What the Numbers Tell Us About the U.S. Drug Crisis
The U.S. has been battling a growing drug use epidemic for several years. This crisis has resulted in an increase in overdose-related deaths as well as an increase in prescription drug spending. By looking at how much people are spending on prescription drugs in each state, we begin to understand the scope of the problem.
So how large of an impact has this drug crisis had on each state? Let’s take a look at the numbers to get an idea.
- North Dakota has both the lowest spending per capita on prescription drugs ($734) and the lowest number of deaths by overdose (68).
- Low spending per capita on prescription drugs does not necessarily indicate a lower number of overdose-related deaths. California, for example, has the second lowest spending per capita ($788) and the fourth most deaths from overdose (4,868).
- The largest spending category on prescription drugs in the country is commercial — meaning, the majority of these prescriptions are covered by commercial and government programs.
- 25 out of 50 states had over 1,000 fatal opioid overdoses in 2019 with only three states having less than 100 fatal overdoses — indicating that the drug epidemic is a national concern, not a localized issue.
Fatal overdoses in the United States are continuing to increase. The age-adjusted rate of overdose deaths increased significantly by 9.6% from 2016 (19.8 per 100,000) to 2017 (21.7 per 100,000).
Unsurprisingly, this correlates with an increase in prescription drug spending. In 2018, individuals in the United States spend a total of nearly $400 million on prescription drugs.
States With Highest Spending Per Capita On Prescription Drugs:
1. Delaware - $1,693
2. Tennessee - $1,655
3. Kentucky - $1,612
4. West Virginia - $1,507
5. Connecticut - $1,423
States With Lowest Spending Per Capita On Prescription Drug:
1. North Dakota - $734
2. California - $788
3. Montana - $828
4. Washington - $833
5. South Dakota - $862
States With Highest Opioid Prescriptions per 100 People:
1. Alabama - 107.2
2. Arkansas - 105.4
3. Tennessee - 94.4
4. Mississippi - 92.9
5. Louisiana - 89.5
States With Lowest Opioid Prescriptions per 100 People:
1. District of Columbia - 28.5
2. Hawaii - 37
3. New York - 37.8
4. California - 39.5
5. Massachusetts - 40.1
Most people would assume that either larger populations or some other correlating factor would present itself in the data. What we tend to see is that the higher the expenses per capita, the higher the opioid prescription rates. You see this in places like Alabama, Mississippi, and Arkansas.
However, this isn’t always true. States like Wisconsin and Connecticut have high per capita spending, but low amounts of opioid prescriptions. In fact, there isn’t necessarily a correlation between opioid deaths per capita and either the total expenses per capita or the prescription rate per 100 people.
To find the prescription drug spending per capita by state, we used U.S. Census data to get the population by state. KFF then provided data from IQVIA to total expenses for each state. We then divided the population to get the per capita spending for each state. The CDC provided the data on the opioid prescription rates.
The figures used in this piece were obtained by dividing all prescription drug expenses by the U.S. Census 2018 population estimates in each state. This data includes prescriptions covered by commercial and government programs, Medicaid, Medicare, and cash.
You were likely already aware of the current drug epidemic in the U.S. It has been the subject of extensive media coverage over the past several years.
However, if you haven’t experienced the effects of this problem in your own life, taking a look at the numbers surrounding this crisis can help give you a better idea of the severity of this issue.
It’s important to understand what this data represents and what it implies for the future. Moreover, it’s important to learn from others that have been affected by this epidemic to better understand the situation at hand. We encourage you to leave a comment about your personal experiences and what conclusions you think can be drawn from these figures.
Data: Table 1.1