Similarly, there can be switching between drinks when prices change, so for example (two cells to the right), a 1% increase in off trade beer was estimated to produce a slight increase of 0.096% in purchasing of off trade wine. To examine the effects of uncertainty, we also carried out runs of the Sheffield Alcohol Policy Model (version 2.5) using alternative definitions to generate the pseudopanel purchasing groups, and using probabilistic sensitivity analysis accounting for variable uncertainty reported in the regression coefficients.15 16Table 2 Base case estimated own price and cross price elasticities for off trade and on trade beer, cider, wine, spirits, and ready to drinks (RTDs, or “alcopops”) in the United KingdomView this table:View popupView inlineNote that different subgroups in Sheffield Alcohol Policy Model (version 2.5) experience different scales of effect due to a price change because for each subgroup we accounted for data on their preferences for the 10 categories of beverage (for example, middle aged women drink more wine at home, younger men drink more beer on nights out) and data on the prices paid for each of the 10 beverages (for example, harmful drinkers spend less per unit on average). Thus, the 1010 matrix of price elasticities means that each of the 96 modelled population subgroups essentially has a different overall price elasticity and a different scale of response to a given policy (for example, drinkers who favour cheaper cider would be more affected by a minimum unit price because their beverages would experience a relatively large increase in price and also because the own price elasticity for off trade cider is relatively large at 1.268).Risk functions and modelling process for health harmsThe baseline and estimated post policy consumption data for each of the population subgroups feed into a second model component relating changes in consumption to changes in harm.
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