Asthma prevalence due to building dampness in Europe: Difference between revisions

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{{variable|moderator=Teemu R|stub=Yes}}
[[Category:Indoor air]]
[[Category:Mega case study]]
[[Category:Health impact]]
{{variable|moderator=Teemu R}}


== Scope ==
== Scope ==
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== Definition ==
== Definition ==


Initial Prevalence data from GINA<ref name=GINA>[http://www.ginasthma.com/download.asp?intId=29 Global Initiative for Asthma report: Global Burden of Asthma]</ref>.  
This variable is calculated entirely from upstream variables listed under [[#Dependencies]]. Mathematical method described under [[#Formula]].
 
Formula calculates number of asthma cases which can be attributed to dampness in Europe in years 2010-2050 by using the following:
#<nowiki><math>extra cases = (prevalence_total - prevalence_nondamp) population</math></nowiki>
#<nowiki><math>prevalence_total = prevalence_nondamp(%damp * OR + 1 - %damp)</math></nowiki>
#<nowiki><math>prevalence_nondamp = \frac{prevalence_0}{%damp * OR + 1 - %damp}</math></nowiki>
 
Population data[http://en.opasnet.org/w/Population_of_Europe] included population growth scenarios so they too are included.
 
Distribution represents uncertainty (mainly of fraction of affected homes).


=== Data ===
=== Data ===
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*[[:heande:Moisture damage]]
*[[:heande:Moisture damage]]
*[[Population of Europe by Country|Population of Europe]]
*[[Population of Europe by Country|Population of Europe]]
*Prevelance of Clinical Asthma<ref name=GINA></ref>
*[[Asthma prevalence]]
*[[ERF of indoor dampness on respiratory health effects]]
*[[ERF of indoor dampness on respiratory health effects]]


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=== Formula ===
=== Formula ===


#<nowiki><math>extra cases = (prevalence_total - prevalence_nondamp) population</math></nowiki>
Formula calculates number of asthma cases which can be attributed to dampness in Europe in years 2010-2050 by using the following:
#<nowiki><math>prevalence_total = prevalence_nondamp(%damp * OR + 1 - %damp)</math></nowiki>
#<math>extra cases = (prevalencetotal - prevalencenondamp) * population</math>
#<nowiki><math>prevalence_nondamp = \frac{prevalence_0}{%damp * OR + 1 - %damp}</math></nowiki>
#<math>prevalencetotal = prevalencenondamp * (%damp * OR + 1 - %damp)</math>
#<math>prevalence_0 = %damp * OR * prevalencenondamp + (1 - %damp) * prevalencenondamp</math>
::<math>prevalencenondamp = \frac{prevalence_0}{%damp * OR + 1 - %damp}</math>


*ERF approximated as that for current asthma (OR = 1.56).  
*Prevalences for nondamp and damp homes are assumed constant, for a given iteration in a given country.  


  <nowiki>
  <nowiki>
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"Finland", "France", "Greece", "Hungary", "Ireland", "Iceland", "Italy", "Lithuania", "Luxembourg", "Latvia", "Malta", "Netherlands",  
"Finland", "France", "Greece", "Hungary", "Ireland", "Iceland", "Italy", "Lithuania", "Luxembourg", "Latvia", "Malta", "Netherlands",  
"Norway", "Poland", "Portugal", "Romania", "Sweden", "Slowenia", "Slovakia", "United Kingdom")
"Norway", "Poland", "Portugal", "Romania", "Sweden", "Slowenia", "Slovakia", "United Kingdom")
levels(pop[,"CountryID"]) <- countries
levels(pop[,"CountryID"]) <- countries #IDs converted to actual names, for compatibility with other data
colnames(pop)[4] <- "Country"
colnames(pop)[c(4,7)] <- c("Country","Population")
colnames(pop)[8] <- "Population"
asthma <- op_baseGetData("opasnet_base", "Op_en4789")
#asthma <- read.csv("C:/Documents and Settings/tris/My Documents/Asthma prevalence.csv", sep = ";")
erf <- op_baseGetData("opasnet_base", "Op_en4716")
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")
poparray <- DataframeToArray(pop, "Population")
dampxpop <- IntArray(dampness, poparray, "Population")
dampxpop <- IntArray(dampness, poparray, "Population")
asthmaarray <- DataframeToArray(asthma, "InitialPrevalence")
asthmaarray <- DataframeToArray(asthma)
dampxpopxasthma <- IntArray(dampxpop, asthmaarray, "InitialPrevalence")
dampxpopxasthma <- IntArray(dampxpop, asthmaarray, "InitialPrevalence")
erf <- rnorm(nrow(dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010",]), 1.56, (1.86-1.30)/3.92)
erfarray <- DataframeToArray(erf)
p_nd <- data.frame(dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010", c("obs","Country","Age","Rate","Sex")],  
dampxpopxasthmaxerf <- IntArray(dampxpopxasthma, erfarray, "ERF")
p_nd=dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010",  
p_nd <- data.frame(dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010", c("obs","Country","Age","Sex")],  
"InitialPrevalence"] * 100 /(dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010", "Result"] * erf + 100 - dampxpopxasthma[dampxpopxasthma[,"Year"]==
p_nd=dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010", "InitialPrevalence"] * 100 /(dampxpopxasthmaxerf[
"2010", "Result"]))
dampxpopxasthmaxerf[,"Year"]=="2010", "Result"] * dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010",
"ERF"] + 100 - dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010", "Result"]))
p_ndarray <- DataframeToArray(p_nd, "p_nd")
p_ndarray <- DataframeToArray(p_nd, "p_nd")
dampxpopxasthmaxp_nd <- IntArray(dampxpopxasthma, p_ndarray, "p_nd")
dampxpopxasthmaxerfxp_nd <- IntArray(dampxpopxasthmaxerf, p_ndarray, "p_nd")
erfdf <- p_nd
final <- data.frame(dampxpopxasthmaxerfxp_nd[,c("obs","Country","policy","Year","Age","Sex")],  
colnames(erfdf)[ncol(erfdf)] <- "erf"
Result=dampxpopxasthmaxerfxp_nd[,"Population"] * dampxpopxasthmaxerfxp_nd[,"p_nd"] *  
erfdf[,"erf"] <- erf
dampxpopxasthmaxerfxp_nd[,"Result"] * (dampxpopxasthmaxerfxp_nd[,"ERF"] - 1) / 10000)
erfarray <- DataframeToArray(erfdf, "erf")
###Fancy alternative below, might be better, requires more testing
dampxpopxasthmaxp_ndxerf <- IntArray(dampxpopxasthmaxp_nd, erfarray, "erf")
#final <- model.frame(I(Result*Population*p_nd*(ERF-1)/10000)~ obs + Country + policy + Age + Sex + Outcome,
final <- data.frame(dampxpopxasthmaxp_ndxerf[,c("obs","Country","policy","Year","Age","Rate","Sex")],  
#data = dampxpopxasthmaxerfxp_nd)</nowiki>
Result=dampxpopxasthmaxp_ndxerf[,"Population"] * dampxpopxasthmaxp_ndxerf[,"p_nd"] *  
dampxpopxasthmaxp_ndxerf[,"Result"] * (dampxpopxasthmaxp_ndxerf[,"erf"] - 1) / 10000)</nowiki>


== Result ==
== Result ==
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{|{{prettytable}}
{|{{prettytable}}
|+'''Asthma prevalence (#) in Europe due to indoor radon in residences (mean and 95% confidence interval).'''
|+'''Asthma cases (prevalence) in Europe due to residential building dampness (mean and 95% confidence interval).'''
! !!colspan="4"|Year
! !!colspan="4"|Year
|----
|----
!Policy!!2010!!2020!!2030!!2050
!Policy!!2010!!2020!!2030!!2050
|----
|----
|BAU || 1719942 (1112299-2501866) || 2074298 (1283407-3166039) || 2307249 (1367574-3736504) || 2415120 (1371764-3983910)
|BAU || 1715846 (794208-2918407) || 2069089 (929518-3645690) || 2300513 (1007103-4193891) || 2417413 (1016202-4559645)
|----
|----
|All || NA || 2077607 (1287267-3174488) || 2642618 (1569616-4149730) || 3004924 (1758236-4783584)
|All || NA || 2071501 (940391-3650210) || 2634778 (1139578-4745158) || 3009693 (1251020-5519308)
|----
|----
|Biomass || NA || NA || NA || 2994210 (1712744-4763987)
|Biomass || NA || NA || NA || 2998888 (1249803-5529395)
|----
|----
|Insulation || NA || NA || NA || 2997521 (1717737-4737750)
|Insulation || NA || NA || NA || 3002498 (1239186-5524389)
|----
|----
|Renovation || NA || NA || NA || 3412703 (1987737-5394455)
|Renovation || NA || NA || NA || 3416010 (1443227-6233562)
|----
|----
|}
|}
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! Country of observation!! Mean!! SD
! Country of observation!! Mean!! SD
|----
|----
|| Austria|| 23958|| 19818
|| Austria || 23661 || 22103
|----
|----
|| Belgium|| 46983|| 24769
|| Belgium || 46341 || 30025.2
|----
|----
|| Cyprus|| 3010|| 706
|| Cyprus || 2988 || 1251
|----
|----
|| Czech Republic|| 65640|| 31215
|| Czech Republic || 65025 || 39220
|----
|----
|| Denmark|| 9088|| 6502
|| Denmark|| 9051 || 7432
|----
|----
|| Estonia|| 8188|| 2735
|| Estonia|| 7828 || 3876
|----
|----
|| Finland|| 10881|| 17198
|| Finland|| 10929 || 18613
|----
|----
|| France|| 303354|| 161230
|| France|| 302344 || 201280
|----
|----
|| Germany|| 379346|| 221077
|| Germany|| 375145 || 265165
|----
|----
|| Greece|| 20517|| 7842
|| Greece|| 20343 || 10627
|----
|----
|| Italy|| 279127|| 99106
|| Italy|| 276619 || 142459
|----
|----
|| Latvia|| 11991|| 3158
|| Latvia|| 11875 || 5228
|----
|----
|| Poland|| 270064|| 49342
|| Poland|| 267934 || 105393
|----
|----
|| Portugal|| 48477|| 18082
|| Portugal|| 47961 || 25133
|----
|----
|| Spain|| 226670|| 93709
|| Spain|| 227574 || 126839
|----
|----
|| Sweden|| 20039|| 24323
|| Sweden|| 20225 || 27113
|----
|----
! Total!! 1727332||  
! Total!! 1715846 ||  
|----
|----
|}
|}

Latest revision as of 12:23, 11 April 2011



Scope

Europe in 2010, 2020, 2030, 2050.

Definition

This variable is calculated entirely from upstream variables listed under #Dependencies. Mathematical method described under #Formula.

Data

Dependencies

Unit

#

Formula

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

  1. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle extra cases = (prevalencetotal - prevalencenondamp) * population}
  2. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle prevalencetotal = prevalencenondamp * (%damp * OR + 1 - %damp)}
  3. Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle prevalence_0 = %damp * OR * prevalencenondamp + (1 - %damp) * prevalencenondamp}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle prevalencenondamp = \frac{prevalence_0}{%damp * OR + 1 - %damp}}
  • Prevalences for nondamp and damp homes are assumed constant, for a given iteration in a given country.
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 #IDs converted to actual names, for compatibility with other data
colnames(pop)[c(4,7)] <- c("Country","Population")
asthma <- op_baseGetData("opasnet_base", "Op_en4789")
erf <- op_baseGetData("opasnet_base", "Op_en4716")
poparray <- DataframeToArray(pop, "Population")
dampxpop <- IntArray(dampness, poparray, "Population")
asthmaarray <- DataframeToArray(asthma)
dampxpopxasthma <- IntArray(dampxpop, asthmaarray, "InitialPrevalence")
erfarray <- DataframeToArray(erf)
dampxpopxasthmaxerf <- IntArray(dampxpopxasthma, erfarray, "ERF")
p_nd <- data.frame(dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010", c("obs","Country","Age","Sex")], 
	p_nd=dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010", "InitialPrevalence"] * 100 /(dampxpopxasthmaxerf[
	dampxpopxasthmaxerf[,"Year"]=="2010", "Result"] * dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010", 
	"ERF"] + 100 - dampxpopxasthmaxerf[dampxpopxasthmaxerf[,"Year"]=="2010", "Result"]))
p_ndarray <- DataframeToArray(p_nd, "p_nd")
dampxpopxasthmaxerfxp_nd <- IntArray(dampxpopxasthmaxerf, p_ndarray, "p_nd")
final <- data.frame(dampxpopxasthmaxerfxp_nd[,c("obs","Country","policy","Year","Age","Sex")], 
Result=dampxpopxasthmaxerfxp_nd[,"Population"] * dampxpopxasthmaxerfxp_nd[,"p_nd"] * 
dampxpopxasthmaxerfxp_nd[,"Result"] * (dampxpopxasthmaxerfxp_nd[,"ERF"] - 1) / 10000)
###Fancy alternative below, might be better, requires more testing
#final <- model.frame(I(Result*Population*p_nd*(ERF-1)/10000)~ obs + Country + policy + Age + Sex + Outcome, 
#data = dampxpopxasthmaxerfxp_nd)

Result

{{#opasnet_base_link:Op_en4723}}


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)
Biomass NA NA NA 2998888 (1249803-5529395)
Insulation NA NA NA 3002498 (1239186-5524389)
Renovation NA NA NA 3416010 (1443227-6233562)
Asthma cases (prevalence) attributable to residential building dampness in Europe in 2010.
Country of observation Mean SD
Austria 23661 22103
Belgium 46341 30025.2
Cyprus 2988 1251
Czech Republic 65025 39220
Denmark 9051 7432
Estonia 7828 3876
Finland 10929 18613
France 302344 201280
Germany 375145 265165
Greece 20343 10627
Italy 276619 142459
Latvia 11875 5228
Poland 267934 105393
Portugal 47961 25133
Spain 227574 126839
Sweden 20225 27113
Total 1715846

See also

Keywords

Asthma, indoor air, dampness, Europe

References


Related files

<mfanonymousfilelist></mfanonymousfilelist>

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