Lung cancer cases due to radon in Europe: Difference between revisions

Latest revision as of 10:58, 26 August 2013

This page is a knowledge crystal of subtype variable. The page identifier is Op_en4715
Moderator:Teemu R (see all)
This page is a stub. You may improve it into a full page.
Upload data {{#opasnet_base_link:Op_en4715}}

Scope

Mortality due to indoor radon concentrations.

Spatial: Europe
Temporal: years 2010-2050

Definition

Lung cancer cases calculated from radon concentration using <math>cases = relative risk * background rate of cases * concentration * population</math>. RR taken from HEIMTSA and INTARESE (Darby 2004; Darby 2005).
- Background rate of new lung cancers is assumed constant for the whole Europe: 58.2 cases per 100,000 population (Globocan 2008, crude rate).
- Linear no threshold model assumed.
- In a given iteration, radon concentrations are assumed equal in all residences in a country.

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 (heande:ERFs of several pollutants)

Unit

cases per year

Formula

This code features R functions described on pages Opasnet Base Connection for R and Operating intelligently with multidimensional arrays in R.

+ Show code - Hide code

library(OpasnetBaseUtils)
conc <- op_baseGetData("opasnet_base", "Op_en4713")
pop <- op_baseGetData("opasnet_base", "Op_en4691", include = 1367, exclude = c(1435, 1436))
erf <- op_baseGetData("opasnet_base", "Erac2987", include = 48847)
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")
levels(pop[,"CountryID"]) <- countries #Country IDs converted to names for compatibility
colnames(pop)[4] <- "Country"
colnames(pop)[7] <- "Population"
concarray <- DataframeToArray(conc)
popxconc <- IntArray(pop, concarray, "Concentration")#[1:1000,,,]
erfarray <- DataframeToArray(erf[,c("obs","Result")])
popxconcxerf <- IntArray(popxconc, erfarray, "ERF")
lungmortality <- data.frame(popxconcxerf[,c("obs","Country","policy","Year","Age","Sex")], Result = popxconcxerf[,"ERF"] * 58.2 * 
popxconcxerf[,"Concentration"] / 100 * popxconcxerf[, "Population"] / 100000)

An alternative code for Finland

This code uses the summary table approach for obtaining data. The data sources are the same except ERF for long-term indoor exposure to radon and lung cancer (summary table Category:Exposure-response functions) and Exposure to indoor radon in Finland (summary table Category:Exposures).

+ Show code - Hide code


cat("Get data from Opasnet Base.\n")

library(OpasnetBaseUtils)
library(xtable)

conc <- summary.bring("Op_en1619")[show, ] # Summary table Category:Exposures
erf <- summary.bring("Op_en1914") # Summary table Category:Exposure-response functions
pop <- summary.bring("Op_en2154") # Summary table Category:Population characteristics
bg <- summary.bring("Op_en1620") # Summary table Category:Health effects

cat("Adjust data for the model.\n")

print(xtable(conc), type = 'html')
print(xtable(erf), type = 'html')
print(xtable(pop), type = 'html')
print(xtable(bg), type = 'html')

out <- merge(conc, erf)
out <- merge(out, pop)
out <- merge(out, bg)

out$ERF <- as.numeric(substr(out$ERF, 1, 6))
out$Exposure <- as.numeric(substr(out$Exposure, 1, 3))
out$Response <- as.numeric(out$Response)

cat("Run the actual model.\n")

out <- dropall(out)
tst <- rep(TRUE, ncol(out)) # tst: are all values in a column the same?
for(i in 1:ncol(out)){tst[i] <- nlevels(as.factor(out[, i])) == 1}
print(xtable(t(out[1, tst])), type = 'html')

out$Out <- (out$ERF - 1) / out$ERF * out$Exposure * out$Response * out$Amount / 100000
for(i in 1:ncol(out)){tst[i] <- nlevels(as.factor(out[, i])) == 1}
print(xtable(out[, !tst]), type = 'html')

Result

**Lung cancer cases in Europe due to indoor radon in residences (mean and 95% confidence interval).**
	Year
Policy	2010	2020	2030	2050
BAU	42691 (22042-95542)	51138 (24733-111979)	57618 (25785-137181)	62461 (27023-143632)
All	NA	53385 (25402-130453)	68338 (28863-171718)	81873 (31372-239696)
Biomass	NA	NA	NA	81849 (32497-218247)
Insulation	NA	NA	NA	80534 (32445-236962)
Renovation	NA	NA	NA	94075 (35396-288323)

**Lung cancer cases attributable to indoor radon in residences in Europe, year 2010.**
Country of observation	Mean	SD
Austria	1105	1146
Belgium	857	796
Bulgaria	303	382
Switzerland	1636	2835
Cyprus	10	14
Germany	4754	4305
Denmark	382	408
Estonia	222	239
Spain	8245	15249
Finland	805	792
France	8204	12176
Greece	857	977
Hungary	1583	1985
Ireland	533	580
Italy	5035	5117
Lithuania	223	233
Luxembourg	68	64
Latvia	229	289
Malta	43	50
Netherlands	504	338
Norway	566	667
Poland	2175	1864
Portugal	1183	1208
Romania	1233	1357
Sweden	1353	1475
Slovakia	586	661
Total	42691

Keywords

References

Related files

@@ Line 1: / Line 1: @@
 {{variable|moderator=Teemu R|stub=Yes}}
+[[Category:Code under inspection]]
 == Scope ==
@@ Line 54: / Line 55: @@
 This code uses the summary table approach for obtaining data. The data sources are the same except [[ERF for long-term indoor exposure to radon and lung cancer]] (summary table [[:Category:Exposure-response functions]]) and [[Exposure to indoor radon in Finland]] (summary table [[:Category:Exposures]]).
-<rcode include="page:OpasnetBaseUtils|name:generic">
+<rcode include="page:OpasnetBaseUtils|name:generic" variables="
+name:show|description:Which exposure parameters do you want to see?|type:checkbox|options:1;Actual population average in Finland;2;Guidance value for new apartments;3;Guidance value for old apartments|default:1;2;3
+">
-############### Bring parts of summary table
+cat("Get data from Opasnet Base.\n")
-#   data is the summary table.
-summary.bring <- function(data){
-pages <- levels(data$Page)
-out <- op_baseGetData("opasnet_base", pages[1])[, -c(1,2)]
-out <- out[out$Observation != "Description", ]
-if(nlevels(pages) > 1){
-for(i in 2:nlevels(pages)){
-out[[i]] <- op_baseGetData("opasnet_base", pages[i])
-}}
-cols <- colnames(out)[!colnames(out) %in% c("Observation", "Result", "Result.Text")]
-out$Result <- ifelse(!is.na(out$Result.Text), as.character(out$Result.Text), out$Result)
-#print(xtable(out), type = 'html')
-out <- out[, colnames(out) != "Result.Text"]
-out <- reshape(out, timevar = "Observation", idvar = cols, direction = "wide")
-#print(xtable(out), type = 'html')
-colnames(out) <- ifelse(substr(colnames(out), 1, 12) == "Result.Text.", substr(colnames(out), 13, 50), colnames(out))
-colnames(out) <- ifelse(substr(colnames(out), 1, 7) == "Result.", substr(colnames(out), 8, 50), colnames(out))
-#print(xtable(out), type = 'html')
-conc <- data[data$Observation != "Description", !colnames(data) %in% c("Result", "Observation")]
-#print(xtable(conc), type = 'html')
-temp <- reshape(conc, idvar = "Page", timevar = "Index", direction = "wide")
-temp <- temp[, colnames(temp) != "Page"]
-colnames(temp) <- ifelse(substr(colnames(temp), 1, 12) == "Result.Text.", substr(colnames(temp), 13, 50), colnames(temp))
-out <- merge(temp, out)
-return(out)
-}
-##########
-cat("Get data from Opasnet Base.\n")
 library(OpasnetBaseUtils)
 library(xtable)
-conc <- op_baseGetData("opasnet_base", "Op_en1619")[, -c(1,2)] # Summary table Category:Exposures
-#pop <- op_baseGetData("opasnet_base", "Op_en4691", include = 1367, exclude = c(1435, 1436))[, -c(1,2)] #
-erf <- op_baseGetData("opasnet_base", "Op_en1914")[, -c(1,2)] # Summary table Category:Exposure-response functions
-out <- summary.bring(conc)
+conc <- summary.bring("Op_en1619")[show, ] # Summary table Category:Exposures
+erf <- summary.bring("Op_en1914") # Summary table Category:Exposure-response functions
+pop <- summary.bring("Op_en2154") # Summary table Category:Population characteristics
+bg <- summary.bring("Op_en1620") # Summary table Category:Health effects
-print(xtable(out), type = 'html')
+cat("Adjust data for the model.\n")
-erfs <- summary.bring(erf)
+print(xtable(conc), type = 'html')
-print(xtable(erfs), type = 'html')
+print(xtable(erf), type = 'html')
+print(xtable(pop), type = 'html')
+print(xtable(bg), type = 'html')
+out <- merge(conc, erf)
+out <- merge(out, pop)
+out <- merge(out, bg)
-cat("Adjust data for the model.\n")
+out$ERF <- as.numeric(substr(out$ERF, 1, 6))
+out$Exposure <- as.numeric(substr(out$Exposure, 1, 3))
+out$Response <- as.numeric(out$Response)
-#conc <- conc[conc$Page == "Op_en5450", ] # Slice exposures to radon
+cat("Run the actual model.\n")
-#conc
-countries <- c("Austria", "Belgium", "Bulgaria", "Switzerland", "Cyprus", "Czech Republic", "Germany", "Denmark", "Estonia", "Spain",
+out <- dropall(out)
-	"Finland", "France", "Greece", "Hungary", "Ireland", "Iceland", "Italy", "Lithuania", "Luxembourg", "Latvia", "Malta", "Netherlands",
+tst <- rep(TRUE, ncol(out)) # tst: are all values in a column the same?
-	"Norway", "Poland", "Portugal", "Romania", "Sweden", "Slowenia", "Slovakia", "United Kingdom")
+for(i in 1:ncol(out)){tst[i] <- nlevels(as.factor(out[, i])) == 1}
-levels(pop[,"CountryID"]) <- countries #Country IDs converted to names for compatibility
+print(xtable(t(out[1, tst])), type = 'html')
-colnames(pop)[4] <- "Country"
-colnames(pop)[7] <- "Population"
-cat("Run the actual model.\n")
+out$Out <- (out$ERF - 1) / out$ERF * out$Exposure * out$Response * out$Amount / 100000
+for(i in 1:ncol(out)){tst[i] <- nlevels(as.factor(out[, i])) == 1}
+print(xtable(out[, !tst]), type = 'html')
-concarray <- DataframeToArray(conc)
-popxconc <- IntArray(pop, concarray, "Concentration")#[1:1000,,,]
-erfarray <- DataframeToArray(erf[,c("obs","Result")])
-popxconcxerf <- IntArray(popxconc, erfarray, "ERF")
-lungmortality <- data.frame(popxconcxerf[,c("obs","Country","policy","Year","Age","Sex")], Result = popxconcxerf[,"ERF"] * 58.2 *
-popxconcxerf[,"Concentration"] / 100 * popxconcxerf[, "Population"] / 100000)
 </rcode>

Lung cancer cases due to radon in Europe: Difference between revisions

Latest revision as of 10:58, 26 August 2013

Contents

Scope

Definition

Data

Dependencies

Unit

Formula

An alternative code for Finland

Result

See also

Keywords

References

Related files

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