Back

Characterizing Declines in US Overdose Deaths Compared to Exponential Predictions

Friedman, J. R.; Palamar, J. J.; Ciccarone, D.; Gaines, T. L.; Borquez, A.; Shover, C. L.; Strathdee, S. A.

2025-10-27 addiction medicine
10.1101/2025.10.24.25338732 medRxiv
Show abstract

BackgroundBetween 1979 and 2016, US overdose death rates rose in a smooth fashion, described by Jalal and Burke using an exponential growth curve that fit observed data nearly perfectly. Fluctuations above this curve have subsequently been seen during shocks related to drug supply and the COVID-19 pandemic. However, large-magnitude dips below the curve have never been demonstrated. Given that overdose mortality began sharply falling during 2023-2024, we assess updated overdose trends against the Jalal-Burke curve. MethodsWe examined US overdose deaths from the National Vital Statistics System between January 1979-December 2024. We recreated the Jalal-Burke curve, fitting an exponential growth curve to overdose rates from 1979 to 2016, linearly projecting through 2024, with 95% confidence intervals. We also examined trends by specific substance involvement. ResultsAfter precipitously surpassing exponential growth predictions in 2020-2023, overdose deaths decreased sharply from approximately 32 per 100,000 in 2021-2023 to 23.7 in 2024, falling below the lower bound of Jalal-Burke curve (24.98 per 100,000) for the first time since 2001. These decreases reflected declining illicit fentanyl-involved deaths (with and without stimulants); however, deaths involving stimulants without fentanyl, and those involving xylazine, represent an increasing share of deaths in 2024. ConclusionsRather than simply representing a return to the Jalal-Burke exponential growth curve, recent decreases in overdose deaths represent the first significant, large-magnitude deviation below exponential growth projections. These trends represent a very positive development. However, challenges in the US drug crisis are shifting, requiring a tailored public health response.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
JAMA Network Open
127 papers in training set
Top 0.1%
27.9%
2
International Journal of Drug Policy
11 papers in training set
Top 0.1%
14.8%
3
Drug and Alcohol Dependence
37 papers in training set
Top 0.1%
14.5%
50% of probability mass above
4
The Lancet Public Health
20 papers in training set
Top 0.1%
10.2%
5
Pharmacoepidemiology and Drug Safety
13 papers in training set
Top 0.1%
4.0%
6
PLOS ONE
4510 papers in training set
Top 39%
3.6%
7
Journal of General Internal Medicine
20 papers in training set
Top 0.3%
2.8%
8
Pharmacology Research & Perspectives
11 papers in training set
Top 0.1%
2.1%
9
Addiction
25 papers in training set
Top 0.2%
2.1%
10
The British Journal of Psychiatry
21 papers in training set
Top 0.4%
1.9%
11
Neuropsychopharmacology
134 papers in training set
Top 2%
1.7%
12
Frontiers in Psychiatry
83 papers in training set
Top 2%
1.2%
13
Frontiers in Artificial Intelligence
18 papers in training set
Top 0.5%
1.0%
14
British Journal of Pharmacology
34 papers in training set
Top 0.5%
0.8%
15
The Lancet Regional Health - Americas
22 papers in training set
Top 0.3%
0.8%
16
npj Digital Medicine
97 papers in training set
Top 3%
0.8%
17
Addiction Neuroscience
17 papers in training set
Top 0.5%
0.8%
18
PeerJ
261 papers in training set
Top 15%
0.8%
19
JAMA Psychiatry
13 papers in training set
Top 0.6%
0.8%
20
American Journal of Preventive Medicine
11 papers in training set
Top 0.6%
0.7%
21
Scientific Reports
3102 papers in training set
Top 78%
0.6%
22
BMC Health Services Research
42 papers in training set
Top 2%
0.6%
23
Addiction Biology
47 papers in training set
Top 0.8%
0.5%