How to quantify deaths averted derived from interrupted time-series analyses

BackgroundInterrupted time series (ITS) are an important tool for determining whether alcohol control policies, as well as other policy interventions, are successful over and above secular trends or chance. Subsequent to estimating whether a policy has had an effect, quantifying the key outcomes, such as the number of prevented deaths, is of primary practical importance. The current paper compares the results of two different methodological approaches to quantify deaths averted using different two standard populations. MethodsTime series methodologies were used to estimate the effect size in deaths averted of a substantial increase in excise taxation in Lithuania in 2017. We compare the impact of a) using ITS methodology vs. fitting the trend before the intervention to predict the following 12 months and comparing the predicted monthly estimates of deaths with the actual numbers; and b) adjusting the time series either using the World Health Organization standard or the age distribution of Lithuania in the month before the intervention. The effect was estimated by sex. ResultsThe increase in excise taxation was associated with a substantial decrease in all-cause mortality in all models considered. ITS methodology and using the age-distribution of Lithuania were consistently associated with higher estimates of deaths averted. Although confidence and prediction intervals were highly overlapping, the point estimates differed substantially. The taxation increase was associated with 1,155 deaths averted in the year following the intervention (95% prediction interval: 729, 1,582), corresponding to 2.80% of all deaths in Lithuania in the respective year, for the model selected as best for planning policy interventions in Lithuania. ConclusionsFitting a time series model for the time until the intervention, and then comparing the predicted time points with the actual mortality, standardizing to country-specific weights, was chosen as the best way to derive practically relevant effect sizes.