Monitoring and Evaluating Scotland’s Alcohol Strategy (MESAS) The short-term impact of the Alcohol Act on alcohol-related health harms in Scotland: an interrupted time series analysis Please make sure Mark Robinson1 Janet Bouttell2, Jim Lewsey2, Danny Mackay2, Gerry McCartney1 and Clare Beeston1 1 NHS Health Scotland 2 University of Glasgow

of the prices at which each alcoholic Multi-buy discount ban ‘A package containing two or more alcoholic products … may only be sold on the premises at a price equal to or greater than the sum of the prices at which each alcoholic product is for sale.’ x

Promotion intervention Change in consumption Change in outcomes or Pricing intervention Promotion intervention

Evaluating the Alcohol Act – phase 1 Quite a clean analysis Not too many challenges Biggest difficulty was the fact that the p-value was 0.07, obviously not below the holy grail of 0.05 Another challenge

Intervention Change in consumption Change in outcomes Say a little about the data here – just mention comprability Hospital admissions Deaths

Analytical approach Alcohol Act introduced General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions

Analytical approach Control group Comparable data Long pre- trend Alcohol Act introduced Analytical approach General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions Control group Comparable data Long pre- trend Underlying trends Sensitivity analysis Adjustment

Analytical approach Control group Comparable data Long pre- trend Alcohol Act introduced Analytical approach General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions Control group Comparable data Long pre- trend Underlying trends Sensitivity analysis Adjustment

Analytical approach Control group Comparable data Long pre- trend Alcohol Act introduced Analytical approach General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions Control group Comparable data Long pre- trend Underlying trends Sensitivity analysis Adjustment

Analytical approach Control group Comparable data Long pre- trend Alcohol Act introduced Analytical approach General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions Control group Comparable data Long pre- trend Underlying trends Sensitivity analysis Adjustment

Analytical approach Control group Comparable data Long pre- trend Alcohol Act introduced Analytical approach General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions Control group Comparable data Long pre- trend Underlying trends Sensitivity analysis Adjustment

Analytical approach Control group Comparable data Long pre- trend Alcohol Act introduced Analytical approach General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions Control group Comparable data Long pre- trend Underlying trends Sensitivity analysis Adjustment

Analytical approach Control group Comparable data Long pre- trend Alcohol Act introduced Analytical approach General approach – before and after – interrupted time series analysis Regression assuming negative binomial distribution Include a binary variable for before/after Need to model seasonality – use a ‘month’ variable Adjust for all the usual suspects (age, sex, Carstairs) Need to take account of trend Difference in difference assumes parallel trends Trends between Scotland and England not parallel Strengthen findings by including England in Scotland analysis but allowing all the variables to interact (ie England and Scotland could have separate age, sex,seasonality, trend effects). Results were not stable for regression method adopted when countries were combined Used a panel data approach – where each subgroup gets its own ID – ie 15-34 year old, men in Carstairs quintile 1 Single countries only We wanted to exploit the individual level data The same analytical framework was used for both deaths and hospital admissions Control group Comparable data Long pre- trend Underlying trends Sensitivity analysis Adjustment

Results – Deaths (seasonal component) The overall crude rate in both of these outcomes over the study time period was plotted in charts. In addition, to ease visual interpretation of trends, the time series for each outcome was decomposed into trend and seasonal components using the stl command (seasonal decomposition by loess) in R Studio 3.0.2 software (R Studio, Boston, USA).14 This decomposition is not adjusted for sex, age or socio-demographic factors and uses a different approach to trends and seasonality than the regression approach described below and so is included for descriptive purposes only.

Results – Deaths (trend component) Alcohol Act introduced The overall crude rate in both of these outcomes over the study time period was plotted in charts. In addition, to ease visual interpretation of trends, the time series for each outcome was decomposed into trend and seasonal components using the stl command (seasonal decomposition by loess) in R Studio 3.0.2 software (R Studio, Boston, USA).14 This decomposition is not adjusted for sex, age or socio-demographic factors and uses a different approach to trends and seasonality than the regression approach described below and so is included for descriptive purposes only.

Results – Deaths (regression) Alcohol Act introduced IRR = 0.99 (0.91 to 1.07) The overall crude rate in both of these outcomes over the study time period was plotted in charts. In addition, to ease visual interpretation of trends, the time series for each outcome was decomposed into trend and seasonal components using the stl command (seasonal decomposition by loess) in R Studio 3.0.2 software (R Studio, Boston, USA).14 This decomposition is not adjusted for sex, age or socio-demographic factors and uses a different approach to trends and seasonality than the regression approach described below and so is included for descriptive purposes only. IRR = 0.99 (0.95 to 1.02)

Results – Admissions (seasonal component) The overall crude rate in both of these outcomes over the study time period was plotted in charts. In addition, to ease visual interpretation of trends, the time series for each outcome was decomposed into trend and seasonal components using the stl command (seasonal decomposition by loess) in R Studio 3.0.2 software (R Studio, Boston, USA).14 This decomposition is not adjusted for sex, age or socio-demographic factors and uses a different approach to trends and seasonality than the regression approach described below and so is included for descriptive purposes only.

Results – Admissions (trend component) Alcohol Act introduced The overall crude rate in both of these outcomes over the study time period was plotted in charts. In addition, to ease visual interpretation of trends, the time series for each outcome was decomposed into trend and seasonal components using the stl command (seasonal decomposition by loess) in R Studio 3.0.2 software (R Studio, Boston, USA).14 This decomposition is not adjusted for sex, age or socio-demographic factors and uses a different approach to trends and seasonality than the regression approach described below and so is included for descriptive purposes only.

Results – Admissions (regression) Alcohol Act introduced IRR = 0.98 (0.95 to 1.02) IRR = 1.05 (1.03 to 1.07) So, in other words

Main finding and interpretation “The implementation of the Alcohol Act was not associated with any measurable, short-term changes in overall rates of alcohol-related deaths or hospital admissions in Scotland.” So how do we interpret these results. Well…

Main finding and interpretation “The implementation of the Alcohol Act was not associated with any measurable, short-term changes in overall rates of alcohol-related deaths or hospital admissions in Scotland.” Intervention effect too small to be detected. So how do we interpret these results. Well…

Main finding and interpretation “The implementation of the Alcohol Act was not associated with any measurable, short-term changes in overall rates of alcohol-related deaths or hospital admissions in Scotland.” Intervention effect too small to be detected. Full effect of changes in consumption takes time. So how do we interpret these results. Well…

Main finding and interpretation “The implementation of the Alcohol Act was not associated with any measurable, short-term changes in overall rates of alcohol-related deaths or hospital admissions in Scotland.” Intervention effect too small to be detected. Full effect of changes in consumption takes time. Narrow focus on 100% attributable conditions. So how do we interpret these results. Well…

Main finding and interpretation “The implementation of the Alcohol Act was not associated with any measurable, short-term changes in overall rates of alcohol-related deaths or hospital admissions in Scotland.” Intervention effect too small to be detected. Full effect of changes in consumption takes time. Narrow focus on 100% attributable conditions. Wine the drink type most affected. So how do we interpret these results. Well…

Main finding and interpretation “The implementation of the Alcohol Act was not associated with any measurable, short-term changes in overall rates of alcohol-related deaths or hospital admissions in Scotland.” Intervention effect too small to be detected. Full effect of changes in consumption takes time. Narrow focus on 100% attributable conditions. Wine the drink type most affected. Rates of alcohol-related harm depend not only on average consumption levels. So how do we interpret these results. Well…

Implications Policy evaluation Policy Policy implications, but perhaps more important is evaluating policy that the analytical challenges presented. Policy

Thanks to the study project team and to you for listening. Report available at www.healthscotland.com Email markrobinson1@nhs.net Telephone 0141-414-2753

Strengths and limitations Comparability Coverage Data Patient level Long pre-intervention trend Study design Analytical approach Sensitivity analysis