# Inferring Effects of Major Events through Discontinuity Forecasting of Population Anxiety

## TL;DR

This paper introduces a novel statistical learning framework adapting econometric discontinuity design to forecast community mental health impacts of events, specifically COVID-19, by predicting shifts in anxiety scores using dynamic data.

## Contribution

It extends Longitudinal Regression Discontinuity Designs into a predictive modeling framework for estimating community-specific effects of events on mental health.

## Key findings

- Modeling with exogenous and dynamic covariates improves accuracy.
- Best models achieved correlations of +.46 for discontinuity and +.65 for slope.
- Framework enables estimation of effects of future or hypothetical events.

## Abstract

Estimating community-specific mental health effects of local events is vital for public health policy. While forecasting mental health scores alone offers limited insights into the impact of events on community well-being, quasi-experimental designs like the Longitudinal Regression Discontinuity Design (LRDD) from econometrics help researchers derive more effects that are more likely to be causal from observational data. LRDDs aim to extrapolate the size of changes in an outcome (e.g. a discontinuity in running scores for anxiety) due to a time-specific event. Here, we propose adapting LRDDs beyond traditional forecasting into a statistical learning framework whereby future discontinuities (i.e. time-specific shifts) and changes in slope (i.e. linear trajectories) are estimated given a location's history of the score, dynamic covariates (other running assessments), and exogenous variables (static representations). Applying our framework to predict discontinuities in the anxiety of US counties from COVID-19 events, we found the task was difficult but more achievable as the sophistication of models was increased, with the best results coming from integrating exogenous and dynamic covariates. Our approach shows strong improvement ($r=+.46$ for discontinuity and $r = +.65$ for slope) over traditional static community representations. Discontinuity forecasting raises new possibilities for estimating the idiosyncratic effects of potential future or hypothetical events on specific communities.