Lung cancer cases due to radon in Europe: Difference between revisions
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#} | #} | ||
#for (i in list(1=1:5, 2=8:3)) print(i[1]+i[2]) | #for (i in list(1=1:5, 2=8:3)) print(i[1]+i[2]) | ||
k <- 0. | k <- rnorm(nrow(popxconc), 0.16, (0.31-0.05)/3.92)*58.2 #RR * background rate | ||
lungmortality <- data.frame(popxconc[,c("obs","Country","policy","Year","Age","Rate","Sex")], Result = k * popxconc[, | lungmortality <- data.frame(popxconc[,c("obs","Country","policy","Year","Age","Rate","Sex")], Result = k * popxconc[, | ||
"Concentration"] / 100 * popxconc[, "Population"] / 100000)</nowiki> | "Concentration"] / 100 * popxconc[, "Population"] / 100000)</nowiki> | ||
Revision as of 10:47, 14 January 2011
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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 <- rnorm(nrow(popxconc), 0.16, (0.31-0.05)/3.92)*58.2 #RR * background rate
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}}
| Year | ||||
|---|---|---|---|---|
| Policy | 2010 | 2020 | 2030 | 2050 |
| BAU | 37091 (36015-38167) | 44370 (43124-45616) | 50295 (48582-52008) | 54519 (52653-56386) |
| All | NA | 45737 (44244-47230) | 58804 (56731-60878) | 68994 (66156-71832) |
| Biomass | NA | NA | NA | 69412 (66698-72125) |
| Insulation | NA | NA | NA | 68539 (65955-71124) |
| Renovation | NA | NA | NA | 80139 (76838-83440) |
| 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
<mfanonymousfilelist></mfanonymousfilelist>