Florida's Opioid Crackdown and Mortality From Drug Overdose, Motor Vehicle Crashes, and Suicide: A Bayesian Interrupted Time-Series Analysis

Estimating the impact of the COVID-19 pandemic on dengue in Brazil

Atypical dengue prevalence was observed in 2020 in many dengue-endemic countries, including Brazil. Evidence suggests that the pandemic disrupted not only dengue dynamics due to changes in mobility patterns, but also several aspects of dengue surveillance, such as care seeking behavior, care availability, and monitoring systems. However, we lack a clear understanding of the overall impact on dengue in different parts of the country as well as the role of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in 2020 using an interrupted time series design with forecasts from a neural network and a structural Bayesian time series model. We also decomposed the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. Increased understanding of the causal mechanisms driving the 2020 dengue season helps mitigate some of the data gaps caused by the COVID-19 pandemic and is critical to developing effective public health interventions to control dengue in the future.

Impact of the COVID-19 Pandemic on Opioid Overdose Deaths: a Spatiotemporal Analysis

The effects of the opioid crisis have varied across diverse and socioeconomically defined urban communities, due in part to widening health disparities. The onset of the COVID-19 pandemic has coincided with a spike in drug overdose deaths in the USA. However, the extent to which the impact of the pandemic on overdose deaths has varied across different demographics in urban neighborhoods is unclear. We examine the influence of COVID-19 pandemic on opioid overdose deaths through spatiotemporal analysis techniques. Using Milwaukee County, Wisconsin as a study site, we used georeferenced opioid overdose data to examine the locational and demographic differences in overdose deaths over time (2017-2020). We find that the pandemic significantly increased the monthly overdose deaths. The worst effects were seen in the poor, urban neighborhoods, affecting Black and Hispanic communities. However, more affluent, suburban White communities also experienced a rise in overdose deaths. A better understanding of contributing factors is needed to guide interventions at the local, regional, and national scales.

A (Flexible) Synthetic Control Method for Count Data and Other Nonnegative Outcomes

The synthetic control method is a covariate balancing method that exploits data from untreated regions to construct a synthetic control that approximates a single, aggregate treatment unit on a time series of preintervention outcomes and covariates. The method is increasingly being used to evaluate population-level interventions in epidemiology. Although the original version can be used with bounded outcomes, it imposes strong constraints on the balancing weights to ensure that the counterfactuals are based solely on interpolation. This feature, while attractive from a causal inference perspective, is sometimes too conservative and can lead to unnecessary bias due to poor covariate balance. Alternatives exist that allow for extrapolation to improve balance but existing procedures may produce negative estimates of the counterfactual outcomes and are therefore inappropriate for count data. We propose an alternative way to allow for extrapolation, although ensuring that the estimated counterfactuals remain nonnegative. Following a related proposal, we add a penalty to the balancing procedure that favors interpolation over extrapolation whenever possible. As we demonstrate theoretically and using empirical examples, our proposal can serve as a useful alternative when existing approaches yield demonstrably poor or unrealistic counterfactuals. Finally, we provide functions to implement the method in R.

Invited Commentary: The Association Between Florida's Opioid Crackdown and Opioid-Related Mortality-The Roles of Economic Factors and Mortality Misclassification

Opioid overdose mortality has been increasing in the United States, and other types of mortality, such as motor vehicle crash deaths, may also be linked to opioid use. In this issue of the Journal, Feder et al. (Am J Epidemiol. 2020;189(9):885-893) examine the association between Florida's opioid crackdown laws, implemented in 2010-2011, and opioid-related mortality. They found a decrease in numbers of opioid-overdose and car-crash deaths compared with what would have been expected in the absence of such policies. They also found no evidence of any unintended increase in suicides due to poor pain management. The results were robust to alternative methodological approaches. Florida's opioid policy reforms coincided with the state's convergence towards national unemployment rates, as well as a new state law prohibiting texting while driving. Because opioid overdose mortality is often associated with economic conditions and because car crashes and suicides may also be linked to the macroeconomic environment, future research should take such factors into account when studying the outcomes of opioid prescribing laws. Another data-related aspect to consider is the misclassification of suicides as car crashes or opioid overdoses. Overall, the findings by Feder et al. are encouraging and can inform policy in other countries facing increasing numbers of opioid overdose deaths.