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

Latest revision as of 10:33, 26 August 2013

Scope

In this study, the research question was the following: What are the effects of different building policies on dampness and mould in residential buildings, and consequently on asthma prevalence in Europe? The building policies considered aim at reducing greenhouse gas emissions and thus mitigate climate change. 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. A technical objective was to test feasibility of web workspace and on-line modelling tools developed in the projects.

Boundaries

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 (in other scenarios, insulation is always combined with improved ventilation).

Result

Results

Asthma prevalence due to building dampness in Europe: Show results ^[1]

Conclusions

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. It is important to maintain good air exchange and humidity conditions even when energy saving measures are taken.

The value of information analysis showed that further information is worth about 500 to 1000 million euros. In this assessment, all of the value goes into the most critical issue, namely the impacts of policy on dampness prevalence, which in this assessment was modelled by using air exchange rate. Of course, some dampness problems are not related to air exchange at all, so focussing on that specific topic will not be the optimal solution. Instead, wide and robust understanding is needed about why harmful dampness occurs and what can be done to repair such problems and especially to prevent them in advance. A thorough examination of current knowledge was not done in this assessment. It seems obvious that collecting and organising existing information is a cost-effective way to reduce uncertainty in this issue, because that would cost only a small fraction of the value of that information according to this assessment.

In theory, the reasons for hazardous microbial growth and mould in building structures are simple: if there is constant moisture, microbes start growing. Therefore, buildings should be built and repaired to avoid moisture. However, the issue is much more complex in practice.

Even if there is moisture and microbial growth, it is sometimes very harmful to health but sometimes less harmful; determinants of this are poorly understood. In many cases, dampness is invisible and the building owner is unaware of the problem. Also, many owners are unaware of risks of dampness and ignore the problem. Even if the problems is acknowledged, there may be insufficient expertise or resources to repair the problem. Further on, the building industry has varying expertise and capability to build such buildings where typical dampness risks are managed beforehand. Especially, there are limited knowledge and expertice on moisture control of highly energy efficient buildings. And finally, the changing climate is likely to cause problems that were not anticipated at the time when buildings were built.

In conclusion, more should be known about determinants of dampness-related health problems and moisture control of highly energy efficient buildings to be able to design climate-friendly building policies that also reduce dampness in homes.

Rationale

The assessment is based on a causal model presented in the figure. Each node in the graph (also called a variable in the model) are described in more detail elsewhere; only a summary of the model is presented here.

Building policies

European building policies described above are considered. The aim of the policies is to mitigate climate change by reducing greenhouse gas emissions from heating and cooling of buildings. In this sub-assessment, we do not consider greenhouse gas emissions or climate impacts, but only health impacts occurring as collateral damages or benefits. The purpose of the assessment is to estimate the impacts of each building policy and identify those policies that produce the best health outcomes.

Exposure estimation

The logic of the assessment is that the climate change mitigation policies considered affect air exchange rates in buildings. It is expected that moisture problems become more likely if the air exchange decreases. Nation-wide dampness estimates were obtained from several studies reviewed in this sub-assessment (http://heande.opasnet.org/wiki/Moisture_damage). However, several countries (Luxembourg, Netherlands, Switzerland, Ireland, Norway, United Kingdom, Bulgaria, Hungary, Lithuania, Romania, Slovakia, Slovenia, Malta) were rejected due to lack of data.

Health impact estimation

Asthma prevalence (number of asthma cases) attributable to indoor problems due to residential mould or dampness was chosen as the outcome of interest for two reasons. First, there are plausible information about the causal association between dampness and asthma; second, asthma is a fairly common and severe disease, and therefore it is likely that focusing on this single endpoint can produce a reasonable estimate about the total magnitude of the problem.

The current scientific epidemiological literature contains plausible exposure-response functions for the association of moisture problems and asthma. In this assessment, number of current asthma cases (i.e., prevalence) is used as the outcome indicator. The current exposure-response estimate is 1.56 (odds ratio OR) for current asthma risk (prevalence) due to existing dampness problem (Fisk et al., 2007). ^[2] Linear no-threshold exposure-response function was assumed for the whole population in each country.

The prevalence of dampness-induced asthma depends also on the background prevalence of asthma and the population size. Asthma and population size differ by country and the population also changes in time. However, the determinants of asthma are not known well enough to predict time trends into the future and so the asthma background is assumed constant in time in this assessment.

Policy evaluation

Finally, the asthma prevalence under each policy scenario are compared and optimum scenario found. It should be noted, however, that this sub-assessment only has a very narrow view on all impacts of the policies and therefore it cannot be used as an ultimate guidance for policy selection. Instead, this sub-assessment gives important information for the Common Case Study as a whole, which may produce such overall conclusions.

Impacts

For overall conclusions, it is crucial that the impacts observed in a sub-assessment can be compared with other impacts observed in other sub-assessment. To this aim, we expressed the outcome using two alternative summary indicators: disability-adjusted life years (DALY) and euros (€). DALYs are computed by multiplying the number of cases of a disease with a respective disability or severity weight and the duration of the disease. The idea is to measure the overall healthy years that are lost due to several diseases. The disability weight (estimated by WHO) for a treated asthma case is 0.059.

Monetary valuation

The costs of diseases include direct costs of treatment, indirect costs due to loss of productivity (absence from work), and willingness of a person to pay extra to avoid the disease. Because the monetary estimation of impacts was not the main objective in this sub-assessment, we did not go through this laborious path. Instead, we simply assumed that the DALY estimate also provides a reasonable indicator of all monetary costs of the asthma cases. Thus, we multiplied the DALY estimate with an estimate of willingness to pay to avoid a loss of one healthy life year. This has typically been in the order of 30000 - 60000 euros per saved life year. This results in a preliminary estimate of monetary impact, which can be used in comparisons in other parts of the Common Case Study and the value of information analysis (see below).

A methodological objective was a proof of concept for running assessment models via open internet interface. Therefore, 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/Dampness_and_asthma .

Analyses

Two analyses were performed in the sub-assessment. First, the main analysis was the optimisation of the health impact across different policy options as described before. Second, a value of information analysis was performed based on the monetary impact estimates.

Value of information is a statistical method that estimates the largest sum of money a decision maker should be willing to pay to be able to reduce uncertainty in the decision before actually making the decision. The analysis is based on the idea that even if one of the options looked the best based on the expected value of impact, it is possible that, due to uncertainties described in the decision model, in some cases some other option could actually be the best. The decision maker would be better off, if she could do more research, reduce the uncertainty and actually find out whether the alternative indeed turns out to be better. The beauty of value of information analysis is that it can be performed before the decision, but more importantly, before any further research is done. If the value of information analysis shows low value, the decision maker can decide now with only a low probability of regret afterwards. On the other hand, if it shows high value, the decision-maker would be better off if she postponed the actual decision and put effort in further research and analysis (assuming that such work is feasible).

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(opasnetBaseUtils)
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 
#and one intermediate variable that could make a difference.
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

The parts of the sub-assessment model about dampness and asthma.

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
• DALY to money conversion

Indicators

• Asthma prevalence due to building dampness in Europe

You can also click the nodes on the graph to go to pages with more detailed description about that topic.

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Keywords

Dampness, indoor air, asthma, Europe

References

Related files

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