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72 results for “Prescription opioids”
The Effect of Prescription Drug Monitoring Programs (PDMPs) and Overdose Reversal Drug Accessibility Laws on Opioid Analgesic Mortality in the United States
<p><strong>Purpose: </strong>This analysis focuses on the impact of Prescription Drug Monitoring Programs (PDMPs) and increased layperson access to the overdose reversal drug Naloxone on prescription opioid mortality rates. The prescription opioid mortality rate was analyzed against state laws governing PDMPs and Naloxone accessibility to laypersons to evaluate if there was a correlation between mortality reduction and implementation of the laws.</p> <p>Three main analyses were conducted:</p> <ol> <li>Does a state’s opioid mortality reduction correlate to an effective PDMP and/or Naloxone law?</li> <li>Do states with strong PDMP laws have a corresponding opioid overdose mortality rate reduction?</li> <li>Do states with strong Naloxone accessibility laws have a corresponding opioid overdose mortality rate reduction?</li> </ol> <p><strong>Conclusion:</strong> Patterns show that PDMP and Naloxone accessibility can be successful in reducing mortality rates, but implementations and results vary dramatically between states. Further changes to both PDMPs and Naloxone accessibility are needed for states to see reliable and consistent mortality reductions. Changes to regulations need time to implement and take effect, meaning longer term measurements and data will be required to see if positive impacts can be sustained.</p> <ol> </ol> <p><strong>Data and Datasets:</strong></p> <p>There are three main datasets used in the analysis.</p> <ul> <li>Opioid mortality rate by state from 2006 to 2016. <ul> <li>This datasetis from the CDC website (<a href="https://www.cdc.gov/drugoverdose/data/statedeaths.html">https://www.cdc.gov/drugoverdose/data/statedeaths.html</a>). I used web scraping and regular expression search to extract the data and calculated mortality reduction of each state.</li> <li><em>Mortality reduction formula: (Highest mortality rate) – (2016 mortality rate)</em><br> </li> </ul> </li> <li>PDMP law implementation in each state and its timelinefrom January 1, 1998 to July 1, 2016. <ul> <li>Dataset is from the Prescription Drug Abuse Policy System(<a href="http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372">http://pdaps.org./datasets/prescription-monitoring-program-laws-1408223332-1502818372</a>). This dataset encompasses laws regulating which professions have access to the database and for what purpose, whether practitioners can delegate their access, whether patients can see their own information, and the extent to which access to individually-identified records may be granted for law enforcement purposes.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing the PDMP.I performed the following data preparation to allow for analysis and visualization:</li> <li><em>PDMP law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul> <ul> <li>Naloxone accessibility to laypersons law implementation in each state and its timeline from January 1, 2001 to December 31, 2016. <ul> <li>Dataset is also from the Prescription Drug Abuse Policy System (<a href="http://pdaps.org./datasets/laws-regulating-administration-of-naloxone-1501695139">http://pdaps.org./datasets/laws-regulating-administration-of-Naloxone-1501695139</a>).This dataset focuses on state laws that provide civil or criminal immunity to licensed healthcare providers or lay responders for opioid antagonist administration.</li> <li>This dataset is not quantitative, but a set of questions on the characteristics of the laws governing Naloxone accessibility to laypersons. I performed the following data preparation to allow for analysis and visualization:</li> <li><em>Naloxone accessibility law effectiveness calculation:Each question with a “Yes” answer adds one point to the law’s cumulative effectiveness score on the year it was implemented.</em></li> </ul> </li> </ul>
Evaluation of Medical Cannabis and Prescription Opioid Taper Support for Reduction of Pain and Opioid Dose in Patients With Chronic Non-Cancer Pain
ClinicalTrials.gov study NCT04827992. IPD Sharing: YES. Countries: 1. Publications: 1.
Default Dosing Settings for Opioid Prescriptions to Adolescents and Young Adults After Tonsillectomy
ClinicalTrials.gov study NCT04066829. IPD Sharing: NO. Countries: 1. Publications: 1.
Improving Treatment Outcomes for Prescription Opioid Dependence
ClinicalTrials.gov study NCT02543944. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Opioid Prescription After Cesarean Trial
ClinicalTrials.gov study NCT04296396. IPD Sharing: YES. Countries: 1. Publications: 1.
Personalization of Opioid Prescription Following Orthognathic Surgery
ClinicalTrials.gov study NCT05708521. IPD Sharing: NO. Countries: 1. Publications: 20.
Buprenorphine Maintenance vs. Detoxification in Prescription Opioid Dependence
ClinicalTrials.gov study NCT00555425. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Shared Decision Making for Prescription Opioids After Cesarean Delivery
ClinicalTrials.gov study NCT02770612. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Efficacy of Extended Release Tramadol for Treating Prescription Opioid Withdrawal
ClinicalTrials.gov study NCT00980044. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prescription Opioid Effects in Abusers Versus Non-Abusers
ClinicalTrials.gov study NCT00158184. IPD Sharing: NO. Countries: 1. Publications: 1.
Primary Care Intervention to Reduce Prescription Opioid Overdoses
ClinicalTrials.gov study NCT02464410. IPD Sharing: NO. Countries: 1. Publications: 1.
Ambulatory Gynecologic Surgery: Finding the Optimal Opioid Prescription
ClinicalTrials.gov study NCT03588910. IPD Sharing: NO. Countries: 1. Publications: 6.
STandard Versus No Opioid Prescription After Prolapse and Anti-INcontinence Surgery Trial
ClinicalTrials.gov study NCT04491617. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Prescription Opioid Addiction Treatment Study (POATS)
ClinicalTrials.gov study NCT00316277. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Reducing High School Athletes' Prescription Opioids Misuse and Diversion Through the Student Athlete Wellness Portal
ClinicalTrials.gov study NCT05209191. IPD Sharing: YES. Countries: 1. Publications: 1.
Comparing Opioid Prescription Patterns in Total Joint Arthroplasty Patients
ClinicalTrials.gov study NCT03236155. IPD Sharing: NO. Countries: 1. Publications: 6.
Text Intervention to Facilitate Secure Storage and Disposal of Prescription Opioids
ClinicalTrials.gov study NCT05503186. IPD Sharing: YES. Countries: 1. Publications: 1.
Benzodiazepine Taper With Cognitive Behavioral Therapy in Patients Using Prescription Opioids
ClinicalTrials.gov study NCT05573906. IPD Sharing: YES. Countries: 1. Publications: 1.
CSD Opioid Prescriptions vs. Stimulants Trend
<p>Notes: The data reported in this dashboard (<a href="https://docs.google.com/spreadsheets/d/1iV2ZVv1lfD7aaN0OHZdA43RzA6VR4-7__UOWEl1WztE/edit?usp=sharing">Link</a>)reflects the data quality and reporting of controlled substance database. A new database schema was updated by DOPL in March 2017 that follows the ASAP 4.2 standard. The data is reported based on the current data. As a result, these numbers may not be consistent with previous reports. The aggregate numbers in this table does not take into account of prescription counts in CSD that were revisions (reporting status <> 02). CDC's latest drug file (2018) was used to compute these numbers. The Rx numbers are based on NDC files updated in production tables on May 2018. A new NDC file updating procedure is in progress, which might increase/decrease the MME, Rx numbers and compounds. This dashboard can be accessed at: Stimulants vs. Opioids. For any questions regarding this report contact DOPL Senior Health Informaticist, nsr@utah.gov</p>
Data from: Co-prescription network reveals social dynamics of opioid doctor shopping
This paper examines network prominence in a co-prescription network as an indicator of opioid doctor shopping (i.e., fraudulent solicitation of opioids from multiple prescribers). Using longitudinal data from a large commercially insured population, we construct a network where a tie between patients is weighted by the number of shared opioid prescribers. Given prior research suggesting that doctor shopping may be a social process, we hypothesize that active doctor shoppers will occupy central structural positions in this network. We show that network prominence, operationalized using PageRank, is associated with more opioid prescriptions, higher predicted risk for dangerous morphine dosage, opioid overdose, and opioid use disorder, controlling for number of prescribers and other variables. Moreover, as a patient's prominence increases over time, so does their risk for these outcomes, compared to their own average level of risk. Results highlight the importance of co-prescription networks in characterizing high-risk social dynamics.
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International Brain Laboratory public data
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OpenNeuro
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