Unmet Needs: Healthcare Crowd-out During the COVID-19 Pandemic

Efficient resource allocation, during a possible new wave of the COVID-19 pandemic and similar public health crisis, requires understanding whether (and to which extent) COVID-related healthcare demand may displace or crowd-out non-COVID care. We study this crowd-out hypothesis using a large sample of online drug retailing transactions in Mainland China which covers the height of the pandemic's first wave (January-February 2020). Since an interaction with healthcare providers is required to purchase prescription drugs (Rx) but not for over-the-counter drugs (OTC), crowd-out can be inferred based on relative Rx/OTC demand changes. Built on a triple-differences (DDD) identification framework, our results are consistent with the presence of crowd-out, estimating it equivalent to a 10% healthcare capacity decrease for non-COVID care at peak. Such a crowd-out effect also varies across different therapeutic classes. This variation is consistent with medically-guided healthcare prioritization. Based on these results we propose and evaluate an α-reserve healthcare capacity reallocation policy, which could be implemented at large scale using tele-health infrastructure. Significant crowd-out reduction could be achieved with limited capacity reallocation.