Substance Use & Misuse, Early Online:1–12, 2014Copyright C© 2014 Informa Healthcare USA, Inc.ISSN: 1082-6084 print / 1532-2491 onlineDOI: 10.3109/10826084.2014.913433

ORIGINAL ARTICLE

Alcohol Consumption and its Related Harms in the Netherlands Since1960: Relationships With Planned and Unplanned Factors

Ronald A. Knibbe1, Mieke Derickx1, Allaman Allamani2 and Giulia Massini3

1Department of Health Promotion, Maastricht University, Maastricht, The Netherlands; 2Agenzia Regionale di SanitaToscana, Villa la Quiete alle Montalve, Firenze, Italy; 3SEMEION Research Centre of Sciences of Communication,Rome, Italy

Aim: to establish which unplanned (social develop-ments) and planned (alcohol policy measures) factorsare related to per capita consumption and alcohol-related harms in the Netherlands. Methods: linear re-gression was used to establish which of the plannedand unplanned factors were most strongly connectedwith alcohol consumption and harms. Artificial Neu-ral Analysis (ANN) was used to inspect the intercon-nections between all variables. Results: mothers ageat birth was most strongly associated with increasein consumption. The ban on selling alcoholic bever-ages at petrol station was associated with a decreasein consumption. The linear regression of harms did notshow any relation between alcohol policymeasures andharms. The ANN-analyses indicate a very high inter-connectedness between all variables allowing no causalinferences. Exceptions are the relation between price ofbeer and wine and the consumption of these beveragesand the relation between a decrease in transport mor-tality and the increased use of breathalyzers tests anda restriction of paracommercial selling. Conclusions:unplanned factors are most strongly associated withper capita consumption and harms. ANN-analysis in-dicates that price of alcoholic beverages, breath testing,and restriction of sales may have had some influence.The study’s limitations are noted.

Keywords alcohol policy measures, social developments, percapita consumption, alcohol-related harms

INTRODUCTION

This study aims to identify changes in selected unplannedcontextual factors that are most strongly correlated withchanges in alcohol consumption, drinking patterns, andalcohol-related harm between 1960 and 2008 in theNetherlands. The Netherlands was part of an European

Address correspondence to Ronald A. Knibbe, Department of Health Promotion, Maastricht University, P. O. Box 616, 6200MD Maastricht, TheNetherlands; E-mail: r.knibbe@maastrichtuniversity.nl.

study including 12 countries in which associations of percapita alcohol consumption and alcohol-related harmssince 1960 (or later if no data was available) with con-textual variables were studied. Liver cirrhosis mortalityand mortality due to traffic accidents were selected asindicators of alcohol consumption and related harms. Inall these studies, both the outcome measures (e.g. percapita consumption) and the contextual measures concernaggregated data.

The contextual factors used in the Netherlands and theother countries can be divided into two broad categories:planned and unplanned social changes. The “planned”changes refer to social changes that (intentionally) aim toreduce alcohol consumption and alcohol-related harms ina society. Price of alcoholic beverages is a well knownexample of a measure by which alcohol consumption isstimulated (in case of price-decrease) or discouraged (incase of price increase). Other well known examples of“planned” changes are density of selling points of alco-holic beverages, opening times of pubs, minimum agefor buying alcoholic beverages and enforcement of this;minimum blood alcohol level when participating in traf-fic and enforcement. For these and other measures, it israther well documented that they influence alcohol con-sumption and alcohol consumption-related harms (e.g.Babor et al., 2010). However, the evidence for the effectof these measures is mainly established in northern Euro-pean and northern American countries (Room, Babor, &Rehm, 2005). In Mediterranean countries, fewer of thesemeasures were implemented in the second half of the 20thcentury. Nevertheless, in countries like France, Italy, andSpain, a rather stable decrease in alcohol consumptioncan be observed since 1960. In West and Northern Eu-ropean countries, where more alcohol control policy mea-sures are implemented, the consumption has, despite thesemeasures, increased since 1960. This raises the questionwhether other factors than the “planned” factors perhaps

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2 R. A. KNIBBE ET AL.

are responsible for changes in alcohol consumption andalcohol-related harms. Concerning these unplanned fac-tors, one has to think of factors like urbanization, emanci-pation, changes in income, etc. For many of these factors,it is also documented that they are related to alcohol con-sumption. The major question—not to be answered in thispaper—is of course whether one can estimate for a vari-ety of European countries to what extent alcohol controlpolicy measures (e.g. planned factors) have influenced al-cohol consumption when one also takes into account the“unplanned” factors influencing alcohol consumption atthe same time (Allamani et al., 2011). This paper concen-trates on the Netherlands and the period 1960–2010 andallow for the Netherlands specification of (1) which socialchanges are most strongly associated with the changes inalcohol consumption and types of harms and (2) the extentto which alcohol control policy measures influence alco-hol consumption and alcohol consumption-related harms,considering the effect of unplanned factors on alcoholconsumption.

We will start with describing the main social and cul-tural changes in the Netherlands and some of the majorchanges in alcohol consumption and its harms. Thereafter,the data and methods used in the study will be described,the results will be presented and discussed.

The Netherlands, in 1951 was one of the co-founders ofthe predecessor of the EU: the European Community forCoal and Steel and of the European Community in 1958.

In 1960, relative prosperity and social security wasone of the defining characteristics of the post SecondWorld War reconstruction. A new generation, focusedupon a break with traditional values, initiated new cul-tural developments. The breakwith traditional values gov-erning, for example gender relations, political system,social hierarchy, etc. shows in rather popular anarchis-tic movements like “provo” (http://en.wikipedia.org/wiki/Provo (movement). It also perhaps shows even clearer inan increased rate of secularization since 1960. In 1960,a minority of 18% of the population reported not to be-long to any religious denomination. Eighty-two percentreported belonging to the Roman Catholic church, oneof the Protestant denominations, a Jewish or Moslem de-nomination or another religious denomination. However,in 2009, the proportion reporting not to belong to anyreligious domination had increased to 44%. The propor-tion of the population attending regularly, for examplechurches and the frequency of going to church decreasedeven more strongly. In the Netherlands, the Protestant andCatholic political parties dominated the political life sinceabout the start of the 20th century. The major religiousdenominations each also had their own system of insti-tutions, ranging from trade unions, football (and other)clubs, schools, etc. This meant that in almost all sectors ofeveryday life, one’s “lifestyle,” including choices when,where, and how frequent to use alcoholic beverages,was monitored by people having the same denominationand therefore the same values about lifestyle (Schuyt,Taverne & Taverne, 2000). Most of these denominations,including the “socialist” one, favored a very moderate use

of alcoholic beverages. Secularization in the Netherlandsmeant that since 1960, the political and social power ofdenominations actively advocating abstention and moder-ation decreased. The Dutch government operated withoutthe Christian parties for the first time in the 20th centuryduring the late 1990s.

The gradual decline of the power of Christian, andmore specifically Protestant political parties meant thatabstention- and moderation-oriented values played lessrole in the political debate about alcohol consumptioncontrol. This is demonstrated by a strong reductionof measures limiting alcohol consumption since 1967.For example, selling of beer and wine in supermarketsbecame allowed; opening time of pubs were liberalized, asteady decline in the enforcement of prohibition of publicdrunkenness and enforcement of the law concerningminimum age (16 years for beer and wine; 18 years forstrong beverages) for buying alcoholic beverages wereseen. A growing increase of more hedonistic values hasmanifested itself since 1960 as evidenced, for exampleby alcohol consumption, which increased between 1960and 2008 with 249%. The association between theincrease in per capita consumption and, on the one hand,a decrease of traditional elements in the everyday menuof Dutch people: milk products and fresh vegetables, onthe other hand, an increase in consumption of a relativelyexpensive product like meat can also be taken to indicatea more hedonistic lifestyle.

Secularization is, of course, not unique for the Nether-lands, although the extent to which social life and socialcontrol till about 1960 was organized around confes-sional lines is rather unique for the Netherlands (Lijphart,1975). Next to and almost at the same time one finds inthe Netherlands as in many other European countries,social developments like, urbanization, emancipation,increase in income, etc. In the context of this article, thesedevelopments are classified as “unplanned” factors. Toincrease comparability between countries participating inthis study, the selected unplanned factors that are includedin this study concern factors indicating economic changes(e.g. income), sociodemographic changes (proportionof older people, proportion of people living in largercities), and cultural changes, more specifically indicatorsof emancipation (e.g. proportion of working women; ageof child birth).

Changes in Alcohol Consumption, Drinking Pattern,and Alcohol Consumption-Related HarmsFigure 1 demonstrates that alcohol consumption in theNetherlands peaked in 1979, when 12.20 L of pure alcoholper capita (15 years and older) was consumed. Thereafter,there was a slight decrease, ending with 9.25 L in 2008.Over the whole period 1961–2008, there was a remark-able increase of 249%. These figures are based on (offi-cial) sales data of alcoholic beverages in the Netherlandsand cover actual consumption rather better than self re-ports of consumption do. As there is only very limitedunrecorded alcohol consumption in the Netherlands, theyrepresent rather well the actual per capita consumption.

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ALCOHOL CONSUMPTION AND ITS RELATED HARMS IN THE NETHERLANDS SINCE 1960 3

FIGURE 1. Recorded alcohol consumption in litres of pure alcohol per inhabitant 15 years and older, Netherlands, 1961–2008. Source:WHOGISAH 2011∗).

The increase between 1960 and 1980 was 300%,greater than in any other country and about the samein subpopulations defined by gender, age, religious de-nomination, or social class. The increase in prevalence ofdrinking 22 glasses or more alcoholic beverages per weekwas during 1960–1980 even greater than the increase inper capita consumption (Knibbe, Drop, Reek J van, &Saenger, 1985).

The drinking pattern in terms of beverage choice is dur-ing this entire period rather clear. Beer has been the mostpopular beverage for the whole period and the popularityincreased till about 1980. Wine is a relatively “new” bev-erage for the Netherlands for which the popularity con-stantly increased over the entire period. Since about 1990,it is more popular than the more traditional strong alco-holic beverages or spirits. Spirits are a traditional beveragein the Netherlands. However, the popularity of spirits hasbeen declining since the Second World War. This showsin changes in contribution to the total amount of alcoholof the different beverages. In 1961, 46% of the total con-sumption of alcohol was beer, 44% spirits, and 10%wine;in 2008, 49% of the total consumption was beer, 17% wasspirits, and 34% wine.

A constant element of the Dutch drinking pattern is thatalcohol is consumed at special occasions like birthdaysand celebrations. However, since about 1960, drinking be-came customary and legitimate at more occasions than be-fore. The start of the weekend, holidays, a day out withfamily, friends, or colleagues became occasions to havesome drinks. Another change in drinking pattern startingin the 1960s was that the frequency of drinking at homeincreased strongly. The exception to this are the youngestwhere, till now, still most of the drinking is done in publicdrinking places. The young people were characterized byan intensification of an actually rather traditional way ofdrinking: drinking when going out in the weekend. Theolder population also concentrated its drinking during theweekend, however, much less often in public drinkingplaces and drinking a smaller number of glasses per oc-casion than among the young (<25 year; Knibbe, Drop,& Muytjens, 1987). The better-educated older (>35 year)adopted the habit of drinking wine with their meal, alsoon working days (Hupkens, Knibbe, & Drop, 1993).

De Lint (1981) reports a steady increase between 1950and 1975 in liver cirrhosis mortality. As one of the indica-tors of alcohol consumption-related harms, however, the

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4 R. A. KNIBBE ET AL.

association between increase in per capita consumptionand liver cirrhosis mortality is not as strong as seen inother countries. Nevertheless, morbidity data, based uponhospital admissions for the period 1969–1981 indicates a300% increase in admissions for Laennec’s cirrhosis ofthe liver and about similar increase in admissions for al-coholism (Hoogendoorn, 1983). Studies on prevalence ofproblem drinking in the general population (16–69 yearsold) show that in 1980 the prevalence is estimated at 10%and in 2001 at 10.3% (Dijck & Knibbe, 2005).

The selected alcohol consumption-related harms in thisstudy (and the other AMPHORA study countries) con-cern liver cirrhosis mortality and transport accident mor-tality. We were able to collect this data for only the period1979–2009. This is the period during which per capitaconsumption in the Netherlands started to decline. Be-

tween 1979 and 2009, liver cirrhosis mortality has de-creased among both men and women (Figure 2).

A similar declining trend is observed in the period1979–2009 for transport-related accident mortality. Formen, these types of deaths declined from about 24 deathsper 100,000 inhabitants to about 7. For women, it declinedfrom 7 deaths per 100,000 to about 2.

DATA AND ANALYSIS

DataAlcohol Consumption and its HarmsPer Capita alcohol consumption data available from 1960to 2008/2010 for each European country on per capitarecorded alcohol consumption for those aged 15 yearsand older are provided by GISAH (Global Information

FIGURE 2. Liver disease mortality rate (per 100,000 population) per gender and age. The Netherlands, 1979–2009. source WHO mortalitydata, 2012∗.

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ALCOHOL CONSUMPTION AND ITS RELATED HARMS IN THE NETHERLANDS SINCE 1960 5

System on Alcohol and Health) through AMPHORAWork package 2 (World Health Organization, 2012). Mor-tality for liver disease and liver cirrhosis (or, simply, liverdisease), and the number of persons killed in transport ac-cidents, are traditional indirect indicators of chronic andacute consequences of alcohol consumption, respectively(Edwards et al., 1994; Rehm & Scaffato, 2011). WHO’s“Health For All” Database (HFA DB) is providing stan-dardized death rates (SDR) for both genders and for allages per 100,000 inhabitants (World Health Organization,2010). The latter will be used in the analysis since the dataare more comparable with GISAH alcohol consumptiondata, representing all ages and both genders. A limitationof both series is that these are available only for the pe-riod after 1979. As noted before, the major increase inper capita consumption in the Netherlands took place be-tween 1960 and 1979, after which per capita consumptiondecreased.

Planned Determinants: Alcohol Consumption ControlPolicy MeasuresThe 12 participating EU countries involved in the AM-PHORA study (Allamani et al., 2011) agreed on the basisof a literature search that the following five categories ofplanned determinants were most relevant:

1. Advertising policies,2. Minimum age to buy alcoholic beverages,3. Restrictive availability policies (licensing rules and

trading hours sales),4. Permissive availability policies (licensing rules and

trading hours sales), and5. Laws concerning maximumBlood Alcohol Concentra-

tion (BAC limit).

While taxes were originally taken into account as(planned) policy measures, (retail) prices are not includedin the list above. The reason for this is that alcoholic bev-erage prices have been included in the analysis as an in-dicator of the market, which is an unplanned factor ratherthan a “planned factor.” This led the Netherlands researchgroup to include the following planned variables in theanalysis:

1. 1967: acceptance of new set of alcohol laws, regu-lating the retail selling of alcoholic beverages. Themajor change compared with the former law is thatthere is no local maximum in the number of sell-ing points (both on and off premise) of alcoholicbeverages (http://www.wetboek-online.nl/wet/Drank-%20en%20Horecawet.html).

2. 1974: a statutory BAC-limit of .5 g/L for driving anyvehicle came in effect (http://www.swov.nl/rapport/Factsheets/NL/Factsheet Alcohol.pdf).

3. 1987–1989: increasing repression of drunk driv-ing. The background is that from 1987 on portablebreathalyzers (instead of blood tests by a doctor atthe police station) accepted as legal evidence. Thismarked the start of a 2-year campaign with more

frequent actions of police teams to control drivers(http://www.draeger.net/IK/nl/alcoholwetgeving/strafrechtelijke maatregelen/historie in alcohol wetgeving/).

4. 1987: from 1987 on there has been yearly a nation-wideeducational campaign. The most important elementsof these campaigns were media messages (television,daily journals; posters, other media) putting problem-atic alcohol consumption of adolescents on the agenda(e.g. a slogan like “Alcoholic beverages destroy morethan you want”).

5. 1991: a new law obliging local communities to testwhether paracommercial outlets (e.g. sport canteens)were taking unfair advantage compared with commer-cial enterprises (e.g. pubs, restaurants) when sellingalcoholic beverages. In practice, this meant that para-commercial outlets may have had to increase price andhad to restrict hours (http://resourcessgd.kb.nl/SGD/19901991/PDF/SGD 19901991 0005190.pdf).

6. 2001: the law making it impossible for petrol/gasstations to sell alcoholic beverages (http://www.draeger.net/IK/nl/alcoholwetgeving/strafrechtelijkemaatregelen/historie in alcohol wetgeving/).

Selected Unplanned VariablesAs mentioned before, secularization is likely to be an im-portant “unplanned” factor in the Netherlands explainingthe relatively strong increase in per capita alcohol con-sumption between 1960 and 1980. Nevertheless, this fac-tor is not included in the list of unplanned factors. Avail-ability of data over the whole period and comparability offactors for the 12 different AMPHORA countries led tothe selection of seven variables being used for the analysison the basis of their potential relevance as variables re-lated to alcohol consumption as well as the availability ofthese data in most of the participating AMPHORA studycountries. These variables cover economic development:changes in income and prices ofmost drunk alcoholic bev-erages (beer and spirits in the Netherlands); indicators foremancipation: female educational level, female employ-ment, mother’s age at childbirth; and changes in socialde-mographic factors: proportion of older people and peopleliving in larger cities (urbanization). For the Netherlands,it can be noted that over the study’s whole period, theincome has generally increased, as has the proportion ofolder people in the population, the level of female edu-cation, and the Netherland’s urban level. Mothers age atchildbirth is an exception: between 1960 and 1970, the ageat childbirth decreased from 26 years to about 24. Afterthat, mothers’ age at birth increased to age 29 in 2008. Thefact that, with one exception all variables monotonouslyincreased over the whole period, means that the variableswill be very strongly intercorrelated. Data on prices had tobe transformed from absolute prices into real prices andfrom local currency or Euro into price index numbers (butfor some countries, which already provided transformeddata). Missing data were also not infrequent in the Nether-lands, so that also EUROSTAT figures (Price index num-bers) were considered—generally data are available since1996.

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6 R. A. KNIBBE ET AL.

AnalysisBoth the dependent variables (per capita consumption,liver cirrhosis, and transport accidents) and the unplannedfactors (e.g. urbanization) had the format of time series ofthese variables (e.g. per capita alcohol consumption) overa longer period. The time series structure was exploredand ARIMA first-order integrated models were found tobe appropriate for most countries. However, the results ofthis model appeared very close to those obtained from alinear regression with a time trend among the explana-tory variables. Therefore, we used this second approach,which is both simpler and easier to understand (Baccini &Carreraas, 2014).

For each country, a core model was specified for thelogged per capita total alcoholic beverage consumption,accounting for income and alcohol prices of beer andspirits (most drunk types of beverages over the studyperiod). This approach rests on the relative importanceof economic factors in determining changes on alcoholconsumption (see e.g. Nelson, 2010). Moreover, the coremodel also took into account the males over 65 in the pop-ulation (percentage of elderly), so that the demographicage structure of the population was considered. All thesevariables were included in the linear predictor after a log-arithm transformation. A time trend was also added, tocapture the long-term behavior in consumption that couldbe related to unobservable factors. The assumption of in-dependent normally distributed error terms was made.

The other four unplanned variables selected for theanalysis (“Female educational level,” “Female employ-ment,” “Mothers’ age at all childbirths,” and “Urbanlevel”) that are socially relevant were then inserted inthe core model one at time after a log transformation(Nelson, 2010). Then the AIC (Akaike information cri-terion) for each model was calculated, and the model withminimum AIC was detected. This was done for the prac-tical reason of avoiding the effects of the high correlationbetween these variables, what would lead to very unstableresults if they were included in the model at the same time.The Confidence Interval (CI) was set at .90 instead of .95,which is the usual value for CI. This liberal use of CI, wasused in order to capture evenminor information from data.This analysis allows conclusions about which of the un-planned factors is most strongly associated with changesin consumption, after controlling for economic factors (in-come and price) and age structure of the population.

After having selected the best unplanned variable onthe basis of the minimum AIC, for each main policy mea-sure, a model was specified by adding a dummy variablewhich was equal to 0 before the policy introduction and 1afterward. In this way, we estimated the net policy mea-sure effect over the period between the introduction of thepolicy measure and the end of the study period (2008),given long-term trend and social, demographic, and eco-nomic factors. The impact of each policy was modeledas being immediate and constant over time. This step al-lows conclusions about the extended alcohol control pol-icy measures, after controlling for unplanned factors, thatare related to changes in per capita consumption.

This kind of analysis is affected by some limitations.In particular, even if other studies consider a differentscaling of alcohol policies across countries (Karlsson &Osterberg, 2001, 2007), here the effect of each policyis assumed as having the same value. It is also assumedconstant over time and evaluated marginally, without ac-counting for the impact of other interventions. Moreover,the analysis did not consider the level of consumptionat the time of the introduction of a policy measure, e.g.whether a restrictive policy measure was introduced atthe highest level of alcohol consumption, or at the lowest,to test the hypothesis of a maximum effect at the higherlevel, and a minimum effect at the lower level. Also, sincethe policies are analyzed one at a time, the effect esti-mated for each policy estimates the changes in the meantotal alcohol consumption values before and after thepolicy implementation, up to the end of the study period,resulting in an underestimation of the policy effect.

The public health approach assumes according to thetotal consumption theory that restrictive alcohol policies,which reduce alcohol consumption, should, as a conse-quence, also be able to produce a reduction in alcoholicbeverage consumption-related harms (Ledermann, 1956;Bruun et al., 1975; Anderson, Møller, & Galea, 2012).For this reason, our model considers the indirect effectof policy measures on harm, i.e. the effects on harmmediated by consumption changes. However, also thedirect effect of policy measures on harmwas studied. Thiswould allow us to see the effects on harm of those policymeasures, which may hypothetically be effective evenwhen they are not mediated by total alcohol consumption.As an example, this would be the case of a restrictivedrink and driving measure that would induce drinkingdrivers to stop drinking, or to drink only when they donot drive. In this case, this limited drinking reductionwould not be capable of causing an overall reduction inthe alcohol consumption of the general population.

In order to study the direct effect of each policy mea-sures on liver mortality and transport mortality, a time se-ries model with the log-rate of each type of harm as de-pendent variable was estimated for each country. In orderto study the effect of policy measures, the effect of eachpolicy was estimated one at a time in a model adjusted forthe unplanned indicator that provided the minimum AICin the Netherlands.

This study also used a new data analysis methodologybased on a new type of Artificial Neural Network (ANN)architecture, i.e. the Auto Contractive Maps (AutoCMs),which allows for basic improvements in both robustnessof use in poorly specified and/or computationally de-manding problems, and output usability and intelligibility(Buscema, Grossi, Snowdon,&Antuono, 2008; Buscema,Massini, &Maurelli, 2014). These analyses allow a betterinterpretation of the interrelations between variablesand allow more informative conclusions. A completedescription of all these analyses for the Netherlandsand other countries can be found in the AMPHORA-report (2013) and Allamani, Voller, Baccini, Massini, &Pepe (2014).

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ALCOHOL CONSUMPTION AND ITS RELATED HARMS IN THE NETHERLANDS SINCE 1960 7

AutoCMs spatialize the correlation among variablesby constructing a suitable embedding space where a vi-sually transparent and cognitively natural notion such ascloseness among variables reflects accurately their asso-ciations. Such representation is not actually constructedthrough some form of cumbersome aggregation of two-by-two associations between couples of variables, butrather by building a complex global picture of the wholepattern of variation. Moreover, it fully exploits the topo-logical meaningfulness of graph-theoretic representationssince actual paths connecting nodes (i.e. variables) carrya definite meaning in terms of logical interdependence inexplaining the data set’s variability. The weight of connec-tions between any couple of variables is a number between0 (minimum) and 1 (maximum), indicating the strength ofconnection, also considering the connections among allthe variables.

To view and maximize the information contained in thematrix of relationships between variables obtained from

AutoCM, the Minimum Spanning Tree (MST) has to becalculated. MST is a graph that connects all the nodes, orvertices (= variables), so that the arcs, or lines (= rela-tionships, or connections) are only those that are neededto interconnect all vertices with one and only one path,the shortest among any other possible, without creatingany circuit. In order to obtain major complexity in infor-mation, Maximally Regular Graph (MRG) is to be used.

MRG describes other high-value relationships betweenvariables, which are obtained from AutoCM, not beingshown by the Minimum Spanning Tree. Such relation-ships in MRG are represented as arcs (i.e. lines) creatingcircuits. MRG is able to identify the relevant connectionthat we do not see in MTS.

The analyses which were provided using AutoCM foreach country, were the following:

(a) Unplanned variables and consumption of beer, wine,and spirits;

FIGURE 3. Upper graph: total alcohol consumption. The vertical dotted lines indicate the occurrence of policy measures. Lower graph:estimated policy effect with 90%CI.

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8 R. A. KNIBBE ET AL.

(b) The selected policy measures, and consumption ofbeer, wine, and spirits;

(c) Unplanned variables and policy measures together,and consumption of beer, wine, and spirits in orderto check the interaction among the two sets of inde-pendent variables;

(d) Unplanned variables and policy measures together,and HFA database deaths for liver disease & transportaccidents.

RESULTS

Unplanned Variables and Alcohol ConsumptionThe unplanned variables, female education, female em-ployment, mother’s age at child birth and urbanization,

are very strongly correlated with each other (between .92and .99). Therefore, four separate models were estimatedfor each of the selected unplanned indicators. The laggedvalues of these variables (mean value of the unplannedindicator in the year under study up to two years before)were inserted in the analysis. Since the analysis wascarried out on the data resulting from a multiple imputa-tion procedure, each model was estimated on a differentimputed dataset (five datasets). The results of the modelssummarized on the five imputed datasets are used in thispaper. Then the AIC for each model was calculated. TheAIC is a measure that accounts for goodness of fit andparsimony of the model. By and large, lower AIC valuesindicate better models.

Table1 demonstrates that the model with “mother’s av-erage age at all “Childbirths” generates the minimumAIC

FIGURE 4. Policy measures and unplanned variables with consumption of beer, wine, spirits, Netherlands—ANN analysis.

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ALCOHOL CONSUMPTION AND ITS RELATED HARMS IN THE NETHERLANDS SINCE 1960 9

TABLE 1. Regression coefficients describing the relationship between 15+ per capita recorded alcohol consumption (source: WHO 2009)and four selected unplanned indicators in the Netherlands∗, with 90% confidence intervals (CI 90), and AIC (Akaikeinformation criterion)values∗∗

The Netherlands Coefficient CI90 AIC

Female education 0,02 (−1.06, 1.11) −156,98Female employment −0,02 (−0.25, 0.22) −146,13Urbanization −3,07 (−4.08, −2.05) −160,97Mother’s age, all childbirths −1,97 (−2.46, −1.48) −163,03

∗Results from 4 separate regression models for each unplanned factor, adjusted for time trend, income, proportion of males >65 of age, andprices of spirits & beer.∗∗Lower AIC values indicate better models which explain consumption changes.

in the Netherlands, i.e. it is a better model for the interpre-tation of results. The negative correlation of this variablewith total alcohol consumption suggests that overall a 1year increase of mothers’ average age at all childbirths(which started its constant increase in the 1970s, after adecrease in the 1960s) is associated with a 1.97 decreaseof consumption. The model with urbanization also showsa negative correlation with total alcoholic beverage con-sumption, with a higher rate of 3.07, indicating that thealmost continuous increase of the proportion of the pop-ulation living in large cities is associated with a decreasein consumption.

In order to study the effect of planned indicators, the ef-fect of each control policy was estimated one at a time in amodel adjusted for the unplanned indicator that providedthe minimum AIC in the Netherlands, i.e. mother’s aver-age age at childbirths. Figure 3 presents the policy mea-sures; in paragraph 3.2 the policy measures are describedmore fully. In Figure 3, the direct relation between thesemeasures and per capita alcohol consumption is visualized(Upper graph) and the estimated effect of these measureswith CI.

The results of the analysis on policy measures show(surprisingly) that the 1974 BAC measure and the 1991alcohol sale restrictions on paracommercial outlets are as-sociated with increases in alcohol consumption over time,by about 12% and 4% respectively. On the other hand,the 1991 prohibition of selling alcoholic drinks in gas sta-tions is associated with a 5% decrease in consumption.The other policy measures show no significant effect.

The ANN analysis qualifies the outcomes presentedabove (Figure 4).

It appears that the defining characteristic of changingdrinking pattern in the Netherlands, that is the combi-nation of wine increase (wine max in the figure), beerincrease (beer max), and spirit decrease (spirit min), isstrongly connected with all the unplanned variables andall policy measures except the 2001 ban on sales at petrolstations. The price of beer and wine is far less stronglyinterconnected with the unplanned and planned variables.Interestingly, it appears that the increase of price of wineis linked with the decrease of wine, while increasing priceof beer has a connection, although less strong, with reduc-tion in consumption of beer.

FIGURE 5. Regression coefficients of direct and indirect liver deaths∗ and selected policy measures and their year of introduction in theNetherlands∗∗, with 90%CI. (Continuous line = direct effects; dotted lines = indirect effects).

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10 R. A. KNIBBE ET AL.

FIGURE 6. Liver disease (cirrhosis) and transport accidents mortality with wine, beer, and spirits consumption, alcohol control policy mea-sures, and socio-demographic & economic factors. The Netherlands—ANN analysis.

HarmsIn order to study the effect of policy measures, the effectof each control policy was estimated one at a time in amodel adjusted for the unplanned indicator that providedthe minimum AIC which in the Netherlands is mother’saverage age at all childbirths.

The figure below indicates that none of the policy mea-sures appears to be associated with alcoholic beverageconsumption-related livermortality and transport accidentdeaths. Figure 5.

The ANN-analysis allows us to qualify the above con-clusion (Figure 6).

It shows that the decreasing trend in transport mor-tality (transport accidents min) is associated with a de-creased consumption of both spirits and beer, as well asto the 1987 introduced breath testing and the nation-wide

educational campaign and the 1991 introduced restrictionof sales in paracommercial outlet. In addition, the increaseof liver disease-related deaths and of transport deaths areassociated with increases in the consumption of beer andof spirits, respectively.

DISCUSSION

This study’s two main questions are:

1. which social changes are most strongly associated withchanges in per capita alcoholic beverage consumptionand their selected consumption-related harms and,

2. the extent to which alcohol control policy measuresmay have influenced per capita consumption andharms, considering the relation of the afore mentioned

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ALCOHOL CONSUMPTION AND ITS RELATED HARMS IN THE NETHERLANDS SINCE 1960 11

social changes with per capita consumption and se-lected harms.

The results from the linear regression analyses allow arather straightforward answer to these questions. Yet, wewill also point out the limitations of these regression anal-yses and use the results from the ANN-analysis to qualifythe answers.

The social change most strongly associated with percapita consumption appeared to be mothers’ age at child-birth. Most likely this variable appeared to be moststrongly correlated with per capita consumption becausethe trend in this variable—an initial decrease (1960–1970)and a subsequent increase since 1970—corresponded bet-ter than any of the other social changes, like urbanization,female employment with trends in alcohol consumption(increase till 1979; decrease since 1980), etc. The ANN-analysis shows interconnectedness between all of the se-lected social changes and increase in consumption of wineand beer.

The answer to the question about the effects of alco-hol control policy measures is rather simple when theoutcomes of the regression analyses are taken: with theexception of the 2001 ban on selling alcoholic bever-ages at petrol stations, none of the alcohol control pol-icy measures had the expected and planned for influenceon per capita consumption, liver diseases, or transportaccidents.

However, a major limitation of this type of statisticalanalysis is that it very imperfectly controls for all otherinfluences on alcohol consumption. The ANN-analysisallows qualification of the outcome that for almost noneof the alcohol control policy measures the expectedrelation with per capita consumption could be found inthe regression analysis. The ANN analysis shows thatall variables—whether they were selected and consid-ered to indicate unplanned factors, planned factors oroutcomes—are that closely interconnected that orderingthese variables in a linear causal sequence is not war-ranted. However, for three variables a farmore isolated po-sition in the graph can be observed. One variable concernsthe 2001 measure banning sales of alcoholic beverages atpetrol stations. In the ANN-analysis, there is no direct re-lation of this variable with any of the alcohol consumptionmeasures. This means that the significant relation betweenthis ban and per capita consumption found in the regres-sion analysis should not be interpreted as due to a “causal”relation. The other “isolated” variables are price increaseof wine and beer which are directly associated withdecrease in wine and beer, respectively. In the regressionanalyses, prices were used as a control variable indicatingthe market. Yet, the ANN analysis allows the conclusionthat increasing prices of alcoholic beverages as a plannedmeasure is likely to have the expected effect on con-sumption, even in a historical-social situation were otheralcohol control policy measures are too interconnectedwith other variables influencing consumption to show aneffect.

With respect to alcohol consumption-related harm, theANN-analysis allows some specification of the outcomeof the regression analysis that none of the alcohol mea-sures were related to alcohol consumption. It appears thatthe increase in enforcement of the BAC-limit, the 1987yearly campaign, and the 1991 restriction on paracom-mercial selling have the expected relation with a decreasein transport accidents. No relationship was documentedfor liver cirrhosis and alcohol control policy measures. Inconclusion, for most of the alcohol control policy mea-sures, the expected effects on alcohol consumption andon harms could not be found.

The conclusion that most alcohol control policymeasures did not have the expected effect is due the factthat these measures are part of a process of social changeembracing social aspects that we thought could clearlycategorize into planned, unplanned factors and outcomes.Especially the ANN analysis shows that with this type ofaggregated data, the interconnections between variablesof these categories are such strong that linear orderinginto causes and effect is not warranted.

Declaration of Interest

The authors declare no conflicts of interest. The authorsalone are responsible for the content and writing of thearticle.

THE AUTHORSRonald A.Knibbe, PhD isemeritus professor at MaastrichtUniversity, regional editor(Europe) for Drug and AlcoholReview and member of theresearch council of the NationalMonitor of Alcohol and Druguse. He has supervised manyresearch projects in the fieldof Alcohol and Drug researchand have been author or co-author of many papers. Twoimportant subjects in his work

are international comparisons of alcohol use and the relationbetween structure of everyday life and substance use.

Mieke Derciks, B.Sc, works asresearch assistant at MaastrichtUniversity, Department ofHealth Promotion and ascurator art & heritage CouncilMaastricht University. Most ofher work as research assistant isin the field of alcohol and drugresearch. Her expertise concernsmethodology for collecting data,storing and analyzing data, andencompass both quantitative andqualitative research methods.

She is co-author of many papers.

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Allaman Allamani, M.D., isPsychiatrist, Family Therapist,and Researcher. He has beencoordinator of the AlcoholCentre, Florence Health Agency(1993–2009); since 2009, hehas been consultant to theRegion of Tuscany HealthAgency for research on socialepidemiology and preventionpolicy First non-alcoholictrustee of Italian AlcoholicsAnonymous (1997–2003). He is

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

Giulia MassiniSenior Researcher ofSemeion—Research Centerof Sciences of Communication,Rome (Italy). She is inventorof new algorithms of NeuralNetworks and AdaptiveArtificial Systems. She’sresponsible of the applicationof Neural Computation mainlyin medical and social field. Sheis author of many researchsoftware packages. She has

published several scientific papers in peer reviewed journal andbook chapters.

GLOSSARY

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