Substance Use & Misuse, 49:1684–1691, 2014 C 2014 Informa Healthcare USA, Inc. Copyright ISSN: 1082-6084 print / 1532-2491 online DOI: 10.3109/10826084.2014.914373
ORIGINAL ARTICLE
Analyzing and Comparing the Association Between Control Policy Measures and Alcohol Consumption in Europe Michela Baccini1,2 and Giulia Carreras2 1 2
Department of Statistics, Informatics and Applications “G.Parenti,” University of Florence, Florence, Italy; ISPO Cancer Prevention and Research Institute, Florence, Italy sumption, depending on the way they were implemented and on the context where they were introduced. This paper investigates whether during the study period alcohol consumption was associated with the introduction of control policy measures. We explored heterogeneity among countries and tried to detect the typologies of interventions that appeared more related to changes in the outcome. In Allamani, Pepe, Baccini, Massini, and Voller (2014), the results are presented concerning the relative importance of unplanned contextual variables and policy measures on changes in alcohol consumption during the study period in the 12 countries. In that paper, the association between each intervention and the outcome was examined trough time series regressions, where the policies were included one at time, adjusting for the unplanned variables (see the country-specific analysis described in Baccini & Carreras, 2014). However, by fitting a model for each policy, without considering the others, the estimated association could be biased and confounded by the effect of previous or subsequent interventions. Here, we deepen this last point by investigating the “net” associations between intervention policies and total alcohol consumption, taking into account contextual factors, and including all policies in the same regression model (see the European level analysis described in Baccini & Carreras, 2014). The associations were estimated for each country, and the results were compared in a random-effects meta-analysis. We first describe the typologies of policy considered in the analysis. Then, details on the regression model and on the meta-analysis model are provided.
This paper focuses on the association between alcohol consumption and the introduction of control policy measures, within the AMPHORA 12 country European project. We estimated the “net” associations between intervention policies and total alcohol consumption, taking into account contextual socioeconomic factors and including all policies in the same regression model. The associations were estimated for each country, and the country-specific results were compared in a random-effects meta-analysis. The association between policy measures and total alcohol consumption was very heterogeneous among countries. Policies on restricting alcohol availability and on enhancing the minimum age for alcohol purchase appeared to be related to decreasing alcohol consumption. The evidence regarding the effect of the others kinds of interventions was more contradictory. Keywords meta-analysis, policy measures, time series, alcohol consumption
INTRODUCTION
Variations in alcohol consumption in a population can depend on changes in unplanned socioeconomic and cultural factors, demographic dynamics, and implementation of planned control policies. The AMPHORA project examined the pattern over time of alcohol consumption, as related to policy measures and time-varying unplanned factors, in 12 European countries1 during 1960s to 2000s. The participating countries were characterized by different climate, geography, culture, and socioeconomic and political structure; the relevance of each unplanned factor on alcohol consumption was expected to be different among countries or groups of countries. In addition, similar policy measures when applied in different countries could have had different impacts on alcohol con-
POLICIES TYPOLOGY
During the study period, a very large number of different control policy measures have been introduced in
Address correspondence to Michela Baccini, Department of Statistics, Informatics and Applications “G.Parenti,” University of Florence, Florence, Italy; E-mail: [email protected] 1 Austria, Finland, France, Hungary, Italy, Netherlands, Norway, Poland, Spain, Sweden, Switzerland, and United Kingdom.
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the enrolled countries, so that considering the association of each intervention with alcohol consumption was not possible. In order to reduce the problem complexity, we investigated for each country a restricted set of policies. In particular, for each country, the most relevant interventions introduced during the study period, for a maximum of six, were a priori identified by a team of experts (see Allamani et al., 2014). Moreover, after having defined five different policy typologies, we classified the selected policies according to this partition. The five defined typologies were the following:
• Restrictive advertising policies: policies that introduced limitations in alcohol advertising • Restrictive availability policies: restrictive policies acting on licensing rules and trading hour sales • Permissive availability policies: permissive policies acting on licensing rules and trading hour sales • Changes of the legal minimum age to buy alcohol • Changes of the legal blood alcohol concentration (BAC) limit when driving In Table 1, the year of introduction of each selected policy was reported by country and intervention typology. Intervention policies introducing taxes on alcohol purchase are not included in this classification. However, relying on the importance of the economic factors in determining changes on alcohol consumption (see e.g., Nelson, 2010), in the present work, we considered the association between price of the two main alcoholic beverages (i.e., the most drunk during the study period) and total alcohol consumption. As prices are inclusive of taxes, the estimated associations should partly capture the effect of taxes on consumption (Elder et al., 2010; Sornpaisarn, Shield, Cohen, Schwartz, & Rehm, 2013; Xu & Chaloupka, 2011; Wagenaar, Salois, & Komro, 2009; Nelson 2013). STATISTICAL METHODS
The statistical approach consisted of two steps. First, country-specific analyses were performed according to the same regression model to obtain country-specific estimates of the association between each kind of policy and alcohol consumption, adjusted for contextual unplanned
factors. Second, the country-specific estimates were compared and combined in a random-effects meta-analysis. Regression Model
A regression model was specified to estimate the association between policy measures and pro capita total alcohol consumption on a logarithmic scale (Ledermann, 1956). We adjusted for the confounding effect of the unplanned contextual factors by introducing in the model a linear time trend, which captured the long-term trend in consumption eventually related to unobserved factors, and few other relevant variables: logarithm of income, percentage of men over 65 years of age, and urbanization level (Baccini & Carreras, 2014; Nelson, 2010). Since the effect of this last variable was expected to be delayed in time, its lagged association with the outcome was considered by introducing in the model the mean of the urbanization in the current and in the previous 2 years. Regarding the associations of interest, two linear terms for the logarithmic price of the two main alcoholic beverages (the most drunk during the study period) were included in the model (Nelson, 2010); moreover, relying on the assumption that policies belonging to the same subgroup had the same effect regardless year of introduction, we introduced a regression term for each of the five policy typologies. This reduced the number of parameters needed to investigate the associations of interest and, at the same time, simplified comparison among countries. The effect of restrictive advertising policies, restrictive availability policies, and permissive availability policies was modeled by step variables. In particular, for restrictive (advertising and availability) policies, a variable was defined that was equal to 0 up to the first intervention, one from the first to the second, two from the second to the third, and so on. For permissive policies, analogous but decreasing step functions were specified. For policies acting on BAC limit, we introduced in the model a continuous variable corresponding to the BAC limit, setting to 1% by volume the BAC limit before introduction of any policy. The effect of the minimum age limit was investigated by including in the model a continuous variable corresponding to the minimum age, setting to
TABLE 1. Years of introduction of the most relevant policies by country Country Austria Finland France Hungary Italy The Netherlands Norway Poland Spain Sweden Switzerland UK
Restrictive advertising
Restrictive availability
Permissive availability
Minimum age
BAC limit
1966 1969 1991
1998
1969, 1995
1974 1987 2001
1977 1991, 1998 1991, 2001
1975 1988
1982 1989, 1990 1977, 1982 1980, 1999 1997, 2001
1990 1967 1973, 1998 1984, 1990, 1993, 1996 1982 1965, 2001, 2004 1968 2002
2002
1970, 1995 1975 1988 1974
1992, 1998 1990 2005 1967
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14 the minimum age in the absence of any policy (Nelson, 2010). Summarizing, the final model was the following: log Ct = β0 + β1 t + β2 log It + β3 log Pt + β4 log PtC + β5 log At + β6 log Ut(0−2) + + γ2 Availrestrictive + γ1 Advrestrictive t t permissive
+ γ3 Availt + γ5 BACt + εt
+ γ4 MinAget
where Ct : total pro capita alcohol consumption It : income Pt : price of the main alcoholic beverage PtC : price of its competitor At : percentage of men over 65 years of age Ut(0−2) : average urbanization level in the current and in the previous 2 years : restrictive advertising policies Advrestrictive t : restrictive availability policies Availrestrictive t permissive : permissive availability policies Availt MinAget : minimum age BACt : BAC limit εt : Normally distributed error term (independence was assumed between error terms). The assumption of independent error terms was checked by inspection of the residuals partial autocorrelation function. Neither autocorrelation nor moving average structure of the errors arose, so that the assumption was considered appropriate and substantial underestimation of the standard errors is not expected. Positive/negative regression coefficients for restrictive advertising policies and restrictive availability policies indicate that these policies are associated with increase/reduction of alcohol consumption. On the contrary, positive/negative regression coefficients for permissive policies indicate that these policies are related to reduction/increase of alcohol consumption. A positive regression coefficient for the BAC variable indicates that lowering the limit is associated with alcohol consumption reduction. A negative regression coefficient for the minimum age variable indicates that the increase of the minimum age required to buy alcoholic beverages is associated with total consumption reduction. It should be noticed that the parametrization we adopted to estimate the association between changes in BAC limit or minimum age and alcohol consumption allowed to treat at the same time permissive and restrictive interventions. The regression coefficients for advertising and availability policies quantify the variation in log-consumption after policy implementation. Through a simple transformation, 100 × (exp(coefficient) − 1)—which, for small value of the coefficient is approximately equal to 100 × coefficient—it is possible to obtain the percent change in consumption associated to the policy introduction. As an example, a coefficient equal to −0.0006 indicates that the
policy is associated to 0.06% decrease in consumption. On the basis of the same transformation, it is possible to evaluate the percent reduction in consumption associated to increasing the age limit to buy alcoholic drinks by 1 year or to decreasing the BAC limit by 1% by volume. For example, if the regression coefficient for minimum age (which measures the change in log consumption associated to increasing the limit by 1 year) is equal to 0.003, then the associated percent reduction in consumption is 0.3%. Finally, the coefficients for the prices variables express the elasticity of alcohol consumption to changes in alcohol beverages prices (Nelson and Young, 2001); for example, a coefficient equal to −0.0003 indicates that 1% increase in price is associated to 0.03% decrease in consumption. Meta-Analysis Model
At the second step of the analysis, the country-specific estimates of the coefficients for each of the five policy typologies and for the price variables were combined in random-effects meta-analyses (DerSimonian & Laird 1986). Let βˆc be the estimated value of the association of interest for the c-th country and σˆ c2 the correspondent estimated variance. The random-effects meta-analysis model assumes that: βˆc = β + u c + εc ,
(1)
where β is the overall meta-analytic association, uc is a normal distributed random effect that expresses how much the association in the c-th country differs from the mean, and εc is a normal distributed error term with mean 0 and known variance σˆ c2 . The random effect has mean 0 and variance τ 2 . Large values of τ 2 indicate strong heterogeneity among countries. The random-effects meta-analysis allowed comparison among countries and estimation of an average association between each factor and total alcohol consumption, expressed by the overall meta-analytic estimate. In order to quantify heterogeneity among countries, we calculated the I2 index, i.e., the percentage of the total variability due to discrepancy among countries (Higgins & Thompson, 2002). Large I2 values (close to 100%) indicated relevant differences in the estimated associations among countries. For each meta-analysis, a forest plot was created, where the country-specific estimates of the association between factor and alcohol consumption were reported (points) with their 90% confidence intervals. The overall metaanalytic estimate, i.e., the average association, was also included in the plot, even if in a context of large heterogeneity among countries it should be stressed that this overall estimate is the average of very different results. RESULTS
Table 2 reports the estimated meta-analytic coefficients for the associations between each type of policy measure
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TABLE 2. Meta-analysis results by policy type Policy type
Meta-analytical coefficient (×100)a (90% CI)
I2
Price main beverage 1 Price main beverage 2 Advertising Available restrictive Available permissive BAC Age
−0.03 (−0.16, 0.10) −0.0009 (−0.0025, 0.0007) −0.6 (−3.6, 2.3) −3.9 (−8.8, 1.1) 0.9 (−4.6, 6.3) 0.3 (−10.0, 10.7) −9.8 (−15.4, −4.2)
93.3 88.5 69.1 92.2 95.4 88.9 97.8
a The reported results (coefficient × 100) express the elasticity of consumption relative to changes in prices, the percent change in consumption associated with the introduction of an intervention policy on advertising or availability, and the percent change in consumption associated with 1 unit increase in BAC limit or minimum age.
and alcohol consumption, with their 90% confidence interval, and the corresponding I2 indexes. Regarding the association between price of the main alcoholic beverages and total alcohol consumption, high heterogeneity among countries was observed, with 93.3% and 88.5% of total variability explained by differences among countries for the main beverage and the main com-
petitor, respectively (Table 2). Considering the main alcohol beverage, we found that the confidence interval for the overall estimate of elasticity was almost centered in zero, showing that no indication of a clear positive or negative association between price and total alcohol consumption arise from the country-specific results (Table 2). At the level of single countries, a negative association was found for Norway, Poland, Spain, and United Kingdom, and a positive association was found for France and Italy (Figure 1, first panel). Considering the main competitor, we also found that price was not overall associated with changes in total alcohol consumption, with very heterogeneous results among countries (Table 2 and Figure 1, second panel). The association between policy measures and alcohol consumption appeared very heterogeneous among countries, with I-squared values ranging from 69.1% to 98.2% (Table 2). During the study period, policies that introduced limitations in alcohol advertising were implemented in six countries (Figure 2, first panel). We found that overall there was no evident association with the outcome: the 90% confidence intervals for the meta-analytic estimate ranged from a decrease in consumption of 3.6% to an increase of 2.3%. France was an exception: the implementation in 1987 of a restrictive policy on advertising appeared to be associated with a reduction in consumption
Price main beverage 1
Price main beverage 2
Austria
Austria
Finland
Finland
France
France
Hungary
Hungary
Italy
Italy
Netherlands
Netherlands
Norway
Norway
Poland
Poland
Spain
Spain
Sweden
Sweden
Switzerland
Switzerland
United Kingdom
United Kingdom
pooled
pooled
-1.278
-0.785
-0.292 elasticity
0.201
0.694
-0.678
-0.366
-0.054
0.258
0.570
elasticity
FIGURE 1. Forest plot of the effect of prices of the two main beverages for each countries on alcohol consumption. Results are expressed in terms of elasticity of alcohol consumption to changes in alcohol beverage prices. In grey the area corresponding to values of coefficient indicating that an increase in price corresponds to a decrease in alcohol consumption. In white the area corresponding to values of coefficient indicating that an increase in price corresponds to an increase in alcohol consumption.
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M. BACCINI AND G. CARRERAS Restrictive advertising policies
Restrictive availability policies
Permissive availability policies
Austria
Austria
Austria
Finland
Finland
Finland
France
France
France
Hungary
Hungary
Hungary
Italy
Italy
Italy
Netherlands
Netherlands
Netherlands
Norway
Norway
Norway
Poland
Poland
Poland
Spain
Spain
Spain
Sweden
Sweden
Sweden
Switzerland
Switzerland
Switzerland
United Kingdom
United Kingdom
United Kingdom
pooled
pooled
pooled
-8.475
-4.591
-0.708
3.176
7.059
-30.382
-20.484
% change
-10.585
-0.686
9.212
% change
-13.267
-4.672
3.923
12.518
21.113
% change
FIGURE 2. Forest plot of the association between total alcohol consumption and policies on alcoholic beverages availability and advertisement. Results are expressed in terms of percent change in consumption associated with the policy introduction. The grey area corresponds to values indicating that the policy is associated with a decrease in alcohol consumption; the white area corresponds to values indicating that the policy is associated with an increase in alcohol consumption.
by 4%. Unexpected association with a consumption increase was estimated for Norway. Restrictive policies concerning alcohol availability were introduced in eight out of the 12 enrolled countries (Figure 2, second panel). These policies were associated with decreasing alcohol consumption in Poland, Spain, Sweden, and Switzerland and with unexpected increasing consumption in United Kingdom. No association was estimated in Italy, Hungary, and the Netherlands. Despite the large heterogeneity, we can conclude that on average a certain tendency of this kind of policy to be associated to alcohol consumption reduction was observed, as shown by the confidence interval which is strongly asymmetric towards negative values (Table 2). Also, for permissive availability policies, which were introduced in eight countries, a considerable variability of results was observed (Table 2 and Figure 2, third panel). They were associated with increased alcohol consumption for Netherlands, Norway, Sweden, and United Kingdom. In Finland, Hungary and Poland, permissive availability policies showed association with decreasing consumption. Policy measures acting on BAC limit were introduced in nine countries (Figure 3, first panel). In three countries, Austria, France, and Switzerland, reducing BAC limit was associated with a reduction in alcohol consumption. In the others countries, an opposite relationship was found. Regarding interventions on minimum age allowed to alcohol purchase, if we limited the attention to the set of the restrictive measures, increasing minimum age was significantly associated to consumption reduction in most countries (France, Spain and Switzerland), but, due to the
unexpected consumption increase in Austria, combining these results in a meta-analysis, a relevant overall association did not arise (results not shown). When also the permissive minimum age measure introduced in Finland in 1969 was considered (Table 2 and Figure 3, second panel), the increase of the minimum age allowed to alcohol purchase appeared to be clearly associated to an overall consumption reduction: increasing the minimum age limit by 1 year was overall associated to a decrease of 9.8% in alcohol consumption (90% CI: 15.4%, 4.2%) (Table 2).
DISCUSSION
With this work we investigated the “net” relationship between policy interventions and total alcohol consumption in the European countries involved in the AMPHORA project. Due to the involved countries are not a “random” subset from Europe, the meta-analysis results reported in the present paper cannot be generalized to Europe as a whole, specially because large heterogeneity among countries was measured. The same modeling approach was adopted for all the country-specific analyses. In respect to an extensive review of published studies, the proposed planned metaanalysis does not incur in heterogeneity due to different country-specific analysis methods, allowing at the same time comparison among countries. All policies were introduced in the country-specific regression models at the same time. The estimated associations were adjusted for the confounding effect of unplanned contextual factors, the role of which was
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Change in BAC limit
Change in minimum age
Austria
Austria
Finland
Finland *
France
France
Hungary
Hungary
Italy
Italy
Netherlands
Netherlands
Norway
Norway
Poland
Poland
Spain
Spain
Sweden
Sweden
Switzerland
Switzerland
United Kingdom
United Kingdom
pooled
pooled
-57.492
-29.873
-2.255
25.364
52.982
% change
-53.147
-37.864
-22.581
-7.298
7.986
% change
FIGURE 3. Forest plot of the association between total alcohol consumption and policies on BAC limit and minimum age. Results are expressed in terms of percent change in consumption associated with increasing the BAC limit by 1% by volume or the minimum age by 1 year. The grey area corresponds to values indicating that decreasing the BAC limit or increasing the minimum age required to buy alcoholic beverages is associated with a decrease in alcohol consumption; the white area corresponds to values indicating that decreasing the BAC limit or increasing the minimum age is associated with an increase in alcohol consumption. ∗ The result for Finland refers to a permissive policy on minimum age, which resulted to be associated with an increase in total alcohol consumption.
demonstrated to be predominant in determining changes in consumption over time (Allamani et al., 2014). We found a slight evidence that on average introducing restrictive policies on alcohol availability can be associated with decreasing consumption. This result agrees with the marginal analysis reported in Allamani et al. (2014), where, overall, restrictive interventions on availability after the first one appeared to be associated with consumption decrease. Overall, increasing the minimum age for alcohol purchase appeared associated with a decrease in consumption. This is a clear result even if based on observations from only five countries. It should be noticed that in the present analysis, permissive and restrictive measures were treated together as a whole, enriching the available evidence. The analysis reported in Figure 3 in Allamani et al. (2014), which excluded the permissive intervention introduced in 1969 in Finland, was not able to highlight this result with the same strength. We failed to find an association of total alcohol consumption with permissive availability policies and restrictive advertisement policies. Regarding interventions
on BAC, in principle they could not be causally related with changes in consumption, but rather with changes in transport-related harm (Mitis & Sethis, 2012). Then, it is not surprising that we failed to find a significant association with consumption decrease, if we exclude Austria, France, and Switzerland. Moreover, lowering the BAC limit could be effective in reducing consumption of specific alcoholic beverages but not total consumption. For example, the 1974 Dutch BAC law appeared effective in reducing spirit consumption, but not beer and wine consumption. Regarding elasticity of total alcohol consumption to changes of alcoholic beverages price, if we exclude Italy and France, increases in price of the main alcoholic beverage were related to outcome decreases. The elasticity estimates are coherent with those reported in other international studies (Selvanathan and Selvanathan, 2005; Nelson, 2010). Less clear is the relationship between total consumption and price of the main competing beverage. In interpreting the results, we should account that sometimes interventions of different typology have been implemented within few years. This, together with the fact
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that we did not model the delayed effect of policies, could have brought in some situations to confusing the effect of measures close in time. This could be the case of Norway, where a restrictive policy on advertising (which resulted to be associated with an increase of consumption) was implemented 2 years after the first permissive availability policy in 1973; or of England, where a restrictive policy on availability was introduce in 2001, just 1 year before the introduction of a permissive policy (in this case, both the policy typologies appeared associated with an increase of alcohol consumption). In addition, sometimes the selected policies consisted of a set of measures, and we classified them, according to the defined partition, on the basis of the measure that appeared predominant. In these cases, a great effect may be estimated due to the complex intervention implemented in that year, but the estimated association should not be completely attributed to a specific policy typology. High heterogeneity was observed among countries, with sometimes opposite signs for the estimated associations. Part of this heterogeneity could be explained by the fact that policies of the same typology have been introduced in very different calendar periods, depending on the country. For example, considering the set of the selected policies, the first intervention on BAC limit was in 1967 in UK, the last one in 2005 in Switzerland. We cannot exclude that calendar time acted as an effect modifier. Others sources of heterogeneity could be related to the context where the policies were applied or to the modality and efficacy of their implementation, which can strongly ¨ vary across countries (Karlsson & Osterberg, 2001, 2007). The approach presented in this paper assumes that the effect of policies of the same type introduced in the same country in different years was the same: we estimated for each country an average association between each kind of intervention and total alcohol consumption, without evaluating whether the impact of the first policy on consumption was different from the impact of the second one, and so on. With the aim to estimate the “net” effect of each policy, given the others, this was a needed limitation to avoid introducing a very large number of parameters in the regression model with consequent loss of power. Allamani et al. (2014) partly explored this point. For example, they found that the first introduction of availability permissive measures was associated with an increase in total alcohol consumption overall, but that such association disappeared when the permissive measures were reiterated. Analogously, they found that the second policy on BAC limit, but not the first one, was overall associated with a decrease in total alcohol consumption. However, these evaluations, being based on associations estimated by including one policy at a time in the regression model, are affected by the limitations previously described. Regarding the choice of selecting a subset of relevant policies in each country, this could have brought to lose some potentially relevant policy actions and to biased results for the policies that were taken into account in the model. However, focusing on a restricted set of events was necessary to avoid too many overlapping interven-
tions, whose single contribution could not be otherwise detected. CONCLUSIONS
The present analysis showed that the association between policy measures and total alcohol consumption in Europe during the study period was very heterogeneous. Policies that restricted alcohol availability and lowered minimum age required to buy alcoholic beverages appeared to be related to decreasing alcohol consumption. For minimum age policies, the association was found only if the 1969 Finnish permissive measure was considered in the analysis. The evidence regarding the others kinds of interventions was more contradictory. The results confirm and enforce those obtained including one policy at time in the country-specific model and discussed in Allamani et al. (2014). In interpreting the results one should consider the time series and aggregate nature of the data and the related limitations. Data at the individual level would be more effective in demonstrating efficacy of specific restrictive or permissive interventions. Planning ad hoc prospective cohort studies which monitor alcoholic beverages consumption and related habits in subgroups of population before and after the introduction of policy measures should be encouraged. Declaration of Interest
The authors declare no conflicts of interest. The authors alone are responsible for the content and writing of the paper. THE AUTHORS 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, and multiple imputation.
Giulia Carreras, Ph.D., is a Statistician at the Cancer Prevention and Research Institute in Florence. She is author of several papers in the field of primary prevention and environmental epidemiology. Her key research areas are Markov models and dynamic models for decision making, analysis of prevention studies, and health impact assessment.
POLICY MEASURES AND ALCOHOL CONSUMPTION IN EUROPE
REFERENCES Allamani, A., Pepe, P., Baccini, M., Massini, G., & Voller, F. (2014). 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. Baccini, M., & Carreras, G. (2014). Analyzing the effect of policy measures and socio-demographic factors on alcohol consumption in Europe within the AMPHORA project: Statistical methods. Substance Use & Misuse, 49. DerSimonian, R., & Laird, N. (1986) Meta-analysis in clinical trials. Controlled Clinical Trials, 7, 177–188. Elder, R. W., Lawrence, B., Ferguson, A., Naimi, T. S., Brewer, R. D., Chattopadhyay, S. K., . . . Task Force on Community Preventive Services. (2010). The effectiveness of tax policy interventions for reducing excessive alcohol consumption and related harms. American Journal of Preventive Medicine, 38, 217–229. Higgins, J. P., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539–1558. ¨ Karlsson, T., & Osterberg, E. (2001). A scale of formal alcohol control policy in 15 European countries. Nordic Studies on Alcohol and Drugs. English Supplement, 18, 117–131. ¨ Karlsson, T., & Osterberg, E. (2007). Scaling alcohol control policies across Europe. Drugs: Education, Prevention & Policy, 14, 499–511. Ledermann, S. (1956). Alcool, alcoolisme, alcoolisation. Donn´es scientifiques de caract´ere physiologique, e´ conomique et social. Paris: Presses Universitaires de France.
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Mitis, F., & Sethis, D. (2012). Reducing injuries and death from alcohol-related road crashes. In P. Anderson, L. Møller, & G. Galea (Eds.), Alcohol in the European Union. Consumption, harm and policy approaches (pp. 49–54). Copenhagen, Denmark: World Health Organization, Regional Office for Europe. Nelson, J. P. (2010). Alcohol, unemployment rates and advertising bans: International panel evidence, 1975–2000. Journal of Public Affairs, 10, 74–87. Nelson, J. P. (2013) Meta-analysis of alcohol price and income elasticities—with corrections for publication bias. Health Economics Review, 3, 17. Nelson, J. P., & Young, D. J. (2001). Do advertising bans work? An international comparison. International Journal of Advertising, 20, 273–296. Selvanathan, S. & Selvanathan, E. A. (2005) Empirical regularities in cross-country alcohol consumption. Economic Record, 81, S128–S142. Sornpaisarn, B., Shield, K., Cohen, J., Schwartz R., & Rehm, J. (2013). Elasticity of alcohol consumption, alcohol-related harms, and drinking initiation in low- and middle-income countries: A systematic review and meta-analysis. The International Journal of Alcohol and Drug Research, 2, 45–58. Wagenaar, A. C., Salois, M. J. & Komro, K. A. (2009). Effects of beverage alcohol price and tax levels on drinking: A metaanalysis of 1003 estimates from 112 studies. Addiction, 104, 179–190. Xu, X., & Chaloupka, F. J. (2011). The effects of prices on alcohol use and its consequences. Alcohol Research & Health, 34, 236–245.
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ORIGINAL ARTICLE
Analyzing and Comparing the Association Between Control Policy Measures and Alcohol Consumption in Europe Michela Baccini1,2 and Giulia Carreras2 1 2
Department of Statistics, Informatics and Applications “G.Parenti,” University of Florence, Florence, Italy; ISPO Cancer Prevention and Research Institute, Florence, Italy sumption, depending on the way they were implemented and on the context where they were introduced. This paper investigates whether during the study period alcohol consumption was associated with the introduction of control policy measures. We explored heterogeneity among countries and tried to detect the typologies of interventions that appeared more related to changes in the outcome. In Allamani, Pepe, Baccini, Massini, and Voller (2014), the results are presented concerning the relative importance of unplanned contextual variables and policy measures on changes in alcohol consumption during the study period in the 12 countries. In that paper, the association between each intervention and the outcome was examined trough time series regressions, where the policies were included one at time, adjusting for the unplanned variables (see the country-specific analysis described in Baccini & Carreras, 2014). However, by fitting a model for each policy, without considering the others, the estimated association could be biased and confounded by the effect of previous or subsequent interventions. Here, we deepen this last point by investigating the “net” associations between intervention policies and total alcohol consumption, taking into account contextual factors, and including all policies in the same regression model (see the European level analysis described in Baccini & Carreras, 2014). The associations were estimated for each country, and the results were compared in a random-effects meta-analysis. We first describe the typologies of policy considered in the analysis. Then, details on the regression model and on the meta-analysis model are provided.
This paper focuses on the association between alcohol consumption and the introduction of control policy measures, within the AMPHORA 12 country European project. We estimated the “net” associations between intervention policies and total alcohol consumption, taking into account contextual socioeconomic factors and including all policies in the same regression model. The associations were estimated for each country, and the country-specific results were compared in a random-effects meta-analysis. The association between policy measures and total alcohol consumption was very heterogeneous among countries. Policies on restricting alcohol availability and on enhancing the minimum age for alcohol purchase appeared to be related to decreasing alcohol consumption. The evidence regarding the effect of the others kinds of interventions was more contradictory. Keywords meta-analysis, policy measures, time series, alcohol consumption
INTRODUCTION
Variations in alcohol consumption in a population can depend on changes in unplanned socioeconomic and cultural factors, demographic dynamics, and implementation of planned control policies. The AMPHORA project examined the pattern over time of alcohol consumption, as related to policy measures and time-varying unplanned factors, in 12 European countries1 during 1960s to 2000s. The participating countries were characterized by different climate, geography, culture, and socioeconomic and political structure; the relevance of each unplanned factor on alcohol consumption was expected to be different among countries or groups of countries. In addition, similar policy measures when applied in different countries could have had different impacts on alcohol con-
POLICIES TYPOLOGY
During the study period, a very large number of different control policy measures have been introduced in
Address correspondence to Michela Baccini, Department of Statistics, Informatics and Applications “G.Parenti,” University of Florence, Florence, Italy; E-mail: [email protected] 1 Austria, Finland, France, Hungary, Italy, Netherlands, Norway, Poland, Spain, Sweden, Switzerland, and United Kingdom.
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the enrolled countries, so that considering the association of each intervention with alcohol consumption was not possible. In order to reduce the problem complexity, we investigated for each country a restricted set of policies. In particular, for each country, the most relevant interventions introduced during the study period, for a maximum of six, were a priori identified by a team of experts (see Allamani et al., 2014). Moreover, after having defined five different policy typologies, we classified the selected policies according to this partition. The five defined typologies were the following:
• Restrictive advertising policies: policies that introduced limitations in alcohol advertising • Restrictive availability policies: restrictive policies acting on licensing rules and trading hour sales • Permissive availability policies: permissive policies acting on licensing rules and trading hour sales • Changes of the legal minimum age to buy alcohol • Changes of the legal blood alcohol concentration (BAC) limit when driving In Table 1, the year of introduction of each selected policy was reported by country and intervention typology. Intervention policies introducing taxes on alcohol purchase are not included in this classification. However, relying on the importance of the economic factors in determining changes on alcohol consumption (see e.g., Nelson, 2010), in the present work, we considered the association between price of the two main alcoholic beverages (i.e., the most drunk during the study period) and total alcohol consumption. As prices are inclusive of taxes, the estimated associations should partly capture the effect of taxes on consumption (Elder et al., 2010; Sornpaisarn, Shield, Cohen, Schwartz, & Rehm, 2013; Xu & Chaloupka, 2011; Wagenaar, Salois, & Komro, 2009; Nelson 2013). STATISTICAL METHODS
The statistical approach consisted of two steps. First, country-specific analyses were performed according to the same regression model to obtain country-specific estimates of the association between each kind of policy and alcohol consumption, adjusted for contextual unplanned
factors. Second, the country-specific estimates were compared and combined in a random-effects meta-analysis. Regression Model
A regression model was specified to estimate the association between policy measures and pro capita total alcohol consumption on a logarithmic scale (Ledermann, 1956). We adjusted for the confounding effect of the unplanned contextual factors by introducing in the model a linear time trend, which captured the long-term trend in consumption eventually related to unobserved factors, and few other relevant variables: logarithm of income, percentage of men over 65 years of age, and urbanization level (Baccini & Carreras, 2014; Nelson, 2010). Since the effect of this last variable was expected to be delayed in time, its lagged association with the outcome was considered by introducing in the model the mean of the urbanization in the current and in the previous 2 years. Regarding the associations of interest, two linear terms for the logarithmic price of the two main alcoholic beverages (the most drunk during the study period) were included in the model (Nelson, 2010); moreover, relying on the assumption that policies belonging to the same subgroup had the same effect regardless year of introduction, we introduced a regression term for each of the five policy typologies. This reduced the number of parameters needed to investigate the associations of interest and, at the same time, simplified comparison among countries. The effect of restrictive advertising policies, restrictive availability policies, and permissive availability policies was modeled by step variables. In particular, for restrictive (advertising and availability) policies, a variable was defined that was equal to 0 up to the first intervention, one from the first to the second, two from the second to the third, and so on. For permissive policies, analogous but decreasing step functions were specified. For policies acting on BAC limit, we introduced in the model a continuous variable corresponding to the BAC limit, setting to 1% by volume the BAC limit before introduction of any policy. The effect of the minimum age limit was investigated by including in the model a continuous variable corresponding to the minimum age, setting to
TABLE 1. Years of introduction of the most relevant policies by country Country Austria Finland France Hungary Italy The Netherlands Norway Poland Spain Sweden Switzerland UK
Restrictive advertising
Restrictive availability
Permissive availability
Minimum age
BAC limit
1966 1969 1991
1998
1969, 1995
1974 1987 2001
1977 1991, 1998 1991, 2001
1975 1988
1982 1989, 1990 1977, 1982 1980, 1999 1997, 2001
1990 1967 1973, 1998 1984, 1990, 1993, 1996 1982 1965, 2001, 2004 1968 2002
2002
1970, 1995 1975 1988 1974
1992, 1998 1990 2005 1967
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M. BACCINI AND G. CARRERAS
14 the minimum age in the absence of any policy (Nelson, 2010). Summarizing, the final model was the following: log Ct = β0 + β1 t + β2 log It + β3 log Pt + β4 log PtC + β5 log At + β6 log Ut(0−2) + + γ2 Availrestrictive + γ1 Advrestrictive t t permissive
+ γ3 Availt + γ5 BACt + εt
+ γ4 MinAget
where Ct : total pro capita alcohol consumption It : income Pt : price of the main alcoholic beverage PtC : price of its competitor At : percentage of men over 65 years of age Ut(0−2) : average urbanization level in the current and in the previous 2 years : restrictive advertising policies Advrestrictive t : restrictive availability policies Availrestrictive t permissive : permissive availability policies Availt MinAget : minimum age BACt : BAC limit εt : Normally distributed error term (independence was assumed between error terms). The assumption of independent error terms was checked by inspection of the residuals partial autocorrelation function. Neither autocorrelation nor moving average structure of the errors arose, so that the assumption was considered appropriate and substantial underestimation of the standard errors is not expected. Positive/negative regression coefficients for restrictive advertising policies and restrictive availability policies indicate that these policies are associated with increase/reduction of alcohol consumption. On the contrary, positive/negative regression coefficients for permissive policies indicate that these policies are related to reduction/increase of alcohol consumption. A positive regression coefficient for the BAC variable indicates that lowering the limit is associated with alcohol consumption reduction. A negative regression coefficient for the minimum age variable indicates that the increase of the minimum age required to buy alcoholic beverages is associated with total consumption reduction. It should be noticed that the parametrization we adopted to estimate the association between changes in BAC limit or minimum age and alcohol consumption allowed to treat at the same time permissive and restrictive interventions. The regression coefficients for advertising and availability policies quantify the variation in log-consumption after policy implementation. Through a simple transformation, 100 × (exp(coefficient) − 1)—which, for small value of the coefficient is approximately equal to 100 × coefficient—it is possible to obtain the percent change in consumption associated to the policy introduction. As an example, a coefficient equal to −0.0006 indicates that the
policy is associated to 0.06% decrease in consumption. On the basis of the same transformation, it is possible to evaluate the percent reduction in consumption associated to increasing the age limit to buy alcoholic drinks by 1 year or to decreasing the BAC limit by 1% by volume. For example, if the regression coefficient for minimum age (which measures the change in log consumption associated to increasing the limit by 1 year) is equal to 0.003, then the associated percent reduction in consumption is 0.3%. Finally, the coefficients for the prices variables express the elasticity of alcohol consumption to changes in alcohol beverages prices (Nelson and Young, 2001); for example, a coefficient equal to −0.0003 indicates that 1% increase in price is associated to 0.03% decrease in consumption. Meta-Analysis Model
At the second step of the analysis, the country-specific estimates of the coefficients for each of the five policy typologies and for the price variables were combined in random-effects meta-analyses (DerSimonian & Laird 1986). Let βˆc be the estimated value of the association of interest for the c-th country and σˆ c2 the correspondent estimated variance. The random-effects meta-analysis model assumes that: βˆc = β + u c + εc ,
(1)
where β is the overall meta-analytic association, uc is a normal distributed random effect that expresses how much the association in the c-th country differs from the mean, and εc is a normal distributed error term with mean 0 and known variance σˆ c2 . The random effect has mean 0 and variance τ 2 . Large values of τ 2 indicate strong heterogeneity among countries. The random-effects meta-analysis allowed comparison among countries and estimation of an average association between each factor and total alcohol consumption, expressed by the overall meta-analytic estimate. In order to quantify heterogeneity among countries, we calculated the I2 index, i.e., the percentage of the total variability due to discrepancy among countries (Higgins & Thompson, 2002). Large I2 values (close to 100%) indicated relevant differences in the estimated associations among countries. For each meta-analysis, a forest plot was created, where the country-specific estimates of the association between factor and alcohol consumption were reported (points) with their 90% confidence intervals. The overall metaanalytic estimate, i.e., the average association, was also included in the plot, even if in a context of large heterogeneity among countries it should be stressed that this overall estimate is the average of very different results. RESULTS
Table 2 reports the estimated meta-analytic coefficients for the associations between each type of policy measure
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POLICY MEASURES AND ALCOHOL CONSUMPTION IN EUROPE
TABLE 2. Meta-analysis results by policy type Policy type
Meta-analytical coefficient (×100)a (90% CI)
I2
Price main beverage 1 Price main beverage 2 Advertising Available restrictive Available permissive BAC Age
−0.03 (−0.16, 0.10) −0.0009 (−0.0025, 0.0007) −0.6 (−3.6, 2.3) −3.9 (−8.8, 1.1) 0.9 (−4.6, 6.3) 0.3 (−10.0, 10.7) −9.8 (−15.4, −4.2)
93.3 88.5 69.1 92.2 95.4 88.9 97.8
a The reported results (coefficient × 100) express the elasticity of consumption relative to changes in prices, the percent change in consumption associated with the introduction of an intervention policy on advertising or availability, and the percent change in consumption associated with 1 unit increase in BAC limit or minimum age.
and alcohol consumption, with their 90% confidence interval, and the corresponding I2 indexes. Regarding the association between price of the main alcoholic beverages and total alcohol consumption, high heterogeneity among countries was observed, with 93.3% and 88.5% of total variability explained by differences among countries for the main beverage and the main com-
petitor, respectively (Table 2). Considering the main alcohol beverage, we found that the confidence interval for the overall estimate of elasticity was almost centered in zero, showing that no indication of a clear positive or negative association between price and total alcohol consumption arise from the country-specific results (Table 2). At the level of single countries, a negative association was found for Norway, Poland, Spain, and United Kingdom, and a positive association was found for France and Italy (Figure 1, first panel). Considering the main competitor, we also found that price was not overall associated with changes in total alcohol consumption, with very heterogeneous results among countries (Table 2 and Figure 1, second panel). The association between policy measures and alcohol consumption appeared very heterogeneous among countries, with I-squared values ranging from 69.1% to 98.2% (Table 2). During the study period, policies that introduced limitations in alcohol advertising were implemented in six countries (Figure 2, first panel). We found that overall there was no evident association with the outcome: the 90% confidence intervals for the meta-analytic estimate ranged from a decrease in consumption of 3.6% to an increase of 2.3%. France was an exception: the implementation in 1987 of a restrictive policy on advertising appeared to be associated with a reduction in consumption
Price main beverage 1
Price main beverage 2
Austria
Austria
Finland
Finland
France
France
Hungary
Hungary
Italy
Italy
Netherlands
Netherlands
Norway
Norway
Poland
Poland
Spain
Spain
Sweden
Sweden
Switzerland
Switzerland
United Kingdom
United Kingdom
pooled
pooled
-1.278
-0.785
-0.292 elasticity
0.201
0.694
-0.678
-0.366
-0.054
0.258
0.570
elasticity
FIGURE 1. Forest plot of the effect of prices of the two main beverages for each countries on alcohol consumption. Results are expressed in terms of elasticity of alcohol consumption to changes in alcohol beverage prices. In grey the area corresponding to values of coefficient indicating that an increase in price corresponds to a decrease in alcohol consumption. In white the area corresponding to values of coefficient indicating that an increase in price corresponds to an increase in alcohol consumption.
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M. BACCINI AND G. CARRERAS Restrictive advertising policies
Restrictive availability policies
Permissive availability policies
Austria
Austria
Austria
Finland
Finland
Finland
France
France
France
Hungary
Hungary
Hungary
Italy
Italy
Italy
Netherlands
Netherlands
Netherlands
Norway
Norway
Norway
Poland
Poland
Poland
Spain
Spain
Spain
Sweden
Sweden
Sweden
Switzerland
Switzerland
Switzerland
United Kingdom
United Kingdom
United Kingdom
pooled
pooled
pooled
-8.475
-4.591
-0.708
3.176
7.059
-30.382
-20.484
% change
-10.585
-0.686
9.212
% change
-13.267
-4.672
3.923
12.518
21.113
% change
FIGURE 2. Forest plot of the association between total alcohol consumption and policies on alcoholic beverages availability and advertisement. Results are expressed in terms of percent change in consumption associated with the policy introduction. The grey area corresponds to values indicating that the policy is associated with a decrease in alcohol consumption; the white area corresponds to values indicating that the policy is associated with an increase in alcohol consumption.
by 4%. Unexpected association with a consumption increase was estimated for Norway. Restrictive policies concerning alcohol availability were introduced in eight out of the 12 enrolled countries (Figure 2, second panel). These policies were associated with decreasing alcohol consumption in Poland, Spain, Sweden, and Switzerland and with unexpected increasing consumption in United Kingdom. No association was estimated in Italy, Hungary, and the Netherlands. Despite the large heterogeneity, we can conclude that on average a certain tendency of this kind of policy to be associated to alcohol consumption reduction was observed, as shown by the confidence interval which is strongly asymmetric towards negative values (Table 2). Also, for permissive availability policies, which were introduced in eight countries, a considerable variability of results was observed (Table 2 and Figure 2, third panel). They were associated with increased alcohol consumption for Netherlands, Norway, Sweden, and United Kingdom. In Finland, Hungary and Poland, permissive availability policies showed association with decreasing consumption. Policy measures acting on BAC limit were introduced in nine countries (Figure 3, first panel). In three countries, Austria, France, and Switzerland, reducing BAC limit was associated with a reduction in alcohol consumption. In the others countries, an opposite relationship was found. Regarding interventions on minimum age allowed to alcohol purchase, if we limited the attention to the set of the restrictive measures, increasing minimum age was significantly associated to consumption reduction in most countries (France, Spain and Switzerland), but, due to the
unexpected consumption increase in Austria, combining these results in a meta-analysis, a relevant overall association did not arise (results not shown). When also the permissive minimum age measure introduced in Finland in 1969 was considered (Table 2 and Figure 3, second panel), the increase of the minimum age allowed to alcohol purchase appeared to be clearly associated to an overall consumption reduction: increasing the minimum age limit by 1 year was overall associated to a decrease of 9.8% in alcohol consumption (90% CI: 15.4%, 4.2%) (Table 2).
DISCUSSION
With this work we investigated the “net” relationship between policy interventions and total alcohol consumption in the European countries involved in the AMPHORA project. Due to the involved countries are not a “random” subset from Europe, the meta-analysis results reported in the present paper cannot be generalized to Europe as a whole, specially because large heterogeneity among countries was measured. The same modeling approach was adopted for all the country-specific analyses. In respect to an extensive review of published studies, the proposed planned metaanalysis does not incur in heterogeneity due to different country-specific analysis methods, allowing at the same time comparison among countries. All policies were introduced in the country-specific regression models at the same time. The estimated associations were adjusted for the confounding effect of unplanned contextual factors, the role of which was
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POLICY MEASURES AND ALCOHOL CONSUMPTION IN EUROPE
Change in BAC limit
Change in minimum age
Austria
Austria
Finland
Finland *
France
France
Hungary
Hungary
Italy
Italy
Netherlands
Netherlands
Norway
Norway
Poland
Poland
Spain
Spain
Sweden
Sweden
Switzerland
Switzerland
United Kingdom
United Kingdom
pooled
pooled
-57.492
-29.873
-2.255
25.364
52.982
% change
-53.147
-37.864
-22.581
-7.298
7.986
% change
FIGURE 3. Forest plot of the association between total alcohol consumption and policies on BAC limit and minimum age. Results are expressed in terms of percent change in consumption associated with increasing the BAC limit by 1% by volume or the minimum age by 1 year. The grey area corresponds to values indicating that decreasing the BAC limit or increasing the minimum age required to buy alcoholic beverages is associated with a decrease in alcohol consumption; the white area corresponds to values indicating that decreasing the BAC limit or increasing the minimum age is associated with an increase in alcohol consumption. ∗ The result for Finland refers to a permissive policy on minimum age, which resulted to be associated with an increase in total alcohol consumption.
demonstrated to be predominant in determining changes in consumption over time (Allamani et al., 2014). We found a slight evidence that on average introducing restrictive policies on alcohol availability can be associated with decreasing consumption. This result agrees with the marginal analysis reported in Allamani et al. (2014), where, overall, restrictive interventions on availability after the first one appeared to be associated with consumption decrease. Overall, increasing the minimum age for alcohol purchase appeared associated with a decrease in consumption. This is a clear result even if based on observations from only five countries. It should be noticed that in the present analysis, permissive and restrictive measures were treated together as a whole, enriching the available evidence. The analysis reported in Figure 3 in Allamani et al. (2014), which excluded the permissive intervention introduced in 1969 in Finland, was not able to highlight this result with the same strength. We failed to find an association of total alcohol consumption with permissive availability policies and restrictive advertisement policies. Regarding interventions
on BAC, in principle they could not be causally related with changes in consumption, but rather with changes in transport-related harm (Mitis & Sethis, 2012). Then, it is not surprising that we failed to find a significant association with consumption decrease, if we exclude Austria, France, and Switzerland. Moreover, lowering the BAC limit could be effective in reducing consumption of specific alcoholic beverages but not total consumption. For example, the 1974 Dutch BAC law appeared effective in reducing spirit consumption, but not beer and wine consumption. Regarding elasticity of total alcohol consumption to changes of alcoholic beverages price, if we exclude Italy and France, increases in price of the main alcoholic beverage were related to outcome decreases. The elasticity estimates are coherent with those reported in other international studies (Selvanathan and Selvanathan, 2005; Nelson, 2010). Less clear is the relationship between total consumption and price of the main competing beverage. In interpreting the results, we should account that sometimes interventions of different typology have been implemented within few years. This, together with the fact
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that we did not model the delayed effect of policies, could have brought in some situations to confusing the effect of measures close in time. This could be the case of Norway, where a restrictive policy on advertising (which resulted to be associated with an increase of consumption) was implemented 2 years after the first permissive availability policy in 1973; or of England, where a restrictive policy on availability was introduce in 2001, just 1 year before the introduction of a permissive policy (in this case, both the policy typologies appeared associated with an increase of alcohol consumption). In addition, sometimes the selected policies consisted of a set of measures, and we classified them, according to the defined partition, on the basis of the measure that appeared predominant. In these cases, a great effect may be estimated due to the complex intervention implemented in that year, but the estimated association should not be completely attributed to a specific policy typology. High heterogeneity was observed among countries, with sometimes opposite signs for the estimated associations. Part of this heterogeneity could be explained by the fact that policies of the same typology have been introduced in very different calendar periods, depending on the country. For example, considering the set of the selected policies, the first intervention on BAC limit was in 1967 in UK, the last one in 2005 in Switzerland. We cannot exclude that calendar time acted as an effect modifier. Others sources of heterogeneity could be related to the context where the policies were applied or to the modality and efficacy of their implementation, which can strongly ¨ vary across countries (Karlsson & Osterberg, 2001, 2007). The approach presented in this paper assumes that the effect of policies of the same type introduced in the same country in different years was the same: we estimated for each country an average association between each kind of intervention and total alcohol consumption, without evaluating whether the impact of the first policy on consumption was different from the impact of the second one, and so on. With the aim to estimate the “net” effect of each policy, given the others, this was a needed limitation to avoid introducing a very large number of parameters in the regression model with consequent loss of power. Allamani et al. (2014) partly explored this point. For example, they found that the first introduction of availability permissive measures was associated with an increase in total alcohol consumption overall, but that such association disappeared when the permissive measures were reiterated. Analogously, they found that the second policy on BAC limit, but not the first one, was overall associated with a decrease in total alcohol consumption. However, these evaluations, being based on associations estimated by including one policy at a time in the regression model, are affected by the limitations previously described. Regarding the choice of selecting a subset of relevant policies in each country, this could have brought to lose some potentially relevant policy actions and to biased results for the policies that were taken into account in the model. However, focusing on a restricted set of events was necessary to avoid too many overlapping interven-
tions, whose single contribution could not be otherwise detected. CONCLUSIONS
The present analysis showed that the association between policy measures and total alcohol consumption in Europe during the study period was very heterogeneous. Policies that restricted alcohol availability and lowered minimum age required to buy alcoholic beverages appeared to be related to decreasing alcohol consumption. For minimum age policies, the association was found only if the 1969 Finnish permissive measure was considered in the analysis. The evidence regarding the others kinds of interventions was more contradictory. The results confirm and enforce those obtained including one policy at time in the country-specific model and discussed in Allamani et al. (2014). In interpreting the results one should consider the time series and aggregate nature of the data and the related limitations. Data at the individual level would be more effective in demonstrating efficacy of specific restrictive or permissive interventions. Planning ad hoc prospective cohort studies which monitor alcoholic beverages consumption and related habits in subgroups of population before and after the introduction of policy measures should be encouraged. Declaration of Interest
The authors declare no conflicts of interest. The authors alone are responsible for the content and writing of the paper. THE AUTHORS 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, and multiple imputation.
Giulia Carreras, Ph.D., is a Statistician at the Cancer Prevention and Research Institute in Florence. She is author of several papers in the field of primary prevention and environmental epidemiology. Her key research areas are Markov models and dynamic models for decision making, analysis of prevention studies, and health impact assessment.
POLICY MEASURES AND ALCOHOL CONSUMPTION IN EUROPE
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