Trend CT spent several weeks pulling, requesting, cleaning and analyzing different data sets to better understand Connecticut’s intractable drug-overdose problem. We generated hundreds, if not thousands, of exploratory charts and maps before attempting to publish the first story in our series.
We had to rely on overdose data from the following sources:
- Published raw data from the Office of the Chief Medical Examiner
- Data cleaned by the Connecticut Data Collaborative [individual] [by type]
- Underlying raw map data specific to overdoses in 2014 from the state that contained specific address and latitude and longitude details
- Accidental deaths from 1999 to 2013 by age group, gender, and race
The reproducible scripts detailing our methodology can be found on the Trend CT github page.
One script marked the overdose deaths over time by town. Another created different combinations of small multiple charts for race, gender, and towns based on individual drugs. For the second half of the series, we used our own method of cleaning up the data to categorize deaths by prescription opioids, heroin, and fentanyl in a way that was more consistent than what was noted in the raw data.
Those who are interested in the analysis process can see the dozens of scripts in the repo to see how it was done and exploratory angles that never made it into the final series but might be revisited in the future. Please let us know if there are any suggestions to improve our analysis.
We’d like to extend our thanks to the many researchers and health officials who gave us ideas on how to approach our analysis.
We’ll never know the full scope of the opioid overdose crisis in Connecticut until there is a database of overdose injuries. Analyzing data based on deaths alone is not enough to combat epidemics.
The death data used for this project is collected throughout the year, but it takes time to break out the drugs found in each person’s system.
One way to track might be to see how often Naloxone, the anti-overdose medicine, is used by emergency workers and where. The state has no central database tracking usage, nor is there a master list for which police or fire departments have equipped themselves with the medicine.
This is a data-centric website, so of course the emphasis is on better data.
Treatment advocates would argue that more emphasis should be put on improving drug user access to detox and expanding funding to accommodate more overdose victims. Recovery advocates would say the emphasis should be put on improving community relations and outreach into prisons.
They and others wouldn’t be wrong. This opioid epidemic is huge and complex.
Even if updated daily, a comprehensive database that incorporated the prescription monitoring system with Naxolone tracking and overdose injury reports from hospitals would be useless without good communication.
However, a shift in emphasis and resources toward an evidence-based public health approach to data use for overdose prevention could only help because the data shows that the problem is getting worse much faster than before.