Health impact of radon in Europe

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Main message:
Question:

Radon gas in homes is a major environmental health hazard causing lung cancer. Good building policies can reduce radon concentrations in indoor air in homes. What are the effects of different plausible building policies on radon in homes and consequently on lung cancer mortality in Europe between 2010 and 2050?

Answer:

There are currently 43000 (95 % CI 7186-104660) lung cancer deaths due to indoor radon in Europe. This number is likely to increase in the future due to decreased ventilation in aim to reduce energy consumption, if other measures are not taken. It is important to maintain good air exchange conditions and proper building insulation even when energy saving measures are taken. More should be known about practical conditions and potential to radon reduction, as well as about the knowledge level, preparedness, and practical hindrances to take such actions by building owners. Value of information analysis shows that this information is so valuable that putting resources in this research is clearly cost-effective. The ultimate aim should be to reduce energy consumption, greenhouse gas emissions, and indoor radon in a cost-effective and synergistic way.


Scope

Purpose

What are the health impacts of radon in the indoor air of residential buildings in Europe?

Boundaries etc

Boundaries, scenarios, intended users, and participants are the same as in the Mega case study.

Definition

A causal diagram of health impacts of radon in Europe.
Decision variables
Other variables
Indicators

Analyses

Indoor radon case study

  • Estimates health impacts of radon in indoor air of residences.
  • Nation-wide radon concentration distributions were obtained from EnVIE project and UNSCEAR 2000 report.
  • United Kingdom, Czech Republic, and Slovenia were rejected because of lack of data.
  • Exposure-response function was 16 % increase of lung cancer incidence due to 100 Bq/m3 increase of radon (Darby 2004 and 2005).
  • The same lung cancer background rate was assumed for the whole Europe: 58.2 cases/100000 person-years (Globocan 2008).
  • Linear no-threshold ERF assumed for the whole population in each country.
  • The model development, data storage, and model runs were all performed in Opasnet using R software and Opasnet Base.
  • The main page of the sub-assessment is http://en.opasnet.org/w/Health_impact_of_radon_in_Europe


Outcomes of interest of radon sub-assessment

  • Main health impact: number of lung cancer cases attributable to radon.
  • Preliminary estimate of DALYs attributable to radon (only draft estimates for duration of disease).
    • Disability weight 0.146 (from WHO) used for disease duration.
    • Duration of disease estimated at 2-36 months; life-years lost due to lung cancer death estimated at 1-15 years.
  • Preliminary estimate of monetary impact was obtained indirectly by converting DALYs into euros; other cost types were ignored.
    • One DALY estimated to be worth 30-60 k€.
  • Methodological outcome: proof of concept for running assessment models via open internet interface.

R code

library(ggplot2)
cancer <- op_baseGetData("opasnet_base", "Op_en4715", exclude = 48823)
array <- DataframeToArray(cancer)
array <- array[,,,c(2,1,3,4),,]
##### Cases #####
means <- apply(array, c(2,3,4), mean, na.rm=TRUE)
means <- apply(means, c(2,3), sum, na.rm=TRUE)
plot1 <- as.data.frame(as.table(means))
plot1 <- ggplot(plot1[plot1[,"Freq"]!=0,], aes(Year, weight=Freq, fill=policy)) + geom_bar(position="dodge") + 
scale_x_discrete("Year") + scale_y_continuous("Cases")
plot1
ci <- apply(apply(array, c(1,3,4), sum, na.rm=TRUE), c(2,3), quantile, probs=c(0.025,0.975))
final1 <- means
final1[,] <- paste(round(means), " (", round(ci[1,,]), "-", round(ci[2,,]), ")", sep="")
final1[c(2:4,7:8,11:12)] <- NA
final1
##### DALYs #####
DALY <- array(NA, dim = c(dim(array), 3), dimnames = dimnames(array))
DALY[,,,,1] <- array
DALY[,,,,2] <- runif(dim(array)[1],2,36)/12
DALY[,,,,3] <- runif(dim(array)[1],1,15)
DALY <- DALY[,,,,1]*0.146*DALY[,,,,2]+DALY[,,,,1]*DALY[,,,,3]
means <- apply(DALY, c(2,3,4), mean, na.rm=TRUE)
means <- apply(means, c(2,3), sum, na.rm=TRUE)
plot2 <- as.data.frame(as.table(means))
plot2 <- ggplot(plot2[plot2[,"Freq"]!=0,], aes(Year, weight=Freq, fill=policy)) + geom_bar(position="dodge") + 
scale_x_discrete("Year") + scale_y_continuous("DALYs")
plot2
ci <- apply(apply(DALY, c(1,3,4), sum, na.rm=TRUE), c(2,3), quantile, probs=c(0.025,0.975))
final2 <- means
final2[,] <- paste(round(means), " (", round(ci[1,,]), "-", round(ci[2,,]), ")", sep="")
final2[c(2:4,7:8,11:12)] <- NA
final2
##### Cost #####
mpdaly <- op_baseGetData("opasnet_base", "Op_en4858")
cost <- IntArray(mpdaly, DALY, "DALYs")
cost <- data.frame(cost[,c("obs","Country","policy","Year")], Result=cost[,"Result"]*cost[,"DALYs"])
cost <- DataframeToArray(cost)
cost <- cost[,,c(2,1,3,4),]
means <- apply(cost, c(2,3,4), mean, na.rm=TRUE)
means <- apply(means, c(2,3), sum, na.rm=TRUE)/10^6
plot3 <- as.data.frame(as.table(means))
plot3 <- ggplot(plot3[plot3[,"Freq"]!=0,], aes(Year, weight=Freq, fill=policy)) + geom_bar(position="dodge") + 
scale_x_discrete("Year") + scale_y_continuous("Cost (M€)")
plot3
ci <- apply(apply(cost, c(1,3,4), sum, na.rm=TRUE), c(2,3), quantile, probs=c(0.025,0.975))/10^6
final3 <- means
final3[,] <- paste(round(means), " (", round(ci[1,,]), "-", round(ci[2,,]), ")", sep="")
final3[c(2:4,7:8,11:12)] <- NA
final3
##### Probability density plot #####
costdf <- as.data.frame(as.table(apply(cost, c(1,3,4), sum)/1e9))
costdf <- costdf[is.na(costdf[,"Freq"])==FALSE,]
plot4 <- ggplot(costdf, aes(x=Freq, y=..density.., fill=policy)) + geom_density(alpha=0.2, adjust=4) + 
scale_x_continuous(expression("Cost ("*10^9*"€)"), limits = c(-50,300)) + scale_y_continuous("Density") + facet_wrap(~Year)
plot4
##### Expected Value of Perfect Information #####
evpi <- (apply(apply(cost, c(2,3,4), mean, na.rm=TRUE), c(1,3), min, na.rm=TRUE) - apply(apply(cost, c(1,2,4), min, 
na.rm=TRUE), c(2,3), mean, na.rm=TRUE))/1e6
plot5 <- as.data.frame(as.table(apply(evpi, 2, sum)))
plot5 <- ggplot(plot5, aes(Var1, weight=Freq)) + geom_bar(position="dodge") + 
scale_x_discrete("Year") + scale_y_continuous("Value of perfect information (M€)")
plot5
##### Expected Value of Partial Perfect Information #####
#Same as that of perfect information, because of only one decision variable
ae <- op_baseGetData("opasnet_base", "Erac2499")
aer <- DataframeToArray(ae)
aer <- aer[,,c(2,1,4,5),]
dropnonmax <- function(x) {
	x[x<max(x, na.rm = TRUE)] <- NA
	return(x)
}
aer <- apply(aer, c(1,2,4), dropnonmax)
aer <- as.data.frame(as.table(aer))
aer <- aer[,c(2,3,1,4,5)]
colnames(aer)[3] <- "policy"
aer <- aer[is.na(aer[,"Freq"])==FALSE,]
aer <- IntArray(aer, cost, "Cost")
aer <- DataframeToArray(aer[,c("obs","Country","Year","Cost")],"Cost")
test2 <- (apply(apply(cost, c(2,3,4), mean, na.rm=TRUE), c(1,3), min, na.rm=TRUE) - apply(aer, c(2,3), mean))/1e6
plot6 <- as.data.frame(as.table(apply(test2, 2, sum)))
plot6 <- ggplot(plot6, aes(Var1, weight=Freq)) + geom_bar(position="dodge") + 
scale_x_discrete("Year") + scale_y_continuous("Value of perfect information (M€)")
plot6
test2==evpi #Test if the values are the same
test2-evpi 
# It seems that for some reason aer evppi is slightly smaller than evpi, 
# although it should be the only variable that varies in the "policy" dimension

Result

Results

Lung cancer cases due to radon in Europe: {{#opasnet_base_link:Op_en4715}}

  • Results for the Biomass scenario are wrong and the scenario is perhaps irrelevant because biomass usage does not affect air exchange rates which this assessment is concerned with, so it should be ignored.


The impacts of European building policies on lung cancer due to indoor radon.
Result distributions
Value of information analysis results
Lung cancer cases in Europe due to indoor radon in residences (mean and 95% confidence interval).
Year
Policy 2010 2020 2030 2050
BAU 43074 (7186-104660) 51801 (8934-129303) 58716 (9427-155621) 63718 (10407-178566)
All NA 52660 (8892-130780) 68086 (10544-180827) 81022 (11983-235695)
Insulation NA NA NA 80149 (11898-228747)
Renovation NA NA NA 92783 (13365-275851)
Lung cancer DALYs in Europe due to indoor radon in residences (mean and 95% confidence interval).
Year
Policy 2010 2020 2030 2050
BAU 358244 (54193-940205) 427824 (65203-1156026) 483005 (70921-1335438) 524154 (80215-1545369)
All NA 433106 (65625-1140190) 562607 (78874-1614252) 663170 (89720-2045648)
Insulation NA NA NA 664141 (88638-2108941)
Renovation NA NA NA 773105 (107764-2392599)
Lung cancer monetary impact (based on DALYs) in Europe due to indoor radon in residences (mean and 95% confidence interval). Unit: M€
Year
Policy 2010 2020 2030 2050
BAU 16147 (2519-42699) 19250 (3039-53378) 21770 (3121-60868) 23585 (3590-70455)
All NA 19464 (3009-53226) 25219 (3682-71977) 29715 (3973-94741)
Insulation NA NA NA 29877 (4049-93146)
Renovation NA NA NA 34810 (4748-106804)
Lung cancer cases attributable to indoor radon in residences in Europe, year 2010.
Country of observation Mean SD
Austria 1071 1202
Belgium 885 872
Bulgaria 297 338
Switzerland 1540 2764
Cyprus 9 12
Germany 4843 4207
Denmark 373 400
Estonia 218 245
Spain 8460 18071
Finland 812 802
France 8320 12045
Greece 813 880
Hungary 1617 2161
Ireland 552 636
Italy 4952 4444
Lithuania 236 277
Luxembourg 69 64
Latvia 226 294
Malta 43 50
Netherlands 523 350
Norway 575 673
Poland 2264 2151
Portugal 1140 1214
Romania 1236 1313
Sweden 1375 1600
Slovakia 626 678
Total 43074

Conclusions

See also

Keywords

Radon, indoor air, lung cancer, Europe

References


Related files

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