ultra-violet radiation (UVR) under different climate change scenarios?

Answer:

Given current knowledge, The assessment considered two sets of scenarios for the years 2030 and 2050: the IPCC SRES B1 scenario and SRES A2 scenario.Results suggest that further knowledge about the uncertain variables considered very likely would not have changed the decision


Scope

As part of the EU-funded INTARESE project, which contributed to the development of this Toolbox, a case study was carried out to assess health impacts of exposures to ultra-violet radiation (UVR) under different climate change scenarios.

The assessment considered two sets of scenarios for the years 2030 and 2050: the IPCC SRES B1 scenario and SRES A2 scenario. Health outcomes considered were basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (CMM). Changes in UVR exposure were estimated, taking account of changes in stratospheric ozone associated with decreasing emission of ozone depleting substances and increasing emissions of CH4 and N2O. Allowance was also made for future demographic changes.

Below the methods used to model non-melanoma skin cancer are decribed. Details of the working procedures are given in the attached spreadsheet.


Explanation

INTARESE PROJECT-WP3.7-UVR


Several health impact model runs were carried out. The baseline model and model 1 used the baseline incidence rates per age group and gender. For model 2, future incidence rate per age group and gender were calculated by applying the BAF and the future percental change in modelled ambient UVR to the baseline incidence rates. The (future) incidence rates were combined with the baseline and future population estimates in order to calculate skin cancer incidence (number of cases).

The results were then combined with the baseline and future population estimates in order to calculate skin cancer incidence (number of cases). Thus:

  • Baseline model: baseline incidence rates and baseline population estimates are applied.
  • Model 1: baseline incidence rates and future population estimates are applied.
  • Model 2: estimated future incidence rates and future population estimates are applied.


The input data sheet includes the population (in thousands) data for males and females developed in baseline; 2030 and 2050 B1, and 2030 and 2050 A2. The data also includes the baseline incidence rate per 10000 per year and the incidence mortality ratio for both males and females in all the scenarios. The calculations were carried by dividing the age range starting from 0 – 85+ into a 5-year age group for both the sexes.


The summery tables include the data for both health impact model 1 and 2. The data derived from the calculation of attributable incident cases, attributable deaths and attributable DALY were presented both individually and together for males and females. The attributable incident cases, mortality rate and DALY were also calculated per million combining both males and females. The data were explored for 2001 and 2030 B1 and A2, 2050 B1 and A2, except for the attributable DALY data which was for 2001 and 2030 B1 and A2.


The summery figures contain data for health impact model 1 and 2, including the total incidence, mortality and DALY for males and females together. The total incidence, mortality and DALY rate per million for both males and females were also calculated. The calculations was for 2001, 2030 B1 and A2 and 2050 B1 and A2, except for the DALYs which was for 2001 and 2030 B1 and A2. Data were illustrated graphically and also presented in tables.


The health impact model 2 incidence rate per 100000 includes data for males and females; dividing the 0-85+ age range into 5-year age groups individually for both sexes. The calculation was performed for baseline, B1 (2030, 2050) and A2 (2030, 2050).


The attributable (based on PAF) and the total incidence cases, number of deaths and incidence and mortality rates per 100000 per year were calculated individually for baseline, model 1 B1 2030, model 1 B1 2050, model 1 A2 2030, model 1 A2 2050, model 2 B1 2030, model 2 A2 2030 and model 2 A2 2050. The age range was 0-85+ for both males and females which was categorised into 5-year age group for both attributable and total data in all the respective scenarios.

See Also


References


Related files

<mfanonymousfilelist></mfanonymousfilelist>

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