Europe. An analysis of changes in the consumption of alcoholic beverages: the interaction among consumption, related harms, contextual factors and alcoholic beverage control policies.

Substance Use & Misuse, 49:1692–1715, 2014 C 2014 Informa Healthcare USA, Inc. Copyright ISSN: 1082-6084 print / 1532-2491 online DOI: 10.3109/10826084.2014.925314

ORIGINAL ARTICLE

Europe. An Analysis of Changes in the Consumption of Alcoholic Beverages: The Interaction Among Consumption, Related Harms, Contextual Factors and Alcoholic Beverage Control Policies Allaman Allamani1 , Pasquale Pepe1 , Michela Baccini2 , Giulia Massini3 and Fabio Voller1 1 3

Agenzia Regionale di Sanità Toscana, Firenze, Italy; 2 Department of Statistics, Florence University, Firenze, Italy; Semeion, Roma, Italy correlated with consumption. Population ageing, older mother’s age at childbirths, increased income and increases in female employment, as well as drink driving limitations were associated with the decrease of transport mortality. Study’s limitations are noted.

This AMPHORA study’s aim was to investigate selected factors potentially affecting changes in consumption of alcoholic beverages in 12 European countries during the 1960s–2008 (an average increase in beer, decreases in wine and spirits, total alcohol drinking decrease). Both time series and artificial neural networks-based analyses were used. Results indicated that selected socio-demographic and economic factors showed an overall major impact on consumption changes; particularly urbanization, increased income, and older mothers’ age at their childbirths were significantly associated with consumption increase or decrease, depending on the country. Alcoholic beverage control policies showed an overall minor impact on consumption changes: among them, permissive availability measures were significantly associated with consumption increases, while drinking and driving limits and availability restrictions were correlated with consumption decreases, and alcohol taxation and prices of the alcoholic beverages were not significantly

Keywords Alcohol policies, socio-demographic and economic factors, time series analysis, artificial neural network analysis

INTRODUCTION

Europe is a continent with many languages and cultures that have developed through millennia until the present time. It’s 10,800,000 km2 , contains 45 countries, and has a population of approximately 800 million (Wikipedia, http://en.wikipedia.org/wiki/Europe). In the post-WWII period, there has been a great political integration of the European countries, with increasing coherence in the health and economic policies emanating from the European Union (EU) level of government and administration subsequent to WWII. The Economic

Acknowledgments: Thanks to Peter Anderson, Gerhard Gmel and Francesco Cipriani for their great support in the development of some of the ideas presented in this paper. Also, we want to express our gratitude to the late Eva Buiatti for her strong support and scientific commitment, and to the late Martin Plant for his dedication to this study at its planning stage. We are grateful to Deborah R. Gordon for her suggestions to the text. Also thanks to the AMPHORA 3 partners for their great co-operation in the AMPHORA study: Francesco Maccari, Karin Pantzer, Veronica Casotto, Regional Health Agency, Region of Tuscany, Florence, Italy Giulia Carreras, Department of Statistics, Florence University, Florence, Italy Massimo Paolo Buscema, Guido Maurelli, Semeion, Rome, Italy Peter Anderson, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands Gerhard Gmel, Béatrice Annaheim, Hervé Kuenig, Addiction Info Switzerland, Lausanne, Switzerland Antoni Gual, Silvia Matrai, Cristina Casajuana, Alcoholism Unit, Clinical Hospital Barcelona, Barcelona, Spain Juliette Guillemont, Carmen Kreft-Jais, Chloe Cogordan, INPES, Paris, France Zsuzsanna Elekes, Institute of Sociology and Social Policy, Corvinus University of Budapest, Budapest, Hungary Irmgard Eisenbach-Stangl, Gabriele Schmied, European Centre for Social Welfare Policy and Research, Vienna, Austria Ronald A. Knibbe, Derickx Mieke, Department of Health Promotion, Faculty of Health, Medical, and Life Sciences, University of Maastricht, Maastricht, the Netherlands Sturla Nordlund, Øystein Skjælaaen, Norwegian Institute for Alcohol & Drug Research, Oslo, Norway ¨ Börje Olsson, Jenny Cisneros Ornberg, Filip Roumeliotis, SORAD, Stockholm University, Stockholm, Sweden ¨ Esa Osterberg, Thomas Karlsson, Mikaela Lindeman, National Institute for Health and Welfare (THL), Helsinki, Finland Moira Plant, Nikki Coghill, Patrick Miller Alcohol & Health Research Unit, University of the West of England, Bristol, UK Grazyna Swiatkiewicz, Lukasz Wieczorek, Institute of Psychiatry and Neurology, Warsaw, Poland Address correspondence to Allaman Allamani, Via P. Toselli, 140, 50144 Firenze, Italia; E-mail: [email protected]

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EUROPE. AN ANALYSIS OF CHANGES IN THE CONSUMPTION OF ALCOHOLIC BEVERAGES

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FIGURE 1. Minimum spanning tree of connections among 12 European countries according to their consumption trends of wine, beer and spirits during 1962–2005 (revised, from AMPHORA 2013, Part 3, 6: Far and Near countries in Europe).

European Community (EEC), including six states, was created in 1957; EEC was transformed into the EU in 1992, and in 2013 consists of 28 countries. Big events1 have impacted the lives of populations at country, community and individual levels during this period. Among them, the world-wide young/cultural revolution in 1968; the oil crisis between 1973 and 1980; the USSR collapse, with dissolution of the Iron curtain in 1989; the fall of the Berlin Wall in 1989, a range of revolutions in East Europe, and the more recent massive immigration into the EU from Eastern Europe, Asia, Africa and South America.

1

Big events is a relatively new term, introduced into the intervention literature by Friedman et al. (Samuel R. Friedman, Diana Rossi, Peter L. Flom. (2006). “Big events” and networks: Thoughts on what could be going on. Connections 27(1): 9–14) refers to major events such as mega-disasters, natural, as well as man-made, famine, conflict, genocide, disparities in health, epidemics, mass migrations, economic recessions, etc. which effect adaptation, functioning and quality-of-life of individuals as well as systems. Existential threat, instability and chaos are major dimensions and loss of control over one’s life is experienced (Editor’s note)

The European study AMPHORA, work package 3, partially funded by the Commission for 2009–2012, was a large project involving 14 scientific institutions and 40 researchers. It included 12 countries, representative of north, west, east, central and south Europe (Allamani et al., 2011; AMPHORA, 2013). However, the AMPHORA Report, drawing on the Artificial Neural Network (ANN)-based analysis of country alcoholic beverage consumption trends during the decades 1960s–2000s, has divided the 12 countries into three main areas (AMPHORA, 2013, Part 3, 6: Far and Near countries in Europe). Figure 1 outlines a spatial division of nearby countries that turns out to correspond to diverse drinking cultures. They are grouped into northern countries: Finland, Norway, Sweden, with the addition of United Kingdom and Poland; central countries: Austria, Hungary, Netherlands, Switzerland; and southern countries: France, Italy, Spain. This classification was considered to be a useful and practical tool, so that both each country by itself and their grouping in these three main areas, have been taken into account in this paper for the analysis of data and for the discussion of results.

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Europe: Selected Socio-Demographic Factors, Drinking Patterns and Alcoholic Beverage Consumption-Related Harm

Several socio-demographic and economic factors, like an ageing population, urbanization, mother’s average age at all childbirths, female employment, female tertiary education, income, and price of alcoholic beverages not planned by the health authorities, are posited to be able to represent essential features of the European societies, both their similarities and diversities. They have also been related to the amount of consumption of alcoholic beverages and to drinking patterns by several authors (Sulkunen, 1989; Gual & Colom, 1997; Simpura, Karlsson, & Läppanen, 2002; Allamani & Beccaria, 2007; Rabinovich et al., 2011). Such factors are a main focus of the AMPHORA study and are described in this article. Table 1 summarizes

some quantitative aspects during the period 1960s–2000s (for further details, see Voller, Maccari, Pepe, & Allamani, 2014). This long time frame is appropriate for an adequate overview of changes in Europe as well as to highlight the varying international drinking trends investigated (Simpura et al., 2002). The European population has become older over time. Among the 12 study countries, males over 65, a reasonable indicator of the population’s ageing, have been on the rise, from about 9% of the total population in 1960s to 13.4% in the 2000s, with Southern Europe manifesting the greatest increase from 8.4% to 14.9%. Urbanization, that is the rate of residents living in larger cities, especially increased during the decades 1960s–1980s. Overall, urbanization rose from 60.8 per 100,000 population in 1960s to 73.4% in 2000s (World Bank, 2011a); this was more prominent

TABLE 1. Average decennial values (with standard deviation) of socio-demographic and economic variables in 12 European countries grouped in three macro-areas, during 1960–2009 (national/international sources)

North (FI NO SE PL UK)

Centre (AT CH HU NL)

1960-1969

1970-1979

1980-1989

1990-1999

Males>65

9.1 ±2.4

10.3 ±2.3

11.4 ±2.7

12.2 ±2.3

1999-2010 13.0 ±2

Urban

60.8 ±1.4

68.6 ±1.2

72.4 ±1.2

73.8 ±1.1

74.9 ±1.1

M.age at childbirths

27.4 ±1.2

26.8 ±0.3

27.7±0.5

28.3 ±0.9

28.3 ±0.8

Price 1st bev

91±3

93±11

106±31

95±8

89±13

Income

13,901±2311

16,661±5156

20,731±7157

24,599±9662

32,021±11275

Fem edu

1.3 ±0.8

2.6 ±1.1

4.2 ±1.8

6.9 ±2.7

10.34 ±3.6

Fem empl

39.2 ±0.6

37.6 ±4.5

42.4 ±4.8

41.0 ±3.9

42.5 ±5.1

Males> 65

9.2 ±0.9

10.4± 1.0

10.7 ±0.7

11.3 ±0.6

12.7 ±1.0

Urban

59.3 ±4.2

61.9 ±3

65.3 ±2.5

69.2 ±3.9

71.4 ±5.7


27.4 ±1.2

26.7±1.0

27.0 ±1.4

28.2±1.5

29.5 ±1.0

Price 1st bev

145±19

122±13

107±12

102 ±5

95 ±4

Income

16,515± 4196

19,238 ±6825

22,878 ±7333

26,501 ±9139

32,039 ±9674

Fem edu

1.2 ±1.2

2.1 ±1.0

3.6 ±1.9

6.1 ±2.3

8.5 ±2.5

Fem empl

33.3 ±1.5

31.400 ±2.6

34.874 ±3.6

39.318 ±6.6

42.431 ±6.4 14.9 ±1.2

South

Males> 65

8.4 ±0.8

9.7 ±0.9

10.6 ±0.7

12.925 ±1.0

(FR IT ES)

Urban

62.9 ±3.4

69.3 ±3.1

71.4 ±3.4

72.5 ±4.0

73.6±4.3


28.7 ±0.2

28.2 ±0.5

28.2 ±0.3

29.7 ±0.5

30.4 ±0.6

All 12 European countries

Price 1st bev

106 ±10

109 ±18

94 ±2

101 ±5

105 ±7

Income

10,848 ±2105

16,344 ±2204

20,141 ±2989

24,687 ±2545

29,149 ±1589

Fem edu

0.6 ±0.3

1.6 ±0.8

2.8 ±1.0

4.7 ±1.5

8.2 ±3.29

Fem empl

24.4 ±3.3

23.2 ±5.0

24.1 ±5.9

25.6 ±4.8

32.6 ±4.0

Males> 65

10.2 ±1.7

10.9 ±2.0

12.1 ±1.7

13.4 ±1.6

Urban

9.0 ±1.6 60.8 ±9.7

66.4 ±8.8

69.8 ±8.5

71.9 ±8.1

73.4 ±8.5


27.7 ±1.0

27.0 ±0.9

27.6 ±1.0

28.6 ±1.2

29.6 ±0.9

Price 1st bev

117 ±25

109 ±18

104 ±21

98 ±7

94 ±11

Income

13,769 ±3666

17,441 ±5389

21,299 ±6497

25,255 ±8256

31,309 ±9230

Fem edu

1.1 ±0.9

2.2 ±1.0

3.6 ±1.7

6.1 ±2.3

9.2 ±3.2

Fem empl

29.5 ±6.4

31.4 ±7.6

34.6 ±9.4

36.7 ±8.1

40.2 ±6.8

Males > 65 = % males >65 on male populaon (Naonal stascs) Urbanizaon = % residents in larger cies per total populaon (World Bank, 2011a) st M age 1 child at childbirths = mean mother’s age at all childbirths (EUROSTAT, 2012) Price of 1st preferred alcohol beverage = real price as index number (Naonal stascs) Income = purchase power parity converted gross domesc product pro capita (data in constant 2005 internaonal dollars) (World Bank, 2011b) Female educaon = % fem. compleng terary educaon on all females +15 years old (World Bank, 2011c) Female employment = % females employed per 100,000 female populaon (OECD, 2011).


in the northern European area, where it reached 74.9 in 2000s. Mothers’ average age at all their childbirths also increased, from 27.7 years in the 1960s to 29.6 years in the 2000s again with the Southern countries having the oldest age in the 2000s (EUROSTAT, 2012). Female employment (rate of 15–64-year-old employed females of the 15–64year-old female population), has been on the rise, from 29.5% in the 1960s to 40.2% in the 2000s (OECD, 2011). Also the female tertiary education (rate of females who completed tertiary education per 15 years and older on female population), which may adequately indicate the level of education attained in the country, has impressively increased from 1.1% in 1960 to 9.2% in 2009 (World Bank, 2011b). All these figures represent a remarkable change in the role of women all over Europe during the last five decades. Income has had a remarkable increase in the 12 countries, especially during the last decade. Overall, income has more than doubled from 13.769 per annum in the 1960s to 31.309 per annum during the 2000s, purchase power parity converted gross domestic product per capita (data in constant 2005 international dollars) (World Bank, 2011c). Prices for the first preferred alcoholic beverage (beverage differs according to countries), which are expressed in price index numbers, document an overall drop from 117 in 1960s to 94 during the 2000s, more evident in the central European area (AMPHORA, Part 1, the Study, Unplanned determinants of alcoholic beverage consumption change). Table 2 shows the trends of the consumption of wine, beer, spirits and total alcoholic beverage consumption in the three European macro-areas. There was a remarkable decrease of total alcoholic beverage consumption in the southern macro-area, from an average of 19.4 l of pure

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alcohol for inhabitants of 15 years and over during the period 1960–1969, to 10.4 in 2000–2009. On the other hand, a noticeable increase occurred in the northern macro-area during the same period (from 5.3 to 8.6 litres), while there was a moderate increase in the central macro-area (from 10.3 to 11.5). Overall, the 12 European countries were almost steady in terms of consumption of total alcoholic beverages (even if with a slight decrease from 10.4 to 10.0), showing a certain closure among the different countries. However, the consumption trends of each alcoholic beverage document a different picture for the same period. Wine consumption significantly dropped in the southern area (from 14.9 in 1960s to 6.05 l of pure alcohol for inhabitants of 15 years and older in 2000s), remarkably raised in the Northern macro-area (from 0.5 to 2.1), and slightly increased in the Central area (from 3.5 to 4.3); overall, the wine consumption decreased in the 12 European countries from 5.0 to 3.7. Beer consumption has raised everywhere, while spirits decreased in the Northern and Southern areas, and remained steady in the Central, with an overall slight decrease from 2.5 to 2.0. Such trend differences in the different countries may be explained by the supposition that in a globalized world, there is a tendency to decrease the consumption of traditional beverages and increase the consumption of novel beverages (Knibbe, Drop, & Hupkens, 1996). This opinion supports the relevance of the cultural and economic factors in alcoholic beverage consumption changes. Regarding mortality related to consumption of alcoholic beverages, there has been a decreasing trend in deaths from chronic liver disease and cirrhosis between 1970 and 2010, with a slight 3.0% drop from 16.17 in 1970s, to 15.59 in 2000s, all ages per 100,000 of

TABLE 2. Average decennial litres of pure alcohol (with standard deviation) of recorded alcohol consumption per inhabitant, 15 years and older, for 12 European countries and their grouping in three macro-areas, during 1960–2009 (World Health Organization, 2011)

North (FI NO SE PL UK) Wine Beer Spirits Tot Centre (AT CH HU NL) Wine Beer Spirits Tot South (FR IT ES) Wine Beer Spirits Tot All 12 European countries Wine Beer Spirits Tot

1960–1969

1970–1979

1980–1989

1990–1999

2000–2010

0.50 ± 0.2 2.72 ± 1.8 2.43 ± 1.1 5.32 ± 1.4

0.85 ± 0.3 3.82 ± 1.9 3.45 ± 1.6 7.55 ± 1.6

1.13 ± 0.3 3.64 ± 1.8 3.28 ± 1.6 7.66 ± 1.7

1.47 ± 0.4 4.07 ± 1.4 2.22 ± 1.1 7.54 ± 1.7

2.15 ± 0.7 4.07 ± 1.1 1.89 ± 0.7 8.63 ± 2.0

3.53 ± 1.8 3.93 ± 1.4 2.28 ± 0.4 10.27 ± 3.3

4.16 ± 1.6 5.01 ± 1.18 3.16 ± 1.13 12.97 ± 1.9

4.45 ± 1.5 5.58 ± 1.0 3.38 ± 1.8 13.29 ± 2.1

4.32 ± 1.2 5.23 ± 1.2 2.54 ± 1.1 11.86 ± 1.6

4.28 ± 0.9 4.78 ± 1.2 2.34 ± 1.0 11.54 ± 1.4

14.88 ± 3.7 1.62 ± 0.9 2.77 ± 0.7 19.39 ± 3.6

13.43 ± 2.4 2.29 ± 1.0 3.13 ± 0.5 19.08 ± 1.8

9.72 ± 2.2 2.64 ± 1.1 2.85 ± 0.9 15.63 ± 2.22

6.82 ± 1.8 2.68 ± 1.1 2.36 ± 1.0 12.04 ± 2.0

6.05 ± 1.76 2.62 ± 1.17 1.49 ± 1.04 10.39 ± 2.22

5.01 ± 6.1 2.86 ± 1.7 2.46 ± 0.8 10.41 ± 6.2

5.1 ± 5.2 3.83 ± 1.8 3.27 ± 1.2 12.24 ± 4.9

4.38 ± 3.7 4.04 ± 1.8 3.21 ± 1.5 11.53 ± 4.0

3.76 ± 2.4 4.11 ± 1.6 2.36 ± 1.1 10.11 ± 2.8

3.72 ± 1.9 3.99 ± 1.4 1.95 ± 0.9 9.98 ± 2.3

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TABLE 3. Average decennial values (with standard deviation) of chronic liver disease & cirrhosis and of transport accident deaths – both genders, all ages, per 100,000 population) in 12 European countries grouped in three macro-areas, during 1960–2009 (World Health Organization, 2010)

North (FI NO SE PL UK) Liver Transport Centre (AT CH HU NL) Liver Transport South (FR IT ES) Liver Transport All 12 European countries Liver Transport

1970–1979

1980–1989

1990–1999

2000–2010

7.27 ± 3.58 —

7.66 ± 2.77 13.69 ± 4.27

8.06 ± 3.19 11.27 ± 5.56

10.13 ± 5.18 8 ± 3.8

16.43 ± 8.94 17.69 ± 4.77

20.85 ± 13.95 17.65 ± 5.27

26.09 ± 25.2 13.22 ± 5.59

19.53 ± 18.24 8.63 ± 3.7

30.69 ± 4.03 20.09 ± 0.74

24.77 ± 4.13 17.37 ± 2.07

16.78 ± 2.52 14.65 ± 1.82

10.83 ± 1.72 10.27 ± 2.42

16.17 ± 11.04 17.64 ± 3.88

16.33 ± 11.29 16.32 ± 4.42

16.39 ± 16.67 12.77 ± 5.03

13.44 ± 11.89 8.71 ± 3.59

general population (World Health Organization, 2010). This decrease is the combination of a large 37.7% mortality reduction in the south (shared by France, Italy and Spain) and a notable 14.7% increase in the north area (especially due to Finland, UK and Poland), while the 20.7% increase in the central area is mainly due to the increase in Hungarian liver disease-related deaths. The road traffic death data, the other indicator of alcoholic beverage consumption-related harm, showed a decrease in the three macro-areas; overall, they had a 30.0% drop from 17.64 deaths in 1970s to 12.31 deaths in 2000s, all ages per 100,000 general population (World Health Organization, 2010). Considering the transport accident mortality, one should also take into account the improvement in the safety of motor vehicles and of roads as an important factor in reducing traffic deaths (Table 3). Prevention: Alcoholic Beverage Control Policies

Alcoholic beverage control policies designed for prevention interventions may be usefully grouped in the following categories: minimum age to purchase alcoholic beverages, availability-accessibility sites and sales (spaceplace and time; licenses to sell alcoholic beverages), advertising, drinking and driving, excise and VAT taxes, and country-wide prevention/treatment programs. For many years the alcoholic beverage control policies that aim to control and reduce the consumption of alcoholic beverages and drinking-related harms have been applied and evaluated (Babor et al., 2003, 2010; Anderson, 2009; Rabinovich et al., 2009; Patra, Giesbrecht, Rehm, Bekmuradov, & Popova, 2012; Anderson & Møller, 2012; Treno, Marzell, Gruenenwald, & Holder, 2014). However, although they are often shown to be effective in well-defined experimental contexts of observation, needed generalizable evidence is frequently missing at the macro country level. Among the latter, the introduction of permissive policy measures in 1969 in Finland, such as the lowering of the off-premise age limit from age 21 to 20 years for buying alcoholic beverages with higher levels of alcohol, and from age 21 to age 18 for buying milder alcoholic beverages, as well as the right for grocery stores

and cafés to sell medium beers caused2 a consumption ¨ jump of 46% (Lindeman, Osterberg, & Karlsson, 2014). Further, the introduction of restrictive policies in Slovenia (a 2003 law of minimum drinking age of 18 for purchasing alcohol, as well as hour limitations of off and on premises sales of alcoholic beverages) and in Russia (a 2006 regulation on alcoholic beverage production and the ban of sales in places of large public gatherings of alcoholic beverages with more than 15% ethanol) was associated with a reduction in male suicides in their respective countries by 10% and 9% (Pridemore & Snowden, 2009; Pridemore, Chamlin, & Andreev, 2013). On the other hand, after the abolition of travellers’ allowances for alcoholic beverage imports into Denmark, Sweden, and Finland in 2004, and the lowering of excise taxes on alcoholic beverages in 2003–2005 in Denmark and in 2004 in Finland, the prevalence of alcoholic beverage consumptionrelated problems mainly decreased over the study period (Bloomfield, Wicki, Gustafsson, Mäkelä, & Room, 2010). Moreover, doubts have been repeatedly raised about the effectiveness of both prices and taxation on all types of beverages and on all types of drinkers (among others see Mohler-Kuo, Rehm, Heeb, & Gmel, 2004; Nelson, 2013). The enforcement of measures and their interplay with informal control may greatly vary according to both countries and cultures, making it difficult to attempts to measure and compare the effects of similar interventions in ¨ different areas (Karlsson, Lindeman, & Osterberg, 2012). For example, it is a commonplace that the level of law enforcement in Mediterranean countries is lower than in Scandinavian countries. Further, prevention policies targeted to alcoholic beverages may make sense in cultures and periods of time where “alcohol,” its consumption and its tagged drinkers are perceived as being a social and health problem, while they are less understood in societies 2

The reader is referred to Hiil’s criteria for causation that were developed in order to help assist researchers and clinicians determine if risk factors were causes of a particular disease or outcomes or merely associated. (Hill, A. B. (1965). The environment and disease: associations or causation? Proceedings of the Royal Society of Medicine 58: 295—300. (Editor’s note.).


where “alcohol” receives less public attention (Nordlund, 2012; Room 1999). Another aspect that is usually not taken into account is what type of alcoholic beverage is targeted by a consumption prevention policy. Usually researchers, and possibly policy makers, have typically focused on total alcoholic beverage consumption, while some studies (Knibbe et al., 1996) imply that the country’s traditional alcoholic beverage would be the intervention target. In general, according to some authors, “there is no single optimal set of measures that would work in any country, but the final mix of measures must be determined in each country separately, while the possible joint policies cannot be introduced in a top-down fashion, but sufficient space must be left for national initiatives” (Norström, 2002, pp. 221–225).3 The 12 AMPHORA European countries produced a different number of control policy measures during the period 1960–2008, from less than 20 in Hungary and in Sweden, to more than 100 in Finland. However, the number of the main country policies that were selected for the purpose of this study’s analysis was 6 (7 in Sweden) (Voller et al., 2014). Aims and Methods

Aims The work package 3 of the AMPHORA project focused on the following questions and issues: (1) the extent to which selected demographic and economic changes are most strongly associated with the changes in alcoholic beverage consumption and types of harms; (2) the extent to which alcoholic beverage control policy measures influence alcoholic beverage consumption and alcoholic beverage consumption-related harms, 3

The reader is asked to consider that a range of necessary processes associated with a person’s active involvement, or non-involvement in any of his/her many intoxicating-satiating appetites and behaviors are rarely, if ever, considered by the relevant stakeholders as they plan, implement and assess a range of “appetite”/behavioral control policies (regulations, laws, edicts, traditions, etc.). These processes include a person’s awareness, perceptions, expectations, judgments, decision-making which is or is not implemented, learning or not learning from his/her experience, etc., and are bounded (temporal period, place, culture, traditions, age, gender, ethnicity, religiosity, SES, etc. The influencing processes’ actions can be known, unknown and unknowable, visible and hidden, measureable and unmeasurable. With the advent of complexity, chaos and uncertainty theories it is reasonable to consider that man’s “doing,” as well as “not doing” something, is usefully considered to be the outcome of complex, non-linear, dynamic, multi-dimensional processes and factors which are evidence-informed. Alcoholic beverage control policies have not and are not based upon these considerations. The following references may be of interest to the reader: The reader is referred to Tilly, Charles (2006). Why. Princeton Univ. Press. Princeton, NJ for a stimulating analysis about generic “causative” reasons given in the West; to Tilly, Charles (2008). Credit and Blame Princeton Univ. Press. Princeton, NJ for an important analysis about “blame” and “credit”; Ormerod, Paul, (2005), Why most things fail: Evolution, extinction and economics. Faber & Faber, UK; and to Miller, Matt, (2010). The tyranny of dead ideas; New York: Henry Holt & Co. (Editor’s note).

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considering the effect of socio-demographic and economic factors on alcohol consumption; and (3) how selected policies and socio-demographic /economic factors affect alcohol consumption -related deaths. The study has produced a series of country outcome reports, based upon the last five decades, that have been described elsewhere (AMPHORA, 2013, Part 2; Allamani, Voller, et al., 2014; Cogordan, Guillemont, & Kreft-Jais, 2014; Eisenbach-Stangl, 2014; Elekes, 2014; Gual & Matrai, 2014; Knibbe, Derickx, Allamani, & Massini, 2014; Lindeman et al., 2014; Plant, Allamani, Pepe, & Massini, ´ atkiewicz, Wieczorek, & Allamani, 2014). 2014; Swi This paper is based, mainly, on a second level analysis of the AMPHORA data that were obtained for each country. Its aim was to compare and combine the country results of the associations among selected alcoholic beverage control policies measures and sociodemographic/economic factors, and alcoholic beverage consumption and alcohol consumption-related harms. Variables

Alcoholic Beverage Consumption and Detrimental Consequences Country figures about consumption of alcoholic beverages (beer, wine, spirits and total alcohol) were drawn from World Health Organization GISAH series (World Health Organization, 2009; AMPHORA Final Report, 2013, Part 2, Country Reports). These data series have good time coverage between 1961 and 2007, i.e. throughout the study period, and are comparable among countries. However on some occasions the value of total alcoholic beverage consumption differs from the sum of consumptions of the three different types of beverages, up to about 1 litre of pure alcohol per capita per year. Also, GISAH figures do not include unrecorded consumption data that are estimated to be a significant part of the overall alcoholic beverage consumption especially in Finland, Poland, and Sweden. Mortality data for liver disease and liver cirrhosis (or, simply, liver disease), and for transport accidents, which are traditionally considered and consensualized to be indicators of chronic and acute consequences of alcoholic beverage consumption respectively (Edwards et al., 1994), were drawn from WHO’s “Health For All” Database (HFA DB) that standardised annual death rates for both genders and for all ages per 100,000 inhabitants (World Health Organization, 2010). A limitation of these series is that liver disease mortality data is only available, at best, from the 1970s, while transport accident death data start at earliest in 1979, or even later, with some missing data for some countries. Selected Socio-Demographic and Economic Factors Seven variables, with good data coverage, were selected for the analysis of all of the 12 countries. They are indicators of (a) economic changes: income; prices of the first preferred alcoholic beverage in each country, (b)

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socio-demographic changes: proportion of males over 65 years, proportion of people living in larger cities (urbanization), and three indicators related to women: average age of mothers at all their child births; proportion of working women; rate of women’s tertiary education (Voller, Maccari, Pepe, & Allamani, 2014). Data were collected from national sources for rates of people over age 65 and prices; from World Bank for urbanization, income and female education (World Bank, 2011a–c); from EUROSTAT for the mean age of women at all their childbirths (EUROSTAT, 2012); and from the International Organisation for Economic Co-operation and Development (OECD) for female employment (OECD, 2011).

justed for the “core model” variables. Also, the estimates of each policy measure were adjusted by both the “core model” and the “main” variables. Data are presented through forest plots, where xvalues represent % variation in total alcohol consumption when there is a 1% increase change for each sociodemographic/economic variable, and % variation in total alcohol consumption after the introduction of a policy measure. In cases in which the policy measure was introduced in 1960, as were the first of the two restrictive availability measures in Poland, it was not considered, since its possible effect on change in alcohol consumption, whose series begins with 1961, could not be estimated.

Alcoholic Beverage Consumption Prevention Control Policy Measures The list of alcoholic beverage control policy measures collected by each country partner over the period 1960–2008, varied remarkably according to the countries. A smaller number of the main policy measures, consensually set at 6 (7 in the case of Sweden), were considered appropriate to better analyse the effect of policies within each country and across the 12 countries (AMPHORA 2013, Part 1; Baccini & Carreras, 2014). They were restrictive and permissive, and they were distributed differently over time according to the countries (Voller et al., 2014). The category of taxes was excluded in the Time series analysis (TS), since they were considered to be highly correlated with prices, which in turn had been taken into account as part of the contextual economic indicators. However, taxes were part of the ANN analysis.

PSC PSC measures the association between two variables, while adjusting for the effect of a set of other variables (Baba, Ritei, & Masaaki, 2004). Here, PSC was used to quantify the overall conditional effect of sociodemographic/economic (unplanned) variables, taken as a whole, and of the policies, taken as a whole, on total alcoholic beverage consumption for each country. Also, it allowed to overcome the multi-colinearity problems that may occur through TS of data. The PSC coefficients between total consumption (C) and socio-demographic and economic, also defined as unplanned, variables (U)—given the policies (P) and the time trend (T)—can be interpreted as the portion of variance in alcoholic beverage consumption explained by unplanned variables and not explained by the policy measures and the time trend (1). On the other hand the PSC coefficients between total consumption (C) and policies (P)—given the unplanned variables (U), and time trend (T)—can be interpreted as the portion of variance in alcoholic beverage consumption explained by policy measures and not explained by unplanned variables and the time trend (2).

METHODS

TS, partial square correlations (PSC) and ANN analysis have been used in this data analysis. TS

Due to some incompleteness of the aforementioned seven socio-demographic and economic data during the study period, a multiple imputation of missing values was performed by Multivariate Imputation by Chained Equations (Baccini & Carreras, 2014). The aforementioned seven selected economic and socio-demographic factors were considered: income, prices of alcoholic beverages, males over age 65 (“core model” variables), female educational level, female employment, mother’s age at all childbirths, and urban level (“main” variables). Results obtained from the country specific Time Series (see AMPHORA, 2013, Part 2, Country Reports) were combined in a random effect meta-analysis (DerSimonian & Laird, 1986) in order to have an overview of the differences and similarities of the influences of economic and socio-demographic factors, as well as policy measures, on alcoholic beverage consumption across Europe. Specifically, the estimates of the “core model” variables were adjusted by the most representative among the “main” variables for each country. Moreover, the estimates of the “main” socio-demographic/economic variables were ad-

(1) PSC (C,U|P, T) = (SSEC∼PT − SSE C∼UPT )/SSE C∼PT (2) PSC (C,P|U, T) = (SSEC∼UT − SSEC∼UPT) /SSE C∼UT SSEC∼PT , and SSEC∼UT are error sum of squares of the TS between total consumption and policies, and between total consumption and unplanned variables, respectively; and SSEC∼ UPT is the error sum of squares of the TS between total consumption and both unplanned factors and policies. The models used considered the socio-demographic economic (unplanned) factors as being the best variable selected for each country on the basis of the “minimum Akaike Information Criteria” (that is a measure of the relative quality of a statistical model for a given set of data), as well as those variables in the country analysis that had been included in the core model (i.e. males >65 years, income, prices or the two most preferred alcoholic beverage for each country, time) (see Baccini & Carreras, 2014). The time trend was added to capture the long-term behaviour in drinking that could be related to unobservable factors.


Different from the models used in country report TS, here all of the 6 control policy measures (7 for Sweden) selected for each country were taken into account as a whole, not one at a time (Baccini & Carreras, 2014), in order to observe their overall impact on alcoholic beverage consumption. Regarding harm, an overview of the 12 European countries’ PSC coefficients between harms and policies—given socio-demographic and economic (unplanned) variables, consumption and time trend—, as well as between harms and unplanned variables—given policies, consumption and the time trend—, is presented for each country. The analyses were conducted in a similar way as described above for consumption, but using rates of both liver disease and transport accident deaths as dependent variables, while consumption was considered to be an independent variable. Note that PSC analysis can only indicate the dimension of change, but cannot indicate the direction of the effect, whether there is an increase or decrease of consumption. A revision of data already presented in AMPHORA report (AMPHORA 2013), and in AMPHORA e-book (Allamani et al., 2012) elicited updated results that are presented in the Results section.

ANN-Based Analysis A relatively new method based on an ANN architecture, i.e. the Auto Contractive Maps (AutoCMs) that spatialize the correlation among variables, was also used in this study (Buscema, Massini, & Maurelli, 2014). The connection between any couple of variables, which also contemporarily take into account all of the other connections between all of the variables, has a connection weight, which can be transformed into values between 0 (minimum strength) and 1 (maximum strength). The strength of the connection drops gradually in relation to its value. In this analysis the connections between 0.90 and 0.99, which are the strongest, were usually considered. A Minimum Spanning Tree (MST) graph was calculated in order to maximize the information contained in the matrix of relationships between the variables. The highest-value relationships, or connections (lines in the graph) between the variables (= nodes in the graph) are expressed in visually transparent notions of closeness and distance. In addition to graphs, a connecting value table is also used to describe the interactions among the different types of variables. ANN-based analysis used all of the variables that were analysed though the TS, but 1 imputed dataset instead of 5 was used. The total consumption of alcohol was split into wine, beer and spirits. Only the price of wine was investigated because of some country missing data from the other beverages; all of the 6 or 7 selected policy measures, including taxes, were taken into account. Alcoholic beverage related liver disease and transport accident deaths were analysed in connection with consumption, policy measures, and the study’s selected socio-demographic/economic factors.

1699

ANN analysis does not take the temporal trend into account, assuming that there are no unobservable factors related to the long-term trend in consumption. RESULTS Selected Socio-Economic and Demographic Factors and Consumption of Alcoholic Beverages-TS

The meta-analysis of the country results indicates that in Europe some socio-demographic and economic variables, namely the growing urbanization, maternal age at childbirths, and income, were significantly associated with the change in alcoholic beverage consumption (Figure 2). Overall, a 1% increase in urbanization was significantly associated with a 2.63% increase of total alcoholic beverage consumption. However, looking at countries separately, the increased urbanization was also associated with a consumption increase in those countries (e.g. Italy and Spain) where consumption decreased during the study period. The rise in maternal age at childbirths was significantly associated with total alcoholic beverage consumption: overall, for every delayed year in a mother age at childbirth, alcoholic beverage consumption increased by 1.78%. However, looking at countries separately, increasing maternal age at childbirths was also associated with decreases in drinking reduction in those countries (Hungary, the Netherlands, Norway and Sweden) where consumption has in fact increased over the study period. The increase in total income was significantly associated with the increase of total alcoholic beverage consumption: for every 1% increase of income, alcoholic beverage consumption increased by 0.41%. This metaanalysis found no association among total consumption and women’s employment and education, the ageing population, and the prices of the country’s most popular beverages. In general, there was a great variability among the estimates of the effects of the study’s selected sociodemographic and economic factors on consumption in AMPHORA’s 12 different countries, with the I-squared (a measure of variability among countries) being between 80% and 97.45%. Alcoholic Beverage Control Policies Measures and Consumption of Alcoholic Beverages

The meta-analysis of the country results indicates that in Europe the introduction of permissive measures, which increased the availability of alcoholic beverages and the first introduction of blood alcohol concentration (or BAC) limits when driving, were significantly correlated with the change in consumption (Figure 3). The introduction of the availability permissive measures was associated with a 5.95% increase in total alcoholic beverage consumption overall. At the level of single countries, the increase occurred for Poland, Switzerland, UK, and it is especially impressive (42%) for Finland. However, at the level of the countries as a whole, such association disappeared when another permissive measure was introduced a second time. On

1700

A. ALLAMANI ET AL.

Mother age at all childbirths

Female terary educaon

COUNTRY

COEFF (90% CI)

AT FI FR HU IT NL NO PL ES SE CH UK Overall (I-squared = 93.5%)

1.73 (0.38, 3.08) -0.65 (-3.24, 1.94) -3.52 (-4.75, -2.29) -3.38 (-3.97, -2.79) -4.01 (-4.52, -3.50) -1.97 (-2.46, -1.48) -3.17 (-3.61, -2.73) 1.63 (0.62, 2.64) -2.20 (-4.55, 0.15) -4.35 (-7.09, -1.61) -1.12 (-1.52, -0.72) -0.57 (-1.11, -0.03) -1.78 (-2.60, -0.96)

-7

0

COUNTRY

COEFF (90% CI)

FI FR HU IT NL NO PL ES SE CH UK Overall (I-squared = 91.2%)

-0.33 (-0.47, -0.19) 0.04 (-0.02, 0.10) 0.22 (0.12, 0.32) 0.10 (0.06, 0.14) 0.02 (-1.07, 1.11) -0.98 (-1.20, -0.76) 0.19 (0.14, 0.24) 0.17 (-0.01, 0.35) 0.07 (0.01, 0.13) -0.01 (-0.08, 0.06) 0.02 (-0.05, 0.09) -0.01 (-0.09, 0.07)

7

-1.2

Female employment COEFF (90% CI)


0.53 (0.06, 1.00) 0.77 (0.39, 1.15) 2.46 (2.02, 2.90) 0.48 (-0.02, 0.98) 0.11 (-0.11, 0.33) -0.02 (-0.26, 0.22) 0.76 (0.49, 1.03) -0.29 (-0.52, -0.06) -0.11 (-0.45, 0.23) -0.64 (-0.82, -0.46) -0.13 (-0.40, 0.14) -0.24 (-0.72, 0.24) 0.29 (-0.04, 0.61)

0

1.2

Urbanizaon

COUNTRY

-3

0

COUNTRY

COEFF (90% CI)


23.60 (19.22, 27.98) 1.23 (0.91, 1.55) 0.21 (-0.65, 1.07) 4.51 (3.60, 5.42) 6.96 (6.31, 7.61) -3.07 (-4.09, -2.05) 1.22 (1.00, 1.44) 5.67 (2.02, 9.32) 3.14 (1.13, 5.15) 3.15 (2.41, 3.89) 0.75 (0.58, 0.92) 0.43 (-0.49, 1.35) 2.63 (1.73, 3.53)

-28

3

Males>65

0

28

Income

COUNTRY

COEFF (90% CI)

COUNTRY

COEFF (90% CI)


0.55 (0.29, 0.81) 1.82 (0.75, 2.90) -0.06 (-0.36, 0.24) 0.45 (0.26, 0.64) 0.22 (-0.19, 0.64) -3.31 (-4.81, -1.81) -1.19 (-1.52, -0.86) -0.32 (-0.61, -0.03) 1.96 (0.69, 3.22) 0.37 (-0.14, 0.87) 0.47 (0.13, 0.82) 1.52 (0.84, 2.20) 0.23 (-0.13, 0.58)


0.60 (0.37, 0.83) 0.43 (-0.00, 0.87) -0.78 (-1.03, -0.54) -0.23 (-0.40, -0.05) -0.41 (-0.80, -0.01) 1.81 (1.48, 2.15) 0.99 (0.62, 1.37) 0.40 (0.13, 0.66) 0.39 (-0.17, 0.94) 0.85 (0.56, 1.14) 0.56 (0.35, 0.77) 0.28 (-0.30, 0.86) 0.41 (0.07, 0.74)

-5

0

-2

5

Price 1st preferred beverage COUNTRY

COEFF (90% CI)

AT FI FR HU IT NL NO PL ES SE CH Overall (I-squared = 80.0%)

0.12 (-0.11, 0.35) 0.03 (-0.31, 0.36) 0.14 (-0.16, 0.44) 0.07 (-0.08, 0.22) 0.50 (0.24, 0.76) -0.12 (-0.24, -0.01) -0.17 (-0.39, 0.05) -0.28 (-0.45, -0.10) -0.09 (-0.22, 0.04) -0.57 (-0.74, -0.40) 0.15 (-0.09, 0.39) -0.04 (-0.17, 0.09)

-0.8

0

0.8

0

2

Price 2nd preferred beverage

COUNTRY

COEFF (90% CI)

AT

-0.13 (-0.32, 0.07)

FI

0.41 (0.13, 0.68)

FR

-0.24 (-0.54, 0.06)

IT

0.84 (0.40, 1.28)

NL

-0.37 (-0.52, -0.21)

NO

-0.52 (-0.73, -0.30)

SE

0.05 (-0.12, 0.22)

CH

0.13 (-0.11, 0.36)

Overall (I-squared = 84.7%)

-0.02 (-0.22, 0.18)

-1.3

0

1.3

FIGURE 2. Forest plots reporting country time series coefficients and their meta-analytic estimates of Mother’s age at childbirths, Female education, Female employment, Urbanization, M > 65, income, Price of 1st and 2nd country preferred beverage for 12 European countries (x-values = % variation in total alcohol consumption if there is a 1% increase for each variable).

1701


Minimum age

Adversing

COUNTRY

COEFF (90% CI)

AT

19.00 (15.00, 23.00)

FR

-18.00 (-21.00, -15.00)

ES

-14.00 (-19.00, -9.00)

CH

-12.00 (-14.00, -10.00)

Overall(I-squared = 98.2%)

-6.29 (-18.26, 5.68)

-23

0

COUNTRY

COEFF (90% CI)

AT

10.00 (4.00, 16.00)

FR

-11.00 (-16.00, -6.00)

IT

-2.00 (-6.00, 2.00)

NO

2.00 (-1.00, 5.00)

ES

-12.00 (-17.00, -7.00)

Overall(I-squared = 88.6%)

-2.65 (-8.49, 3.19)

23

-17

Availability permissive 1st

0

17

Availability permissive 2nd

COUNTRY

COEFF (90% CI)

FI

42.00 (31.00, 53.00)

HU

-2.00 (-6.00, 2.00)

NL

-1.00 (-5.00, 3.00)

NO

1.00 (-2.00, 4.00)

COUNTRY

COEFF (90% CI)

FI

-9.00 (-21.00, 3.00)

NO

8.00 (5.00, 11.00)

PL

-18.00 (-25.00, -11.00)

PL

13.00 (8.00, 18.00)

SE

-5.00 (-11.00, 1.00)

SE

12.00 (8.00, 16.00)

CH

7.00 (3.00, 11.00)

UK

9.00 (5.00, 13.00)

UK

6.00 (2.00, 10.00)


1.68 (-5.50, 8.85)

5.95 (1.25, 10.64)


-53

0

-25

53

st

Availability restricve 1

COUNTRY

COEFF (90% CI)

HU

2.00 (-2.00, 6.00)

IT NL PL

5.00 (1.00, 9.00)

ES

-12.00 (-17.00, -7.00)

0

25

Availability restricve 2

nd

COUNTRY

COEFF (90% CI)

3.00 (-1.00, 7.00)

IT

-2.00 (-6.00, 2.00)

4.00 (-2.58, 10.58)

NL

-5.00 (-8.00, -2.00)

ES

-7.00 (-10.04, -3.96) -11.00 (-15.00, -7.00)

SE

-7.00 (-14.00, -0.00)

SE

CH

13.00 (9.00, 17.00)

UK

7.00 (3.00, 11.00)

UK

7.00 (3.00, 11.00)


-3.67 (-8.18, 0.85)


2.16 (-2.18, 6.50)

-17

0

-15

17

Drink driving BAC 1st (establishment)

0

15

Drink driving BAC 2nd

COUNTRY

COEFF (90% CI)

AT

-17.00 (-20.00, -14.00)

FR

-11.00 (-16.00, -6.00)

4.00 (-1.00, 9.00)

IT

-2.00 (-6.00, 2.00)

IT

3.00 (-2.00, 8.00)

NL

3.00 (-1.00, 7.00)

NL

12.00 (9.00, 15.00)

ES

14.00 (8.00, 20.00)

ES

2.00 (-4.00, 8.00)

SE

-7.00 (-10.00, -4.00)

UK

3.00 (-2.00, 8.00)

CH

-13.00 (-18.00, -8.00)

UK

3.00 (-1.00, 7.00)


6.76 (3.03, 10.49)


-3.91 (-9.40, 1.58)

COUNTRY

ES (90% CI)

FR

14.00 (10.00, 18.00)

HU

-18

0

18

-20

0

20

FIGURE 3. Forest plots reporting country time series coefficients and their meta-analytic estimates of Minimum age, Advertising, Availability permissive and restrictive 1st and 2nd measures, Drink driving 1st and 2nd measures, for 12 European countries (x-values = % variation in total alcohol consumption after the introduction of measure).

0.74 0.92 0.80 0.74 0.91 0.95 0.84 0.83 0.33 0.75 0.82 0.46 0.79 0.85 0.86 0.73 0.30 0.58 0.43 0.34 0.88 0.89 0.66 0.89 0.11 0.35 0.56 0.18 0.91 0.86 0.65 0.89 P = permissive, R = restrictive. Bold numbers indicate the strongest connections.

0.19 0.43 0.30 0.25 0.86 0.59 0.68 0.68 0.81 0.85 0.85 0.82 0.88 0.95 0.85 0.84 0.87 0.97 0.85 0.85 0.88 0.97 0.88 0.87 0.80 0.96 0.86 0.75 0.89 0.97 0.87 0.84 Wine decrease Beer increase Spirits decrease Total decrease

Drunk driving Availability (P) Availability (R) Minim age (P) Minim age (R) Advertising (P) Advertising (R) Taxes (P) Taxes (R) Price wine Income Urban Mother age

The ANN based analysis done on the 12 AMPHORA European countries together, considering their overall consumption trends over the study period (i.e. beer increase, wine and spirit decrease) documented that the overall increase of beer consumption was rather strongly related (0.96–0.97) to the increase in rate of males aged over 65, older age of mothers at childbirth, and urbanization. Also the increase of female employment and income, as well as the introduction of limitations of blood alcohol concentration (BAC) when driving, were significantly associated with the rise in the consumption of beer (Table 4). The reduction in wine was weakly connected with BAC measures, and advertising restrictions, and even more weakly with the introduction of taxes, and with most socio-demographic and economic variables. The reduction in spirits consumption showed weak or no connection with the independent variables. Looking at the three macro-areas where the 12 countries have been grouped, in each area beer consumption shows an increase, that is more or less strongly connected (connection values 0.95–0.98) with the increase of urbanization, age of mothers at childbirths, rates of males over 65 years, and income (Table 5). Increased beer drinking had no substantial connections with planned control policy measures, except with the drinking and driving limi-

Fem empl

Selected Socio-Economic and Demographic Factors, Policy Measures and Consumption of Alcoholic Beverages. ANN Analysis

M>65

the other hand, the introduction of BAC limits was significantly, positively, correlated with the increase in the total alcoholic beverage consumption. However, a second introduction of BAC limit, reducing the BAC limit previously introduced, showed an association, although not statistically significant, with a 3.91% reduction of total consumption. At the individual country level, this negative association holds true for Austria, Switzerland, France and Sweden, and not significantly for Italy. While the first introduction of an alcoholic beverage availability restrictive policy had no overall significant correlation with total consumption, the next introduction of another availability restriction measure appeared to be associated, even if not significantly, with the 3.87% reduction of total consumption. At the level of single countries, this holds true for Sweden, Spain and Netherlands. Policies related to minimum age to purchase alcoholic beverages, and advertising did not show significant associations in the European countries as a whole, although this may not be the case at the level of individual countries. Minimum age was significantly associated with a decrease in alcoholic beverage consumption in France, Spain and Switzerland and advertising limitation measures were significantly associated with a decrease in alcoholic beverage consumption in France and Spain. In general, there was a great variability among the estimates of the effects of control policy measures on consumption in different countries, with the I-squared (a measure of variability among countries) being between 75.85% and 98.2%.

Prev & Treat

A. ALLAMANI ET AL.

TABLE 4. Alcoholic beverages, socio-demographic and economic factors, and selected alcohol policies in 12 European countries altogether, 1962–2005. ANN Connection values

1702

1703 0.95 0.89 0.49 0.90 0.98 0.95 0.91 0.93 0.90 0.92 0.94 0.82 P = permissive, R = restrictive. Bold numbers indicate the strongest connections.

0.97 0.95 0.93 0.93 Wine decrease Beer increase Spirits decrease Total decrease

0.98 0.94 0.88 0.91

0.96 0.96 0.93 0.94

0.98 0.98 0.88 0.96

0.98 0.97 0.92 0.94

0.68 0.81 0.80 0.71

0.91 0.76 0.79 0.76

0 0 0 0

0 0 0 0 Southern Area (France Italy Spain) 0.97 0 0.96 0.96 0 0.96 0.84 0 0.84 0.95 0 0.96

0 0 0 0

0.95 0.97 0.82 0.95 0.93 0.97 0.82 0.95 0.82 0.72 0.58 0.77 0.98 0.97 0.83 0.97 Wine increase Beer increase Spirits increase Total increase

0.97 0.95 0.86 0.97

0.97 0.96 0.75 0.96

0.97 0.97 0.84 0.97

0.97 0.97 0.70 0.97

0.86 0.85 0.77 0.86

0.80 0.78 0.88 0.82

Central Area (Austria Hungary Switzerland Netherlands) 0.57 0.84 0.6 0.91 0 0.26 0.92 0.52 0.94 0 0.27 0.63 0.38 0 0.14 0.43 0.84 0.60 0.91 0

0.95 0.92 0.77 0.96

0.68 0.63 0.58 0.54 0.86 0.87 0.55 0.83 0.91 0.85 0.89 0.89 0.91 0.85 0.83 0.90

Drunk driving Availability (P) Availability (R) Minim age (P) Minim age (R) Advertising (P) Advertising (R) Taxes (P)

Northern Area (Finland Norway Poland Sweden UK) 0.62 0 0 0 0.76 0.62 0 0 0 0.81 0.39 0 0 0 0.55 0.68 0 0 0 0.85 0.68 0.61 0.59 0.52 0.91 0.94 0.82 0.96 0.94 0.94 0.81 0.93 0.93 0.96 0.82 0.97 0.95 0.96 0.86 0.97 0.93 0.96 0.84 0.96 0.93 0.96 0.83 0.96 Wine increase Beer increase Spirits decrease Total increase

Considering the 12 European countries altogether over the period 1979–2005, the ANN-based analysis showed that the decreasing trend in transport accident mortality was strongly associated (connection value 0.96–0.97) with an ageing population, older maternal age, increased female employment, and increased income as well as with the introduction of drinking and driving measures—but also with the consumption increase of both beer and total alcohol (Table 6). Looking at the three macro-areas where the 12 countries have been grouped, and considering their different trends regarding transport deaths, connections were similar to those of all of the countries together, but in the central European area preventive measures were strongly connected (0.98) with decreased mortality (Table 7). Considering all of the 12 European countries together over the period 1979–2005, the ANN-based analysis shows that liver mortality decreases had no substantial connection with any variable (Table 6). Looking at the grouping of the 12 countries into the three macro-areas, and considering their different liver mortality trends, the decrease of liver deaths in the Southern countries appeared

Fem Mother Price Taxes M>65 empl age Urban Income wine (R)

Harm Related to Consumption of Alcoholic Beverages, Selected Socio-Economic and Demographic Factors, and Control Policy Measures. ANN Analysis

TABLE 5. Alcoholic beverages, socio-demographic and economic factors, and selected alcohol policies in three European macro-areas, 1962–2005. ANN Connection values

tations both in the Central and in the Southern European areas, and with prevention/treatment programs in Central Europe. The decrease in wine consumption, that occurred in Southern Europe countries was mainly associated (0.96–0.98) with the rise in urbanization, income, rates of males aged over 65, and the age of the mothers at childbirths. Wine consumption decreases had strong connections (0.97–0.98) with restrictions in advertising and drinking and driving policy measures. The increase in wine consumption in Central Europe is strongly connected (0.97–0.98) with most of the study’s selected socio-demographic and economic factors, and less strongly with alcoholic beverage availability permissive measures, and prevention/treatment programs. The increase in wine consumption in Northern Europe showed less strong connections with rates of males aged over 65, women’s employment, mother age at childbirths, urbanization and income, and more weakly, with price of wine. Wine consumption increase had no appreciable associations with the control policy measures. The decrease in spirits consumption of AMPHORA’s Northern and Southern European areas did not show any substantial connection with the study’s selected sociodemographic, economic and political factors, but manifested a weak association (0.94, 0.91) with the restrictions on availability and drinking and driving in the South. The increase of spirits consumption in the Central European area had no substantial connection with any variable. The introduction of taxes on alcoholic beverages was not associated with consumption trend changes, except in the case of the Southern area, where a tax increase was relatively weakly connected (0.91) with a reduction in the consumption of wine.

Prev & Treat


0.89

0.90

Wine decrease

0.81

Wine increase

0.81

Beer increase

0.96

0.80

Beer decrease 0.90

0.76

Spirits increase 0.94

0.87

Spirits decrease 0.94

0.79

Total increase 0.97

0.84

Total decrease 0.88

0.75

0.72 0.92

0.39 0.51

0.83 0.85

Price wine 0.83 0.87

Income 0.75 0.96

Urban 0.72 0.93

Mother age 0.80 0.97

Fem empl 0.80 0.96

M > 65 0.97

Taxes (P)

0.82

Advertising (R)

Taxes (R)

Advertising (P) 0.71

0.70

Minim age (R) 0.92

0.70

Minim age (P) 0.63

0.46

Availability (R) 0.91

0.67

0.94

0.96

0.56 0.85

0.93 0.96

0.92 0.90

Liver disease increase Transport accident decrease

Liver disease decrease Transport accident decrease

Wine increase

0.98 0.96

0.93 0.95

0.88 0.92

Beer increase 0.94 0.97 0.95 0.95

0.94 0.94

0.93 0.91

Beer decrease

0.92 0.97

0.92 0.94

Spirits increase 0.94 0.93

0.92 0.88

0.93 0.89

0.93 0.93

0.92 0.88

0.95 0.96

0.94 0.97

Spirits decrease 0.96 0.97

Total increase

0.92 0.93

0.95 0.95

0.89 0.97

0.94 0.96

Total decrease

P = permissive, R = restrictive. Bold numbers indicate the strongest connections.

0.90 0.93

Wine decrease

Liver disease increase Transport accident decrease

Advertising (R) Taxes (P)

Taxes (R)

Price wine Income Urban

Mother age

Fem empl

M > 65

Northern Area (Finland Norway Poland Sweden UK) 0.90 0.91 0.93 0.81 0.90 0.92 0.45 0.81 0 0.96 0.96 0.97 0.93 0.96 0.96 0.78 0.76 0 Central Area (Austria Hungary Switzerland Netherlands) 0.90 0.90 0.86 0.59 0.68 0.82 0.96 0.13 0.75 0.97 0.95 0.97 0.84 0.95 0.86 0.94 0.36 0.92 Southern Area (France Italy Spain) 0.97 0.97 0.97 0.96 0.98 0.74 0.93 0 0.96 0.97 0.96 0.96 0.95 0.97 0.78 0.89 0 0.95

Advertising (P) 0 0

0.90 0.88

0 0

Minim age (R)

Minim age (P) 0.91 0.88

0.95 0 0.96 0

0.73 0 0.92 0

0 0

Availability (R)

0.87 0.93

0.95 0.96

0.94 0 0.93 0

0.72 0.87

0.82 0.92

0.98 0.97

0.88 0.98

0.76 0.84

0.94 0.90

0.92 0.98

0.45 0.78

0.76

0.82

Availability (P)

TABLE 7. Chronic liver disease and cirrhosis, transport accident deaths, alcoholic beverages, socio-demographic and economic factors, and selected alcohol policies in three European macro-areas, 1979–2005. ANN Connection values

P = permissive, R = restrictive. Bold numbers indicate the strongest connections.

Liver disease decrease Transport accident decrease

Drunk driving Drunk driving

TABLE 6. Chronic liver disease and cirrhosis, transport accident deaths, alcoholic beverages, socio-demographic and economic factors, and selected alcohol policies in 12 European countries altogether, 1979–2005. ANN Connection values Prev & Treat Prev & Treat

Availability (P)

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to be strongly connected (0.97–0.98) with most indicators of the study’s socio-demographic and economic factors, with the drinking and driving measures, and with wine consumption decrease. The Northern European countries showed weak connections of an increase in pathological liver-related deaths with both an increase and a decrease of spirits and beer consumption—as well as with both an increase and a decrease in total alcoholic beverage consumption. PSC Following is an overview of the PSC coefficients between total alcoholic beverage consumption and control policies—given the socio-demographic and economic variables and time trend-, as well as between total consumption and the socio-demographic and economic variables—given the policies and time trend, for each country (see Figure 4). The overall data analysis documents that the partial square correlation between the AMPHORA selected socio-demographic and economic (unplanned) factors and alcoholic beverage consumption—their mean value across the 12 countries being 71%—were more able to explain the observed changes in total alcoholic beverage consumption than policies did. By and large this held true for each European country, where the socio-demographic and economic factors have larger values than the alcoholic beverage policy measures, with the exception of Poland. However, values vary according to the country. They were higher for the Netherlands, Switzerland, and Finland, and lower for Poland and Austria. As far as the control policy measures the partial squared correlation (the posited effects of policy measures on alcoholic beverage consumption which are not explained by the sociodemographic/economic variables and the time trend) have a mean value of 38% across the 12 countries, even if there is a notable country variability. The correlation values were higher for Finland and Poland, and lower for Italy and the Netherlands. This study documented, for example, that in Italy the six control policy measures globally explained about 11% of the total variability not explained by the socio-demographic/economic (unplanned) predictors and the time trend. The unplanned variables in Italy explained about 80% of the total variability not explained by the policy predictors and the time trend. For Finland policy measures globally explained 74% of the total alcoholic beverage consumption changes, while the socio-demographic/ economic variables explained 84%. For Switzerland these proportions were 46% and 84%, respectively. PSC coefficients between both chronic liver disease deaths and transport deaths, and sociodemographic/economic variables (given the policies and the time trend) have been calculated for each country (Figures 5 and 6). Also the PSC coefficients between both chronic liver disease deaths and transport accident deaths, and policy measures (given the sociodemographic/economic variables and the time trend) have

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been calculated for each country. The data analysis has again documented that socio-demographic and economic (unplanned) factors—their mean value across the 12 countries being 43% for liver disease-related deaths and 52% for transport accident deaths—were better able to explain the observed changes in alcoholic beverage consumption than the policy measures. The mean values of policy measures across the 12 countries were 30% and 31% for liver disease and transport-related deaths, respectively. DISCUSSION

The average recorded total alcoholic beverage consumption in the 12 European study countries during the period 1960–2009 manifested a notable increase peak during both the 1970s and the 1980s, while during the following decades it dropped to the consumption rates of the 1960s. Overall, there was a quite small decrease in terms of recorded total alcoholic beverage consumption, from 10.4 litres of pure alcohol for inhabitants of 15 years and over during 1960–1969 to 10.0 litres during 2000–2009. However, if we consider the overall consumption trends of beer, wine and spirits, and the group of three European macro-areas described above, there are other clear differences. First, the average total alcoholic beverage consumption data are in fact the result of a certain closure among the different countries. The Southern European area has experienced a remarkable decrease, while the Northern area manifested a noticeable increase of alcoholic beverage consumption during the five decades. Second, considering the consumption of the three main alcoholic beverage, each manifested a different trend during the same period. Wine consumption significantly decreased in the South while remarkably increasing in the North. Beer consumption increased everywhere, while consumption of spirits decreased particularly in the North and the South. For the diverse trends of wine and spirits in the three European macro-areas, see Figure 7. In contrast to the consumption trends of the alcoholic beverages, the socio-demographic and economic factors investigated have shown a rather similar trend for the study countries, increasing during the 1960–2009 period, that is an index of the progressive wellbeing achieved in the Europe during the decades after WW II (Voller et al., 2014). However, during the same period the prices of the country-preferred alcoholic beverages decreased overall. Among these factors, the increase of urbanization, of mothers’ age at their childbirths, and of income was most connected with the changes in total alcoholic beverage consumption, according to the TS. This is confirmed for beer consumption by the ANN-based analysis, which also identified the ageing of the population (as measured by the increased rates of men older than 65 years), as well as the rise of female employment as being relevant factors. Nonetheless, the TS, indicated that urbanization is positively associated to the total alcoholic beverage consumption increase in France, Italy and Spain. This finding

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100

Unplanned Policies

90 80 70 60 50 40 30 20 10 0 AT

FI

FR

HU

IT

NL

NO

PL

ES

SE

CH

UK

FIGURE 4. Policy measures and socio-demographic and economic (unplanned) variables: their PSC (in per cent) with total alcohol consumption, 12 EU countries, 1960–2008.

contrasted with the observed evidence that urbanization has grown and total alcoholic beverage consumption has decreased in those countries. According to TS, also the variable of older mothers’ age at childbirths is associated with decreased alcoholic beverage consumption in Hungary, Netherlands, Norway and Sweden. This trend contrasted with the observed evidence that mothers’ childbirths’ age has grown and that alcoholic beverage consumption has increased in those countries. These findings creates a problem of interpretation. As noted later, these inconsistencies in the TS may be related to the high correlation within the study’s selected socio-demographic and economic variables, and between the latter and time trend—that is, to the co-linearity of variables. This is why we also chose to include an additional data analysis method, ANN, which is capable of analysing large number of variables, and where time trends were not considered. In the ANN-based analysis total alcohol consumption was divided into the three cate-

gories of wine, beer and spirits, and the 12 countries were grouped into three macro-areas according to their consumption patterns. The ANN analysis has made it clear that urbanization was strongly connected with both decreased wine and increased beer consumption in the Southern European area, as well as with an increase in both wine and beer consumption in the Central and Northern European areas. On the other hand, older mothers’ age at childbirths was rather strongly connected with both decreased wine and increased beer consumption in the Southern area, and with both increased wine and beer consumption in the other two macro-areas. The ANN-based analysis confirmed the importance of urbanization, mother’s age at childbirths, as well as income as factors associated with alcoholic beverages’ consumption changes. It also documented that in Southern Europe the growth in urbanization paralleled the rise of beer consumption and the decrease in drinking the traditional beverage, wine. Also, in the Central

100

Unplanned Planned

80 60 40 20 0 AT

FI

FR

HU

IT

NL

NO

PL

ES

SE

CH

UK

FIGURE 5. Policy measures and socio-demographic and economic (unplanned) variables: their PSC (in per cent) with chronic liver deaths and transport accident deaths, 12 EU countries, 1970–2008.


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Unplanned

100

Planned

80 60 40 20 0 AT

FI

FR

HU

IT

NL

NO

PL

ES

SE

CH

UK

FIGURE 6. Policy measures and unplanned variables: PSC coefficients (×100) with transport accident deaths in 12 EU countries, 1979–2008.

European area, the growth in urbanization paralleled the rise in beer and wine consumption; this was also the case in the Northern European area, albeit with a weaker association. The increase of urbanization and income indicates the progressive transformation of the European countries into post-industrial and affluent societies (see Clark, 1998), while the older maternal age at childbirths is an indicator of a higher economic status of women and their emancipation (Schmidt, Sobotka, Bentzen, & Nyboe Andersen, 2011). The relevance of economic factors (Wagenaar, Salois, & Konro, 2009) was confirmed by the significant correlation shown by income and alcohol beverages’ consumption by both time series and ANN-based analysis. Increased income appears to favor alcoholic beverage consumption, and more specifically, the increase of wine and beer in Central and Northern Europe, and the decrease of wine and increase of beer in Southern Europe. This may be explained by the interaction of a more affluent society with more purchase power and more choices among the different types of beverages, with a preference for the novel beverages—typically wine in the North and beer in the South. This would be in keeping with the theory that during the last decades, in each country, drinking the traditional beverages have been partially replaced by the new beverages that have become popular at least among the more innovative and educated groups of society (Hupkens, Knibbe, & Drop, 1993; Knibbe, Drop, & Hupkens, 1996). Contrary to a substantial literature (see, among others, Meier, Purshouse and Brennan, 2010; Patra et al., 2012; ¨ Osterberg 2012b), but in keeping with a few other studies (Mohler-Kuo et al., 2004; Nelson, 2013) our study was not able to find any relevance of price impacting alcoholic beverage consumption. According to TS, no correlation was found between total alcoholic beverage consumption and price of both 1st and 2nd preferred country beverage, while ANN-based analysis showed that in the Northern European countries, where there was only a weak connection between wine price and both wine and beer increased consumption. Nevertheless, no connection

was found between wine price and consumption changes in the two other European macro-areas. In conclusion, the progressive move from rural to urban culture, the increased income, and women’s emancipation from traditional limiting roles of being housewives and mothers to an expanding working role outside of the family context that occurred in Europe, at least since the 1960s, was reflected in notable changes in the consumption of wine, beer and spirits, and total alcoholic beverage drinking. Our findings are in keeping with previous research, which was limited to a few countries (Cipriani & Prina, 2007; Simpura, Karlsson, Leppänen, 2002; Sulkunen, 1989). Were the public health policies that were introduced effective in decreasing alcoholic beverage consumption trends and harms related to alcoholic beverages? The answers are not obvious. In general, our study has shown that both selected societal factors and drinking patterns can and do matter both at the country and macro-area levels in Europe. Any introduction of planned control policy measures necessarily interacts with the different contexts where it is shaped, introduced, abided by and enforced. First of all, the PSC analysis showed for all of the AMPHORA study countries except Poland that the selected socio-demographic and economic factors are more able to explain the observed changes in alcoholic beverage consumption than the selected control policies; the former giving a mean value across the 12 countries of 71%, the latter of 38%. This should not be surprising if we consider the great complexity of society compared to the relatively limited efforts of the alcohol policies’ enforcement. As for Poland, the analysis showed an inversion of the relative importance of consumption and policies. This result may be explained by the relatively higher AIC values observed in Poland when compared to the other countries—this would suggest that the “main” socio-demographics and economic variables used were less capable of reasonably explaining the alcoholic beverage consumption changes. However, in this country the policy measures were historically heavily conditioned by socio-economic and political factors.

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FIGURE 7. Wine and spirits consumption increase (↑) and decrease (↓) during 1960–2009 in the 12 AMPHORA study countries, classified according to three European drinking pattern macro-areas.

Moreover, a sort of natural experiment has occurred in southern Europe, particularly in France and Italy, where restrictive alcoholic beverage control policies measures were introduced well after the alcoholic beverage consumption trend had already started to decrease (AMPHORA, 2013, Part 2, country Reports). In France, the first policy measure—a drinking and driving law—was introduced in 1970 after more than 10 years of reduction in total alcoholic beverage consumption. In Italy, an

alcoholic beverage control policies measure—the first drinking and driving law—was introduced in 1988 after at least 15 years during which the consumption curve had begun to decrease. In Spain, too, the decreasing consumption trend began in 1977, just before the first control policy measure—a partial ban on alcoholic beverage advertisements—was introduced in 1978. These observations suggest that factors other than the selected control policy may be able to explain these documented


consumption changes. Our study has demonstrated that such factors are to be found and operate in societal and demographic changes throughout all of the European countries, which are represented by the increased urban population, increased income, and the women’s emancipated roles. However, our analysis indicated that the policy measures can be effective in changing consumption of the alcoholic beverages when they are introduced, adding to the impact of socio-demographic and economic factors. Alcoholic beverage availability liberalization policies have been shown to be associated with increased consumption, especially at their initial introduction, in those countries that had a tradition of restrictive measures; in Finland, Poland, Switzerland and UK, as well as in Norway and Sweden. This is in keeping with other studies (see Mäkelä, 2002; Moskalewicz & Simpura, 2000; Plant & Plant, 2006; Room, 2002; Room, Babor, & Rehm, 2005; Sulkunen, Sutton, Tigerstedt, & Warpenius, 2000). Restrictive availability measures are proposed as being relevant in many evidence-based studies (see among others Bryden, Roberts, McKee, & Petticrew, 2012; Hahn ¨ et al., 2010; Osterberg 2012a). In our study these types of restrictive measures have generally been shown to be effective if repeated over the years, and this holds true for Sweden, the Netherlands and Spain. Drinking and driving BAC measures have been shown to be effective in several investigations (see Bernhoft & Behrensdorff. 2003; Mitis & Sethi, 2012). In our study they appear to be generally effective only when they are reiterated; at the individual country level the negative association between measures and consumption is evident for Austria, Switzerland, France and Sweden. Differently from other research (see Møller, 2002; Waagenar & Toomey, 2002) our study was not able to show any negative correlation among total alcoholic beverage consumption changes and minimum age, except in the cases of France, Spain and Switzerland. In respect to alcoholic beverage advertising restrictions, whose effectiveness is questioned in the literature (see e.g. Nelson 2010; Safffer & Dave, 2006) our study was also not able to show any negative correlation among total alcoholic beverage consumption changes and advertising restrictions except for France and Spain. The ANN-based analysis was not able to find any connection between alcoholic beverage consumption changes and taxes, which compared to a wealth of literature (see e.g. ¨ Osterberg, 2012a; Wagenaar, Salois, & Konro, 2009) is an unexpected finding. Considering wine, beer and spirits consumption separately and according to the three European macro-areas, as was done in the ANN-based analysis, control policy measures may have manifested different impacts on the different types of beverages, being more linked to the traditional beverage in keeping with the position of Knibbe and colleagues (Knibbe et al., 1996). This seems evident in the case of the Southern European area, where the drinking and driving measures were: (1) strongly linked to the reduction in wine consumption and relatively weakly con-

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nected to spirits drinking reduction, but were (2) associated, even if less strongly with beer increase, as if beer was not perceived as being a planned prevention target by the population and the policy makers. Further investigation on this issue is needed. Not infrequently both TS and ANN-based analysis showed that the introduction of restrictive measures was unexpectedly connected with an increase in alcoholic beverage consumption (see Allamani, Olimpi, Pepe, & Cipriani, 2014). One possible explanation would rely on a hypothetical circularity between the two phenomena: the policy measures brought in to reduce alcoholic beverage consumption could in fact be generated by the consumption increase, that might lead both citizens and politicians to introduce the measures (see Nordlund, 2012). Baccini and Carreras performed a further analysis of AMPHORA data, considering the core model using a different model for each country (Baccini & Carreras, 2014b). Here, the model accounted for time trend, “core model” variables. while urbanization was added as the most representative “main” variable. Differently from what was done in the country report analysis the alcohol control policy measures, were treated as being either step or continuous variables. A single consistent model was adopted, which allowed a simultaneous analysis of the 12 European countries. The estimated coefficients arising from this comprehensive country model were then combined in a random effects meta-analyses. By and large, the results obtained through such analysis are in line with our study. Regarding liver disease mortality as an indicator of chronic alcoholic beverage consumption- related harm, the slight documented decrease in liver disease-related deaths observed in the 12 countries altogether between 1970 and 2010 is, in fact, a combination of a large mortality decrease in the South and a notable increase in North and Central Europe. Where a decrease in harm occurred, it was associated with an improvement of social and economic conditions, with the drinking and driving measures, and with wine consumption decrease. The socio-demographic and economic factors are more able to explain the changes in liver disease-related deaths than the control policy measures—their mean value across the 12 country being 43% versus 30% of policy measures. The transport traffic death data, an indicator of acute alcoholic beverage consumption-related harm, showed a 30% decrease for the 12 study countries overall. Again, mortality decrease was associated with an improvement of social and economic conditions and drinking and driving measures. Also, in the central European area legislated preventive measures were strongly connected with mortality reductions. The socio-demographic and economic factors are more able to explain the observed changes in transport deaths than policies—their mean value across the 12 country being 52% versus 31% of policy measures. In any case, one should also consider the improvement in the safety of motor vehicles and roads as being important and relevant factors in decreasing traffic-related deaths.

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The inconsistencies observed between the association of policy measures and consumption on the one hand, and policy measures and the study’s selected harms, on the other, might be attributed to a double effect of harm measures: (a) an effect on consumption, and as a consequence, indirectly on alcoholic beverage consumptionrelated harms; and (b) a direct effect on harm—a typical case being a drinking and driving measure that might induce drivers to delay their consumption of an alcoholic beverage until after the car has stopped. The Study Limitations

This study has several limitations. First, the AMPHORA study investigated variations in the aggregated amounts of alcoholic beverages, while consumption patterns were restricted to the three main types of beverages, i.e., wine, beer and spirits (as they were available in GISAH dataset). Other aspects of consumption patterns, such as place, time and context of drinking, as well as gender or age classes, were not considered due to lack of information over the study period. Furthermore, consumption data are recorded data, while the unrecorded consumption for all the 12 countries for the 5 decades was not available and therefore not taken into account. During the analysis process, the traditional statistical tools such as TS proved to be relatively inadequate to tackle the great amount of data generated by the study variables over 50 years, sometimes generating outcomes at odds with the observed phenomena (see Rehm & Gmel, 2001). In fact the focus of the AMPHORA project was on detecting—often long-term—changes in alcohol use related to policy changes and to indicators such as increasing economy, increasing gender equity or population mobility, e.g., movement from rural to urban areas. Such analysis needs comparable alcohol use data over a longer period. One difficulty here is that series—even when covering 50 years—may be too short from a time series perspective. A second difficulty is that most time series in the “alcohol field” are rather smooth, showing long-term waves or trends (see Skog, 1986), while de-trending of series by means of introducing a linear time trend—an often assumed necessity in TS—leaves little variation in the de-trended series. For example, if alcoholic beverage consumption more or less decreases linearly over a long period, most of the variance is captured by adjusting for a simple time trend variable, as was used in the present analyses. This problem is amplified if predictor variables (i.e., the selected socio-demographic and economic variables) also can and do manifest long term trends. For example, if alcohol use is more or less linearly decreasing and mother’s age at childbirth is increasing, then the correlation between the two series can be very high, leaving little to be explained by other variables. Thus, analysis may be problematic due to coinciding trends, which may not necessarily be causally related, and the remaining variance may often represent not much more than random noise; thus effects estimated on this remaining noise series may be spurious and random (Gmel, 2013).

A third difficulty is that the study predictor variables may be highly correlated themselves. Higher income, increasing prices, later age at childbirth, increasing urbanization, higher female education may all just reflect a single underlying development, e.g., a development toward a better welfare state. However in this study an attempt has been made to add a single socio-demographic and economic variable selected on the basis of Akaike information criteria (either urbanization, or mother’s age at childbirths, or female education or female employment), to the core model variables (income, price of alcoholic beverage, age, time), in order to reduce the risk of co-linearity among them. Moreover, PSC was added in this study as a measure of associations between variables since it is not influenced by multi-co-linearity problems that may occur through TS of data. During the process of analysis we also chose to add a relatively new data analysis method based on ANN, which appears capable of dealing with a large number of variables and which is not often used in the broad substance use research field. This type of analysis also shows some weakness, namely that this is a relatively new method using uncommon theoretical approaches (the auto-contractive maps approach), which even if supported by the evidence of some literature, would need the challenge of further studies. The two methods used in the analysis—TS and ANN based analysis—are not commensurable. The first one is based on statistical probability, the second method is based on the circular interaction of data. Nevertheless, they could allow approaching the phenomena from two different viewpoints, sometimes permitting a mutual confirmation of outcomes. The analysis of alcoholic beverage control policy measures is affected by some other limits. One is that for making TS possible the number of policy measures, so different according to each country, were conventionally set at the 6 or 7 main country policies agreed upon by the study’s country experts. Nevertheless, the selection of measures could be considered to be arbitrary. These measures may be even less in time series analysis (but not ANN-based analysis), which did not take taxes into account. Further, even if other studies consider a different scaling of alcoholic beverage control policies across coun¨ tries (Karlsson & Osterberg, 2001, 2007; Karlsson, Linde¨ man, & Osterberg, 2012a), here the effect of each policy was assumed to have the same value. It was also assumed to be constant over time, and as far as meta-analysis is concerned, has been evaluated individually without accounting for the impact of other policy interventions. Moreover, the analysis did not consider the level of consumption at the time of the introduction of a policy measure; this was done in an attempt not to trigger further co-linearity problems. The issue of the measured being possibly more or less enforced was not taken into account, so that the differences in country relationships between policies and both consumption and harm could at least partly be attributed to different enforcement of policies.


The alcoholic beverage drinking related harms that were analysed were limited to chronic liver disease mortality, cirrhosis, and transport accident deaths. One problem is that the WHO international data series of liver mortality start only from 1970 in most countries; thus the effect of policies on harm was studied only from 1970 and upwards, while policies may have been introduced earlier. Also transport accident mortality data are only available since the 1980s, and not for all countries. Results should therefore be considered with caution. Furthermore, mainly due to lack of data over the period under study, other alcoholic beverage drinking— attributable problems, like violence and cancer, as well as diseases whose risk is reduced with light-moderate drinking, such as coronary heart disease, diabetes, Alzheimer (Anderson & Baumberg, 2006; Schütze, Boeing, Pischon, et al., 2011; Shield, Kehoe, Gmel et al., 2012), were not studied. The AMPHORA dataset consists of a large number of data referring to more than 30 demographic, social, economic, health, political, religious, nutritional variables, and to 11 categories of alcohol control policy measures during 1960–2008 for each of the 12 countries involved (Voller et al., 2014). Other possible analysis, focusing e.g. on smoking and eating lifestyles, and to religion and ethnicity, were not performed because of missing data in several countries. Notwithstanding that the AMPHORA study initially listed the major socio-political events in each country and at the European level as being one of the independent variables, the analysis of connections between “big events” and alcoholic beverage consumption and policy measures was not completed. Big events are of increasing interest among researchers for the far-reaching consequences they are able to set in motion in populations, given necessary critical conditions (see Alexander, 2008; Friedman, Rossi, & Braine, 2009). Some provisional results, still to be completed, appear to indicate that the increase of beer consumption, the more common trend in the 12 AMPHORA study countries, is rather strongly connected with the cultural revolution in 1968, the dissolution of the Iron Curtain in 1989, and the entering the EU, starting from 1992. Finally, a hope that our study could explain the relationships between the different variables in terms of generalizable “cause and effect” processes cannot be satisfied. For example, the association between a control policy measure and a reduction in the consumption of alcoholic beverages in fact means only that the two events go together, rather than one causes the other. At most, these associations may be seen as being co-causal links (Rothman, Greenland, Poole, & Lash, 2008). CONCLUSIONS

This 12 European countries’ study provided different responses to the question: how alcoholic beverage control policies measures are effective in impacting the consumption of alcoholic beverages and the related

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harms? Responses varied according to countries and to their grouping in accordance to their drinking patterns. Overall, the study’s selected socio-demographic and economic factors were better able to explain the observed changes in alcoholic beverage consumption than the selected control policies, the former giving a mean value across the 12 countries of 71%, the latter of 38%. In general, the selected socio-demographic and economic factors, especially urbanization, increased income and women’s greater socio-economic independence, proved to be linked to consumption changes in the direction of increases in Northern and Central Europe, and of decreases in the Southern Europe, with a drop in the traditional beverage and a rise in the “novel beverages.” Among the alcoholic beverage control policies measures, availability (i.e., space and hour limits for sellers to sell alcoholic beverages) permissive measures were generally connected with consumption increases, while drinking and driving norms and availability restrictive measures were usually linked with consumption reduction. This finding is in accordance with previous studies; however, we found that there are differences according to each country and the European latitude. Our analysis, contrary to the findings of many other analyses, was not able to find support for the effectiveness of taxation or of the price of alcoholic beverages. While this study suggests taking the European country cultural diversities into account, it points to the complexity of alcoholic beverage control policies influences and effectiveness, and their relationship with alcoholic beverage consumption changes. Even if some results have been highlighted, much research still needs to be done. Preventive actions have obviously been taken to confront problems in the population. However, while definitive statements are yet to come, the opposite positions of restrictive control and “lassaiz-faire” or self-regulation (De Rita, 2012) ideologies may find ground for bending scientific results toward one or the other side. This may be enhanced by politicians and other relevant individual and systemic influential stakeholders inclined to support programs having media impact, and by researchers who, even when not sharing the funders’ viewpoint, may be unconsciously influenced by the culture in which they live (see Myrdal, 1958). Certainly the authors of this article could not escape this condition as well. The outcome of this study compels us to recommend that the values of urbanization, income, employment, education, ageing, and the changing roles of women, should be included in any future public health program since they accurately represent the complexity of the European context and are able to appropriately shape the design of an effective alcoholic beverage control policies program. Probably the best way to cope with the relative uncertainties of the study results would be to resort to the new simulation techniques (Buscema, 1995) in order to anticipate the success, irrelevance or even regressive outcomes of a programme to be planned.

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Declaration of Interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the article. The research leading to these results has received funding from the European Community’s Seventh Framework Program (FP7/2007–2013) under grant agreement no. 223059—Alcohol Measures for Public Health Research Alliance (AMPHORA). Partners in AMPHORA are: (1) Coordination: Hospital Cl´ıınic de Barcelona (HCB), Spain; (2) Agenzia Regionale di Sanità della Toscana (ARS), Italy; (3) Alcohol & Health Research Unit, University of the West of England, UK; (4) Anderson, Consultant in Public Health, Spain; (5) Anton Proksch Institut (API), Austria; (6) Azienda Sanitaria Locale della Città di Milano (ASL MILANO), Italy; (7) Budapesti Corvinus Egyetem (BCE), Hungary; (8) Central Institute of Mental Health (CIMH), Germany; (9) Centre for Applied Psychology, Social and Environmental Research (ZEUS), Germany; (10) Chemisches und Veterinäruntersuchungsamt Karlsruhe Technische Universität (CVUAKA), Germany; (11) Dutch Institute for Alcohol Policy (STAP), Netherlands; (12) Eclectica snc di Amici Silvia Ines, Beccaria Franca & C. (ECLECTICA), Italy; (13) European Centre for Social Welfare Policy and Research (ECV), Austria; (14) Generalitat de Cataluña (Gencat), Spain; (15) Institute of Psychiatry and Neurology (IPIN), Poland; (16) Institute of Psychiatry, King’s College London (KCL), UK; (17) Istituto Superiore di Sanità (ISS), ˇ Rome, Italy; (18) InStitut za raziskave in razvoj (UTRIP), Slovenia; (19) IREFREA, Spain; (20) Liverpool John Moores University (LJMU), UK; (21) National Institute for Health and Welfare (THL), Finland; (22) Nordiskt välfärdscenter (NVC), Finland; (23) Norwegian Institute for Alcohol and Drug Research (SIRUS), Norway; (24) State Agency for Prevention of Alcohol-Related Problems (PARPA), Poland; (25) Stockholms Universitet (SU), Sweden; (26) Swiss Institute for the Prevention of Alcohol and Drug Problems (SIPA), Switzerland; (27) Technische Universität Dresden (TUD), Germany; (28) Trimbos-instituut (TRIMBOS), Netherlands; (29) University of Bergen (UiB), Norway; (30) Universiteit Twente (UT), Netherlands; (31) University Maastricht (UM), Netherlands; (32) University of York (UoY), UK.

a member of the editorial board of “Substance Use and Misuse.” Coordinator of a few Italian projects on alcohol prevention and policies, he has co-lead work package 3 of the European Commission-funded AMPHORA project. Author and co-author of more than 170 articles, editor and co-editor of 16 books.

Pasquale Pepe, M.Sc., is a Senior Statistician at the Epidemiology Observatory of the Health Agency of Tuscany Region. He has worked for several years on medical statistics, clinical trials and epidemiological studies, and as a statistician has been part of the AMPHORA team in Florence. He has co-authored more than 30 papers.

Michela Baccini, Ph.D., is researcher in medical statistics at the University of Florence. Author of several papers in the field of environmental epidemiology and biostatistics, she worked on time series analysis, meta-analysis, health impact assessment, multiple imputation.

Giulia Massini, Ph.D., Senior Researcher of SemeionResearch Center of Sciences of Communication, Rome (Italy). She is inventor of new algorithms of Neural Networks and Adaptive Artificial Systems. She’s responsible of the application of Neural Computation mainly in medical and social field. She is author of many research software packages. She published several scientific papers in peer reviewed journal and book chapters.

THE AUTHORS Allaman Allamani, M.D., Psychiatrist; Family Therapist; Researcher. He has been coordinator of the Alcohol Centre, Florence Health Agency (1993–2009); since 2009 he has been consultant to the Region of Tuscany Health Agency for research on social epidemiology and prevention policy First non-alcoholic trustee of Italian Alcoholics Anonymous (1997–2003). He is

Fabio Voller, Ph.D. Sociologist, at the Epidemiology Observatory of the Region of Tuscany Health Agency. He has worked on epidemiological studies of lifestyle, alcohol consumption, and psychoactive drug use in the Tuscan population. Among his publications, he is the co-author of a number of monographs about the health consequences of alcohol consumption in Italy. He is a work package 3 leader of the AMPHORA project.


GLOSSARY

AT = Austria FI = Finland FR = France HU = Hungary IT = Italy NL = Netherlands NO = Norway PL = Poland ES = Spain SE = Sweden CH = Switzerland UK = United Kingdom Alcohol policies = measures that are planned by governments to control consumption and drinking patterns, and alcoholic beverage-consumption-related harm Socio-demographic and economic, or unplanned, factors = the unplanned determinants of consumption changes are a broad number of social, cultural, economic, and demographic factors, that are not planned by governments and health authorities in order to control alcohol consumption, but may nevertheless affect consumption, and alcoholic beverage-consumption-related harm. Time series analysis = a time series is a sequence of data points, typically measured at successive points in time that are spaced at uniform time intervals. Data are usually analysed by linear regression or autoregressive integrated moving average (ARIMA) models. Artificial neural network analysis = A method based on an Artificial Neural Network architecture, i.e. the Auto Contractive Maps, spatializes the correlation among variables that are under investigation, and the connection strength between any couple of variables is measured with values between 0 (minimum strength) and 1 (maximum strength). REFERENCES Alexander, B. K. (2008) The globalisation of addiction: A study in poverty of the spirit. Oxford: Oxford University Press. Allamani, A., & Beccaria, F. (Eds.). (2007). Changes in the consumption of alcoholic beverages in Italy: Studies of the decrease in consumption between 1970 and 2000. Contemporary Drug Problems, 34(2), 183–378. Allamani, A., Olimpi, N., Pepe, P., Cipriani, F. (2014). Trends in consumption of alcoholic beverages and policy interventions in Europe: An uncertainty “associated” perspective. Substance Use and Misuse, 49(in press, this issue). Allamani, A., Voller, F., Decarli, A., Casotto, V., Pantzer, K., Anderson, P., . . . Gmel, G. (2011). Contextual determinants of alcohol consumption changes and preventive alcohol policies: A 12-country european study in progress. Substance Use & Misuse, 46(10), 1288–1303. Allamani, A., Voller, F., Pepe, P., Baccini, M., Carreras, G., Massini, G., . . . Kendig, H. (2012). Balance of power in alcohol policy. Balance across different groups and as a whole between societal changes and alcohol policy. In P. Anderson, F. Braddick, J. Reynolds, & A. Gual (Eds.), Alcohol Policy in Europe: Evi-

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