Potential impact of the 2023 Lahaina wildfire on the marine environment: Modeling the transport of ash-laden benzo[a]pyrene and pentachlorophenol.

The Lahaina urban/wildland fire event is considered the deadliest wildfire in the past century of U.S. history.

Risk benefit analysis to evaluate risk of thromboembolic events after mRNA COVID-19 vaccination and COVID-19.

We compared the risks and benefits of COVID-19 vaccines using a causal pathway analysis to weigh up possible risk factors of thromboembolic events post-vaccination. The self-controlled case series (SCCS) method examined the association between thromboembolic events and vaccination while a case-control study assessed the association between thromboembolic events and COVID-19, addressing under-reported infection data issues. The net vaccine effect was estimated using results from SCCS and case-control studies.

Risk of Blood Clots After COVID-19 Vaccination and Infection: A Risk-Benefit Analysis.

We analyzed the risk-benefit of COVID-19 vaccine using a causal model to explain and weigh up possible risk factors of blood clots after vaccination. A self-controlled case series method was used to examine the association between blood clots and COVID-19 vaccination. To avoid bias due to the under-reported infection among non-hospitalized subjects, a case-control study was used to compare the risk of blood clots in infected subjects to control subjects who were hospitalized due to physical injury.

Cytoreductive nephrectomy in metastatic renal cell carcinoma.

Background: Nephrectomy is the surgical removal of all or part of a kidney. When the aim of nephrectomy is to reduce tumor burden in people with established metastatic disease, the procedure is called cytoreductive nephrectomy (CN). CN is typically combined with systemic anticancer therapy (SACT).

Online causal inference with application to near real-time post-market vaccine safety surveillance.

Streaming data routinely generated by social networks, mobile or web applications, e-commerce, and electronic health records present new opportunities to monitor the impact of an intervention on an outcome via causal inference methods. However, most existing causal inference methods have been focused on and applied to static data, that is, a fixed data set in which observations are pooled and stored before performing statistical analysis. There is thus a pressing need to turn static causal inference into online causal learning to support near real-time monitoring of treatment effects.