X-ray emission is one of the signposts of circumstellar interaction in supernovae (SNe), but until now, it has been observed only in core-collapse SNe. The level of thermal X-ray emission is a direct measure of the density of the circumstellar medium (CSM), and the absence of X-ray emission from Type Ia SNe has been interpreted as a sign of a very low density CSM. In this paper, we report late-time (500-800 d after discovery) X-ray detections of SN 2012ca in Chandra data. The presence of hydrogen in the initial spectrum led to a classification of Type Ia-CSM, ostensibly making it the first SN Ia detected with X-rays. Our analysis of the X-ray data favours an asymmetric medium, with a high-density component which supplies the X-ray emission. The data suggest a number density >10⁸ cm^-3 in the higher density medium, which is consistent with the large observed Balmer decrement if it arises from collisional excitation. This is high compared to most core-collapse SNe, but it may be consistent with densities suggested for some Type IIn or superluminous SNe. If SN 2012ca is a thermonuclear SN, the large CSM density could imply clumps in the wind, or a dense torus or disc, consistent with the single-degenerate channel. A remote possibility for a core-degenerate channel involves a white dwarf merging with the degenerate core of an asymptotic giant branch star shortly before the explosion, leading to a common envelope around the SN.

Abstract Background Public health triangulation is a process for reviewing, synthesising and interpreting secondary data from multiple sources that bear on the same question to make public health decisions. It can be used to understand the dynamics of HIV transmission and to measure the impact of public health programs. While traditional intervention research and metaanalysis would be ideal sources of information for public health decision making, they are infrequently available, and often decisions can be based only on surveillance and survey data. Methods The process involves examination of a wide variety of data sources and both biological, behavioral and program data and seeks input from stakeholders to formulate meaningful public health questions. Finally and most importantly, it uses the results to inform public health decision-making. There are 12 discrete steps in the triangulation process, which included identification and assessment of key questions, identification of data sources, refining questions, gathering data and reports, assessing the quality of those data and reports, formulating hypotheses to explain trends in the data, corroborating or refining working hypotheses, drawing conclusions, communicating results and recommendations and taking public health action. Results Triangulation can be limited by the quality of the original data, the potentials for ecological fallacy and "data dredging" and reproducibility of results. Conclusions Nonetheless, we believe that public health triangulation allows for the interpretation of data sets that cannot be analyzed using meta-analysis and can be a helpful adjunct to surveillance, to formal public health intervention research and to monitoring and evaluation, which in turn lead to improved national strategic planning and resource allocation.

Background

Prescription drug monitoring programs (PDMPs) that collect and distribute information on dispensed controlled substances have been adopted by nearly all US states. We know little about program characteristics that modify PDMP impact on prescription opioid (PO) overdose deaths.

Methods

We measured associations between adoption of any PDMP and changes in fatal PO overdoses in 2002-2016 across 3109 counties in 49 states and D.C. We then measured changes related to the adoption of "proactive PDMPs," which report outlying prescribing/dispensing patterns and provide broader access to PDMP data by law enforcement. Comparisons were made within 3 time intervals that broadly represent the evolution of PDMPs (2002-2004, 2005-2009, and 2010-2016). We modeled overdoses using Bayesian space-time models.

Results

Adoption of electronic PDMP access was associated with 9% lower rates of fatal PO overdoses after three years (rate ratio [RR] = 0.91, 95% credible interval [CI]: 0.88-0.93) with well-supported effects for methadone (RR = 0.86,95% CI: 0.82-0.90) and other synthetic opioids (RR = 0.82, 95% CI: 0.77-0.86). Compared with states with no/weak PDMPs, proactive PDMPs were associated with fewer deaths attributed to natural/semi-synthetic opioids (2002-2004: RR = 0.72 [0.66-0.78]; 2005-2009: RR = 0.93 [0.90-0.97]; 2010-2016: 0.89 [0.86-0.92]) and methadone (2002-2004: RR = 0.77 [0.69-0.85]; 2010-2016: RR = 0.90 [0.86-0.94]). Unintended effects were observed for synthetic opioids other than methadone (2005-2009: RR = 1.29 [1.21-1.38]; 2010-2016: RR = 1.22 [1.16-1.29]).

Conclusions

State adoption of PDMPs was associated with fewer PO deaths overall while proactive PDMPs alone were associated with fewer deaths related to natural/semisynthetic opioids and methadone, the specific targets of these programs. See video abstract at, http://links.lww.com/EDE/B619.

Background and aims

Prescription drug monitoring programs (PDMP), defined as state-level databases used in the United States that collect prescribing information when controlled substances are dispensed, have varied substantially between states and over time. Little is known about the combinations of PDMP features that, collectively, may produce the greatest impact on prescribing and overdose. We aimed to (1) identify the types of PDMP models that have developed from 1999 to 2016, (2) estimate whether states have transitioned across PDMP models over time and (3) examine whether states have adopted different types of PDMP models in response to the burden of opioid overdose.

Methods

A latent transition analysis of PDMP models based on an adaptation of nine PDMP characteristics classified by prescription opioid policy experts as potentially important determinants of prescribing practices and prescription opioid overdose events.

Results

We divided the time-period into three intervals (1999-2004, 2005-09, 2010-16), and found three distinct PDMP classes in each interval. The classes in the first and second interval can be characterized as 'no/weak', 'proactive' and 'reactive' types of PDMPs, and in the third interval as 'weak', 'cooperative' and 'proactive'. The meaning of these classes changed over time: until 2009, states in the 'no/weak' class had no active PDMP, whereas states in the 'proactive' class were more likely to proactively provide unsolicited information to PDMP users, provide open access to law enforcement, and require more frequent data reporting than states in the 'reactive' class. In 2010-16, the 'weak' class resembled the 'reactive' class in previous intervals. States in the 'cooperative' class in 2010-16 were less likely than states in the 'proactive' class to provide unsolicited reports proactively or to provide open access to law enforcement; however, they were more likely than those in the 'proactive' class to share PDMP data with other states and to report more federal drug schedules.

Conclusions

Since 1999, US states have tended to transition to more robust classes of prescription drug monitoring programs. Opioid overdose deaths in prior years predicted the state's prescription drug monitoring program class but did not predict transitions between prescription drug monitoring program classes over time.

BACKGROUND:Prescription drug monitoring programs (PDMP), by reducing access to prescribed opioids (POs), may contribute to a policy environment in which some people with opioid dependence are at increased risk for transitioning from POs to heroin/other illegal opioids. This study examines how PDMP adoption and changes in the characteristics of PDMPs over time contribute to changes in fatal heroin poisoning in counties within states from 2002 to 2016. METHODS:Latent transition analysis to classify PDMPs into latent classes (Cooperative, Proactive, and Weak) for each state and year, across three intervals (1999-2004, 2005-2009, 2010-2016). We examined the association between probability of PDMP latent class membership and the rate of county-level heroin poisoning death. RESULTS:After adjustment for potential county-level confounders and co-occurring policy changes, adoption of a PDMP was significantly associated with increased heroin poisoning rates (22% increase by third year post-adoption). Findings varied by PDMP type. From 2010-2016, states with Cooperative PDMPs (those more likely to share data with other states, to require more frequent reporting, and include more drug schedules) had 19% higher heroin poisoning rates than states with Weak PDMPs (adjusted rate ratio [ARR] = 1.19; 95% CI = 1.14, 1.25). States with Proactive PDMPs (those more likely to report outlying prescribing and dispensing and provide broader access to law enforcement) had 6% lower heroin poisoning rates than states with No/Weak PDMPs (ARR = 0.94; 95% CI = 0.90, 0.98). CONCLUSION:There is a consistent, positive association between state PDMP adoption and heroin poisoning mortality. However, this varies by PDMP type, with Proactive PDMPs associated with a small reduction in heroin poisoning deaths. This raises questions about the potential for PDMPs to support efforts to decrease heroin overdose risk, particularly by using proactive alerts to identify patients in need of treatment for opioid use disorder. Future research on mechanisms explaining the reduction in heroin poisonings after enactment of Proactive PDMPs is merited.

Owing to its extremely high luminosity and long duration, supernova (SN) 2006gy radiated more energy in visual light than any other known SN. Two hypotheses to explain its high luminosity at early times - that it was powered by shock interaction with circumstellar material (CSM) as implied by its Type IIn spectrum, or that it was fueled by radioactive decay from a large mass of 56Ni synthesized in a pair-instability SN-predicted different late-time properties. Here we present observations of SN 2006gy obtained more than a year after discovery. We were unable to detect it at visual wavelengths, but clear near-infrared (IR) K′ and H-band detections show that it is still at least as luminous as the peak of a normal Type II SN. We also present spectra giving an upper limit to the late-time Ha luminosity of ≲S10 39 erg s-1. Based on the weak late-time Ha, X-ray, and radio emission, combined with the difficulty of explaining the shift to IR wavelengths, we can rule out ongoing CSM interaction as the primary late-time power source of SN 2006gy. Instead, we propose that the evolution of SN 2006gy is consistent with one of two possible scenarios: (1)apairinstability SN plus modest CSM interaction, where the radioactive decay luminosity shifts to the IR because of dust formation; or (2) an IR echo, where radiation emitted during peak luminosity heats a pre-existing dust shell at radii near 1 light year, requiring the progenitor star to have ejected another shell of ∼10 M ⊙ about 1500 yr before the SN. © 2008. The American Astronomical Society. All rights reserved.

Building a diverse laboratory that is equitable is critical for the retention of talent and the growth of trainees professionally and personally. Here, we outline several strategies including enhancing understanding of cultural competency and humility, establishing laboratory values, and developing equitable laboratory structures to create an inclusive laboratory environment to enable trainees to achieve their highest success.

The supernova (SN) PTF11iqb was initially classified as a Type IIn event caught very early after explosion. It showed narrowWolf-Rayet (WR) spectral features on day 2 (as in SN 1998S and SN 2013cu), but the narrow emission weakened quickly and the spectrum morphed to resemble Types II-L and II-P. At late times, Ha exhibited a complex, multipeaked profile reminiscent of SN 1998S. In terms of spectroscopic evolution, we find that PTF11iqb was a near twin of SN 1998S, although with somewhat weaker interaction with circumstellar material (CSM) at early times, and stronger interaction at late times. We interpret the spectral changes as caused by early interaction with asymmetric CSM that is quickly (by day 20) enveloped by the expanding SN ejecta photosphere, but then revealed again after the end of the plateau when the photosphere recedes. The light curve can be matched with a simple model for CSM interaction (with a mass-loss rate of roughly 10-4 M⊙ yr-1) added to the light curve of a normal SN II-P. The underlying plateau requires a progenitor with an extended hydrogen envelope like a red supergiant at the moment of explosion, consistent with the slow wind speed ( < 80 km s-1) inferred from narrow Ha emission. The cool supergiant progenitor is significant because PTF11iqb showed WR features in its early spectrum - meaning that the presence of such WR features does not necessarily indicate a WR-like progenitor. Overall, PTF11iqb bridges SNe IIn with weaker pre-SN mass-loss seen in SNe II-L and II-P, implying a continuum between these types.