Using Simulation to Analyze Interrupted Time Series Designs.

We are sometimes forced to use the Interrupted Time Series (ITS) design as an identification strategy for potential policy change, such as when we only have a single treated unit and cannot obtain comparable controls. For example, with recent county- and state-wide criminal justice reform efforts, where judicial bodies have changed bail setting practices for everyone in their jurisdiction in order to reduce rates of pre-trial detention while maintaining court order and public safety, we have no natural and available comparison group other than the past. In these contexts, it is imperative to model pre-policy trends with a light touch, allowing for structures such as autoregressive departures from any pre-existing trend, in order to accurately and realistically assess the uncertainty of our projections. We aim to provide a methodological approach rooted in commonly understood and used modeling tools to achieve this. We quantify uncertainty with simulation, generating a distribution of plausible counterfactual trajectories to compare to the observed; this approach naturally allows for incorporating seasonality and other time-varying covariates, and provides confidence intervals along with point estimates for the potential impacts of policy change. We find simulation provides a natural framework to capture and show uncertainty in the ITS designs. It also allows for easy extensions such as nonparametric smoothing in order to handle multiple post-policy time points.

Sex disparities in COVID-19 outcomes in the United States: Quantifying and contextualizing variation.

This paper presents the first longitudinal study of sex disparities in COVID-19 cases and mortalities across U.S. states, derived from the unique 13-month dataset of the U.S. Gender/Sex COVID-19 Data Tracker. To analyze sex disparities, weekly case and mortality rates by sex and mortality rate ratios were computed for each U.S. state, and a multilevel crossed-effects conditional logistic binomial regression model was fitted to estimate the variation of the sex disparity in mortality over time and across states. Results demonstrate considerable variation in the sex disparity in COVID-19 cases and mortalities over time and between states. These data suggest that the sex disparity, when present, is modest, and likely varies in relation to context-sensitive variables, which may include health behaviors, preexisting health status, occupation, race/ethnicity, and other markers of social experience.

Changes in Children's Behavioral Health and Family Well-Being During the COVID-19 Pandemic.

OBJECTIVE: The coronavirus disease 2019 pandemic and associated public health measures have influenced all aspects of life for children and families. In this study, we examine changes in children's behavioral health and families' well-being at the start of the pandemic. METHOD: We used longitudinal data on 2880 children from 1 US state collected over 3 waves to compare family and child well-being before and after a state-wide stay-at-home advisory set in March 2020. We descriptively examined levels and changes in 4 child behavioral health outcomes (externalizing, internalizing, adaptive, and dysregulated behaviors) and 4 family well-being outcomes (parental mental health, parental stress, parent-child relationship conflict, and household chaos) across the preshutdown and postshutdown periods. Fixed effects regression models were used to predict within-child and within-family differences in preshutdown and postshutdown outcomes. RESULTS: Fixed effects analyses showed children's externalizing (0.09 points; 95% confidence interval [CI] 0.05-0.13), internalizing (0.04 points; 95% CI, 0.01-0.08), and dysregulated (0.11 points; 95% CI, 0.06-0.16) behaviors increased after the shutdown, whereas children's adaptive behaviors declined (-0.10 points; 95% CI, -0.15 to -0.05). Parental mental health issues (0.22 points; 95% CI, 0.17-0.27), parental stress (0.08 points; 95% CI, 0.03-0.12), parent-child relationship conflict (0.10 points; 95% CI, 0.04-0.16), and household chaos (0.10 points; 95% CI, 0.05-0.14) all increased relative to preshutdown levels. CONCLUSION: Many children experienced declines in behavioral health and many families experienced declines in well-being in the early months of the public health crisis, suggesting the need for family-focused and child-focused policies to mitigate these changes.

Urban building demolitions, firearm violence and drug crime.

Although multiple interventions to remediate physical blight have been found to reduce urban firearm violence, there is limited evidence for demolishing vacant buildings as a violence reduction strategy. Starting in 2014, Detroit, MI launched a large-scale program that demolished over 10,000 buildings in its first 3 years. We analyzed the pre-post effects of this program on fatal and nonfatal firearm assaults and illegal drug violations at the U.S. Census block group level, using propensity score matching and negative binomial regression. Receiving over 5 demolitions was associated with a 11% reduction in firearm assaults, relative to comparable control locations, 95% CI [7%, 15%], p = 0.01. The program was associated with larger reductions in firearm assaults for the locations receiving moderate numbers of demolitions (between 6 and 12) than for locations receiving high numbers of demolitions (13 and over). No effects were observed for illegal drug violations and no evidence of spatial crime displacement was detected. These findings suggest that vacant building demolitions may affect gun violence.