As pressures mount to reopen U.S. businesses that were shuttered to prevent COVID-19 spread, a pressing question is, at what level of government should these judgments be made? A new model of disease transmission shows that city- and county-level characteristics have significant impact.

Researchers often seek to predict outcomes, detect distinct subgroups within their data, or estimate causal treatment effects. But in empirical analyses, outcomes with many observed zeros can complicate these tasks. PhD candidate Arman Oganisian recently led development of a method that helps researchers faced with such outcomes.

Unmeasured confounding almost always exists in observational studies—and it can bias our analysis of the effects we see in patients. These new findings can point researchers in various situations toward the best statistical methods to combat dominant types of unmeasured confounders.

The importance of a study is often inappropriately defined by the P value, the authors write, and this problem is highlighted by the use of “trend”—a term for which there is no definition—to refer to statistically non-significant results. To make a case in point, they set out to characterize the degree of overreliance on P values in high-impact oncology literature.

Many people in the U.S. take multiple prescription drugs, but very few studies have explored the relationships between these "high-order" drug combinations. The authors set out to make a general tool for quickly and accurately estimating drug-drug interactions, or DDIs, in cases where a patient already on a given drug adds a new one and three or more drugs are involved.

When cardiac arrest occurs outside the home, men are more likely than women to receive cardiopulmonary resuscitation (CPR) from a bystander—and are more likely to survive, found a study team led by Audrey Blewer, MPH, who is assistant director for Educational Programs at Penn’s Center for Resuscitation Science and is pursuing a PhD in Epidemiology at Penn.

Type-2 diabetes is a significant risk factor for coronary heart disease, and these two common conditions are leading causes of disease and death globally. But the biological pathways that explain the connection between them have remained somewhat murky, as have the causes of T2D. In a large analysis of genetic data, an international team led by researchers in the Perelman School of Medicine sought answers about what causes T2D and clarified how the two diseases are linked.