The Impact of the Macroeconomy on Health Insurance Coverage: Evidence from the Great Recession

This paper investigates the impact of the macroeconomy on the health insurance coverage of Americans using panel data from the Survey of Income and Program Participation (SIPP) for 2004-2010, a period that includes the Great Recession of 2007-09. We find that a one percentage point increase in the state unemployment rate is associated with a 1.67 percentage point (2.12%) reduction in the probability that men have health insurance; this effect is strongest among college-educated, white, and older (50-64 year old) men. We estimate that 9.3 million Americans, the vast majority of whom were adult men, lost health insurance due to a higher unemployment rate alone during the 2007-09 recession. We conclude with a discussion of how components of recent health care reform may influence this relationship in the future.health insurance, Medicaid, SCHIP, recession, unemployment

The Impact of the Macroeconomy on Health Insurance Coverage: Evidence from the Great Recession

This paper investigates the impact of the macroeconomy on the health insurance coverage of Americans using panel data from the Survey of Income and Program Participation (SIPP) for 2004-2010, a period that includes the Great Recession of 2007-09. We find that a one percentage point increase in the state unemployment rate is associated with a 1.67 percentage point (2.12%) reduction in the probability that men have health insurance; this effect is strongest among college-educated, white, and older (50-64 year old) men. For women and children, health insurance coverage is not significantly correlated with the unemployment rate, which may be the result of public health insurance acting as a social safety net. Compared to the previous recession, the health insurance coverage of men is more sensitive to the unemployment rate, which may be due to the nature of the Great Recession.

Improving sensitivity and coverage of structural health monitoring using bulk ultrasonic waves

Practical ultrasonic structural health monitoring systems must be able to deal with temperature changes and some signal amplitude/phase drift over time; these issues have been investigated extensively with low-frequency-guided wave systems but much less work has been done on bulk wave systems operating in the megahertz frequency range. Temperature and signal drift compensation have been investigated on a thick copper block specimen instrumented with a lead zirconate titanate disc excited at a centre frequency of 2 MHz, both in the laboratory at ambient temperature and in an environmental chamber over multiple 20 degrees C-70 degrees C temperature cycles. It has been shown that the location-specific temperature compensation scheme originally developed for guided wave inspection significantly out-performs the conventional combined optimum baseline selection and baseline signal stretch method. The test setup was deliberately not optimised, and the signal amplitude and phase were shown to drift with time as the system was temperature cycled in the environmental chamber. It was shown that the ratio of successive back wall reflections at a given temperature was much more stable with time than the amplitude of a single reflection and that this ratio can be used to track changes in the reflection coefficient from the back wall with time. It was also shown that the location-specific temperature compensation method can be used to compensate for changes in the back wall reflection ratio with temperature. Clear changes in back wall reflection ratio were produced by the shadow effect of simulated damage in the form of 1-mm diameter flat-bottomed holes, and the signal-to-noise ratio was such that much smaller defects would be detectable

Characterization of hallucinogenic phenethylamines using high-resolution mass spectrometry for non-targeted screening purposes

Copyright © 2017 John Wiley & Sons, Ltd. Hallucinogenic phenethylamines such as 2,5-dimethoxyphenethylamines (2C–X) and their N-(2-methoxybenzyl) derivatives (25X–NBOMe) have seen an increase in novel analogues in recent years. These rapidly changing analogues make it difficult for laboratories to rely on traditional targeted screening methods to detect unknown new psychoactive substances (NPS). In this study, twelve 2C–X, six 2,5-dimethoxyamphetamines (DOX), and fourteen 25X–NBOMe derivatives, including two deuterated derivatives (2C–B-d6 and 25I–NBOMe-d9), were analyzed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Collision-induced dissociation (CID) experiments were performed using collision energies set at 10, 20, and 40 eV. For 2C–X and DOX derivatives, common losses were observed including neutral and radical losses such as NH3 (17.0265 Da), •CH6N (32.0500 Da), C2H7N (45.0578 Da) and C2H9N (47.0735 Da). 2C–X derivatives displayed common product ions at m/z 164.0837 ([C10H12O2]+•), 149.0603 ([C9H9O2]+), and 134.0732 ([C9H10O]+•) while DOX derivatives had common product ions at m/z 178.0994 ([C11H14O2]+•), 163.0754 ([C10H11O2]+), 147.0804 ([C10H11O]+), and 135.0810 ([C9H11O]+). 25X–NBOMe had characteristic product ions at m/z 121.0654 ([C8H9O]+) and 91.0548 ([C7H7]+) with minor common losses corresponding to 2-methylanisole (C8H10O, 122.0732 Da), 2-methoxybenzylamine (C8H11NO, 137.0847 Da), and •C9H14NO (152.1074 Da). Novel analogues of the selected classes can be detected by applying neutral loss filters (NLFs) and extracting the common product ions. Copyright © 2017 John Wiley & Sons, Ltd

Libraries as Indispensable Partners and Connectors in Data Science

Libraries play an integral role as learning partners and connectors throughout the data science lifecycle in curriculum and research. We will discuss the UNC library system-wide data science initiative around the following issues (1) how to scaffold the integration of data science competencies through layers of education and help solve real-world problems; (2) how to harness the expertise from interdisciplinary experts, and incorporate pedagogical approaches that facilitate problem-based learning; (3) how to bridge the gaps in skill acquisition, application of data science tools, and allocation of resources by playing an intermediary role. The discussion will be framed around social sciences and humanities, digital humanities, and a special emphasis on biomedical and health sciences. Examples will include the research collaborations that the UNC Health Sciences Library established with several campus units, including North Carolina Translational and Clinical Science Institute, UNC School of Medicine, School of Pharmacy, and other entities on campus that host large volumes of data

The Impact of the Macroeconomy on Health Insurance Coverage: Evidence from the Great Recession

This paper investigates the impact of the macroeconomy on the health insurance coverage of Americans using panel data from the Survey of Income and Program Participation (SIPP) for 2004-2010, a period that includes the Great Recession of 2007-09. We find that a one percentage point increase in the state unemployment rate is associated with a 1.67 percentage point (2.12%) reduction in the probability that men have health insurance; this effect is strongest among college-educated, white, and older (50-64 year old) men. We estimate that 9.3 million Americans, the vast majority of whom were adult men, lost health insurance due to a higher unemployment rate alone during the 2007-09 recession. We conclude with a discussion of how components of recent health care reform may influence this relationship in the future

Towards Compound Identification of Synthetic Opioids in Non-targeted Screening Using Machine Learning Techniques.

The constant evolution of the illicit drug market makes the identification of unknown compounds problematic. Obtaining certified reference materials for a broad array of new analogues can be difficult and cost prohibitive. Machine learning provides a promising avenue to putatively identify a compound before confirmation against a standard. In this study, machine learning approaches were used to develop class prediction and retention time prediction models. The developed class prediction model used a Naïve Bayes architecture to classify opioids as belonging to either the fentanyl analogues, AH series or U series, with an accuracy of 89.5%. The model was most accurate for the fentanyl analogues, most likely due to their greater number in the training data. This classification model can provide guidance to an analyst when determining a suspected structure. A retention time prediction model was also trained for a wide array of synthetic opioids. This model utilised Gaussian Process Regression to predict the retention time of analytes based on multiple generated molecular features with 79.7% of the samples predicted within ± 0.1 min of their experimental retention time. Once the suspected structure of an unknown compound is determined, molecular features can be generated and input for the prediction model to compare with experimental retention time. The incorporation of machine learning prediction models into a compound identification workflow can assist putative identifications with greater confidence and ultimately save time and money in the purchase and/or production of superfluous certified reference materials

Collision-induced dissociation studies of synthetic opioids for non-targeted analysis

© 2019 Klingberg, Cawley, Shimmon and Fu. The continual introduction of a large number of new psychoactive substances, along with the large turnover of these substances, necessitates the development of non-targeted detection strategies to keep pace with the ever-changing drug market. The production of certified reference materials often lags behind the introduction of new substances to the market, therefore these detection strategies need to be able to function without relying on reference materials or library spectra. Synthetic opioids have recently emerged as a drug class of particular concern due to the health issues caused by their incredibly high potency. A common method which has been used for non-targeted analysis in the past involves the identification of common product ions formed as a result of the fragmentation of the parent molecule. These common fragments can then potentially be used as markers to indicate the presence of a particular class of compounds within a sample. In this study, standards of a number of different synthetic opioids, including 14 fentanyl derivatives, 7 AH series opioids, 4U series opioids, 4W series opioids and MT-45, were subjected to collision-induced dissociation studies to determine how the compounds fragment. The spectra obtained from these studies included a number of diagnostic fragments common to the different opioid classes that, when used in combination, show potential for use as class predictors. By using simple data processing techniques, such as extracted ion chromatograms, these diagnostic product ions identified can be applied to a non-targeted screening workflow