The Automated Reports and Consolidated Orders System III (ARCOS-3) tracks legally manufactured drugs that can be diverted and used for illicit purposes from manufacture through distribution, and maintains an inventory of each of these drugs. The system ma

ARCOS Database provided by the Washington Post. We use the data for the empirical analysis in our article`` Retail Pharmacies and Drug Diversion during the Opioid Epidemic’’.

2006--2012 data from the Automation of Reports and Consolidated Orders System (ARCOS), maintained by the Diversion Control Division of the US Drug Enforcement Administration (DEA).

The data can be downloaded from https://www.washingtonpost.com/national/2019/07/18/how- download-use-dea-pain-pills-database/ in raw format and until 2021 through an R package (API) on https://github.com/wpinvestigative/arcos. Please follow the requirement of the Washington Post: ‘If you publish an online story, graphic, map or other piece of journalism based on this data set, please credit The Washington Post, link to the original source, and send us an email when you’ve hit publish. We want to learn what you discover and will attempt to link to your work as part of cataloguing the impact of this project.” (The Washington Post, 2019)

The Washington Post. How to download and use the DEA pain pills database, 2019. https://www.washingtonpost.com/national/2019/07/18/how-download-use-dea-pain-pills-database/

From the Web site: The Post gained access to the Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System, known as ARCOS, as the result of a court order. The Post and HD Media, which publishes the Charleston Gazette-Mail in West Virginia, waged a year-long legal battle for access to the database, which the government and the drug industry had sought to keep secret.

The version of the database published by The Post allows readers to learn how much hydrocodone and oxycodone went to individual states and counties, and which companies and distributors were responsible.

Also: Guidelines for using this data Fill out the form below to establish a connection with our team and report any issues downloading the data. This will also allow us to update you with any additional information as it comes out and answer questions you may have. Because of the volume of requests, we ask you use this channel rather than emailing our reporters individually. If you publish an online story, graphic, map or other piece of journalism based on this data set, please credit The Washington Post, link to the original source, and send us an email when you’ve hit publish. We want to learn what you discover and will attempt to link to your work as part of cataloguing the impact of this project. Post reporting and graphics can be used on-air. We ask for oral or on-screen credit to The Washington Post. For specific requests, including interview with Post journalists, please email postpr@washpost.com.

Objective: To evaluate the changing pattern of distribution of Schedule II and III opioids, barbiturates, and stimulants to veterinary educational institutions in the United States.Design: Longitudinal study.Sample: Veterinary teaching institutions that use Schedule II and III drugs.Procedures: Distribution of controlled substances to veterinary teaching institutions was obtained from the Drug Enforcement Administration's Automated Reports and Consolidated Orders System (ARCOS) for opioids (e.g., methadone, fentanyl, codeine), barbiturates (pentobarbital, butalbital), and stimulants (amphetamine, methylphenidate, lisdexamfetamine) from 2006–2019. Opioids were converted to their morphine milligram equivalents (MME) for evaluation over time.Results: Controlled substance distribution to veterinary schools exhibited dynamic, and agent specific, changes. The total MME for 11 opioids peaked in 2013 and decreased by 17.3% in 2019. Methadone accounted for two-fifths (42.3%) and fentanyl over one-third (35.4%) of the total MME in 2019. Pentobarbital distribution was greatest by weight of all substances studied and peaked in 2011 at 69.4 kg. Stimulants underwent a pronounced decline and were very modest by 2014.Conclusions and Clinical Relevance: Opioids by total MME in veterinary teaching practice have undergone more modest changes than opioids used with humans. Hydrocodone, codeine and recently fentanyl use have declined while methadone increased. Stimulant distribution decreased to become negligible. Together, this pattern of findings warrant continued monitoring.

This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2019), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS).

Please cite the following publication when using this dataset:

KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (in press). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access, Data in Brief.

These data were originally collected for the following research article:

Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (in press). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence.

This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2019), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS).

Please cite the following publication when using this dataset:

KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (2021). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access. Data in Brief 35: e106779. https://doi.org/10.1016/j.dib.2021.106779

These data were originally collected for the following research article:

Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (2021). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence 219: e108501. https://doi.org/10.1016/j.drugalcdep.2020.108501

Scaling coefficients from a piecewise linear model.