A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided

Evidence Map of Pancreatic Surgery–A living systematic review with meta-analyses by the International Study Group of Pancreatic Surgery (ISGPS)

Background: Pancreatic surgery is associated with considerable morbidity and, consequently, offers a large and complex field for research. To prioritize relevant future scientific projects, it is of utmost importance to identify existing evidence and uncover research gaps. Thus, the aim of this project was to create a systematic and living Evidence Map of Pancreatic Surgery. Methods: PubMed, the Cochrane Central Register of Controlled Trials, and Web of Science were systematically searched for all randomized controlled trials and systematic reviews on pancreatic surgery. Outcomes from every existing randomized controlled trial were extracted, and trial quality was assessed. Systematic reviews were used to identify an absence of randomized controlled trials. Randomized controlled trials and systematic reviews on identical subjects were grouped according to research topics. A web-based evidence map modeled after a mind map was created to visualize existing evidence. Meta-analyses of specific outcomes of pancreatic surgery were performed for all research topics with more than 3 randomized controlled trials. For partial pancreatoduodenectomy and distal pancreatectomy, pooled benchmarks for outcomes were calculated with a 99% confidence interval. The evidence map undergoes regular updates. Results: Out of 30, 860 articles reviewed, 328 randomized controlled trials on 35, 600 patients and 332 systematic reviews were included and grouped into 76 research topics. Most randomized controlled trials were from Europe (46%) and most systematic reviews were from Asia (51%). A living meta-analysis of 21 out of 76 research topics (28%) was performed and included in the web-based evidence map. Evidence gaps were identified in 11 out of 76 research topics (14%). The benchmark for mortality was 2% (99% confidence interval: 1%–2%) for partial pancreatoduodenectomy and <1% (99% confidence interval: 0%–1%) for distal pancreatectomy. The benchmark for overall complications was 53% (99%confidence interval: 46%–61%) for partial pancreatoduodenectomy and 59% (99% confidence interval: 44%–80%) for distal pancreatectomy. Conclusion: The International Study Group of Pancreatic Surgery Evidence Map of Pancreatic Surgery, which is freely accessible via www.evidencemap.surgery and as a mobile phone app, provides a regularly updated overview of the available literature displayed in an intuitive fashion. Clinical decision making and evidence-based patient information are supported by the primary data provided, as well as by living meta-analyses. Researchers can use the systematic literature search and processed data for their own projects, and funding bodies can base their research priorities on evidence gaps that the map uncovers. © 2021 The Author

Multi-Log Cytotoxicity of Carbocyclic 2′-Deoxyguanosine in HSV-TK-Expressing Human Tumor Cells

Ganciclovir (GCV) is widely used as a prodrug for selective activation in tumor cells expressing herpes simplex virus thymidine kinase (HSV-TK) because of its ability to induce multi-log cytotoxicity to HSV-TK-expressing as well as nonexpressing bystander cells. We now report that another substrate for HSV-TK, D-carbocyclic 2′-deoxyguanosine (CdG), induces multi-log cytotoxicity in HSV-TK-expressing and bystander cells at concentrations ≤3 μM. We have compared the cytotoxicity and cell cycle effects of CdG to that observed with GCV in two human tumor cell lines. The results demonstrated that cytotoxicity of CdG was similar to that of GCV in both U251 glioblastoma and SW620 colon carcinoma cells that stably expressed HSV-TK. In addition, CdG induced a potent bystander effect in both cell types in co-cultures consisting of HSV-TK-expressing and nonexpressing bystander (lacZ-expressing) cells at ratios of 50:50 or 10:90. Selectivity for HSV-TK-expressing compared to lacZ-expressing cells was similar for CdG and GCV in the U251 cells, however CdG was less selective than GCV in the SW620 cell lines. Despite their ability to induce multi-log cytotoxicity at similar concentrations, CdG and GCV exhibited differential effects on cell cycle progression. Cells incubated with 1 μM CdG for 24 hr accumulated in S-phase and G2/M after drug washout, and the majority of cells died prior to cell division. This contrasts with the delayed effects of 1 μM GCV that were not evident until after cell division when cells attempted S-phase for the second time. Thus, CdG is a potent cytotoxic agent that merits further investigation to determine whether it will be therapeutically effective in enzyme-prodrug therapy with HSV-TK.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63436/1/10430340252809838.pd