A comparison of opioid‐involved fatalities captured in the National Poison Data System to data derived from US death certificate literal text

doi:10.1002/pds.4869

In developing a national energy strategy, the United States has a number of objectives, including increasing economic growth, improving environmental quality, and enhancing national energy security. Wind power contributes to these objectives through the deployment of clean, affordable and reliable domestic energy. To achieve U.S. wind generation objectives, the Wind and Water Power Program within the Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE) instituted the U.S. Offshore Wind: Removing Market Barriers Program in FY 2011. Accurate and comprehensive information on offshore wind resource characteristics across a range of spatial and temporal scales is one marketmore » barrier that needs to be addressed through advanced research in remote sensing technologies. There is a pressing need for reliable offshore wind-speed measurements to assess the availability of the potential wind energy resource in terms of power production and to identify any frequently occurring spatial variability in the offshore wind resource that may impact the operational reliability and lifetime of wind turbines and their components and to provide a verification program to validate the “bankability” of the output of these alternative technologies for use by finance institutions for the financing of offshore wind farm construction. The application of emerging remote sensing technologies is viewed as a means to cost-effectively meet the data needs of the offshore wind industry. In particular, scanning and buoy mounted LIDAR have been proposed as a means to obtain accurate offshore wind data at multiple locations without the high cost and regulatory hurdles associated with the construction of offshore meteorological towers. However; before these remote sensing technologies can be accepted the validity of the measured data must be evaluated to ensure their accuracy. The proposed research will establish a unique coastal ocean test-bed in the Mid-Atlantic for the evaluation of LIDAR-based wind measurement systems to validate the accuracy of remotely measured wind data in marine applications. Specifically, the test-bed will be utilized to systematically evaluate the capability of emerging scanning LIDAR and buoy mounted vertically profiling LIDAR by: (1) Evaluating a fixed scanning LIDAR against land-based 50 and 60 meter high meteorological masts fitted with research quality cup-vane and/or sonic anemometers; (2) Evaluating a buoy mounted vertically profiling LIDAR fixed on land and floating in a sheltered bay against a co-located 60 meter high meteorological mast fitted with a research quality cup-vane and/or sonic anemometers and the fixed scanning LIDAR; and (3) Offshore field evaluation of both LIDAR platforms through a comparison of the fixed scanning LIDAR data and data obtained by the buoy mounted LIDAR located 10 miles offshore. The proposed research will systematically validate Light Detection and Ranging (LIDAR) based wind measurement systems and assess the temporal and spatial variability of the offshore wind resource in the Mid-Atlantic east of New Jersey. The goal of the proposed project is to address the technical and commercial challenges of the offshore wind energy industry by validating and assessing cost-effective, over ocean wind resource characterization technologies. The objective is to systematically evaluate the capability of both scanning and vertically profiling LIDARs to accurately measure 3D wind fields through comparison with fixed met masts and intercomparison among LIDAR platforms. Once validated, data collected by both buoy mounted vertically profiling LIDARs and shore-based, pulsed horizontally scanning LIDARs can be used to accurately assess offshore wind resources and to quantify the spatial and temporal variability in the offshore wind fields. One of the fundamental research questions to be addressed in phase 1 is the assessment of various measurement and data processing schemes to retrieve accurate wind vectors in the marine environment over large sampling ranges (10 to 12 km) and varying atmospheric aerosol levels. Atmospheric conditions and aerosol content within the coastal ocean region of the Mid-Atlantic seaboard of the US can vary significantly over short time periods in response to frontal passages and extratropical and tropical low pressure system passage offshore of the coast. Since aerosols provide the scattering medium for the determination of LIDAR Doppler shifts in the atmosphere the accuracy and range of LIDAR derived velocity measurements as a function of variation in aerosol content in the marine environment is a key research question to be addressed. In phase 1, it is desired to capture as much variation in atmospheric conditions and aerosol content as possible. To this end, collocated measurements of LIDAR and standard anemometer wind fields will be captured by the project PIs over all four seasons and during specific events (e.g., coastal low pressure system passage) in year 1. Additionally, since the meteorological masts are permanent structures, additional events can be captured over the three year duration of the field research project. All research instruments are owned by Fishermen’s Energy and made available to the PIs though a lease agreement as part of the DOE grant. Energy Fishermen’s Energy will be responsible for the operation and maintenance of the scanning LIDAR and met mast anemometers. On a daily basis, environmental data and systems performance indicators will be transmitted from each measurement station to the Fishermen’s project team consisting of both in-house personnel and equipment manufacturer engineers. Data sets include compiled LIDAR files as well as data sets from ancillary sensors. Diagnostic parameters to be monitored include standard deviations of measured values, battery levels and charging systems output, and the operational status. Once data have been confirmed as complete and reliable, files will be transferred to the Garrad Hassan (a subcontractor to Fishermen’s Energy) for incorporation in to the validation database, which is accessible to other scientific team members. Data collection times and durations will be determined by the PI and Co-PIs in consultation with instrument engineers to ensure the capture of data representative of the expected range of mid-Atlantic atmospheric conditions (e.g., temperature, moisture, coastal low pressure systems, tropical systems, rain, snow, fog). The collection and processing of the data is a function of site specific measurement requirements (Kelley et.al. 2007; Hannon et.al. 2008). To determine the optimal profiles of wind speed and direction from the LIDAR radial velocities as a function of azimuth angle, rigorous estimates of the bias and random error of each radial velocity estimate are required. Lockheed Martin Coherent Technologies, Inc., under contract with Fishermen’s Energy, will provide analyses of raw and processed data using various scan patterns to determine optimal performance settings for the pulsed scanning LIDAR. Once optimized, appropriate processing and analyses techniques will be evaluated by Garrad Hassan for use in validating the accuracy of the LIDAR wind field measurements against the standard anemometer measurements from the meteorological masts. The most attractive capability of the scanning LIDAR is the ability to provide high spatial resolution observations in a three-dimensional volume which provides superior statistical accuracy due to the large number of samples obtained. Each radial scan provides measurements in 100 range gates over a distance of 10 to 12 km at an update rate of 5 to 10 Hz and rotation of 2.5° per second. Each rotation at a fixed azimuth requires 2.4 minutes. Depending on the number of azimuths desired a complete scan can take up to 10 minutes or longer to complete. Once collected the radial velocities are processed to produce vector wind velocity estimates based on a set of data distributed in angle and range around points of interest, typically a standard grid within the radial wind map. To calculate wind vectors over a limited spatial area of interest for the comparison of data with other measurement platforms a localized least-squares approach has been applied by Hannon et.al. (2008) and a Variation Assimilation (VAR) processing technique has been applied by Chan and al Assimilation (VAR) processing technique has been applied by Chan and Shao (2006). Additionally, Kelley et.al. (2007) applied a “stare” technique that fixed a scanning LIDAR in both azimuth and elevation angles to measure over collocated sampling volumes of the LIDAR and a 3D sonic anemometer mounted to a fixed mast. Although the stare technique is limited to wind directions aligned with the sampling radial of the LIDAR, it does provide a direct comparison of sampling volumes. Each of the processing techniques described above (and possibly others) will be evaluated to determine the validity of the LIDAR derived wind fields in the marine environment. Numerical methods such as linear regression and comparison of probability density functions of wind fields measured by each instrument platform will be used to assess the processing techniques. Linear regression has the advantage of directly evaluating corresponding pairs of wind data measured by each instrument and can lend insight into deviations and bias between instruments as a function of wind speed. Assessment of the coherency between the probability density function of the wind measured by each instrument provides insight into processes that may not be accurately resolved by each instrument at specific frequencies. Once processed and assessed the most appropriate technique will be utilized to provide valid wind measurements from the pulsed scanning LIDAR. A detailed analysis of the measurement data from the LIDAR and the three meteorological towers and comparison of the coastal wind characteristics from the different systems will be performed by the CO_PI at NREL. This analysis will evaluate how the WT LIDAR performance and measurement of the wind characteristics vary with distance from the LIDAR and by atmospheric conditions; using the tower measurements at different distances (4.8, 9.6, and 19 km) from the LIDAR as a reference. The comparative analysis will include, to the extent possible, evaluation of parameters such as wind speed and direction distributions, wind shear, turbulence intensity and their variations by atmospheric conditions, month or season, and time of day.

This content will become publicly available on August 10, 2020

Title: A comparison of opioid‐involved fatalities captured in the National Poison Data System to data derived from US death certificate literal text

Citation Formats

Surveillance of poisoning and drug overdose through hospital discharge coding, poison control center reporting, and the Drug Abuse Warning Network
journal, January 1993

Medical examiner and medical toxicologist agreement on cause of death
journal, March 2011

Using poison center exposure calls to predict prescription opioid abuse and misuse-related emergency department visits: PC EXPOSURE CALLS TO PREDICT ED VISITS
journal, October 2013

Urinary Excretion Profiles for Total Morphine, Free Morphine, and 6-Acetylmorphine Following Smoked and Intravenous Heroin
journal, October 2001

Reliability of data sources for poisoning deaths in Massachusetts
journal, March 1992

Medical Outcomes Associated with Nonmedical Use of Methadone and Buprenorphine
journal, August 2013

2016 Annual Report of the American Association of Poison Control Centers’ National Poison Data System (NPDS): 34th Annual Report
journal, June 2017

Uniform Standards and Case Definitions for Classifying Opioid-Related Deaths: Recommendations by a SAMHSA Consensus Panel
journal, July 2013

2008 Annual Report of the American Association of Poison Control Centers’ National Poison Data System (NPDS): 26th Annual Report
journal, December 2009

Syndrome surveillance of fentanyl-laced heroin outbreaks: Utilization of EMS, Medical Examiner and Poison Center databases
journal, November 2017

Deciphering Suicide and Other Manners of Death Associated with Drug Intoxication: A Centers for Disease Control and Prevention Consultation Meeting Summary
journal, August 2017

Surveillance of methadone-related poisonings in Kentucky using multiple data sources
journal, February 2010

Poisoning Mortality in the United States: Comparison of National Mortality Statistics and Poison Control Center Reports
journal, May 2000

Using Poison Center Exposure Calls to Predict Methadone Poisoning Deaths
journal, July 2012

Geographic Variation in Opioid and Heroin Involved Drug Poisoning Mortality Rates
journal, December 2017

Trends in Deaths Involving Heroin and Synthetic Opioids Excluding Methadone, and Law Enforcement Drug Product Reports, by Census Region — United States, 2006–2015
journal, September 2017

Consistency Between Opioid-Related Mortality Trends Derived From Poison Center and National Vital Statistics System, United States, 2006–2016
journal, December 2018

Increases in Drug and Opioid-Involved Overdose Deaths — United States, 2010–2015
journal, December 2016

2015 Annual Report of the American Association of Poison Control Centers’ National Poison Data System (NPDS): 33rd Annual Report
journal, August 2016

Real-time surveillance of illicit drug overdoses using poison center data
journal, June 2009

Clinician Validation of Poison Control Center (PCC) Intentional Exposure Cases Involving Prescription Opioids
journal, January 2006

This content will become publicly available on August 10, 2020

Title: A comparison of opioid‐involved fatalities captured in the National Poison Data System to data derived from US death certificate literal text

Citation Formats

Surveillance of poisoning and drug overdose through hospital discharge coding, poison control center reporting, and the Drug Abuse Warning Network journal, January 1993

Medical examiner and medical toxicologist agreement on cause of death journal, March 2011

Using poison center exposure calls to predict prescription opioid abuse and misuse-related emergency department visits: PC EXPOSURE CALLS TO PREDICT ED VISITS journal, October 2013

Urinary Excretion Profiles for Total Morphine, Free Morphine, and 6-Acetylmorphine Following Smoked and Intravenous Heroin journal, October 2001

Reliability of data sources for poisoning deaths in Massachusetts journal, March 1992

Medical Outcomes Associated with Nonmedical Use of Methadone and Buprenorphine journal, August 2013

2016 Annual Report of the American Association of Poison Control Centers’ National Poison Data System (NPDS): 34th Annual Report journal, June 2017

Uniform Standards and Case Definitions for Classifying Opioid-Related Deaths: Recommendations by a SAMHSA Consensus Panel journal, July 2013

2008 Annual Report of the American Association of Poison Control Centers’ National Poison Data System (NPDS): 26th Annual Report journal, December 2009

Syndrome surveillance of fentanyl-laced heroin outbreaks: Utilization of EMS, Medical Examiner and Poison Center databases journal, November 2017

Deciphering Suicide and Other Manners of Death Associated with Drug Intoxication: A Centers for Disease Control and Prevention Consultation Meeting Summary journal, August 2017

Surveillance of methadone-related poisonings in Kentucky using multiple data sources journal, February 2010

Poisoning Mortality in the United States: Comparison of National Mortality Statistics and Poison Control Center Reports journal, May 2000

Using Poison Center Exposure Calls to Predict Methadone Poisoning Deaths journal, July 2012

Geographic Variation in Opioid and Heroin Involved Drug Poisoning Mortality Rates journal, December 2017

Trends in Deaths Involving Heroin and Synthetic Opioids Excluding Methadone, and Law Enforcement Drug Product Reports, by Census Region — United States, 2006–2015 journal, September 2017

Consistency Between Opioid-Related Mortality Trends Derived From Poison Center and National Vital Statistics System, United States, 2006–2016 journal, December 2018

Increases in Drug and Opioid-Involved Overdose Deaths — United States, 2010–2015 journal, December 2016

2015 Annual Report of the American Association of Poison Control Centers’ National Poison Data System (NPDS): 33rd Annual Report journal, August 2016

Real-time surveillance of illicit drug overdoses using poison center data journal, June 2009

Clinician Validation of Poison Control Center (PCC) Intentional Exposure Cases Involving Prescription Opioids journal, January 2006