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

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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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A causal diagram of health effects of dampness in Europe.
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.

R code for detailed analysis

##### Plot #####
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

Result

Asthma prevalence due to building dampness in Europe: Show results

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The impacts of European building policies on asthma attributable to residential building dampness.
Asthma cases (prevalence) in Europe due to residential building dampness (mean and 95% confidence interval).
Year
Policy 2010 2020 2030 2050
BAU 1719942 (1112299-2501866) 2074298 (1283407-3166039) 2307249 (1367574-3736504) 2415120 (1371764-3983910)
All NA 2077607 (1287267-3174488) 2642618 (1569616-4149730) 3004924 (1758236-4783584)
Biomass NA NA NA 2994210 (1712744-4763987)
Insulation NA NA NA 2997521 (1717737-4737750)
Renovation NA NA NA 3412703 (1987737-5394455)
Asthma DALYs in Europe due to residential building dampness (mean and 95% confidence interval).
Year
Policy 2010 2020 2030 2050
BAU 101477 (65626-147610) 122384 (75721-186796) 136128 (80687-220454) 142492 (80934-235051)
All NA 122579 (75949-187295) 155914 (92607-244834) 177291 (103736-282231)
Biomass NA NA NA 176658 (101052-281075)
Insulation NA NA NA 176854 (101346-279527)
Renovation NA NA NA 201349 (117277-318273)
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 4568 (2872-6849) 5504 (3290-8627) 6122 (3549-10224) 6404 (3545-10730)
All NA 5515 (3343-8632) 7021 (4087-11318) 7976 (4505-12864)
Biomass NA NA NA 7946 (4446-12860)
Insulation NA NA NA 7961 (4445-13017)
Renovation NA NA NA 9060 (5182-14706)
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 1727332

See also

Keywords

Dampness, indoor air, asthma, Europe

References

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