Lung cancer cases due to radon in Europe: Difference between revisions
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|+ '''Lung cancer cases attributable to indoor radon in residents in Europe, year 2010. | |||
! Country of observation!! Mean!! SD | |||
|---- | |||
|| Austria|| 954|| 866 | |||
|---- | |||
|| Belgium|| 733|| 566 | |||
|---- | |||
|| Bulgaria|| 265|| 279 | |||
|---- | |||
|| Switzerland|| 1389|| 2101 | |||
|---- | |||
|| Cyprus|| 8|| 9 | |||
|---- | |||
|| Germany|| 4080|| 2857 | |||
|---- | |||
|| Denmark|| 326|| 300 | |||
|---- | |||
|| Estonia|| 190|| 177 | |||
|---- | |||
|| Spain|| 7491|| 14273 | |||
|---- | |||
|| Finland|| 699|| 563 | |||
|---- | |||
|| France|| 6903|| 8818 | |||
|---- | |||
|| Greece|| 725|| 708 | |||
|---- | |||
|| Hungary|| 1374|| 1558 | |||
|---- | |||
|| Ireland|| 463|| 439 | |||
|---- | |||
|| Italy|| 4355|| 3537 | |||
|---- | |||
|| Lithuania|| 191|| 176 | |||
|---- | |||
|| Luxembourg|| 59|| 47 | |||
|---- | |||
|| Latvia|| 195|| 196 | |||
|---- | |||
|| Malta|| 38|| 38 | |||
|---- | |||
|| Netherlands|| 445|| 217 | |||
|---- | |||
|| Norway|| 492|| 463 | |||
|---- | |||
|| Poland|| 1914|| 1387 | |||
|---- | |||
|| Portugal|| 1020|| 889 | |||
|---- | |||
|| Romania|| 1063|| 1039 | |||
|---- | |||
|| Sweden|| 1206|| 1233 | |||
|---- | |||
|| Slovakia|| 514|| 494 | |||
|---- | |||
|| '''Total'''|| '''37091'''|| | |||
|---- | |||
|} | |||
==See also== | ==See also== |
Revision as of 12:54, 13 January 2011
Moderator:Teemu R (see all) |
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Scope
Mortality due to indoor radon concentrations.
- Spatial: Europe
- Temporal: years 2010-2050
Definition
- Lung cancer cases calculated from radon concentration using <math>cases = impact function * concentration * population</math>. IF taken from HEIMTSA and INTARESE (Darby 2004; Darby 2005).
- The referenced impact function is scaled down in assuming only 4.6% of population is exposed, but we assume all are exposed.
Data
Dependencies
- Radon concentrations in European residences
- Population of Europe
- Lung cancer mortality in Europe (from WHO mortality data)
- ERF of radon exposure on lung cancer mortality (heande:ERFs of several pollutants), impact function on the same page used for code below.
Unit
cases per year
Formula
R code
- This code features R functions described on pages Opasnet Base Connection for R and Operating intelligently with multidimensional arrays in R.
- Possible extensions, to accomodate more complex models, are left commented out with # for now.
#library(ff) #mortlocs <- op_baseGetLocs("opasnet_base", "Op_en2778") #mort <- op_baseGetData("opasnet_base", "Op_en2778", include = mortlocs[grep("C34|1034|UE15|All Ages", mortlocs$loc),"loc_id"]) #poplocs <- op_baseGetLocs("opasnet_base", "Op_en4691") pop <- op_baseGetData("opasnet_base", "Op_en4691", include = 1367, exclude = c(1435, 1436)) #countries <- c("AT", "BE", "BG", "CH", "CY", "CZ", "DE", "DK", "EE", "ES", "FI", "FR", "GR", "HU", "IE", "IS", "IT", "LT", "LU", # "LV", "MT", "NL", "NO", "PL", "PT", "RO", "SE", "SI", "SK", "UK") countries <- c("Austria", "Belgium", "Bulgaria", "Switzerland", "Cyprus", "Czech Republic", "Germany", "Denmark", "Estonia", "Spain", "Finland", "France", "Greece", "Hungary", "Ireland", "Iceland", "Italy", "Lithuania", "Luxembourg", "Latvia", "Malta", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Sweden", "Slowenia", "Slovakia", "United Kingdom") conc <- op_baseGetData("opasnet_base", "Op_en4713") levels(pop[,"CountryID"]) <- countries colnames(pop)[4] <- "Country" colnames(pop)[8] <- "Population" #y <- 0 #mort0 <- mort[1,] #temp <- mort[1,] #for (i in 1:length(levels(mort[,"Country"]))) { # icountry <- levels(mort[,"Country"])[i] # temp <- mort[mort[,"Country"] == icountry&mort[,"Age"] == "All Ages"&mort[,"Year"] == as.character(max(as.numeric(as.character(mort[ # mort[, "Country"] == icountry, "Year"])))),] # if(nrow(temp)>0) mort0[(y+1):(y+nrow(temp)),] <- temp[,] # y <- nrow(mort0) #} #mort0 <- mort0[,c(3,4,5,6,7,9)] pop <- pop[,c(3,4,5,6,7,8)] #conc <- conc[,c(2,3,4,5)] #mort0array <- DataframeToArray(mort0) levels(pop[,"Age"])[1] <- "All Ages" #Fixed to match mortality format #popxmort0 <- IntArray(pop, mort0array, "Mortality") #ffpopxmort0 <- ffdf(Age=as.ff(popxmort0[,1]), Country=as.ff(popxmort0[,2]), Rate=as.ff(popxmort0[,3]), Sex=as.ff(popxmort0[,4]), # Year=as.ff(popxmort0[,5]), Population=as.ff(popxmort0[,6]), Cause=as.ff(factor(popxmort0[,7])), List=as.ff(factor(popxmort0[,8])), # Mortality=as.ff(popxmort0[,9])) concarray <- DataframeToArray(conc) #meanconcarray <- apply(concarray, c(2,3), mean) #ffconcarray <- as.ff(concarray) popxconc <- IntArray(pop, concarray[1:1000,,,], "Concentration") #popxmort0xconc <- IntArray(popxmort0, concarray[1:1000,,]) #y <- 1 #temp <- IntArray(ffpopxmort0[1,], concarray) #ffpopxmort0xconc <- temp[] #for (i in 1:(nrow(ffpopxmort0)%/%5)) { # temp[] <- IntArray(ffpopxmort0[(1+(i-1)*5):(i*5),], concarray) # ffpopxmort0xconc[y:(y+nrow(temp)-1)] <- temp[] # y <- y + nrow(temp) #} #for (i in list(1=1:5, 2=8:3)) print(i[1]+i[2]) k <- 0.37/0.046 lungmortality <- data.frame(popxconc[,c("obs","Country","policy","Year","Age","Rate","Sex")], Result = k * popxconc[, "Concentration"] / 100 * popxconc[, "Population"] / 100000)
Result
{{#opasnet_base_link:Op_en4715}}
Country of observation | Mean | SD |
---|---|---|
Austria | 954 | 866 |
Belgium | 733 | 566 |
Bulgaria | 265 | 279 |
Switzerland | 1389 | 2101 |
Cyprus | 8 | 9 |
Germany | 4080 | 2857 |
Denmark | 326 | 300 |
Estonia | 190 | 177 |
Spain | 7491 | 14273 |
Finland | 699 | 563 |
France | 6903 | 8818 |
Greece | 725 | 708 |
Hungary | 1374 | 1558 |
Ireland | 463 | 439 |
Italy | 4355 | 3537 |
Lithuania | 191 | 176 |
Luxembourg | 59 | 47 |
Latvia | 195 | 196 |
Malta | 38 | 38 |
Netherlands | 445 | 217 |
Norway | 492 | 463 |
Poland | 1914 | 1387 |
Portugal | 1020 | 889 |
Romania | 1063 | 1039 |
Sweden | 1206 | 1233 |
Slovakia | 514 | 494 |
Total | 37091 |
See also
Keywords
References
Related files
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