Asthma prevalence due to building dampness in Europe

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Scope

Europe in 2010, 2020, 2030, 2050.

Definition

Initial Prevalence data from GINA[1].

Formula calculates number of asthma cases which can be attributed to dampness in Europe in years 2010-2050 by using the following:

  1. <math>extra cases = (prevalence_total - prevalence_nondamp) population</math>
  2. <math>prevalence_total = prevalence_nondamp(%damp * OR + 1 - %damp)</math>
  3. <math>prevalence_0 = %damp * OR * prevalence_nondamp + (1 - %damp) * prevalence_nondamp</math>
<math>prevalence_nondamp = \frac{prevalence_0}{%damp * OR + 1 - %damp}</math>
  • Prevalences for nondamp and damp homes are assumed constant, for a given iteration. Fraction of damp homes varies, hence total prevalence varies.

Population data[1] included population growth scenarios so they too are included.

Distribution represents uncertainty (mainly of fraction of affected homes).

Data

Dependencies

Unit

#

Formula

  • ERF approximated as that for current asthma (OR = 1.56).
dampness <- op_baseGetData("opasnet_base", "Erac2988")
pop <- op_baseGetData("opasnet_base", "Op_en4691", include = 1367, exclude = c(1435, 1436))
countries <- c("Austria", "Belgium", "Bulgaria", "Switzerland", "Cyprus", "Czech Republic", "Germany", "Denmark", "Estonia", "Spain", 
	"Finland", "France", "Greece", "Hungary", "Ireland", "Iceland", "Italy", "Lithuania", "Luxembourg", "Latvia", "Malta", "Netherlands", 
	"Norway", "Poland", "Portugal", "Romania", "Sweden", "Slowenia", "Slovakia", "United Kingdom")
levels(pop[,"CountryID"]) <- countries
colnames(pop)[4] <- "Country"
colnames(pop)[8] <- "Population"
#asthma <- read.csv("C:/Documents and Settings/tris/My Documents/Asthma prevalence.csv", sep = ";")
asthma <- data.frame(Country=c('Scotland','Jersey','Guernsey','Wales','Isle of Man','England','New Zealand','Australia','Republic of Ireland', 
'Canada','Peru','Trinidad & Tobago','Costa Rica','Brazil','United States of America','Fiji','Paraguay','Uruguay','Israel','Barbados','Panama', 
'Kuwait','Ukraine','Ecuador','South Africa','Czech Republic','Finland','Malta','Ivory Coast','Colombia','Turkey','Lebanon','Kenya','Germany', 
'France','Norway','Japan','Sweden','Thailand','Hong Kong','Philippines','United Arab Emirates','Belgium','Austria','Spain','Saudi Arabia', 
'Argentina','Iran','Estonia','Nigeria','Chile','Singapore','Malaysia','Portugal','Uzbekistan','FYR Macedonia','Italy','Oman','Pakistan', 
'Tunisia','Cape Verde','Latvia','Poland','Algeria','South Korea','Bangladesh','Morocco','Occupied Territory of Palestine','Mexico','Ethiopia', 
'Denmark','India','Taiwan','Cyprus','Switzerland','Russia','China','Greece','Georgia','Nepal','Romania','Albania','Indonesia','Macau'), 
InitialPrevalence=c(18.4,17.6,17.5,16.8,16.7,15.3,15.1,14.7,14.6,14.1,13,12.6,11.9,11.4,10.9,10.5,9.7,9.5,9,8.9,8.8,8.5,8.3,8.2,8.1,8,8,8,7.8,7.4, 
7.4,7.2,7,6.9,6.8,6.8,6.7,6.5,6.5,6.2,6.2,6.2,6,5.8,5.7,5.6,5.5,5.5,5.4,5.4,5.1,4.9,4.8,4.8,4.6,4.5,4.5,4.5,4.3,4.3,4.2,4.2,4.1,3.9,3.9,3.8, 
3.8,3.6,3.3,3.1,3,3,2.6,2.4,2.3,2.2,2.1,1.9,1.8,1.5,1.5,1.3,1.1,0.7))
#asthma <- data.frame(Country=asthma[1:26,1], Casesper1000=(asthma[1:26,2]+asthma[1:26,3]))
poparray <- DataframeToArray(pop, "Population")
dampxpop <- IntArray(dampness, poparray, "Population")
asthmaarray <- DataframeToArray(asthma, "InitialPrevalence")
dampxpopxasthma <- IntArray(dampxpop, asthmaarray, "InitialPrevalence")
erf <- rnorm(nrow(dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010",]), 1.56, (1.86-1.30)/3.92)
p_nd <- data.frame(dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010", c("obs","Country","Age","Rate","Sex")], 
p_nd=dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010", 
"InitialPrevalence"] * 100 /(dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010", "Result"] * erf + 100 - dampxpopxasthma[dampxpopxasthma[,"Year"]==
"2010", "Result"]))
p_ndarray <- DataframeToArray(p_nd, "p_nd")
dampxpopxasthmaxp_nd <- IntArray(dampxpopxasthma, p_ndarray, "p_nd")
erfdf <- p_nd
colnames(erfdf)[ncol(erfdf)] <- "erf"
erfdf[,"erf"] <- erf
erfarray <- DataframeToArray(erfdf, "erf")
dampxpopxasthmaxp_ndxerf <- IntArray(dampxpopxasthmaxp_nd, erfarray, "erf")
final <- data.frame(dampxpopxasthmaxp_ndxerf[,c("obs","Country","policy","Year","Age","Rate","Sex")], 
Result=dampxpopxasthmaxp_ndxerf[,"Population"] * dampxpopxasthmaxp_ndxerf[,"p_nd"] * 
dampxpopxasthmaxp_ndxerf[,"Result"] * (dampxpopxasthmaxp_ndxerf[,"erf"] - 1) / 10000)

Result

{{#opasnet_base_link:Op_en4723}}


Asthma prevalence (#) in Europe due to indoor radon in residences (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 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

Asthma, indoor air, dampness, Europe

References

  1. ↑ 1.0 1.1 Global Initiative for Asthma report: Global Burden of Asthma

Related files

<mfanonymousfilelist></mfanonymousfilelist>

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