*Geek Box: Interrupted Time Series Analysis
Add this to your research vocabulary; interrupted time series (ITS). ITS analysis is a method used in epidemiology to evaluate the effect of population-wide public health interventions. Breaking down the name may help to understand what this analysis is evaluating. Imagine there is a public health policy that came into place in January 2022; this policy is the ‘interruption’ from the previous status quo. And let’s say we were interested in the effect of the intervention 1-year later; we could establish a ‘time series’ of 1-year prior to the intervention and 1-year post intervention.
It is important to note that this type of analysis is not looking at individual-level effects and means calculated from those, as would be done in a randomised controlled trial [e.g., measuring every participant’s cholesterol levels] or prospective cohort study [e.g., assessing every participant’s diet with a food-frequency questionnaire]. Rather, ITS is specifically looking at population-level effects, e.g., does the introduction of a vaccine reduce prevalence and incidence of a disease in the population compared to before.
While this is a strength of ITS, it also opens the limitations of this analytical approach unless other methods are considered in the analysis. For example, if you introduced the vaccine while there was a ‘lockdown’ stay-at-home order in place, how would you know which of these interventions was responsible for any reduction in disease?
This is where ‘control’ comes into play, and recall that the present study used a controlled ITS design. So, what does this mean? Generally, this means comparing the population who received the intervention to a control population that did not receive it. This allows for the framing of a counterfactual, which describes the effect of an exposure on an outcome contrasting two potential outcomes. For example, the effect of a vaccine on a disease can be contrasted between those who received the vaccine and those who did not; it is not possible for someone to have had both outcomes.
In effect, the control serves as the counterfactual scenario for what would have happened in the absence of the intervention. Thus, the control should be unaffected by the intervention, but also share similar potential confounders with the intervention population group. This allows for effective use of ITS analysis to compare trends in the exposure-outcome relationship of interest in the group exposed to the intervention compared to the counterfactual situation. To understand the concept of counterfactuals in more detail, and the design of the present study, read the Key Characteristic, below.