This paper shows the problem with interrupted time series analyses. Mainly, (1) there is an external shock that happened in 2020 that interrupts the historical

This paper shows the problem with interrupted time series analyses. Mainly, (1) there is an external shock that happened in 2020 that interrupts the historical trend in the data. This shock is probably the covid pandemic, but the time series can't really disentangle how covid impacts death rates (i.e., is it the vaccine? Is it an undiagnosed infection? Is it putting off care because hospitals are full? Is it stress? Is it something else?). In other words, interrupted time series don't really isolate the effect beyond the general shock. (2) It is possible that a similar pattern is present in a different subpopulation that hasn't experienced the same shock. Unfortunately, I cannot think of anyone that hasn't experienced in some way or another the covid pandemic that the authors can use as a possible counterfactual for these analyses. Without that, the interrupted time series could just be an example of an omitted variable that correlated with both the external shock and the outcome and that it is the actual "root cause" of the outcome. For example, it could be that covid lead athletes practice less and become more sedentary. When they resumed their training regimens in the second half of 2020, they were out of shape leading to heart problems. I'm not a heart disease researcher so I don't know if this happens, but it could be.