Interrupted Time Series Analysis Of Count Data With Nuisance Interruptions

Interrupted time series analysis has been used to model the effect of policy and other interventions on public health by forecasting a counterfactual time series during the intervention period using data from prior to the intervention.

However, due to typically relying on a single study unit, this approach risks not adjusting for other interruptions that precede and co-occur during the intervention period.

The COVID-19 pandemic is a prominent example of this phenomenon of nuisance interruptions in contemporary public health research.

To address this complication, we propose using Bayesian stacking over a range of functional forms for the impact of the nuisance interruption in order to make counterfactual forecasts for the intervention period.

We used our proposed methods to estimate the impact of the 2021 Texas six-week abortion ban on documented pregnancies among women in Texas while adjusting for the impact of the COVID-19 pandemic.