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

Revision as of 07:33, 31 January 2011

Scope

Purpose

What are the effects of different building policies on dampness and asthma prevalence in Europe?

Boundaries etc.

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

Definition

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.

library(ggplot2)
asthma <- op_baseGetData("opasnet_base", "Op_en4723")
array <- DataframeToArray(asthma)
array <- array[,,,c(2,1,3,4,5),,]
##### 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(2:5,8:10,13:15)] <- 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(2:5,8:10,13:15)] <- NA
final2
##### Cost #####
cost <- array(NA, dim = c(dim(DALY), 2), dimnames = dimnames(DALY))
cost[,,,,1] <- DALY
cost[,,,,2] <- runif(prod(dim(DALY)),3*10^4,6*10^4)
cost <- cost[,,,,1]*cost[,,,,2]
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(2:5,8:10,13:15)] <- NA
final3
##### Probability density plot #####
test <- as.data.frame(as.table(apply(cost, c(1,3,4), sum)))
plot4 <- ggplot(test, aes(x=Freq, y=..count..)) + geom_density() + scale_x_continuous("Cost (M€)") + 
scale_y_continuous("Density") + facet_wrap(~Year ~policy)
plot4

Result

Asthma prevalence due to building dampness in Europe: Show results

See also

Keywords

Dampness, indoor air, asthma, Europe

References