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This article applies analytical approaches to map illegal psychostimulant (cocaine and methamphetamine) trafficking networks in the US using purity-adjusted price data from the System to Retrieve Information from Drug Evidence. We use two assumptions to build the network: (i) the purity-adjusted price is lower at the origin than at the destination and (ii) price perturbations are transmitted from origin to destination. We then adopt a two-step analytical approach: we formulate the data aggregation problem as an optimization problem, then construct an inferred network of connected states and examine its properties. We find, first, that the inferred cocaine network created from the optimally aggregated dataset explains 46% of the anecdotal evidence, compared with 28.4% for an over-aggregated and 14.5% for an under-aggregated dataset. Second, our network reveals a number of phenomena, some aligning with what is known and some previously unobserved. To demonstrate the applicability of our method, we compare our cocaine data analysis results with parallel analysis of methamphetamine data. These results likewise align with prior knowledge, but also present new insights. Our findings show that an optimally aggregated dataset can provide a more accurate picture of an illicit drug network than can suboptimally aggregated data. [ABSTRACT FROM AUTHOR]

BackgroundHeart failure (HF) is associated with high mortality rates and high costs, and self-care is crucial in the management of the condition. Telehealth can promote patients’ self-care while providing frequent feedback to their health care providers about the patient’s compliance and symptoms. A number of technologies have been considered in the literature to facilitate telehealth in patients with HF. An important factor in the adoption of these technologies is their ease of use. Conversational agent technologies using a voice interface can be a good option because they use speech recognition to communicate with patients. ObjectiveThe aim of this paper is to study the engagement of patients with HF with voice interface technology. In particular, we investigate which patient characteristics are linked to increased technology use. MethodsWe used data from two separate HF patient groups that used different telehealth technologies over a 90-day period. Each group used a different type of voice interface; however, the scripts followed by the two technologies were identical. One technology was based on Amazon’s Alexa (Alexa+), and in the other technology, patients used a tablet to interact with a visually animated and voice-enabled avatar (Avatar). Patient engagement was measured as the number of days on which the patients used the technology during the study period. We used multiple linear regression to model engagement with the technology based on patients’ demographic and clinical characteristics and past technology use. ResultsIn both populations, the patients were predominantly male and Black, had an average age of 55 years, and had HF for an average of 7 years. The only patient characteristic that was statistically different (P=.008) between the two populations was the number of medications they took to manage HF, with a mean of 8.7 (SD 4.0) for Alexa+ and 5.8 (SD 3.4) for Avatar patients. The regression model on the combined population shows that older patients used the technology more frequently (an additional 1.19 days of use for each additional year of age; P=.004). The number of medications to manage HF was negatively associated with use (−5.49; P=.005), and Black patients used the technology less frequently than other patients with similar characteristics (−15.96; P=.08). ConclusionsOlder patients’ higher engagement with telehealth is consistent with findings from previous studies, confirming the acceptability of technology in this subset of patients with HF. However, we also found that a higher number of HF medications, which may be correlated with a higher disease burden, is negatively associated with telehealth use. Finally, the lower engagement of Black patients highlights the need for further study to identify the reasons behind this lower engagement, including the possible role of social determinants of health, and potentially create technologies that are better tailored for this population.

We present a theory of how a rational, profit-maximizing firm would respond to pressure for gender pay equity by strategically distributing raises to reduce the pay gap between its female and male employees at minimum cost. Using formal analysis and pay data from a real employer, we show that (1) employees in low-paying jobs and whose pay-related observables are similar to those of men at the firm are most likely to get raises; (2) counterintuitively, some men may get raises, and giving raises to certain women would increase the pay gap; and (3) a firm can reduce the gender pay gap as measured by a much larger percentage than the overall increase in pay to women at the firm. We also identify the conditions under which a firm could 'explain away' a gender pay gap using other pay-related observables, such as job category, as well as the conditions under which this strategy would backfire. Our paper helps explain some empirical puzzles, such as the tendency for some men to get raises after gender equity pay reviews, and yields a rich set of implications for empirical research and practice. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2018.1248. Keywords: gender pay gap * human capital management * discrimination * compensation * formal models, 1. Introduction After decades of progress, firms still seem to have trouble ensuring gender equity despite the benefits to firm performance from doing so (Eagly 2007, Dezso and Ross 2012). [...]