Assessment of building policies' effect on dampness and asthma in Europe

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Main message:
Question:

Dampness in homes is a major environmental health hazard causing asthma and allergic or respiratory symptoms. Good building policies can reduce dampness in homes. What are the effects of different plausible building policies on dampness in homes and consequently on asthma prevalence in Europe between 2010 and 2050?

Answer:

There are currently 1.7 million (95 % CI 0.8 - 2.9 million) cases of asthma due to indoor dampness 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 and humidity conditions even when energy saving measures are taken.


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 Common Case Study. In brief, the situation is assessed in EU-30 (the current 27 EU member states plus Norway, Iceland, and Switzerland) for the next forty years. Four scenarios are considered: 1) BAU: business as usual contains the implementation of already made decisions but no further actions; 2) ALL: all such policies are implemented that are required to reduce the total greenhouse gas emissions by 70 % by 2050; 3) INSULATION: only building insulation policies from ALL are implemented (ALL also contains policies to increase biomass use, but these are not implemented here); 4) RENOVATION: same as ALL except that ventilation is not improved in 50 % of those buildings that are insulated up to tighter standards.

Rationale

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A causal diagram of health effects of dampness in Europe.

The assessment is based on a causal model presented in the figure. Different European building policies described above are considered. The policies affect air exchange rates in buildings. It is expected that moisture problems become more likely if the air exchange decreases. An exposure-response

Decision variables
Other variables
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.

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.


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The impacts of European building policies on asthma attributable to residential building dampness.
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Result distributions.
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Value of information analysis result (EVPI).
Asthma cases (prevalence) in Europe due to residential building dampness (mean and 95% confidence interval).
Year
Policy 2010 2020 2030 2050
BAU 1715846 (794208-2918407) 2069089 (929518-3645690) 2300513 (1007103-4193891) 2417413 (1016202-4559645)
All NA 2071501 (940391-3650210) 2634778 (1139578-4745158) 3009693 (1251020-5519308)
Insulation NA NA NA 3002498 (1239186-5524389)
Renovation NA NA NA 3416010 (1443227-6233562)
Asthma DALYs in Europe due to residential building dampness (mean and 95% confidence interval).
Year
Policy 2010 2020 2030 2050
BAU 101235 (46858-172186) 122076 (54842-215096) 135730 (59419-247440) 142627 (59956-269019)
All NA 122219 (55483-215362) 155452 (67235-279964) 177572 (73810-325639)
Insulation NA NA NA 177147 (73112-325939)
Renovation NA NA NA 201545 (85150-367780)
Asthma monetary impact (based on DALYs) in Europe due to residential building dampness (mean and 95% confidence interval). Unit: M€
Year
Policy 2010 2020 2030 2050
BAU 4552 (2065-7861) 5478 (2464-9800) 6105 (2617-11307) 6404 (2622-12279)
All NA 5491 (2434-9869) 7012 (2981-13005) 7989 (3285-14872)
Insulation NA NA NA 7995 (3244-15283)
Renovation NA NA NA 9059 (3827-16881)
Asthma cases (prevalence) attributable to residential building dampness in Europe in 2010.
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 1715846


R code for detailed analysis

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

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Keywords

Dampness, indoor air, asthma, Europe

References


Related files

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