Analysis
REGRESSION
For all outcomes of interest, we estimate intention-to-treat (ITT) effects and complier average causal effects (CACE). For both analyses, we restrict our analysis to jurisdictions participating in the IQSP, as cases elsewhere were routed to their city-specific support programs and not considered for most IQSP services [For the ITT effects, we consider only the assignment status of the cases by the weekly ZIP Code randomization. Since treatment assignment is randomized by week and ZIP Code, we cluster standard errors by week-ZIP Code.To calculate the CACE, we leverage the weekly ZIP Code randomization as high-touch contact tracing was deployed. Specifically, we estimate the effect among the subgroup of cases that were assigned to the high-touch team solely due to the ZIP Code randomization. This allows us to account for “noncompliance” from randomizing at the ZIP Code level and fluctuating case rates and operational capacity, which led to some deviations from the assignment protocol. We conduct an instrumental variables (IV) analysis, with the instrumental variable being whether the case was assigned to the treatment group via randomization. We include demographic covariates (gender, race/ethnicity, age) as covariates in our regression, specify time (week) fixed effects as controls, and cluster standard errors by week-ZIP Code. More details and robustness checks are provided in 