Asthma prevalence due to building dampness in Europe

This page is a knowledge crystal of subtype variable. The page identifier is Op_en4723
Moderator:Teemu R (see all)
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Scope

Europe in 2010, 2020, 2030, 2050.

Definition

Data

Description of the data used for obtaining the value of the variable
(e.g. measurement data; mathematical method and its parameters). 

Please include references (preferably using the <ref> </ref> tags)
and links to original data, as appropriate.

Dependencies

• heande:Moisture damage
• Population of Europe
• Prevelance of Clinical Asthma
• ERF of indoor dampness on respiratory health effects

Unit

#

Formula

1. <math>extra cases = (prevalence_total - prevalence_nondamp) population</math>
2. <math>prevalence_total = prevalence_nondamp(%damp * OR + 1 - %damp)</math>
3. <math>prevalence_nondamp = \frac{prevalence_0}{%damp * OR + 1 - %damp}</math>

• ERF approximated as that for current asthma (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]))
erf <- 1.56
poparray <- DataframeToArray(pop, "Population")
dampxpop <- IntArray(dampness, poparray, "Population")
asthmaarray <- DataframeToArray(asthma, "InitialPrevalence")
dampxpopxasthma <- IntArray(dampxpop, asthmaarray, "InitialPrevalence")
p_nd <- data.frame(dampxpopxasthma[dampxpopxasthma[,"Year"]=="2010", c(2,3,6,7,8)], 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")
final <- data.frame(dampxpopxasthmaxk[,c(2,3,4,6,7,8)], Result=dampxpopxasthmaxp_nd[,"Population"] * dampxpopxasthmaxp_nd[,"p_nd"] * 
dampxpopxasthmaxp_nd[,"Result"] * (erf - 1) / 10000)

Result

{{#opasnet_base_link:Op_en4723}}

Keywords

Asthma, indoor air, dampness, Europe

Related files

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