Web-Appendix 1:Prevalence (%) and number of people in the ... · Web viewSlovakia 1.1 10.2 20,200 184,800 2000/2001 MCSS Slovenia 2.0 10.5 13,200 71,300 1999 GSRA (adjusted for screening

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

http://www.gencat.cat/salut/phepa/units/phepa/pdf/phepa_final_report_annex4_hungary.pdf%20

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


Liver cancer C22


Laryngeal cancer C32


Breast cancer C50


Colon cancer C18


Rectal cancer C20


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


Falls W00-W19


Fires X00-X09


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


Intentional injuries


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


Other intentional injuries


§ 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

REFERENCES

Aromaa, A. and Koskinen, S., 2002. Health and functional capacity in Finland baseline results on the Health 2000 Health Examination Survey. National Public Health Institute, Helinski.

Baan, R., Straif, K., Grosse, Y., Secretan, B., El Ghissassi, F., Bouvard, V., Alteri, A., Cogliano, V., On behalf of the WHO International Agency for Research on Cancer monograph working group, 2007. Carcinogenicity of alcoholic beverages. Lancet Oncol 8, 292-293.

Baliunas, D., Taylor, B., Irving, H., Roerecke, M., Patra, J., Mohapatra, S., Rehm, J., 2009. Alcohol as a risk factor for type 2 diabetes - A systematic review and meta-analysis. Diabetes Care 32, 2123-2132.

Cherpitel, C., 1994. Alcohol and casualties: a comparison of emergency room and coroner data. Alcohol Alcohol 29, 211-218.

Cherpitel, C., 1996. Alcohol in fatal and nonfatal injuries: a comparison of coroner and emergency room data from the same country. Alcohol Clin Exp Res 20, 338-342.

Corrao, G., Rubbiati, L., Bagnardi, V., Zambon, A., Poikolainen, K., 2000. Alcohol and coronary heart disease: A meta-analysis. Addiction 95, 1505-1523.

Corrao, G., Bagnardi, V., Zambon, A., La Vecchia, C., 2004. A meta-analysis of alcohol consumption and the risk of 15 diseases. Prev Med 38, 613-619.

de Girolamo, G., Polidori, G., Morosini, P., Scarpino, V., Reda, V., Serra, G., Mazzi, F., Alonso, J., Vilagut, G., Visona, G., Falsirollo, F., Rossi, A., Warner, R., 2006. Prevalence of common mental disorders in Italy: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD). Soc Psychiatry Psychiatr Epidemiol 41, 853-861.

de Graaf, R., Ten Have, M., van Gool, C., van DDorsselaer, S., 2011. Prevalence of mental disorders and trends from 1996 to 2009. Results from the Netherlands Mental Health Survey and Incidence Study-2. Soc Psychiatry Psychiatr Epidemiol [Epub ahead of print].

Eide, G., Heuch, I., 2001. Attributable fractions: fundamental concepts and their visualization. Stat Methods Med Res 10, 159-193.

Florescu, S., Moldovan, M., Mihaescu-Pintia, C., Ciutan, M., Sorel, G.E., 2009. The Mental Health Study. Romania 2007. Prevalence, severity, and treatment of 12-Month DSM-IV disorders. Management in Health 13, 23-31.

Gmel, G., Shield, K., Rehm, J., 2011a. Developing a methodology to derive alcohol-attributable fractions for HIV/AIDS mortality based on alcohol's impact on adherence to antiretroviral medication. Popul Health Metr 9, 5.

17

Gmel, G.j., Shield, K., Frick, H., Kehoe, T., Gmel, G.s., Rehm, J., 2011b. Estimating uncertainty for alcohol-attributable fractions for infectious and chronic disease. BMC Medical Research Methodology 11, 48.

Hansen, A.B.G., Hvidtfeldt, U.A., Gronbaek, M., Backer, U., Nielsen, A.S., Tolstrup, J.S., 2011. The number of persons with alcohol problems in the Danish population. Scandinavian Journal of Public Health 39, 128-136.

International Agency for Research on Cancer, 2011. IARC Monograph 96 on the Evaluation of Carcinogenic Risks to Humans. Alcoholic beverage consumption and ethyl carbamate (urethane). International Agency for Research on Cancer (IARC), Lyon, France.

Irving, H.M., Samokhvalov, A., Rehm, J., 2009. Alcohol as a risk factor for pancreatitis. A systematic review and meta-analysis. JOP 10, 387-392.

Jacobi, F., Wittchen, H., Holting, C., Hofler, M., Pfister, H., Muller, N., Lieb, R., 2004. Prevalence, co-morbidity and correlates of mental disorders in the general population: results from the German Health Interview and Examination Survey (GHS). Psychol. Med. 34, 1-15.

Jacobi, F., Wittchen, H., Holting, C., Sommer, S., Lieb, R., 2002. Estimating the prevalence of mental and somatic disorders in the community: aims and methods of the German National Health Interview and Examination Survey. Int J Methods Psychiatr Res 11, 1-18.

Kessler, R. C. and Üstün, B., 2008. The WHO world mental health surveys. Global perspectives of mental health surveys. First Edition. Cambridge University Press, New York.

Kringlen, E., Torgersen, S., Cramer, V., 2001. A Norwegian psychiatric epidemiological study. Am. J. Psychiatry 158, 1091-1098.

Kuendig, H., 2010. Alcohol dependence figures in the Swiss general population: a sisyphean challenge for epidemiologists. Eur Addict Res 16, 185-192.

Laslett, A.M., Catalano, P., Chikritzhs, T., Dale, C., Doran, C., Ferris, J., Jainullabudeen, T., Livingston, M., Matthews, S., Mugavin, S., Room, R., Schlotterlein, M., Wilkinson, C., 2010. The range and magnitude of alcohol's harm to others. Turning Point Alcohol & Drug Centre, Fitzroy, Vic.

Latvala, A., Tuulio-Henriksson, A., Perälä, J., Saarni, S., Aalto-Setälä, T., Aro, H., Korhonen, T., Koskinen, S., Lönnqvist, J., Kaprio, J., Suvisaari, J., 2009. Prevalence and correlates of alcohol and other substance use disorders in young adulthood: A population-based study. BMC Psychiatry 9, 73.

Lönnroth, K., Williams, B., Stadlin, S., Jaramillo, E., Dye, C., 2008. Alcohol use as a risk factor for tuberculosis - a systematic review. BMC Public Health 8, 289.

Manwell, L., Ignaczak, M., Czabala, J., 2002. Prevalence of tobacco and alcohol use disorders in Polish primary care settings. Eur J Public Health 12, 139-144.

Murray, C.J.L., Lopez, A., 1997. Global mortality, disability, and the contribution of risk factors: global burden of disease study. Lancet 349, 1436-1442.

18

National Institute for Health and Clinical Excellence, 2011. Alcohol use disorders: diagnosis, assessment and management of harmful drinking and alcohol dependence. National Institute for Health and Clinical Excellence, London.

Patra, J., Taylor, B., Irving, H., Roerecke, M., Baliunas, D., Mohapatra, S., Rehm, J., 2010. Alcohol consumption and the risk of morbidity and mortality from different stroke types - a systematic review and meta-analysis. BMC Public Health 10, 258.

Patra, J., Bakker, R., Irving, H., Jaddoe, V.W.V., Malini, S., Rehm, J., 2011. Dose-response relationship between alcohol consumption before and during pregnancy and the risks of low birthweight, preterm birth and small for gestational age (SGA)-a systematic review and meta-analyses. BJOG: International Journal of Obstetrics and Gynaecology 118, 1411-1421.

Pirkola, S.P., Isometsa, E., Suvisaari, J., Aro, H., Joukamaa, M., Poikolainen, K., Koshinen, S., Aromaa, A., Lonnqvist, J.K., 2005. DSM-IV mood-, anxiety- and alcohol use disorders and their comorbidity in the Finnish general population. Results from the Health 2000 Study. Soc Psychiatry Psychiatr Epidemiol 40, 1-10.

Puddey, I.B., Rakic, V., Dimmitt, S.B., Beilin, L.J., 1999. Influence of pattern of drinking on cardiovascular disease and cardiovascular risk factors - a review. Addiction 94, 649-663.

Rehm, J., Room, R., Van den Brink, W., Jacobi, F., 2005. Alcohol use disorders in EU countries and Norway: an overview of the epidemiology. Eur Neuropsychopharmacol 15, 377-388.

Rehm, J., Samokhvalov, A.V., Neuman, M.G., Room, R., Parry, C.D., Lönnroth, K., Patra, J., Poznyak, V., Popova, S., 2009. The association between alcohol use, alcohol use disorders and tuberculosis (TB). A systematic review. BMC Public Health 9, 450.

Rehm, J., Sempos, C., Trevisan, M., 2003. Average volume of alcohol consumption, patterns of drinking and risk of coronary heart disease - a review. J Cardiovasc Risk 10, 15-20.

Rehm, J., Taylor, B., Mohapatra, S., Irving, H., Baliunas, D., Patra, J., Roerecke, M., 2010. Alcohol as a risk factor for liver cirrhosis - a systematic review and meta-analysis. Drug Alcohol Rev 29, 437-445.

Roerecke, M., Rehm, J., 2010. Irregular heavy drinking occasions and risk of ischemic heart disease: a systematic review and meta-analysis. Am J Epidemiol 171, 633-644.

Roerecke, M., Rehm, J., 2012. The cardioprotective association of average alcohol consumption and ischaemic heart disease: a systematic review and meta-analysis. Addiction [Epub ahead of print].

Ronksley, P.E., Brien, S.E., Turner, B.J., Mukamal, K.J., Ghali, W.A., 2011. Association of alcohol consumption with selected cardiovascular disease outcomes: a systematic review and meta-analysis. BMJ 342, d671.

Rothman, K.J., Greenland, S., Lash, T.L., 2008. Modern Epidemiology, 3rd ed. Lippincott Williams & Wilkins, PA, USA.

19

Samokhvalov, A.V., Irving, H., Mohapatra, S., Rehm, J., 2010a. Alcohol consumption, unprovoked seizures and epilepsy: a systematic review and meta-analysis. Epilepsia 51, 1177-1184.

Samokhvalov, A.V., Irving, H.M., Rehm, J., 2010b. Alcohol as a risk factor for atrial fibrillation: a systematic review and meta-analysis. Eur J Cardiovasc Prev Rehabil 17, 706-712.

Samokhvalov, A.V., Irving, H.M., Rehm, J., 2010c. Alcohol consumption as a risk factor for pneumonia: systematic review and meta-analysis. Epidemiol Infect 138, 1789-1795.

Scafato, E., Allamani, A., Patussi, V., Codenotti, T., Marcomini, F., Struzzo, P., the Italian WHO Phase IV EIBI Working Group, 2005. Italy. in: Heather, N. (Ed.), WHO Collaborative project on identification and managment of alcohol-related problems in primary health care - Report on Phase IV World Health Organization - Department of Mental Health and Substance Abuse, Geneva, Switzerland, pp. 131-144.

Taylor, B., Irving, H.M., Baliunas, D., Roerecke, M., Patra, J., Mohapatra, S., Rehm, J., 2009. Alcohol and hypertension: gender differences in dose-response relationships determined through systematic review and meta-analysis. Addiction 104, 1981-1990.

Taylor, B., Irving, H.M., Kanteres, F., Room, R., Borges, G., Cherpitel, C., Greenfield, T., Rehm, J., 2010. The more you drink, the harder you fall: a systematic review and meta-analysis of how acute alcohol consumption and injury or collision risk increase together. Drug Alcohol Depend 110, 108-116.

Taylor, B., Shield, K., Rehm, J., 2011. Combining best evidence: A novel method to calculate the alcohol-attributable fraction and its variance for injury mortality. BMC Public Health 11, 265.

Uhl, A., Bachmayer, S., Kobrna, U., Puhm, A., Springer, A., Kopf, N., Beiglböck, W., Eisenbach-Stangl, I., Preinsperger, W., Musalek, M., 2009. Handbuch: Alkohol - Österreich: Zahlen, Daten, Fakten, Trends 2009. dritte überarbeitete und ergänzte Auflage. BMGFJ, Wein, Austria.

Üstün, T.B., Chatterji, S., Mechbal, A., Murray, C.J.L., WHS Collaborating Groups, 2003a. The world health surveys. in: Murray, C.J.L., Evans, D. (Eds.), Health systems performance assessment: debates, methods, and empiricism. WHO, Geneva, pp. 797-808.

Üstün, T.B., Chatterji, S., Villanueva, M., Bendib, L., Celik, C., Sadana, R., Valentine, N., Ortiz, J., Tandon, A., Saloman, J., Cao, Y., Xie-Wan, J., Ozaltin, E., Mathers, C.D., Murray, C.J.L., 2003b. The WHO multicountry household survey study on health and responsiveness 2000-2001. in: Murray, C.J.L., Evans, D. (Eds.), Health systems performance assessment: debates, methods and empiricism. WHO, Geneva, Switzerland.

Walter, S.D., 1976. The estimation and interpretation of attributable risk in health research. Biometrics 32, 829-849.

Walter, S.D., 1980. Prevention of multifactorial disease. Am J Epidemiol 112, 409-416.

World Health Organization, 2004. Global status report on alcohol and health. World Health Organization, Geneva.

20

World Health Organization Regional Office for Europe, 2010. European Status Report on Alcohol and Health 2010. World Health Organization Regional Office for Europe, Copenhagen.

21

Documents

Web-Appendix 1:Prevalence (%) and number of people in the ... · Web viewSlovakia 1.1 10.2 20,200 184,800 2000/2001 MCSS Slovenia 2.0 10.5 13,200 71,300 1999 GSRA (adjusted for screening