Lung cancer cases due to radon in Europe: Difference between revisions
Jump to navigation
Jump to search
m (→R code) |
(corrected impact function) |
||
| Line 9: | Line 9: | ||
== Definition == | == Definition == | ||
Lung cancer cases calculated from radon concentration using <nowiki><math>cases = impact function * concentration * population</math></nowiki>. IF taken from HEIMTSA and INTARESE (Darby 2004; Darby 2005). | *Lung cancer cases calculated from radon concentration using <nowiki><math>cases = impact function * concentration * population</math></nowiki>. 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 === | === Data === | ||
| Line 79: | Line 80: | ||
#} | #} | ||
#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 <- 0.37 | k <- 0.37/0.046 | ||
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 11:47, 13 January 2011
| Moderator:Teemu R (see all) |
| This page is a stub. You may improve it into a full page. |
| Upload data
|
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}}
See also
Keywords
References
Related files
<mfanonymousfilelist></mfanonymousfilelist>