Difference between revisions of "Assessment of building policies' effect on dampness and asthma in Europe"

Revision as of 14:38, 9 February 2011

Scope

In this study, the research question was the following: What are the effects of different building policies on dampness and asthma prevalence in Europe? We looked specifically at years 2010, 2020, 2030, and 2050 in the European Union. The study was performed as an open assessment in the internet as a part of the so called Common Case Study of INTARESE and HEIMTSA projects.

Boundaries etc.

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

Result

Asthma prevalence due to building dampness in Europe: Show results

• 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.

Rationale

Decision variables

• Building policies in Europe

Other variables

• Population of Europe by Country
• Asthma prevalence
• heande:HI:Air exchange rate for European residences
• heande:Moisture damage
• ERF of indoor dampness on respiratory health effects
• Disability weights

Indicators

• Asthma prevalence due to building dampness in Europe

Analyses

Building mould and dampness case study

• Estimates health impacts of dampness and mould in residential buildings on asthma prevalence in Europe.
• Nation-wide dampness estimates were obtained from several studies reviewed in this sub-assessment (http://heande.opasnet.org/wiki/Moisture_damage).
• Several countries (Luxembourg, Netherlands, Switzerland, Ireland, Norway, United Kingdom, Bulgaria, Hungary, Lithuania, Romania, Slovakia, Slovenia, Malta) were rejected due to lack of data.
• Exposure-response function was 1.56 (OR) for current asthma risk (prevalence) due to existing dampness problem (Fisk et al., 2007).
• Linear no-threshold ERF was 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/Assessment_of_building_policies%27_effect_on_dampness_and_asthma_in_Europe

Outcomes of interest of mould and dampness sub-assessment.

• Main health impact: number of asthma cases (prevalence) attributable to indoor problems due to residential mould or dampness.
• Preliminary estimate of DALYs attributable to asthma were based on disability weight 0.056 (weight of a treated asthma case from WHO).
• 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 for detailed analysis

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

library(ggplot2)
asthma <- op_baseGetData("opasnet_base", "Op_en4723", exclude = 48823)
array <- DataframeToArray(asthma)
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*0.059
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*"€)")) + 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 whether the values are the same

See also

Keywords

Dampness, indoor air, asthma, Europe

References

Related files

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