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Web-Appendix 1: Prevalence (%) and number of people in the age group 18-64 affected with alcohol dependence (2005; best estimates)
W MW
affectedM
affected Year SourceAustria 2.5 7.5 66,800 204,800 2008 (Uhl et al., 2009)
Belgium 1.9 5.4 51,800 177,100 2001 median of two major surveys; (WMHS corrected and Belgian Health Survey 2001, cf. GSRA
Bulgaria 1.4 7.3 35,900 184,500 2004 (World Health Organization Regional Office for Europe, 2010)
Cyprus 1.6 5.3 4,400 13,800 2004 (World Health Organization Regional Office for Europe, 2010)
Czech Republic 0.8 5.0 27,600 173,400 2004 WHS
Denmark 1.9 4.8 32,300 83,000 2005 (Hansen et al., 2011)
Estonia 2.1 11.0 9,500 45,400 2004 (World Health Organization Regional Office for Europe, 2010) (own calculations)
Finland 1.9 7.2 31,400 121,500 2000 (Latvala et al., 2009) for under 30 year olds; (Aromaa and Koskinen, 2002) for 30-64 year old (see also (Pirkola et al., 2005))
France 1.5 5.3 284,700 1,001,700 2001-2002 WMHS, adjusted
Germany1 1.3 5.4 338,900 1,445,000 1997-1999 (Jacobi et al., 2004; Jacobi et al., 2002) (personal communication)
Greece 1.5 4.8 53,400 173,800 2004 (World Health Organization Regional Office for Europe, 2010)
Hungary2 3.4 18.3 114,800 598,600 2004 (World Health Organization Regional Office for Europe, 2010)
Iceland 1 3.3 910 3,100 2004 (World Health Organization Regional Office for Europe, 2010)
Ireland 2.0 6.4 26,600 86,100 2004 (World Health Organization Regional Office for Europe, 2010)
Italy3 0.5 0.8 93,600 149,800 2001-2003 (de Girolamo et al., 2006) (adjusted)
Latvia 1.6 8.4 12,400 60,300 2004 (World Health Organization Regional Office for Europe, 2010) (own calculations)
Lithuania 1.9 9.9 21600 104,200 2004 (World Health Organization Regional Office for Europe, 2010) (own calculations)
Luxembourg 1.4 5.4 2,000 8,000 2000 median of France and Germany
Malta 0.8 2.8 1,000 3,800 2004 (World Health Organization Regional Office for Europe, 2010)
Netherlands4 0.5 1.0 26,000 53,100 2007/2009 (de Graaf et al., 2011)
Norway 3.5 10.5 50,000 154,500 1994-1997 (Kringlen et al., 2001)
Poland5 1.6 8.4 205,500 1,058,200 2004 (World Health Organization Regional Office for Europe, 2010)
Portugal 1.7 5.6 58,600 187,700 2004 (World Health Organization Regional Office for Europe, 2010)
Romania 0.7 2.2 50,000 155,000 2007 (Florescu et al., 2009) (adjusted)
Slovakia 1.1 10.2 20,200 184,800 2000/2001 MCSS
1
Slovenia 2.0 10.5 13,200 71,300 1999 GSRA (adjusted for screening scale)
Spain 0.2 1.2 28,410 173,600 2000/2001 WMHS, adjusted
Sweden 3.3 7.7 91,200 219,400 1998-2003 PART study cf. (Rehm et al., 2005)
Switzerland 1.6 8.1 39,300 194,300 2007 (Kuendig, 2010)
UK 6
(England only) 3.6 9.3 683,300 1,745,500 2007 http://www.ic.nhs.uk/webfiles/publications/alcoholeng2009/Final%20Format%20draft%202009%20v7.pdf
EU 1.5 5.4 2,400,00 8,500,000 Own calculations
GSRA: Global Status Report on Alcohol (World Health Organization, 2004)MCSS: Multi-Country Survey Study (Üstün et al., 2003b)WHS: World Health Survey (Üstün et al., 2003a)WMHS: World Mental Health Survey (Kessler and Üstün, 2008)
1 There are several regional studies in Germany (see overview in (Rehm et al., 2005), but this is the latest national survey with comparable methodology. The next national mental health survey is ongoing.2 Indirect estimations are around 8% for both genders combined, based on a variant of Jellinek's formula: (http://www.gencat.cat/salut/phepa/units/phepa/pdf/phepa_final_report_annex4_hungary.pdf 3 This is the most conservative estimate. The Istituto Superiore di Sanita estimates the prevalence to be 2% overall (Scafato et al., 2005).4 The prevalence of abuse was found to be 5 times higher. AUDs have been stable for the past decade, but AD estimates had been considerably higher in the past surveys.5 The prevalence of AD in primary health care was found to be 19% using the CAGE screening scale (Manwell et al., 2002).6 There are other estimates such as the one cited by the National Institute for Health and Clinical Excellence (National Institute for Health and Clinical Excellence, 2011), which amounted to 6% of men and 2% of women.
The red shaded cells indicate more than 150%, the green shaded cells less than 50% of the EU average. If both women and men have the same shading, the country name is also shaded.
2
Web Appendix 2.1
In alcohol epidemiology the number of deaths caused by alcohol consumption is
calculated using an alcohol Alcohol-Attributable Fraction (AAF), which is defined as the fraction
of mortality that would not be present if exposure to alcohol was 0 (in this case, if every person
was a lifetime abstainer) (Eide and Heuch, 2001; Murray and Lopez, 1997; Rothman et al., 2008;
Walter, 1976; Walter, 1980).
This web appendix outlines the modelling strategies used to estimate the AAFs for
chronic diseases and infections, injuries, heavy drinking, and alcohol dependence.
Alcohol Relative Risks
Sources for Relative Risk (RR) functions by ICD-10 code are outlined in Table A1.
Alcohol-attributable harms were calculated where there existed a meta-analysis reporting a
continuous RR function. An outline of the causal relationship between alcohol consumption and
the ICD code categories is described elsewhere in detail (Rehm et al., 2010).
AAFs for chronic and infectious diseases, except ischemic heart disease
The AAF for most chronic diseases is calculated using the counterfactual scenario
where every person is a lifetime abstainer, which can be expressed as follows:
AAF=(Pabs+Pform RRform+∫
¿0
150
Pcurrent ( x )RRcurrent ( x )dx)−1
Pabs+P formRR form+∫¿0
150
Pcurrent( x )RRcurrent ( x )dx
where Pabs represents the proportion of lifetime abstainers, Pform represents the proportion
of former drinkers, RRform represents the relative risk of an outcome for former drinkers (as
compared to lifetime abstainers), Pcurrent(x) is the prevalence of current drinkers who
consume on average x grams of alcohol per day, RRcurrent(x) is the relative risk for current
3
drinkers who consume on average x grams of alcohol per day (as compared to lifetime
abstainers). The cap at 150 grams per day is an arbitrary limit to insure the RR functions are
only used in the range in which they have been defined.
AAFs for ischemic heart disease
The risk of Ischemic Heart Disease (IHD) is impacted by average volume of alcohol
consumption and patterns of consumption (Puddey et al., 1999; Rehm et al., 2003). To
model the AAF for IHD, we utilized the J-shaped RR curve for IHD based on average alcohol
consumption (see (Corrao et al., 2000; Roerecke and Rehm, 2012; Ronksley et al., 2011)) for
all individuals who did not have irregular heavy drinking occasions. For people with at
least one irregular heavy drinking occasion per month, we used the RR from the respective
meta-analysis (Roerecke et al., 2012) and assumed no cardio-protective effect. The AAF for
HD was estimated as follows:
AAF=(Pabs+Pform RRform+Pcurrent(binge )+ ∫
¿ 0
binge
Pcurrent (non−binge)( x )RR current(non−binge )( x )dx)−1
Pabs+P formRR form+Pcurrent (binge )+ ∫¿ 0
binge
Pcurrent(non−binge )( x )RRcurrent (non−binge )( x )dx
where Pcurrent(binge) represents the proportion of binge drinkers, where binge is defined as the
threshold of alcohol in grams per day that is considered binge drinking (4 standard drinks
or 48 grams of alcohol per day for women and 5 standard drinks or 60 grams of alcohol per
day for men), Pcurrent(non-binge)(x) is the prevalence of current drinkers who do not binge drink
and consume on average x grams of alcohol per day, and RRcurrent(non-binge)(x) is the relative
risk of current drinkers who do not binge drink and consume on average x grams of alcohol
per day.
4
Estimating the AAFs for low birth weight
To estimate the AAFs for mortality and morbidity caused by low birth weight
attributable to alcohol consumption, we used a modelling strategy which took into account
the prevalence of those women who consumed alcohol while pregnant (including those who
consumed less alcohol while pregnant and those who consumed the same amount of alcohol
while pregnant). The AAFs for low birth weight were calculated as follows:
AAF=(Pabs+∫
¿ 0
150
Psame (x )RR( x )dx+ ∫¿ 0
150
Pless( x )RR (x )dx )−1
Pabs+∫¿0
150
P same( x )RR( x )dx+ ∫¿0
150
Pless ( x )RR( x )dx
where Pabs represents the proportion of women who abstained from consuming alcohol
while pregnant, Psame represents the proportion of pregnant women who consumed the
same amount of alcohol as pre-pregnancy, and Pless represents the proportion of pregnant
women who consumed less alcohol than pre-pregnancy.
AAFs for injuries
Estimating the AAFs for harms caused to oneself
The AAFs for injuries were modelled according to methodology which takes into
account two dimensions of alcohol consumption:
1) binge drinking (both the number of occasions and the amount consumed
per occasion), and
2) average daily alcohol consumption (on non-binge days).
When calculating the AAFs, we also included alcohol metabolism rates for men and
women to calculate a person’s time at risk of an injury outcome, according to methods
5
outlined by Taylor and colleagues (Taylor et al., 2011). The AAFs for intentional and
unintentional injuries attributable to alcohol consumption were calculated as follows:
AAF=(Pabs+Pcurrent (non−binge )RRcurrent (non−binge )+Pcurrent (binge )RRcurrent (binge ) )−1
Pabs+Pcurrent (non−binge)RRcurrent (non−binge )+Pcurrent (binge )RRcurrent (binge )
where Pabs represents the prevalence of current abstainers, and Pcurrent(binge) and Pcurrent(non-binge)
are the prevalence of current drinkers who engage in binge drinking and the prevalence of
current drinkers who do not engage in binge drinking, respectively.
RRs were calculated separately for:
1) current drinkers who do not engage in binge drinking (RRcurrent(non-binge))
2) current drinkers who do engage in binge drinking (RRcurrent(binge)).
RRcurrent(non-binge) was calculated as follows:
R Rcurrent (non−binge )=(R Raverage−1 )∗Pnonbingedays+¿1
and RRcurrent(binge) was calculated as follows:
R Rcurrent (binge )=(R Raverage−1 )∗Pnonbingedays+(R Rbinge−1 )∗Pbingedays+1
where injury risk on average drinking days (RRaverage) was calculated as follows:
R Raverage=Pdayatrisk(x )∗(R Rinjury ( x )−1 )+1
and where injury risk on binge drinking days (RRbinge) was calculated as follows:
R Rbinge=Pdayatrisk( x)∗(RR injury ( x )−1 )+1
where Pdayatrisk represents the proportion of a day at risk (calculated based on the average
rate at which alcohol is metabolized), where x is the average daily consumption on non-
6
binge days or binge days, Pnonbingedays represents the proportion of drinking days when the
drinker does not binge, Pbingedays represents the proportion of drinking days when the
drinker does binge, and RRinjury represents the RR function for injuries where x is the
average alcohol intake (this function calculates the relative risk for both non-binge days and
binge days, given an average amount of alcohol consumed (x) on non-binge days or binge
days).
Since these AAFs were calculated based on samples of emergency room patients, we
estimated the AAF for mortality from motor vehicle accidents by multiplying the AAF for
morbidity for motor vehicle accidents by 3/2. Similarly, to estimate the AAF for mortality
due to non-motor vehicle accidents, we multiplied the AAF for morbidity due to non-motor
vehicle accidents by 9/4. These methods were based on two studies that compared blood
alcohol levels of emergency room patients, where blood alcohol levels were obtained from
coroners’ reports of patients who died from an injury (Cherpitel, 1994; Cherpitel, 1996).
For women, the AAF for motor vehicle accidents was calculated by multiplying the
AAF for motor vehicle accidents for men by the product of the per capita consumption of
alcohol for women divided by the per capita consumption of alcohol for men.
Estimating the AAFs for harms caused to others
The AAFs for deaths and morbidity caused by drinkers to others due to motor
vehicle accidents were calculated based on recent data reported by Laslett et al., 2010
(Laslett et al., 2010). The AAFs for the alcohol-attributable injuries to others were
calculated as follows:
AAFOtherage=(1−AAF selfagecountryi)∗(1−exp [ ln (1−AAFOtherageAustralia )∗AAF selfcountryi
AAF selfAustralia ])
7
where AAFotherage represents the AAF for motor vehicle accident injuries caused by others,
AAFselfcountryi represents the AAF for motor vehicle accident injuries caused to oneself for an
entire country i, and AAFselfagecountryi represents the AAF for motor vehicle accident injuries
caused to oneself for each specific age group. AAFselfAustralia represents the AAF for motor
vehicle accident injuries in Australia caused to oneself, and AAFOtherageAustralia represents the
AAF for motor vehicle accident injuries caused by others in Australia for each specific age
group.
The AAFs for deaths and injuries caused by an assault by someone who has been
drinking were calculated based on recent data reported by Laslett et al., 2010 (Laslett et al.,
2010). These AAFs were calculated as follows:
AAF Assaultage countryi=AAF AssaultageAustralia
∗(AA FAssault countryi/AA F AssaultAustralia
)
where AAFAssaultage_countryi represents the age specific AAF for deaths or injuries caused by
assault, AAFAssault_countryi represents the AAF for assaults for an entire country, AAFAssault_Australia
represents the AAF for assaults for Australia and AAFAssaultage_Australia represents the AAF for
deaths or injuries caused by assaults in Australia for each specific age group.
AAFs for heavy drinking
The “Heavy Drinker Attributable Fraction” (HDAF), i.e. the fraction of deaths,
Potential Years of Life Lost, and Years Lived with Disability attributable to heavy drinking, is
defined as follows:
HDAF=(c⋅P formRR form+∫
HD
150
Pcurrent (x )RRcurrent ( x )dx)−(c⋅P form+∫HD
150
Pcurrent (x )dx)Pabs+P fromRR form+∫
¿ 0
150
Pcurrent ( x )RR current( x )dx
8
where Pform represents the proportion of former drinkers, Pcurrent(x) represents the
prevalence of current drinkers who on average consume x grams of alcohol per day, Pabs
represents the proportion of abstainers, RRform represents the RR for former drinkers (as
compared to lifetime abstainers) and RRcurrent(x) represents the RR for current drinkers who
consume on average x grams of alcohol per day (as compared to lifetime abstainers). The
variable HD is the threshold after which a drinker is considered a heavy drinker (i.e., 40 g of
pure alcohol per day for women and 60 g of pure alcohol per day for men).
The proportion of harm caused to former drinkers who were former heavy drinkers
(represented as c in the formula above) was estimated as the ratio of harm among current
heavy drinkers as compared to the total harm among the current alcohol consuming
population. This can be expressed as follows:
c=∫HD
150
Pcurrent (x )RRcurrent ( x )dx
∫¿0
150
Pcurrent( x )RRcurrent ( x )dx
Estimating the 95% confidence intervals for the AAFs
To calculate the 95% Confidence Intervals (CIs) for the AAFs, we used a Monte Carlo
type approach described by Gmel and colleagues (Gmel et al., 2011b) for chronic and
infectious diseases, and by Taylor and colleagues (Taylor et al., 2011) for injuries.
The AAF function is made up of different parameters, for example the RR, the
prevalence of abstainers and former drinkers and the gamma distribution representing the
prevalence of average alcohol consumption. The latter is entirely defined with 2
parameters, the shape and scale parameters of the gamma distribution. Around each
parameter, there is a certain error (sampling error for the prevalence, uncertainty around
the scale and shape parameters) and all parameters follow a normal or asymptotically
9
normal distribution. The variance around the final AAF is a very complex function of the
variances of each parameter and there is no closed mathematical expression to calculate it.
Therefore, we used a Monte Carlo type method to estimate the final variance.
In order to do so, we generated 40,000 sets of the lowest level parameters and then
used these sets of parameters to calculate 40,000 AAFs. From these AAFs we calculated the
variances which were then used to calculate the 95% CIs.
All statistical analyses and modelling were performed using R version 2.11.1.
Alcohol AAFs for alcohol dependence
In general, the AAF for alcohol dependence can be written as follows:
AAF=∑i=1
n
PiRRi−∑i=1
n
Pi' RRi
∑i=1
n
PiRRi
where Pi represents the proportion of the population with alcohol dependence, Pi’
represents the proportion of the population under the counterfactual exposure level, i.e., no
alcohol dependence, and RRi represents the relative risk of a given outcome at exposure
level i. Information on the RRs used in the above formula can be found in the main article.
10
Table A1: Categories of alcohol-attributable diseases and sources used for determining
relative risks
Condition ICD-10 Code Source for Relative Risk
Infectious and parasitic diseases
Tuberculosis A15-A19Lönroth et al.(Lönnroth et al., 2008); for causal relationship see: Rehm et al. (Rehm et al., 2009)
Human immunodeficiency virus/ Acquired immune deficiency syndrome
B20-B24
Gmel et al.(Gmel et al., 2011a) for estimate on the impact of alcohol on worsening the disease course via disrupting the medication schedule
Malignant neoplasms
Mouth and oropharynx cancers C00-C14
Baan et al., IARC (Baan et al., 2007; International Agency for Research on Cancer, 2011) (based on Relative Risks from Corrao et al. (Corrao et al., 2004))
Esophageal cancer C15
Baan et al., IARC (Baan et al., 2007; International Agency for Research on Cancer, 2011) (based on Relative Risks from Corrao et al. (Corrao et al., 2004))
Liver cancer C22
Baan et al., IARC (Baan et al., 2007; International Agency for Research on Cancer, 2011) (based on Relative Risks from Corrao et al. (Corrao et al., 2004))
Laryngeal cancer C32
Baan et al., IARC (Baan et al., 2007; International Agency for Research on Cancer, 2011) (based on Relative Risks from Corrao et al. (Corrao et al., 2004))
Breast cancer C50
Baan et al., IARC (Baan et al., 2007; International Agency for Research on Cancer, 2011) (based on Relative Risks from Corrao et al. (Corrao et al., 2004))
Colon cancer C18
Baan et al., IARC (Baan et al., 2007; International Agency for Research on Cancer, 2011) (based on Relative Risks from Corrao et al. (Corrao et al., 2004))
Rectal cancer C20
Baan et al., IARC (Baan et al., 2007; International Agency for Research on Cancer, 2011) (based on Relative Risks from Corrao et al. (Corrao et al., 2004))
11
Diabetes
Diabetes mellitus E10-E14 Baliunas et al.(Baliunas et al., 2009)
Neuro-psychiatric conditionsAlcoholic psychoses (part of AUD)
F10.0, F10.3-F10.9 100% AAF per definition
Alcohol abuse (part of AUD) F10.1 100% AAF per definitionAlcohol dependence (part of AUD)
F10.2 100% AAF per definition
Epilepsy G40-G41Samokhvalov et al. (Samokhvalov et al., 2010a)
Cardiovascular disease
Hypertensive disease I10-I15 Taylor et al. (Taylor et al., 2009)
Ischemic heart disease I20-I25
Roerecke et al. (Roerecke et al., 2012) et al. for volume; Roerecke et al. (Roerecke and Rehm, 2010) for pattern
Cardiac arrhythmias I47-I49Samokhvalov et al. (Samokhvalov et al., 2010b)
Ischemic stroke I60-I62 Patra et al. (Patra et al., 2010)Hemorrhagic and other non-ischemic strokes
I63-I66 Patra et al. (Patra et al., 2010)
Digestive diseases
Cirrhosis of the liver K70, K74 Rehm et al. (Rehm et al., 2010)
Acute and chronic pancreatitis K85, K86.1 Irving et al. (Irving et al., 2009)
Respiratory infections
Lower respiratory infections J10–J18, J20–J22Samokhvalov et al. (Samokhvalov et al., 2010c)
Conditions arising during the prenatal period
Low birth weight: as defined by the GBD
P05-P07 Patra et al. (Patra et al., 2011)
Unintentional injuries
Motor vehicle accidents §
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Poisonings X40-X49
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Falls W00-W19
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Fires X00-X09
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Drowning W65-W74Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted
12
from Taylor et al. (Taylor et al., 2011)
Other unintentional injuries
†Rest of V-series and W20-W64, W 75-W99, X10-X39, X50-X59, Y40-Y86,
Y88, and Y89
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Intentional injuries
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Self-inflicted injuriesX60-X84 and
Y87.0
Taylor, (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Homicide X85-Y09, Y87.1
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
Other intentional injuries
Taylor et al. (Taylor et al., 2010) for Relative Risk, methodology adopted from Taylor et al. (Taylor et al., 2011)
§ V021–V029, V031–V039, V041–V049, V092, V093, V123–V129, V133–V139, V143–V149, V194–V196, V203–V209, V213–V219, V223–V229, V233–V239, V243–V249,V253–V259, V263–V269, V273– V279, V283–V289, V294–V299, V304–V309, V314–V319, V324–V329, V334–V339, V344–V349, V354–V359, V364–V369, V374–V379, V384–V389, V394–V399, V404–V409, V414–V419, V424–V429, V434–V439, V444–V449, V454–V459, V464– V469, V474–V479, V484–V489, V494–V499, V504–V509, V514–V519, V524–V529, V534–V539, V544–V549, V554–V559, V564–V569, V574–V579, V584–V589, V594–V599, V604–V609, V614–V619, V624–V629, V634–V639, V644–V649, V654– V659, V664–V669, V674–V679, V684–V689, V694–V699, V704–V709, V714–V719, V724–V729, V734–V739, V744–V749, V754–V759, V764–V769, V774–V779, V784–V789, V794–V799, V803–V805, V811, V821, V830–V833, V840–V843, V850– V853, V860–V863, V870–V878, V892. †Rest of V = V-series MINUS §.
13
Web Appendix 2.2
This web appendix outlines the modelling strategies used to estimate the variance around
the effects of treatment interventions for alcohol dependence.
Estimating the standard error of the effects of treatment interventions for alcohol
dependence
As the number of lives saved is obtained by comparing the AAF of the original
population and the AAF of the population in which treatment interventions were simulated,
a great part of the variance of the AAFs stems from the same sources as the variance of the
original AAFs. Due to the complex nature of these treatment simulations (we simulated
individual people and shifted their consumption or relative risk according to their
treatment), a closed mathematical expression is impossible to obtain and a Monte Carlo
type method would take several months or even years to run. Therefore, we assumed that
the part of the AAF that was common between the original and the modified scenario
(treatment applied to the people with alcohol dependence) would preserve, proportionally,
the same standard error as that of the AAF of the original population. As a conservative
estimate, the standard error of the remaining part of the AAF of the treated population was
set to be proportionally twice the standard error of the remaining original AAF.
Mathematically we can write:
c=AAFtreated
AAForiginal
where AAFtreated is the overall AAF after the treatment intervention was applied to the
population, and AAForiginal is the AAF in a population where no treatment is applied. With this
we assumed the following:
14
SE [AAF treated ]=c ∙SE [AAF original ]+2∙ (1−c )∙ SE [AAForiginal ]
This formula produces a conservative estimate of the error around the treatment
interventions.
Web Appendix 3: Proportion of deaths avoided over one year in men by treatment for AD in the EU in 2004 by five different treatment modalities
15
Web Appendix 4: Proportion of deaths avoided over one year in women by treatment for AD in the EU in 2004 by five different treatment modalities
16
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