Opioid overdoses and drug overdoses have been in the news a lot recently. Yet the increase in overdoses has been geographically imbalanced. It has not been uniform across the USA.
This project is the study of the geographic distribution of drug overdoses in the United States and a correlation with other potentially predictive risk factors that lead to drug overdoses.
The distribution of drug overdoses was plotted nationally to get an overall distribution of the problem. From the national map, regions of high drug overdose rates were identified and examined in more detail.
For the states with the highest drug overdose rate, specific counties within the states where drug overdose rates are high were examined. Both at the state and county level, the drug overdose rates were compared with the frequency of a number of demographic markers. The initial set of demographic markers to be investigated includes:
Uninsured adults Value
Median household income Value
Children eligible for free and reduced lunch Value
Homicide rate Value
Child mortality Value
Frequent physical distress Value
HIV prevalence rate Value
Percent of population that is non-Hispanic African American
Percent of population that is American Indian or Alaskan Native
Percent of population that is Asian
Percent of population that is Native Hawaiian or Other Pacific Islander
Percent of population that is Hispanic
Percent of population that is non-Hispanic White
All of these demographic markers are self-explanatory except for the Frequent physical distress value.
The Frequent Physical Distress Value is the percentage of adults who reported ≥14 days in response to the question, “Thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?” (Ref. http://www.countyhealthrankings.org)
In other words, it is an indicator of a person’s self-evaluation of their recent state of health.
The current rate of drug overdose were compared with the current levels of various demographic markers. In reality, there should probably be a time lag between the level of drug overdose and certain demographic features. But I’ll just stick with current levels for all data.
In addition to maps of drug overdose rates at the state and county levels along with maps of demographic features, the study included scatter plots and linear regression of the drug overdose rate compared with the demographic indicators.
Full article can be read here https://lbcc.maps.arcgis.com/apps/MapSeries/index.html?appid=9d8f9cf602bf48e093a739cf333c4a97