Aims Previously, a retrospective cohort study found no increased risk of acute pancreatitis with current or recent use of exenatide twice daily compared with use of other anti-diabetic drugs. This follow-up study investigated incident acute pancreatitis, with the use of a different data source and analytic method, in patients exposed to exenatide twice daily compared with patients exposed to other anti-diabetic medications.Methods A large US health insurance claims database was used. Eligible patients had ≥months continuous enrollment without a claim for pancreatitis and a claim for a new anti-diabetic medication on or after 1 June 2005 to 31 March 2009. Cases of acute pancreatitis were defined as hospitalized patients with an Internation Classification of Disease9 code of 577.0 in the primary position. A discrete time survival model was used to evaluate the relationship between exenatide twice daily and acute pancreatitis.Results Of 482034 eligible patients, 24237 initiated exenatide twice daily and 457797 initiated another anti-diabetic medication. Initiators of exenatide twice daily had more severe diabetes compared with initiators of other anti-diabetic medications. After adjustments for propensity score, insulin and use of medication potentially associated with acute pancreatitis, the odds ratio with exenatide twice daily exposure was 0.95 (95%CI 0.65–1.38). A secondary analysis that examined current, recent and past medication exposure found no increased risk of acute pancreatitis with exenatide twice daily, regardless of exposure category.Conclusion This study indicates that exposure to exenatide twice daily was not associated with an increased risk of acute pancreatitis compared with exposure to other anti-diabetic medications. These results should be interpreted in light of potential residual confounding and unknown biases.

Public and private institutions must often allocate scarce resources under uncertainty. Banks, for example, extend credit to loan applicants based in part on their estimated likelihood of repaying a loan.But when the quality of information differs across candidates (e.g., if some applicants lack traditional credit histories), common lending strategies can lead to disparities across groups. Here we consider a setting in which decision makers-before allocating resources-can choose to spend some of their limited budget further screening select individuals. We present a computationally efficient algorithm for deciding whom to screen that maximizes a standard measure of social welfare. Intuitively, decision makers should screen candidates on the margin, for whom the additional information could plausibly alter the allocation. We formalize this idea by showing the problem can be reduced to solving a series of linear programs. Both on synthetic and real-world datasets, this strategy improves utility, illustrating the value of targeted information acquisition in such decisions. Further, when there is social value for distributing resources to groups for whom we have a priori poor informationlike those without credit scores-our approach can substantially improve the allocation of limited assets.

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A potential voter must incur a number of costs in order to successfully cast an in-person ballot, including the costs associated with identifying and traveling to a polling place. In order to investigate how these costs affect voter turnout, we introduce two quasi-experimental designs that can be used to study how the political participation of registered voters is affected by differences in the relative distance that registrants must travel to their assigned Election Day polling place and whether their polling place remains at the same location as in a previous election. Our designs make comparisons of registrants who live on the same residential block, but are assigned to vote at different polling places. We find that living farther from a polling place and being assigned to a new polling place reduce in-person Election Day voting, but that registrants largely offset for this by casting more early in-person and mail ballots.

Past studies have found that racial and ethnic minorities are more likely than white drivers to be pulled over by the police for alleged traffic infractions, including a combination of speeding and equipment violations. It has been difficult, though, to measure the extent to which these disparities stem from discriminatory enforcement rather than from differences in offense rates. Here, in the context of speeding enforcement, we address this challenge by leveraging a novel source of telematics data, which include second-by-second driving speed for hundreds of thousands of individuals in 10 major cities across the United States. We find that time spent speeding is approximately uncorrelated with neighborhood demographics, yet, in several cities, officers focused speeding enforcement in small, demographically non-representative areas. In some cities, speeding enforcement was concentrated in predominantly non-white neighborhoods, while, in others, enforcement was concentrated in predominately white neighborhoods. Averaging across the 10 cities we examined, and adjusting for observed speeding behavior, we find that speeding enforcement was moderately more concentrated in non-white neighborhoods. Our results show that current enforcement practices can lead to inequities across race and ethnicity.