Difference between revisions of "Assessment of building policies' effect on dampness and asthma in Europe"
From Testiwiki
(country-specific BAU results for 2010 added + analysis descriptions) |
(→R code for detailed analysis: code for DALYs and cost added) |
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===R code for detailed analysis=== | ===R code for detailed analysis=== | ||
− | + | <nowiki>##### Plot ##### | |
− | + | asthma <- op_baseGetData("opasnet_base", "Op_en4723", include = 25658, iterations = 1000) | |
− | <nowiki>asthma <- op_baseGetData("opasnet_base", "Op_en4723") | + | array <- DataframeToArray(asthma)#[asthma[,"Rate"]=="m",]) |
− | array <- DataframeToArray(asthma) | + | final <- apply(array, c(3,4,7), mean, na.rm=TRUE) |
− | final <- apply(array, c( | + | final <- apply(final, c(2,3), sum, na.rm=TRUE) |
− | final <- apply(final, c(2,3 | + | #final1 <- apply(final, c(1,2), mean, na.rm=TRUE) |
− | final1 <- apply(final, c(1,2), mean, na.rm=TRUE) | + | finaldf1 <- as.data.frame(as.table(final)) |
− | finaldf1 <- as.data.frame(as.table( | ||
finaldf1 <- finaldf1[finaldf1[,"Freq"]!=0,] | finaldf1 <- finaldf1[finaldf1[,"Freq"]!=0,] | ||
barplot(finaldf1[,"Freq"], names.arg=c("2010", "2020 Policy", "2020 BAU", "2030 Policy", "2030 BAU", "2050 Policy", "2050 BAU", | barplot(finaldf1[,"Freq"], names.arg=c("2010", "2020 Policy", "2020 BAU", "2030 Policy", "2030 BAU", "2050 Policy", "2050 BAU", | ||
Line 66: | Line 65: | ||
#for (i in 1:4) {temp[i,1,] <- final[,2,]-final[,2,]} | #for (i in 1:4) {temp[i,1,] <- final[,2,]-final[,2,]} | ||
#final <- final[,]-final[rep(1,4),] | #final <- final[,]-final[rep(1,4),] | ||
− | #barplot(final, beside=TRUE, legend.text=c("2010","2020","2030","2050"))</nowiki> | + | #barplot(final, beside=TRUE, legend.text=c("2010","2020","2030","2050")) |
+ | ##### Cases ##### | ||
+ | normerror <- function(input){qnorm(0.975)*sd(input)/(length(input)^0.5)} | ||
+ | means <- apply(array, c(3,4,7), mean, na.rm=TRUE) | ||
+ | means <- apply(means, c(2,3), sum, na.rm=TRUE) | ||
+ | errors <- apply(array, c(1,4,7), sum, na.rm=TRUE) | ||
+ | errors <- apply(errors, c(2,3), normerror) | ||
+ | ci <- array(NA, dim = c(dim(errors),2), dimnames = dimnames(errors)) | ||
+ | dimnames(ci)[[3]] <- c("Lower","Higher") | ||
+ | names(dimnames(ci))[3] <- "Boundary" | ||
+ | ci[,,1] <- means - errors | ||
+ | ci[,,2] <- means + errors | ||
+ | final1 <- means | ||
+ | final1[,] <- paste(round(means), " (", round(ci[,,1]), "-", round(ci[,,2]), ")", sep="") | ||
+ | final1[c(1,3:5,8:10,13:15)] <- NA | ||
+ | final1 | ||
+ | #finaldf1 <- as.data.frame(as.table(final1)) | ||
+ | #finaldf1 <- finaldf1[finaldf1[,"Freq"]!=0,] | ||
+ | #barplot(finaldf1[,"Freq"], names.arg=c("2010", "2020 Policy", "2020 BAU", "2030 Policy", "2030 BAU", "2050 Policy", "2050 BAU", | ||
+ | #"2050 Biomass", "2050 Insulation", "2050 Renovation")) | ||
+ | ##### DALYs ##### | ||
+ | DALY <- array*0.059 | ||
+ | means <- apply(DALY, c(3,4,7), mean, na.rm=TRUE) | ||
+ | means <- apply(means, c(2,3), sum, na.rm=TRUE) | ||
+ | errors <- apply(DALY, c(1,4,7), sum, na.rm=TRUE) | ||
+ | errors <- apply(errors, c(2,3), normerror) | ||
+ | ci <- array(NA, dim = c(dim(errors),2), dimnames = dimnames(errors)) | ||
+ | dimnames(ci)[[3]] <- c("Lower","Higher") | ||
+ | names(dimnames(ci))[3] <- "Boundary" | ||
+ | ci[,,1] <- means - errors | ||
+ | ci[,,2] <- means + errors | ||
+ | final2 <- means | ||
+ | final2[,] <- paste(round(means), " (", round(ci[,,1]), "-", round(ci[,,2]), ")", sep="") | ||
+ | final2[c(1,3:5,8:10,13:15)] <- 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) | ||
+ | errors <- apply(cost, c(1,3,4), sum, na.rm=TRUE) | ||
+ | errors <- apply(errors, c(2,3), normerror) | ||
+ | ci <- array(NA, dim = c(dim(errors),2), dimnames = dimnames(errors)) | ||
+ | dimnames(ci)[[3]] <- c("Lower","Higher") | ||
+ | names(dimnames(ci))[3] <- "Boundary" | ||
+ | ci[,,1] <- means - errors | ||
+ | ci[,,2] <- means + errors | ||
+ | final3 <- means | ||
+ | final3[,] <- paste(round(means), " (", round(ci[,,1]), "-", round(ci[,,2]), ")", sep="") | ||
+ | final3[c(1,3:5,8:10,13:15)] <- NA | ||
+ | final3</nowiki> | ||
==Result== | ==Result== |
Revision as of 08:48, 14 January 2011
This page is a assessment.
The page identifier is Op_en4731 |
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Moderator:Teemu R (see all) |
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Contents
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
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- Decision variables
- 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
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
##### Plot ##### asthma <- op_baseGetData("opasnet_base", "Op_en4723", include = 25658, iterations = 1000) array <- DataframeToArray(asthma)#[asthma[,"Rate"]=="m",]) final <- apply(array, c(3,4,7), mean, na.rm=TRUE) final <- apply(final, c(2,3), sum, na.rm=TRUE) #final1 <- apply(final, c(1,2), mean, na.rm=TRUE) finaldf1 <- as.data.frame(as.table(final)) finaldf1 <- finaldf1[finaldf1[,"Freq"]!=0,] barplot(finaldf1[,"Freq"], names.arg=c("2010", "2020 Policy", "2020 BAU", "2030 Policy", "2030 BAU", "2050 Policy", "2050 BAU", "2050 Biomass", "2050 Insulation", "2050 Renovation")) #temp <- array(NA, dim = c(4,3,3), dimnames = list(dimnames(final)[[1]][c(1,3:5)],dimnames(final)[[2]][2:4],dimnames(final)[[3]] )) #for (i in 1:4) {temp[i,1,] <- final[,2,]-final[,2,]} #final <- final[,]-final[rep(1,4),] #barplot(final, beside=TRUE, legend.text=c("2010","2020","2030","2050")) ##### Cases ##### normerror <- function(input){qnorm(0.975)*sd(input)/(length(input)^0.5)} means <- apply(array, c(3,4,7), mean, na.rm=TRUE) means <- apply(means, c(2,3), sum, na.rm=TRUE) errors <- apply(array, c(1,4,7), sum, na.rm=TRUE) errors <- apply(errors, c(2,3), normerror) ci <- array(NA, dim = c(dim(errors),2), dimnames = dimnames(errors)) dimnames(ci)[[3]] <- c("Lower","Higher") names(dimnames(ci))[3] <- "Boundary" ci[,,1] <- means - errors ci[,,2] <- means + errors final1 <- means final1[,] <- paste(round(means), " (", round(ci[,,1]), "-", round(ci[,,2]), ")", sep="") final1[c(1,3:5,8:10,13:15)] <- NA final1 #finaldf1 <- as.data.frame(as.table(final1)) #finaldf1 <- finaldf1[finaldf1[,"Freq"]!=0,] #barplot(finaldf1[,"Freq"], names.arg=c("2010", "2020 Policy", "2020 BAU", "2030 Policy", "2030 BAU", "2050 Policy", "2050 BAU", #"2050 Biomass", "2050 Insulation", "2050 Renovation")) ##### DALYs ##### DALY <- array*0.059 means <- apply(DALY, c(3,4,7), mean, na.rm=TRUE) means <- apply(means, c(2,3), sum, na.rm=TRUE) errors <- apply(DALY, c(1,4,7), sum, na.rm=TRUE) errors <- apply(errors, c(2,3), normerror) ci <- array(NA, dim = c(dim(errors),2), dimnames = dimnames(errors)) dimnames(ci)[[3]] <- c("Lower","Higher") names(dimnames(ci))[3] <- "Boundary" ci[,,1] <- means - errors ci[,,2] <- means + errors final2 <- means final2[,] <- paste(round(means), " (", round(ci[,,1]), "-", round(ci[,,2]), ")", sep="") final2[c(1,3:5,8:10,13:15)] <- 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) errors <- apply(cost, c(1,3,4), sum, na.rm=TRUE) errors <- apply(errors, c(2,3), normerror) ci <- array(NA, dim = c(dim(errors),2), dimnames = dimnames(errors)) dimnames(ci)[[3]] <- c("Lower","Higher") names(dimnames(ci))[3] <- "Boundary" ci[,,1] <- means - errors ci[,,2] <- means + errors final3 <- means final3[,] <- paste(round(means), " (", round(ci[,,1]), "-", round(ci[,,2]), ")", sep="") final3[c(1,3:5,8:10,13:15)] <- NA final3
Result
Asthma incidence increase due to building dampness in Europe: Show results
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Country of observation | Mean | SD |
---|---|---|
Austria | 23958 | 19818 |
Belgium | 46983 | 24769 |
Cyprus | 3010 | 706 |
Czech Republic | 65640 | 31215 |
Denmark | 9088 | 6502 |
Estonia | 8188 | 2735 |
Finland | 10881 | 17198 |
France | 303354 | 161230 |
Germany | 379346 | 221077 |
Greece | 20517 | 7842 |
Italy | 279127 | 99106 |
Latvia | 11991 | 3158 |
Poland | 270064 | 49342 |
Portugal | 48477 | 18082 |
Spain | 226670 | 93709 |
Sweden | 20039 | 24323 |
Total | 1727332 |
See also
Keywords
Dampness, indoor air, asthma, Europe