Evaluating the impact of social distancing on COVID-19 hospitalizations using interrupted time series regression

Introduction: The quasi-experimental approach of interrupted time series analysis has been used to assess public health interventions by statistically comparing preintervention and postintervention rates. In this study, we apply interrupted time series to assess the effectiveness of social distancing on COVID-19 hospitalizations in a patient population in New York City. Materials and Methods: An interrupted time series design was used to evaluate the impact of the New York State on PAUSE executive order (social distancing measure), on admitted COVID-19 patients, and patients on ventilators, at a single center hospital in Brooklyn, NY. Time series data were collected from March 10, 2020 to April 28, 2020 and were modeled using segmented regression analysis, assuming a 2-week delay in the intervention's effect. ARIMA forecasting was also performed to determine the projected COVID-19 hospitalizations and ventilator use in the absence of social distancing. Results: There was a significant change (decrease) in the upward daily trend in the mean number of COVID-19 admissions and patients on ventilators after the assumed effective date of the New York State on PAUSE mandate. For admitted patients, the coefficient of the variable 'time after intervention,' or change in slope, was - 9.30 (P = 0.0009), and the corresponding value was - 2.27 (P < 0.0001) for patients on ventilators. Conclusion: The assumed effective period of the implementation of the New York State on PAUSE executive order was shown to be significantly correlated with decreased COVID-19 hospitalizations and ventilator use in the population measured. Similar social distancing measures should be adopted in other cities and locales that are currently seeing a surge in COVID-19 transmissions with an assumption of a 2-week delay in impact. The following core competencies are addressed in this article: Medical knowledge, Systems-based practice.