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Modeling environmental burden of disease of asthma: P rotective factors and control options. as part of the TEKAISU project Isabell Rumrich National Institute for Health and Welfare (THL) Kuopio, Finland Master Thesis in the ToxEn program - PowerPoint PPT Presentation
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NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Modeling environmental burden of disease of asthma:Protective factors and control options as part of the TEKAISU project
Isabell RumrichNational Institute for Health and Welfare (THL)Kuopio, Finland
Master Thesis in the ToxEn programUniversity of Eastern Finland, Department of Environmental Science
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Outline
• Introduction
• Background data
• Associated Factors
• Control Policies
• Discussion
2
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Asthma Chronic inflammatory disease Prevalence as high as 9.4 % (2007) Currently only symptomatic treatment Pathology is characterized by miss-regulation of immune
responses
• Various factors have been proposed to be associated with onset or symptoms:
anthropogenic and natural environmental factors, lifestyle related stressors, pharmaceutical
stressors, internal factors, genetic susceptibility and co-morbidities
3
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IHME estimates of BoD (YLDs) in 2010
http://viz.healthmetricsandevaluation.org/gbd-compare/
Asthma:
• 2% of total YLDs in 1990 and
2010
• Maximum for 5-9y old (2010)
13% of total YLDs
biggest contribution to
total YLD
4
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Genes
Environmental Factors
Lifestyle
Co-morbidities
Exposure
Risk Ratio
Exposure can be changed by relatively easy measures- Already existing policies- Development of hypothetical policies Modelling of effect of exposure change
Can not be changed
Can be changed
Other factors
Reducible Fraction
From the Model to Control Policies
5
Asthma BoD
Attributable
Attributable
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Selection of exposure factors
6
Literature Search
Review TableModel PoliciesLack of evidence;
Duplication of factors
Lack of data;Lack of significance
Impact on asthma burden
6
15 factors 6 factors35 factors235 articles
Databases: PubMed, Scopus, Web of Science – WoS (ISI), SpringerLink and Science Direct (Elsevier).
Search queries: asthma; asthma AND environment*; asthma AND risk; asthma AND environment* NOT atopy; asthma AND risk NOT atopy; asthma AND mechanism; asthma AND risk NOT occupation*; asthma AND environment* NOT occupation*
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Outline• Introduction
• Background data
• Associated Factors
• Control Policies
• Discussion
7
Reducible Fraction
Asthma BoD
Attributable
Attributable
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8
Life Table (1986-2040) and Age Distribution
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000Elderly 81-99yPensioner 66-80yWorking Age 26-65yYoung Adult 20-25yTeen 13-19yChild 7-12yPreschool Child 4-6yToddler 1-3yInfant 0yTotalObservedProjection
Year
Popu
latio
n
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Incidence & Prevalence
• Incidence: number of new cases in a specific period of time
number of new individuals entitled to reimburse expenses for asthma medication during one year
• Prevalence: number of all cases at a specific time point
total number of individuals entitled to reimburse expenses for asthma medication at the end of a year
• Data provided by KELA statistics
9
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Incidence and Prevalence – Total number of cases
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
0
5,000
10,000
15,000
20,000
25,000
30,000
0
50,000
100,000
150,000
200,000
250,000
300,000
Start Estimation Incidence Prevalence
Year
Inci
denc
e (c
ases
)
Prev
alen
ce (c
ases
)
10
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Background Rates at Baseline (2011)
11
Infant Toddler Preschool Child
Child Teen Young Adult
Working Age
Pensioner Elderly Total
Inci-dence rate ('000)
0.0499384092952026
6.62483137537616
5.7018279389569
3.68971903579171
2.34084494422293
1.55262728152047
2.30912061047853
3.7317215715274
2.03910383777603
2.74376768668239
1234567
Inci
denc
e ra
te
(per
100
0)
Infant Toddler Preschool Child
Child Teen Young Adult
Working Age
Pensioner Elderly Total
Preva-lence rate ('000)
0.0499384092952026
11.6330509724248
22.8464419475655
27.582444402969
22.2055463464899
18.4908362329946
43.3166347842574
88.8904129195391
91.4031556110138
44.2017308067266
10
30
50
70
90
Prev
alen
ce r
ate
(p
er 1
000)
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Burden of Disease - YLDYears Lived with Disability (YLD)
a) Incidence based:
b) Prevalence based:
YLDI = YLDP
P x DW = I x D x DW
D = I = Incidence; DW = Disability Weight; D = Duration; P = Prevalence
12
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Years Lived with Disability – Total number of years
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
0
2,000
4,000
6,000
8,000
10,000
12,000
Estimation YLD_I YLD_P
Year
Year
s Li
ved
with
Dis
abili
ty (Y
LD) (
Year
s)
13
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Duration estimation
14
WHO Infant Toddler Preschool Child
Child Teen Young Adult
Working Age
Pensioner Elderly Total
1986 15 1 1.38805970149254
2.48523206751055
3.9258883248731
6.31931464174455
7.688 7.34434753438443
5.87721198988805
9.06435643564356
6.3712833545108
3
8
13
18
Dur
ation
(Yea
rs)
WHO Infant Toddler Preschool Child
Child Teen Young Adult
Working Age
Pensioner Elderly Total
2040 15 0.195616342359335
0.806177912580271
3.84778215154842
7.76801055459771
10.7554421016062
13.4208156826173
21.792208371086
27.7883762787015
56.1335667911903
19.1197291353824
5
15
25
35
45
55
Dur
ation
(Ye
ars)
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Outline
• Introduction
• Background data
• Associated Factors– Risk Factors– Protective Factors
• Control Policies
• Discussion
15
Reducible Fraction
Asthma BoD
Attributable
Attributable
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Overview Risk Factors
Factor Exposed Population [%]
RR/OR Target Age [Years]
Dampness and Mold 15 1,34 0-99NO2 100 1,077 0-99Underweight 3 3,14 6PM2.5 100 1,16 0-99SHS (child) 4 1,32 0-13SHS (adult) 14 1,97 21-99Cat Allergy 7 1,67 7-8Dampness and Mold 15 1,37 0-99Dog Allergy 7 2,78 21-99Formaldehyde 2 1,02 0-2
16
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Attributable incident cases and residual at baseline (2011)
17
Overview
Factor
0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000
Incidence (cases)
Residual; 53 %
Residual; 53 %
Attributable; 47 %
PM2.5; 12%
Allergen;11%
NO2; 10%
SHS;7%
Dampness& Mould;5%
Underweight;1%
Dog; 1%
Cat; 0% Formaldehyde; 0%
Smoking; 0%
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
18
Overview Protective Factors
Factor Exposed Population [%]
RR/OR Target Age [Years]
Cat 20 0,47 7-16Dog 24 0,57 7-16Breastfeeding 35 0,48 4-6Eurotium 4 0,57 6-12Penicillium 4 0,57 6-12
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Prevented cases at baseline (2011) and background
19
Overview
Factor
0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000
Incidence (cases)
Prevented; 9%
BackgroundDog; 3%
Breastfeeding; 3% Cat; 2%
Eurotium; 1% Penicillium; 0%
Background
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Outline
• Introduction
• Background data
• Associated Factors
• Control Policies– Tobacco Smoke– PM2.5
– Dampness and Mould– Pets
• Discussion
20
Reducible Fraction
Asthma BoD
Attributable
Attributable
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
21
Summary Risk & Protective Factors
-500
0
500
1,000
1,500
2,000Residual
PM2.5;1720
Allergen;1573
NO2;1560
SHS;985
D&M;719
Under-weight;
159 Dog;82
Smoking;62
Cat;26
Formalde-hyde;
0
Penicil-lium;-50
Euro-tium;-73
Cat;-344 Dog,
-491Breast-
feeding;-495
Attrib
utab
le In
cide
nce
base
line
(201
1) (c
ases
)
Risk Factors
8 000
Protective Factors
Residual;7922
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Control Policies
Policy Factor Reference
Tobacco SHS Kutvonen (2014); Savuton Suomi 2040
Smoking
PM2.5 PM2.5 Kutvonen (2014)
Dampness and Mould
Dampness and Mould HealthVent study
Pets Cat
Dog
22
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Control Policies – TobaccoPolicy Exposure in
2013Change in Exposure
Explanation
BanSHS:4% Children9% Adults
Smoking:15% (15-24y)19% (25-44y)29% (45-64y)8% (65-84y)
Total ban 100% reduction
From 2015 onwards no exposure at all
50% Reduction
50% Reduction In 2015 50% reduction and then constant exposure
10% Reduction
10% Reduction From Exposure 2014 annually 10% reduction
23
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
24
Tobacco Exposure trends
19861988
19901992
19941996
19982000
20022004
20062008
20102012
20142016
20182020
20222024
20262028
20302032
20342036
20382040
0%
5%
10%
15%
20%
25%Smoking BaU Smoking 50% Reduction
Smoking 10% Reduction SHS BaU
SHS 50% Reduction SHS 10% Reduction
Year
Frac
tion
of P
opul
ation
bei
ng e
xpos
ed to
Tob
acco
Sm
oke
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
25
Impact of Tobacco Control Policy
BaU Ban 50% Reduction annual 10% Reduction0
5,000
10,000
15,000
20,000
25y
cum
ulati
ve In
cide
nce
(cas
es)
SHS SHS SHS
Smoking
Smoking
Smoking
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Control Policies – PM2.5Policy Exposure in 2013 Change in
ExposureExplanation
Ban of Small Scale Wood Combustion (SSWC) in Urban Areas
Total: 8mg/m3 and 0,6 mg/m3 due to SSWC
Total ban 100% Reduction
Annually fraction due to SSWC is deleted from total exposure
Reduction of Small Scale Wood Combustion (SSWC) in Urban Areas
Total: 8mg/m3 and 0,6 mg/m3 due to SSWC
50% Reduction
Annually 50% of fraction due to SSWC is deleted from total exposure
Speed Limit of 35km/h in Urban Areas
Total: 8mg/m3 and 0,7 mg/m3 due to resuspension
40% Reduction
Annually 40% of fraction due to resuspension is deleted
26
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
27
Impact of PM2.5 Control Policy
BaU Ban 50% Reduction Speed Limit0
10,000
20,000
30,000
25y
cum
ulati
ve a
ttrib
utab
le In
cide
nce
(cas
es)
Small Scale Wood Combustion
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Control Policies – Dampness and Mould
Policy Exposure in 2013
Change of Exposure Explanation
D&M 15% of total population
50% Reduction In 2015 50% reduction to 7,5% and then constant exposure
28
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
29
Impact of Dampness and Mold Control Policy
BaU 50% Reduction0
5,000
10,000
15,000
20,000
25y
cum
ulati
ve a
ttrib
utab
le In
cide
nce
(cas
es)
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Control Policies – PetsPolicy Exposure in 2013 Change in
ExposureExplanation
Cat Risk20% of total Population
7% atopic 1,5%
50% increase
Increase in 2015, after that constant at 3,5%
Cat Protection
93% non-atopic 18,5%
Increase in 2015, after that constant at 46,5 %
Dog Risk24% of total Population
7% atopic 1,8%
Increase in 2015, after that constant at 3,5%
Dog Protection
93% non-atopic 22,2%
Increase in 2015, after that constant at 46,5%
30
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31
Impact of Pet Control Policy
BaU 50% Increase
-10,000
0
10,000
20,000
30,000
40,000
50,000
25y
cum
ulati
ve a
ttrib
utab
le In
cide
nce
(cas
es)
Dog Protection
Cat Protection
Dog Risk
Cat Risk
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
32
Reduction Potential of Control Policies
Ban 50% Reduction
annual 10%
Reduction
Ban 50% Reduction
Speed Limit
50% Reduction
50% In-
crease Pets
-5,000
0
5,000
10,000
15,000
20,000
SHS16 549
Smoking1 555
SHS4 544
Smoking581
SHS8 774
Smoking847
2 496 1 246 1 345
8 733Cat
10 188
Cat-812
Dog9 382
Dog-1 759
25y
cum
ulati
ve In
cide
nce
(red
uced
cas
es)
Tobacco Wood CombustionDampness
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Impact of combined Control Policies
33
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34
Reducible Fraction of the total 25y cumulative Incidence
Infant Toddler Preschool Child
Child Teen Young Adult
Working Age
Pensioner Elderly
-10%
0%
10%
20%
30%
40% Dog_PDog_RCat_PCat_RD&MPMSmokingSHSNet
Age group
Redu
cibl
e Fr
actio
n of
to
tal 2
5y c
umul
ative
In-
cide
nce
Infant Toddler Preschool Child
Child Teen Young Adult
Working Age
Pensioner Elderly
-10.0 %
0.0 %
10.0 %
20.0 %
30.0 %
40.0 %Dog_PDog_RCat_PCat_RD&MPMSmokingSHSNet
Age group
Redu
cibl
e Fr
actio
n of
tota
l 25
y cu
mul
ative
inci
denc
e
More realistic
Most efficient
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
35
Efficiency Control Scenarios - Incidence
20152017
20192021
20232025
20272029
20312033
20352037
203910,000
11,000
12,000
13,000
14,000
15,000BaU Most Efficient More Realistic
Year
Inci
denc
e (c
ases
)
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
36
Efficiency Control Scenarios - Prevalence
20152017
20192021
20232025
20272029
20312033
20352037
2039150,000
170,000
190,000
210,000
230,000
250,000
270,000 BaU Most Efficient More Realistic
Year
Prev
alen
ce (c
ases
)
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
37
Efficiency Control Scenarios – combined Incidence & Prevalence
20152017
20192021
20232025
20272029
20312033
20352037
2039150,000
175,000
200,000
225,000
250,000
275,000BaU Most efficient More realistic
Year
Prev
alen
ce (c
ases
)
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Outline
• Introduction
• Background data
• Associated Factors
• Control Policies
• Discussion
38
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Limitations• Population Life Table
– Neglecting of (Im-)migration
• Use of YLD instead of DALY– Each year a very low number of death due to asthma
neglected
• Discounting– Discounting decreases estimates for future years compared to
non-discounted estimates
39
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Uncertainties
• Trend estimations– Uncertainty about the future trends in asthma and exposures
• Evidence– Overall very weak (association with atopy)– PM source has impact on toxicological profile
• Duration– Duration has impact on incidence based YLD estimate longer
duration increases YLD estimate
40
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Conclusion
• Accumulation of prevalent cases in older age groups
• Asthma duration is increasing and age dependent
• About half of the total BoD can be theoretically explained
• BoD can be reduced (up to 20%) by reducing exposure to risk factors
41
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42
Thank you for your attention!
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Life Table (1986 – 2040)
43
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
4,800,000.0
5,000,000.0
5,200,000.0
5,400,000.0
5,600,000.0
5,800,000.0
6,000,000.0
Observed Life Table Pop Projection
Year
Popu
lati
on (i
n M
io)
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Age groups
44
Age Group Start End Absolute 1986
% 1986 Absolute 2011
% 2011 Absolute 2040
% 2040
Infant 0 0 67 221 1,3 60 074 1,1 72 314 1,3
Toddler 1 3 211 339 4,2 183 099 3,4 214 476 3,8
Preschool Child
4 6 209 640 4,1 178 890 3,3 210 889 3,7
Child 7 12 441 028 8,7 348 265 6,4 410 752 7,2
Teen 13 19 468 675 9,3 446 420 8,3 460 318 8,1
Young Adult 20 25 465 347 9,2 398 035 7,4 378 248 6,7
Working Age 26 65 2 591 580 51,2 2 885 081 53,4 2 485 483 43,6
Pensioner 66 80 480 214 9,5 670 736 12,4 891 563 15,6
Elderly 80 99 152 366 3,0 230 003 4,3 571 632 10,0
Total 0 99 5 058 012 99,9 5 400 603 99,9 5 695 675 99,9
Absolute 0 >100 5 058 119 5 401 267 5 700 200
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45
Disability Weights
0
0,1 0,2 0,3 0,5 0,6 0,7 0,8 0,9
1
Asthma
Meningitis
PerfectHealth
Death
Dental caries
Acute mycardialinfarction
1st stroke ever
Liverneoplasm
Leukemia
Cretinism
SevereDepressiveEpisode
0,4
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46
Estimation
19861989
19921995
19982001
20042007
20102013
20162019
20222025
20282031
20342037
20400.0
10.0
20.0
30.0
40.0
50.0
60.0
Infant Toddler Preschool Child Child TeenYoung Adult Working Age Pensioner Total Elderly
Year
Dura
tion
(Yea
rs)
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
47
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
Total Years Lived
relative YLD_WHO
relative YLD_P/I
Age Group
Year
s Liv
ed
Rela
tive
Frac
tion
Year
s Liv
ed w
ith D
isabi
lity
(YLD
)
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0.45%
Total Years Livedrelative YLD_WHO
Age Group
Year
s Liv
ed
Rela
tive
Frac
tion
Year
s Liv
ed w
ith D
isabi
lity
(YLD
)
Comparison YLD_I and YLD_P
1986
2011
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
48
Infant
Toddler
Presch
ool Child
ChildTe
en
Young A
dult
Worki
ng Age
Pensio
ner
Elderl
yTo
tal0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%Total Years Livedrelative YLD_WHOrelative YLD_P/I
Age Group
Year
s Liv
ed
Rela
tive
Frac
tion
Year
s Liv
ed w
ith D
isabi
lity
(YLD
)
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
Total Years Livedrelative YLD_WHOrelative YLD_P/I
Age Group
Year
s Liv
ed
Rela
tive
Frac
tion
Year
s Liv
ed w
ith D
isabi
lity
(YLD
)
2015
2040
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Attributable YLD_I & attributable YLD_P – Comparison I
WHO P/I0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
DogCatFormaldehydePM2.5D&MSmoking_aggregatedSHS_aggregatedResidual
Asthma Duration
Attrib
utab
le Y
ears
Live
d w
ith D
isabi
lity
(YLD
) (Ye
ars)
Baseline (2011)
49
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Attributable YLD_I & attributable YLD_P – Comparison
WHO P/I0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000Dog
Cat
Allergen
Formaldehyde
PM2.5
Underweight
NO2
D&M
Smoking_aggregated
SHS_aggregated
Residual
Asthma Duration
Attrib
utab
le Y
ears
Liv
ed w
ith D
isabi
lity
(YLD
) (Ye
ars)
WHO P/I0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Asthma Duration
Attrib
utab
le Y
ears
Liv
ed w
ith D
isabi
lity
(YLD
) (Ye
ars)
1986 2006
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Comparison studies – Methods WHO IHME EBoDE SETURI HealthVent Thesis
Target year 2004 2010 2004 2006 2010 2011
YLD estimate YLD_I YLD_P YLD_I YLD_I YLD_I YLD_IYLD_P
Disability Weight
0,04 0,009-0,132
0,04 0,04 0,04 0,04
Duration 15 years - 15 years 15 years 15 years 15 years-
Discounting Yes No YesNo
No Yes No
Source Asthma Data
WHO ? WHO WHO WHO KELA statistics
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NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Comparison with other studiesStudy Year Factor Estimate
(YLD)Thesis (YLD)
WHO 2002 Asthma 9 526 8 974WHO 2004 Asthma 9 000 8 191HealthVent 2010 Asthma a 2 023* 2 037HealthVent 2010 PM2.5 1,a 8 653* 1 049HealthVent 2010 SHS 2,a 278* 591HealthVent 2010 Dampness & Mould 3,a 340* 397EBoDE 2010 SHS 692 604EBoDE 2010 Formaldehyde 9 0
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* In DALYsa Includes only attributable to poor indoor air quality1 includes asthma, lung cancer, CV-diseases, COPD2 includes lung cancer, ischemic heart disease, asthma3 includes respiratory infections, asthma
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Risk estimates for stressors• Risk estimates for stressor were available for short
window of time linear regression used for extrapolation for longer
period of time
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 221
1.5
2
2.5
3
3.5
f(x) = − 0.142307692307692 x + 3.98846153846154
f(x) = 0.356666666666667 x + 1f(x) = − 0.145 x + 4.01
Age (years)
Rela
tive
Risk
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Tobacco Statistics Finland
19791980
19811982
19831984
19851986
19871988
19891990
19911992
19931994
19951996
19971998
19992000
20012002
20032004
20052006
20072008
20092010
20110
5
10
15
20
25
30
35
40
45
f(x) = − 3.99251291881455 ln(x) + 39.6842744960782R² = 0.717417262429346
Total Logarithmic (Total) 15-24 25-44 45-64
Year
Smok
ing
Popu
latio
n (%
)
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55
PM Exposure trends
20142015
20162017
20182019
20202021
20222023
20242025
20262027
20282029
20302031
20322033
20342035
20362037
20382039
20404.5
5
5.5
6
6.5
7
7.5
8
BaU Ban SSWC Reduction SSWCSpeet Limit
Year
Tota
l am
bien
t PM
con
cent
ratio
n (m
g/m
3)
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2015 2016 2017 2018 2019 2020
BaU Inc
BaUPrev
Control 1Preventing Inc
-x% -x% -x% -x% -x%
+a1-x% +a2-x% +a3-x% +a4-x% +a5-x%
Year
Control Inc
Control 1 Prev
Control 2Preventing Prev
-y% -y% -y% -y% -y%
Control 2 Prev
+a1 +a2 +a3 +a4 +a5
From change in Incidence to change in Prevalence
NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Summary I
• How much of the burden of asthma can be explained by known environmental risk factors? Which are the ones with the most impact?25-50% with PM2.5 and SHS having the biggest
impact
• Are there any protection factors capable of preventing a significant fraction of onset or symptoms of asthma?
Yes, but very weak evidence
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NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Summary II
• Are the two different modeling approaches comparable? Are differences in the burden of disease estimates due to changes in the incidence or prevalence rate?
Incidence based has bigger focus on younger age groups and prevalence based estimates have bigger focus on older age groups
• Does the reduction of environmental exposures lead theoretically to a reduction of burden of disease?
10% of total BoD and 30% of attributable BoD
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NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Summary III • Which control policies approach has theoretically a
bigger impact on DALYs?
Ban of Tobacco (SHS) and Increase of Pets
• Can any causality between onset and aggravation regarding environmental factors be identified?
No
• Does it make a difference to use a constant duration of disease or an age-dependent estimate?
Yes (assumed that duration is equal to Prev/Inc)
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NATIONAL INSTITUTE FOR HEALTH AND WELFARE
Conclusion
”Essentially, all models are wrong, but some are useful” (George Box)
Many uncertainties, but nevertheless, the model gives an overview over the order of magnitude of impact of exposures on asthma
Results can be used as support for decision making in public health policies
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