Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0) United States License.

To cite this case study please use:

Wright, Carrie and Wang, Kexin and Meng, Qier and Jager, Leah and Taub, Margaret and Hicks, Stephanie. (2020). https://github.com/opencasestudies/ocs-bp-opioid-rural-urban Opioids in the United States (Version v1.0.0).

To access the GitHub Repository for this case study see here: https://github.com/opencasestudies/ocs-bp-opioid-rural-urban/

You may also access and download the data using our OCSdata package. To learn more about this package including examples, see this link. Here is how you would install this package:

install.packages("OCSdata")

This case study is part of a series of public health case studies for the Bloomberg American Health Initiative.

The total reading time for this case study is calculated via koRpus and shown below:

Reading Time Method
88 minutes koRpus

Readability Score:

A readability index estimates the reading difficulty level of a particular text. Flesch-Kincaid, FORCAST, and SMOG are three common readability indices that were calculated for this case study via koRpus. These indices provide an estimation of the minimum reading level required to comprehend this case study by grade and age.

Text language: en
index grade age
Flesch-Kincaid 9 14
FORCAST 10 15
SMOG 12 17

Please help us by filling out our survey.

Motivation

In this case study we will be examining the number of opioid pills (specifically oxycodone and hydrocodone, as they are the top two most misused opioids) shipped to pharmacies and practitioners at the county-level around the United States (US) from 2006 to 2014.

This data comes from the DEA Automated Reports and Consolidated Ordering System (ARCOS) and was released by the Washington Post after legal action by the owner of the Charleston Gazette-Mail in West Virginia and the Washington Post.

We will investigate how the number of shipped pills has changed across time and between rural and urban counties in the US. This analysis will demonstrate how different regions of the country may have been more at risk for opioid addiction crises due to differing rates of opioid prescription (using the number of pills as a proxy for prescription rates). This will help inform students about how evidence-based intervention decisions are made in this area.

This case study is motivated by this article:

García, M. C. et al. Opioid Prescribing Rates in Nonmetropolitan and Metropolitan Counties Among Primary Care Providers Using an Electronic Health Record System — United States, 2014–2017. MMWR Morb. Mortal. Wkly. Rep. 68, 25–30 (2019). DOI: 10.15585/mmwr.mm6802a1

This article explores rates of opioid prescriptions in rural and urban communities in the United States using the Athenahealth electronic health record (EHR) system for 31,422 primary care providers from January 2014 to March 2017.

The main takeaways from this article were:

Among 70,237 fatal drug overdoses in 2017, prescription opioids were involved in 17,029 (24.2%).

The percentage of patients prescribed an opioid was higher in rural than in urban areas.

Higher opioid prescribing rates put patients at risk for addiction and overdose.

Indeed, this was confirmed by another article, which surveyed people who use heroin in the Survey of Key Informants’ Patients Program and the Researchers and Participants Interacting Directly (RAPID) program.

Cicero, T. J., Ellis, M. S., Surratt, H. L. & Kurtz, S. P. The Changing Face of Heroin Use in the United States: A Retrospective Analysis of the Past 50 Years. JAMA Psychiatry 71, 821 (2014). DOI:10.1001/jamapsychiatry.2014.366

They found that:

Respondents who began using heroin in the 1960s were predominantly young men (82.8%; mean age, 16.5 years) whose first opioid of abuse was heroin (80%).

Meaning that 80% of the people who use heroin who started using heroin in the 1960s, started with heroin directly, while 20% first used another opioid and then became people who use heroin.

However, more recent users were older (mean age, 22.9 years) men and women living in less urban areas (75.2%) who were introduced to opioids through prescription drugs (75.0%).

[source]

Heroin use has changed from an inner-city, minority-centered problem to one that has a more widespread geographical distribution, involving primarily white men and women in their late 20s living outside of large urban areas.