Evolving regional trends in U.S. opioid-related mortality: Disparities and the impact of COVID-19 (1999-2023)

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This has been further exacerbated by COVID-19, contributing to a surge in substance use and overdose deaths. Methods Data from the CDC Wonder Database was used to identify opioid use-related deaths in the United States from 1999 to 2023. Crude and age-adjusted mortality rates (AAMR) were calculated across different geographic and demographic groups. Temporal trends in mortality were analyzed using the Joinpoint Regression Program. Annual percentage change (APC) and average annual percentage change (AAPC) in mortality rates were calculated. Results Opioid-related mortality surged during the COVID-19 period, with significant regional, racial, and state-level disparities. The Midwest region exhibited the highest AAPC from 1999 to 2023. State-specific analyses showed that West Virginia experienced the highest increase in AAMR from 1999 to 2023. Among racial groups, non-Hispanic American Indians/Alaska Natives had the highest AAPC from 1999 to 2023. Gender-specific analyses revealed that males had a higher AAPC from 1999 to 2023 than females. Conclusion The United States opioid crisis continued to escalate during the COVID-19 period, with significant increases in mortality across the regions. The highest AAPCs were seen in the Midwest and among non-Hispanic American Indians/Alaska Natives. These findings underscore the need for targeted interventions to account for trends in racial and regional disparities in opioid-related mortality that have increased during and post-COVID-19. 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F1000Research 2025, 14 :72 ( https://doi.org/10.12688/f1000research.159967.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Evolving regional trends in U.S. opioid-related mortality: Disparities and the impact of COVID-19 (1999-2023) [version 1; peer review: 1 not approved] Ricky Rana https://orcid.org/0009-0004-0088-9572 1 , Ben Leiken https://orcid.org/0009-0001-9100-7253 1 , Vikram Murugan 1 , Taylor Billion 1 , Abubakar Tauseef 1 , Ali Bin Abdul Jabbar https://orcid.org/0000-0001-5145-8626 1 Ricky Rana https://orcid.org/0009-0004-0088-9572 1 , Ben Leiken https://orcid.org/0009-0001-9100-7253 1 , [...] Vikram Murugan 1 , Taylor Billion 1 , Abubakar Tauseef 1 , Ali Bin Abdul Jabbar https://orcid.org/0000-0001-5145-8626 1 PUBLISHED 13 Jan 2025 Author details Author details 1 Creighton University School of Medicine, Omaha, Nebraska, 68106, USA Ricky Rana Roles: Conceptualization, Data Curation, Formal Analysis, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Ben Leiken Roles: Writing – Original Draft Preparation, Writing – Review & Editing Vikram Murugan Roles: Writing – Review & Editing Taylor Billion Roles: Writing – Review & Editing Abubakar Tauseef Roles: Supervision Ali Bin Abdul Jabbar Roles: Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Addiction and Related Behaviors gateway. Abstract Background The opioid crisis is a public health challenge in the United States, significantly impacting mortality rates. This has been further exacerbated by COVID-19, contributing to a surge in substance use and overdose deaths. Methods Data from the CDC Wonder Database was used to identify opioid use-related deaths in the United States from 1999 to 2023. Crude and age-adjusted mortality rates (AAMR) were calculated across different geographic and demographic groups. Temporal trends in mortality were analyzed using the Joinpoint Regression Program. Annual percentage change (APC) and average annual percentage change (AAPC) in mortality rates were calculated. Results Opioid-related mortality surged during the COVID-19 period, with significant regional, racial, and state-level disparities. The Midwest region exhibited the highest AAPC from 1999 to 2023. State-specific analyses showed that West Virginia experienced the highest increase in AAMR from 1999 to 2023. Among racial groups, non-Hispanic American Indians/Alaska Natives had the highest AAPC from 1999 to 2023. Gender-specific analyses revealed that males had a higher AAPC from 1999 to 2023 than females. Conclusion The United States opioid crisis continued to escalate during the COVID-19 period, with significant increases in mortality across the regions. The highest AAPCs were seen in the Midwest and among non-Hispanic American Indians/Alaska Natives. These findings underscore the need for targeted interventions to account for trends in racial and regional disparities in opioid-related mortality that have increased during and post-COVID-19. READ ALL READ LESS Keywords Opioids, opioid-related mortality, health disparities, regional trends Corresponding Author(s) Ricky Rana ( [email protected] ) Close Corresponding author: Ricky Rana Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Rana R et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Rana R, Leiken B, Murugan V et al. Evolving regional trends in U.S. opioid-related mortality: Disparities and the impact of COVID-19 (1999-2023) [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :72 ( https://doi.org/10.12688/f1000research.159967.1 ) First published: 13 Jan 2025, 14 :72 ( https://doi.org/10.12688/f1000research.159967.1 ) Latest published: 13 Jan 2025, 14 :72 ( https://doi.org/10.12688/f1000research.159967.1 ) Introduction The current opioid crisis began as an influential campaign by pharmaceutical companies. New, potent opioids such as oxycodone were marketed as a less addictive and safer way to treat chronic pain. 1 With this emergence of widespread opioid prescriptions that began in 1999 came a surge in opioid-related overdose deaths. Over the next decade, the opioid-related mortality rate in the United States more than doubled from 2.9 to 6.9 per 100,000 from 1999 to 2010. 2 The second and third waves of the opioid crisis were attributed primarily to a rise in popularity of heroin and fentanyl, respectively. In 2021, this rate soared to 21.4 deaths per 100,000 people, with a record 80,816 deaths that year. 3 Starting in 2016 with the Comprehensive Addiction and Recovery Act, the federal government began enacting multiple pieces of legislation aiming to expand access to care for those suffering from addiction, curtail the overprescription of opioids, and bolster prohibitions and sanctions relating to the import of narcotics into the country. 2 The COVID-19 pandemic put significant strain on the American healthcare system and inhibited routine access to all forms of healthcare, including addiction treatment and rehabilitation services. Epidemiological evidence suggests a disproportionate impact of the opioid crisis across varying regions, ethnicities, races, and socioeconomic groups. 4 Our study expands on the existing literature regarding opioid-related mortality by focusing on regional differences in opioid use, the impact of COVID-19, and various epidemiological factors. Analyzing regional disparities in opioid-related mortality may elucidate underlying issues and reveal insights to facilitate the development of more effective treatment solutions for regions experiencing elevated mortality rates. Furthermore, previous studies are limited to pre-pandemic data and thus do not analyze the effect of COVID-19 on opioid abuse across the country. We aim to use the Centers for Disease Control and Prevention’s WONDER database to improve our understanding of the impact that the COVID-19 pandemic and its various social and economic consequences have had on the number of opioid-related deaths in the United States across race, gender, and region. Methods The Centers for Disease Control and Prevention’s Wide-ranging Online Data for Epidemiologic Research was utilized to track opioid-related deaths in the United States over time. 5 We analyzed records from the Multiple Cause-of-Death Public Use Record and the CDC WONDER database to assess the presence of opioid-related causes of death listed as contributing factors on nationwide death certificates. This database has been employed in various studies to examine mortality trends related to different diseases, including drug overdose. 6 – 8 Data was obtained by selecting current final multiple causes of death and the year ranges of 1999-2020 and 2018-2022. Opioid-related mortality was identified using the International Classification of Diseases, 10th Revision, Clinical Modification codes T40.0-T40.4 for individuals aged 15 and older. This age range was selected because opioid-related deaths are rare among individuals under 15. The results from all states were chosen and split up by region and by state level data. Institutional review board approval was not required for this study, as the CDC WONDER database provides anonymized and publicly accessible data. We extracted data on opioid-related deaths and population sizes from 1999 to 2023, including demographic and regional information such as gender, race/ethnicity, age, region, and state-level information. Racial/ethnic groups were defined as non-Hispanic (NH) White, NH Black, NH American Indian/Alaskan Native, NH Asian/Pacific Islander, and Hispanic people as identified on death certificates. Regions were classified into Northeast, Midwest, South, and West according to the Census Bureau definitions. 9 We calculated both crude and age-adjusted mortality rates (AAMR) for opioid-related deaths. We then used the Joinpoint Regression Program (Joinpoint version 4.9.0.0 available open access from National Cancer Institute, Bethesda, Maryland https://surveillance.cancer.gov/joinpoint/ ) to determine trends in mortality within the study period. 6 – 8 This software employs Joinpoint regression to identify significant shifts in annual mortality patterns by fitting models with linear segments to periods of notable temporal change. The annual percentage change (APC) in age-adjusted mortality rates (AAMRs), along with 95% confidence intervals (CIs), was calculated for each segment defined by a Joinpoint using the Monte Carlo permutation test. To summarize the overall mortality trend across the study period, the weighted average of the APCs was presented as average annual percentage changes (AAPCs), along with their corresponding 95% CIs. An APC or AAPC was classified as increasing or decreasing if the slope representing the change in mortality over the interval significantly differed from zero, determined using a two-tailed t-test. Statistical significance was set at p ≤ 0.05, denoted by an asterisk (*) in the results and figures. Results Demographic differences Gender-stratified From 1999 to 2023, opioids caused 532,307 (68.1%) deaths in males and 249004 (31.9%) in females in the United States ( Table 1 ). Table 1. AAMR per 100,000 U. S Residents from 1999 to 2023 by Gender. Year AAMR female AAMR male 1999 1.41 3.89 2000 1.68 3.96 2001 2.08 4.52 2002 2.83 5.75 2003 3.17 6.32 2004 3.67 6.81 2005 3.95 7.38 2006 4.65 8.98 2007 5.06 9.18 2008 5.23 9.64 2009 5.59 10.11 2010 5.92 10.42 2011 6.32 11.08 2012 6.37 11.31 2013 6.86 12.38 2014 7.9 14.51 2015 8.74 17.13 2016 10.66 22.77 2017 11.88 25.82 2018 11.53 25.71 2019 11.92 27.79 2020 15.83 39.08 2021 18.6 44.7 2022 18.44 45.58 2023 17.42 43.92 The AAMR increased in males from 3.9 (95% CI 3.8 to 4.0) in 1999 to 43.9 (95% CI 43.6 to 44.3) in 2023, with an AAPC of 10.030 (95% CI 9.254 to 11.123)* ( Figures 1 , 2 ). The APC in AAMR was 7.9456 (95% CI CI 4.948 to 10.257) from 1999-2013, which then decelerated to 1.012 (95% CI -6.452 to 10.381) from 2021-2023 ( Figure 1 ). Figure 1. Joinpoint of AAMR in the United States by Gender, 1999 to 2023 (*Indicates Significance). Figure 2. AAMR per 100,000 Residents in the United States by Gender, 1999 to 2023. In females, the AAMR increased from 1.4 (95% CI 1.3 to 1.5) in 1999 to 17.4 (95% CI 17.2 to 17.7) in 2023 with a steady AAPC of 9.819 (95% CI 9.083 to 11.050)* ( Figures 1 , 2 ). Race-stratified American Indian and Native Alaskan people had the highest AAMR from 2.8 (95% CI 2.0 to 3.7) in 1999 to 63.5 (95% CI 59.8 to 67.2) in 2023 with an AAPC of 13.850 (95% CI 12.979 to 15.187)* ( Table 2 , Figure 4 ). The American Indian and Native Alaskan APC increased from 16.279 (95% CI 12.731 to 23.188)* from 1999-2008 and subsequently increased to 40.238 (95% CI 31.162 to 46.105)* from 2018 to 2021 ( Figure 3 ). Table 2. AAMR per 100,000 U.S. Residents from 1999 to 2023 by Race. Year NH American Indian or Alaska Native Age-Adjusted Rate NH Asian or Pacific Islander Age-Adjusted Rate NH Black or African American NH White Hispanic 1999 2.76 0.27 2.1 2.8 3.17 2000 2.82 0.29 2.2 3.15 2.36 2001 3.58 0.28 2.33 3.82 2.37 2002 4.32 0.53 2.78 5.09 3.06 2003 5.95 0.39 2.67 5.76 3.17 2004 7.02 0.45 2.83 6.52 2.87 2005 7.77 0.7 3.28 7.07 3.03 2006 8.26 0.69 4.43 8.57 3.49 2007 8.93 0.55 3.56 9.22 3.46 2008 11.07 0.73 3.47 9.69 3.66 2009 13.39 0.75 3.82 10.24 3.64 2010 11.68 0.85 3.79 10.93 3.33 2011 12.29 0.97 4.08 11.68 3.74 2012 12.72 1.04 4.5 11.72 4.06 2013 13.14 1.02 5.58 12.7 4.58 2014 14.91 1.17 6.89 14.9 4.95 2015 15.05 1.26 8.12 17.34 5.64 2016 17.47 1.96 12.91 22 7.69 2017 20.02 1.98 16.26 24.69 8.68 2018 18.12 1.95 17.84 23.81 9.59 2019 22.42 2.15 21.84 24.38 11.22 2020 34.96 3.81 33.65 32.47 16.71 2021 49.91 3.54 42.75 36.63 20.37 2022 59.87 4.24 46.62 35.55 22.21 2023 63.47 4.62 47.73 32.47 23.04 Figure 3. Joinpoint of AAMR per 100,000 U.S. Residents by Race, 1999 to 2023. Black or African American people had the second highest AAMR over the years, with 2.1 (95% CI 1.9 to 2.3) in 1999 to 47.7 (95% CI 47.0 to 48.5) in 2023 and an AAPC of 13.461 (95% CI 12.622 to 14.66)* ( Figure 4 ). The APC was 4.772 (95% CI 1.888 to 7.559)* from 1999-2012, which increased to 28.785 (95% CI 26.944 to 32.876)* from 2012-2023 ( Figure 3 ). Figure 4. AAMR per 100,000 U. S Residents by Race, 1999 to 2023. Compared to that, Asian and Pacific Islanders had the lowest AAMR from 0.3 (95% CI 0.2 to 0.4) in 1999 to 4.6 (95% CI 4.3 to 4.9) in 2023 with an AAPC of 12.057 (95% CI 9.802 to 13.955)* ( Figure 4 ). The Asian and Pacific Islanders APC increased from 9.401 (95% CI -2.000 to 12.637) from 1999-2014 and subsequently increased to 16.626(95% CI 14.208 to 24.267)* from 2014 to 2023 ( Figure 3 ). Non-Hispanic white people had an AAMR increase from 2.8 (95% CI 2.7 to 2.9) in 1999 to 32.5 (95% CI 32.2 to 32.8) in 2023 with an AAPC of 9.855 (95% CI 9.140 to 12.291)* ( Figure 4 ). The non-Hispanic white APC accelerated 10.986 (95% CI 10.389 to 22.453)* from 1999-2021 and subsequently decreased to -1.855(95% CI -10.503 to 10.102) from 2021 to 2023 ( Figure 3 ). Hispanic people had an AAMR increase from 3.2 (95% CI 2.9 to 3.4) in 1999 to 23.0 (95% CI 22.6 to 23.5) in 2023 with an AAPC of 9.422 (95% CI 8.833 to 10.263)* ( Figure 4 ). The Hispanic white APC was initially low at 2.936 (95% CI -0.019 to 4.763) from 1999-2012 and subsequently increased to 29.143 (95% CI 23.434 to 33.407)* from 2018 to 2021 ( Figure 3 ). Geographic variation Census region difference Every census region saw an increase in AAMR over time, with the Midwest seeing the most significant increase from 1.41 (95% CI 1.31 to 1.52) in 1999 to 29.7 (95% CI 29.3 to 30.2) in 2023 with an AAPC of 12.048 (95% CI 11.318 to 13.524)* from 1999 to 2023 and APC of 13.874 (95% CI 13.135 to 15.713)* from 1999 to 2021 ( Table 3 , Figure 5 , Figure 6 ). Table 3. AAMR per 100,000 U.S. Residents by Census Region, 1999 to 2023. Year Census Region 1: Northeast Age-Adjusted Rate Census Region 2: Midwest Age-Adjusted Rate Census Region 3: South Age-Adjusted Rate Census Region 4: West Age-Adjusted Rate 1999 2.56 1.41 1.93 5.18 2000 2.43 1.88 2.67 4.37 2001 2.9 2.39 3.86 3.59 2002 3.32 3.05 4.57 5.97 2003 3.81 3.12 5.32 6.28 2004 3.58 3.98 6.19 6.38 2005 4.21 4.7 6.3 6.85 2006 5.51 6.24 7.48 7.43 2007 5.99 5.81 8.02 7.92 2008 6.07 6.62 8.02 8.43 2009 5.76 7.08 8.57 9.1 2010 6.47 7.9 8.99 8.45 2011 7.73 8.45 9.19 8.98 2012 8.89 8.96 8.9 8.62 2013 10.61 10.95 9.03 8.74 2014 12.92 13.16 10.6 9.23 2015 16.73 15.19 12.33 9.28 2016 24.27 20.7 15.67 9.59 2017 27.05 24.27 17.83 10.12 2018 28.98 22.08 17.12 10.79 2019 29.32 22.89 18.56 12.76 2020 35.73 30.67 27.07 19.71 2021 38.07 33.42 32.19 24.91 2022 38.78 32.85 32.42 26.16 2023 35.4 29.74 29.93 29.58 Figure 5. Joinpoint of AAMR per 100,000 U.S. Residents by Census Region, 1999 to 2023. Figure 6. AAMR per 100,000 U. S Residents by Census Region, 1999 to 2023. The Northeast had an AAMR rising from 2.6 (95% CI 2.4 to 2.7) in 1999 to 35.4 (95% CI 34.8 to 36.0) in 2023 with an AAPC of 11.902 (95% CI 11.373 to 12.658)* from 1999 to 2023 and APC of 10.382 (95% CI 8.815 to 11.832)* from 1999 to 2013 which then increased to an APC of 34.748 (95% CI 25.850 to 39.232)* from 2013 to 2016 ( Figure 5 , Figure 6 ). The South had the second lowest AAMR rising from 1.9 (95% CI 1.8 to 2.0) in 1999 to 29.9 (95% CI 29.6 to 30.3) in 2023 with an AAPC of 11.549 (95% CI 10.357 to 14.198)* from 1999 to 2023 and APC of 24.421 (95% CI 13.4607 to 59.563)* from 1999 to 2004 which then decreased to an APC of 16.921 (95% CI 14.332 to 27.176)* from 2014 to 2021 ( Figure 5 , Figure 6 ). The West had the lowest AAMR rising from 5.2 (95% CI 5.0 to 5.4) in 1999 to 29.6 (95% CI 29.2 to 30.0) in 2023 with an AAPC of 8.232(95% CI 7.599 to 9.471)* from 1999 to 2023 and APC of 7.9458 (95% CI 5.108 to 18.401)* from 1999 to 2007 which then increased to an APC of 34.922 (95% CI 26.438 to 40.943)* from 2018 to 2021 ( Figure 5 , Figure 6 ). State-level difference Hawaii, Texas, and California had the lowest change in AAMR from 1999 to 2019 ( Table 4 , Figure 7 ). Hawaii had the lowest overall change in AAMR from 1999 to 2019 at 1.2, from 3.3 (95% CI 2.3 to 4.7) to 4.5 (95% CI 3.4 to 5.9), compared to all other states ( Figure 7 ). Texas had the second lowest change in AAMR from 1999 to 2019 at 4.7, from 1.8 (95% CI 1.6 to 2.0) to 6.6 (95% CI 6.2 to 6.9). California had the third lowest change in AAMR from 1999 to 2019 at 5.1, from 5.0 (95% CI 4.7 to 5.3) to 10.1(95% CI 9.8 to 10.5). Table 4. AAMR per 100,000 Residents from 1999 to 2023 by State. State 1999 AAMR 2019 AAMR 2020 AAMR 2021 AAMR 2022 AAMR 2023 AAMR Alabama 1.02 11.36 16.85 27.01 29.74 33.31 Alaska 3.83 16.65 20.73 37.27 34.1 53.85 Arizona 3.86 23.44 34.47 37.3 35.78 36.88 Arkansas 1.23 9.03 12.64 17.49 17.92 14.07 California 5 10.14 17.52 22.8 23.62 25.36 Colorado 3.3 14.07 21.23 28.18 25.53 28.32 Connecticut 4.7 40.83 46.03 48.89 47.15 40.78 Delaware 3.62 54.25 55.52 60.99 63.88 61.14 District of Columbia Unreliable 43.14 57.26 61.97 61.72 63.77 Florida 3.07 23.75 34.35 37.28 33.89 30.58 Georgia 0.81 10.65 16.02 21.97 24.16 22.61 Hawaii 3.33 4.51 6.77 7.77 9.37 12.39 Idaho 2.59 9.78 11.19 16.45 18.79 18.76 Illinois 1.31 22.61 30.08 30.39 31.81 28.83 Indiana 0.71 25.58 39.18 43.86 41.2 34.16 Iowa Unreliable 7.22 10.27 11.07 10.21 10.9 Kansas 1.2 8.91 12.12 20.26 23.68 19.2 Kentucky 1.63 31.67 52.07 57.4 53.53 48.86 Louisiana 0.69 15.91 27.36 39.22 40.35 33.64 Maine 2.18 33.68 43.83 54.02 62.39 51 Maryland 0.56 43.78 52.07 49.71 45.66 43.67 Massachusetts 1.33 37.01 39.44 41.85 42.48 38.47 Michigan 1.52 23.47 29.29 33.57 31.8 29.9 Minnesota 1.14 9.98 16.43 23.05 24.16 23.01 Mississippi Unreliable 10.83 18.43 25.85 25.46 22.98 Missouri 2.33 24.39 31.17 35.21 34.58 32.22 Montana Unreliable 9.43 10.95 13.44 15.84 15.89 Nebraska NONE 4.65 6.99 8.16 8.42 5.61 Nevada 9.68 15.33 23.31 24.44 25.35 31.96 New Hampshire 2.81 37.82 34.57 37.05 40.71 39.19 New Jersey 4.44 36.2 37 36.84 35.99 31.83 New Mexico 12.76 26.23 35.74 49.22 48.33 47.44 New York 1.56 19.24 27.87 31.37 34.07 33.2 North Carolina 2.36 22.95 33.46 42.43 45.64 36.17 North Dakota NONE 6.67 9.68 12.69 16.77 14.76 Ohio 1.76 40.51 51.86 51.67 48.75 43.75 Oklahoma 3.49 8.83 10.54 15.64 25.15 28 Oregon 6.54 9.58 15.25 22.95 28.79 41.24 Pennsylvania 2.85 31.82 41.27 41.89 40.55 36.42 Rhode Island Unreliable 29.69 40.94 46.15 40.31 41.28 South Carolina 1.36 22.41 36.76 44.83 46.68 43.88 South Dakota NONE 5.69 7.65 6.86 6.98 7.16 Tennessee 1.75 30.06 47.15 58.41 57.99 55.34 Texas 1.83 6.56 9.22 11.93 13.54 13.61 Utah 9.18 17.65 18.85 18.39 17.79 20.49 Vermont Unreliable 26.5 36.27 48.61 53.34 50.11 Virginia 3.25 19.27 28.97 33.12 31.25 30.94 Washington 4.64 13.46 19.55 26.27 33.64 45.54 West Virginia 2.05 54.5 92.25 100.47 92.47 94.96 Wisconsin 1.75 21.24 29.59 33.24 32.8 32.23 Wyoming NONE 11.37 14.79 17.12 19.12 20.46 Figure 7. Change in AAMR per 100,000 Residents, 1999 to 2019 (A), 2019 to 2023 (B). From 1999 to 2019 Maryland, West Virginia, and Delaware had the highest AAMR as seen in Figure 7 . Of these 3, West Virginia had the highest increase in AAMR of 52.5 from 2.1 (95% CI 1.4 to 3.0) in 1999 to 54.5 (95% CI 50.4 to 58.6) in 2019. Delaware had the second highest rate of increase with an AAMR of 50.6 from 3.6 (95% CI 2.3 to 5.5) in 1999 to 54.3 (95% CI 48.7 to 59.8) in 2019. Maryland had the third highest rate of increase with an AAMR of 43.2 from 0.6 (95% CI 0.4 to 0.8) in 1999 to 43.8 (95% CI 41.9 to 45.7) in 2019 ( Figure 7 ). From 2019 to 2023, New Jersey had the largest drop in AAMR, while West Virginia had the largest increase in AAMR. New Jersey had a decrease in AAMR of 4.4 from 36.2 (95% CI 34.8 to 37.8) in 2019 to 31.8 (95% CI 30.5 to 33.1) in 2023. On the other hand, in West Virginia the AAMR increased by 40.5, from 54.5 (95% CI 50.4 to 58.6) in 2019 to 95.0 (95% CI 89.5 to 100.4) in 2023 ( Figure 7 ). Discussion Opioid use disorders (OUD) place a significant strain on healthcare systems and come with substantial financial costs to society, estimated at $7.3 billion annually in the U.S. 8 , 10 – 12 Despite this considerable spending, our study shows that OUD-related mortality continues to rise across all regions of the country. We observed an increase in the overall crude mortality rate from 1999 to 2023 in both the Midwest and the entire U.S. This study highlights several key trends in opioid use-related mortality. This study revealed significant regional trends in age-adjusted mortality rates (AAMR). The Midwest exhibited the highest AAMR from 1999 to 2021, followed by the Northeast, which aligns with findings from earlier regional studies. 13 – 15 However, our research indicated that the Midwest’s mortality rates were higher than the average of other U.S. regions between 2014 and 2021. One potential reason for this elevated mortality is limited access to treatment. A study by Cummings et al. showed that the Midwest and Southern regions had the highest number of counties lacking substance use disorder treatment facilities. 16 This lack of access may contribute to a larger number of individuals struggling with severe addiction, thereby increasing the risk of relapse and mortality rates. In addition, other factors contributing to regional differences in opioid use-related mortality include regional drug market differences, differences in opioid use patterns, and overall substance use patterns that differ between regions. Understanding these disparities in opioid use-related mortality in the Midwest can help guide targeted interventions to enhance outcomes. This study also examined AAMR for populations of all races in the United States. The American Indian and Native Alaskan populations experienced the highest AAMR from 1999 to 2023. The Black population experienced the second-highest AAMR from 1999 to 2023. Previous research has identified non-White race as a risk factor for opioid use disorder (OUD). 17 , 18 A study by Quintero et al. found that non-White populations are more likely to transition into OUD, although the rates of disorders varied by drug type and age. 18 These groups may face a higher risk of developing OUD due to significantly lower rates of completing OUD treatment, compounded by various socioeconomic and structural disparities including poverty, social isolation, and treatment access disparities. 18 , 19 Our study also tracked trends before and during the COVID-19 pandemic. From 2006 to 2019, the mortality gap between American Indian and Native Alaskan populations and others narrowed; however, this gap widened again after the onset of COVID-19, reversing earlier progress. Research has shown that non-white communities were disproportionately affected by the pandemic, leading to increased stress for these families and communities. 20 , 21 This heightened stress may have contributed to rising substance use and related mortality during this time. The tight-knit nature of these communities, due to their culture and family units may have served as a source of resilience and acted as protective factors before the pandemic. However, the fragmentation of families and communities during the pandemic has been theorized to have become risk factors for increased behavioral addictions such as gambling and drug use based on current trends. 22 Furthermore, studies have found that along with the social disruptions caused by the pandemic, historical trauma and socioeconomic challenges have also contributed to the increased risk of behavioral addictions in these populations. 22 Overall, our findings underscore the importance of recognizing demographic and socioeconomic disparities and highlighting the specific needs of American Indian and Native Alaskan populations in the United States. From 1999 to 2023, both crude mortality rates and AAMR were higher for men than for women. Men also experienced an increase in APC at the beginning of the COVID-19 outbreak, while women did not show any significant changes in trends during that period. Our findings align with other studies that have identified male sex as a risk factor for substance use-related mortality. 3 , 18 , 19 , 23 This study identified several notable changes following the onset of COVID-19. Opioid-induced deaths in the U.S. increased significantly and remained elevated from March 2020 through the end of 2021. Both the Midwest and other regions experienced a statistically significant increase in APC from 2019 to 2021. Additionally, men, as well as Black American Indian and Native Alaskan populations from all regions, saw a marked rise in AAMR. This timing coincides with the initial COVID-19 surge in the U.S. The pandemic had a profound effect on mental health, leading to increased opioid and substance use. 22 , 24 – 28 A study by Friedman and Akre reported a rise in drug overdoses after the COVID-19 outbreak, peaking in May 2020. 14 Another factor may be the overall decline in visits and access to OUDT facilities in 2020. Research by Cantor et al. indicated that weekly visits to these facilities dropped compared to 2019, with the most significant declines occurring in counties experiencing higher COVID-19 burdens. 28 Overall, our findings align with existing research on the impact of COVID-19 on OUD. 7 , 28 To mitigate spikes in opioid-related deaths during future crises, the U.S. must find ways to maintain access to OUD treatment and address negative coping strategies involving substances. Limitations The CDC WONDER database, which is based on death certificates, may sometimes misclassify causes of death. Our analysis had opioid-related mortality data as the primary or contributing cause of death. While this strategy improves sensitivity by capturing the maximum number of cases where opioid use may have directly or indirectly contributed to mortality, it decreases specificity. Additionally, the regional and state-level data may be misrepresented because a patient’s recorded location at the time of death may not accurately represent their original residence if they relocated during the study period. Furthermore, differences in how states and medical examiners code mortality in death certificates could bias the results. The database also lacks information on social determinants of health and other factors that could affect mortality among the different demographic groups analyzed in this study. Conclusions Our study shows that the United States opioid crisis has escalated during the COVID-19 pandemic, resulting in significant increases in mortality across various demographic groups and regions. The Midwest, especially West Virginia, saw the highest mortality rates, while Hawaii experienced the smallest increase. Despite the overall rise in the Midwest, Nebraska’s lower increase suggests notable regional differences that require further investigation. Our study highlights ongoing demographic and regional disparities in opioid-related behaviors and mortality, with certain disadvantaged groups particularly affected since COVID-19. Addressing these disparities is essential to reducing substance use-related mortality effectively. Data availability statement All the data available in this manuscript is present on CDC Wonder database https://wonder.cdc.gov/mcd.html . The Multiple Cause of Death data set contains mortality and population counts for all U.S. counties. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes, and demographic data. The number of deaths, crude death rates, age-adjusted death rates and 95% confidence intervals for death rates can be obtained by cause of death (4 digit ICD-10 codes, 113 selected causes of death, 130 selected causes of infant death, drug and alcohol related causes of death, injury intent and injury mechanism categories), place of residence (national, region, division, state, and county), age (single-year-of age, 5-year age groups, 10-year age groups and infant age groups), race (American Indian or Alaskan Native, Asian/Pacific Islander, Black or African American, White), Hispanic ethnicity, gender and year. We focused on opioid use-related mortality and therefore used the ICD codes T40-40.4 for age ranges 15 and older. Third party ethics: The CDC Wonder CDC WONDER database contains anonymized, publicly available data on mortality and population counts for all U.S. counties. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Jan 2025 ADD YOUR COMMENT Comment Author details Author details 1 Creighton University School of Medicine, Omaha, Nebraska, 68106, USA Ricky Rana Roles: Conceptualization, Data Curation, Formal Analysis, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing Ben Leiken Roles: Writing – Original Draft Preparation, Writing – Review & Editing Vikram Murugan Roles: Writing – Review & Editing Taylor Billion Roles: Writing – Review & Editing Abubakar Tauseef Roles: Supervision Ali Bin Abdul Jabbar Roles: Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 13 Jan 2025, 14:72 https://doi.org/10.12688/f1000research.159967.1 Copyright © 2025 Rana R et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Rana R, Leiken B, Murugan V et al. Evolving regional trends in U.S. opioid-related mortality: Disparities and the impact of COVID-19 (1999-2023) [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :72 ( https://doi.org/10.12688/f1000research.159967.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 13 Jan 2025 Views 0 Cite How to cite this report: Bhagavathula AS. Reviewer Report For: Evolving regional trends in U.S. opioid-related mortality: Disparities and the impact of COVID-19 (1999-2023) [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :72 ( https://doi.org/10.5256/f1000research.175765.r370946 ) The direct URL for this report is: https://f1000research.com/articles/14-72/v1#referee-response-370946 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Apr 2025 Akshaya Srikanth Bhagavathula , North Dakota State University, Fargo, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.175765.r370946 Thank you for the opportunity to review this manuscript. The topic of opioid-related mortality and its impact during COVID-19 period is of major public health interest. However, the manuscript, in its current version, suffers from multiple methodological and reporting issues ... Continue reading READ ALL Thank you for the opportunity to review this manuscript. The topic of opioid-related mortality and its impact during COVID-19 period is of major public health interest. However, the manuscript, in its current version, suffers from multiple methodological and reporting issues that must be addressed to strengthen its validity, reproducibility, and clarity. The manuscript failed in large and identified several methodological pitfalls. 1. The authors relied on MCOD data using ICD-10 codes T40.0–T40.4. This approach is methodologically inappropriate for estimating opioid-specific mortality trends unless the authors intention is to examine opioid-involved deaths broadly. 2. MCOD includes contributing causes, not necessarily causal agents of death. This can inflate mortality estimates due to increased awareness and recording particularly after post-2015. 3. It was difficult for me to understand various terms used and need to revise terminology throughout the manuscript (change "opioid-related deaths" to "opioid-involved deaths"). 4.If authors aimed to measure opioid-attributable mortality, then it should be using UCOD only, as recommended by CDC, GBD, and NCHS best practices. 5. The authors presented only AAMRs but do not report the total number of opioid-related deaths - this is a key omission. 6. Readers cannot contextualize the burden or interpret the scale of disparities without raw counts. Moreover, Standard epidemiologic practice like STROBE, GATHER requires inclusion of both counts and rates. 7. Potential overlapping: "Data were selected from 1999–2020 and 2018–2022.” This introduces data overlap between 2018–2020. Authors did not justified how the overlapping data were adjusted. This raises concern of duplicate inclusion or inconsistent data treatment. 8. Authors report AAMRs for those aged ≥15 years but do not describe, but the reference population used (e.g., 2000 U.S. standard population), the standardization method (direct or indirect) and how age weights were recalculated for a 15+ population subset were not clearly explained. For example, If the authors are selecting age group from 15-24 or 15-19 the population weights are different and need more clarification on how AAMR are calculated. 9. Some interpretations, particularly those relating to Native American “cultural resilience” or “community fragmentation” during COVID-19, are speculative and unsupported by direct evidence in the manuscript. 10. State-level differences are presented as meaningful, but no adjustment is made for Data quality variability - eg., in toxicology testing) or Population characteristics (e.g., urbanicity, socioeconomic status). 11. Grammatical errors: Data was obtained- data were obtained. 12. The authors omitted T40.6 (unspecified narcotic) — please clarify whether this was excluded intentionally and why. 13. Joinpoint analysis: The results section should provide the number and timing of Joinpoints and model diagnostics (like BIC/AIC, p-values). It is not clearly described how joinpoints were selected and which criteria was applied. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Epidemiology, Opioids, Biostatistics, disparities, and Bigdata anlytics. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bhagavathula AS. Reviewer Report For: Evolving regional trends in U.S. opioid-related mortality: Disparities and the impact of COVID-19 (1999-2023) [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :72 ( https://doi.org/10.5256/f1000research.175765.r370946 ) The direct URL for this report is: https://f1000research.com/articles/14-72/v1#referee-response-370946 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Jan 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 13 Jan 25 read Akshaya Srikanth Bhagavathula , North Dakota State University, Fargo, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Bhagavathula A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Apr 2025 | for Version 1 Akshaya Srikanth Bhagavathula , North Dakota State University, Fargo, USA 0 Views copyright © 2025 Bhagavathula A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thank you for the opportunity to review this manuscript. The topic of opioid-related mortality and its impact during COVID-19 period is of major public health interest. However, the manuscript, in its current version, suffers from multiple methodological and reporting issues that must be addressed to strengthen its validity, reproducibility, and clarity. The manuscript failed in large and identified several methodological pitfalls. 1. The authors relied on MCOD data using ICD-10 codes T40.0–T40.4. This approach is methodologically inappropriate for estimating opioid-specific mortality trends unless the authors intention is to examine opioid-involved deaths broadly. 2. MCOD includes contributing causes, not necessarily causal agents of death. This can inflate mortality estimates due to increased awareness and recording particularly after post-2015. 3. It was difficult for me to understand various terms used and need to revise terminology throughout the manuscript (change "opioid-related deaths" to "opioid-involved deaths"). 4.If authors aimed to measure opioid-attributable mortality, then it should be using UCOD only, as recommended by CDC, GBD, and NCHS best practices. 5. The authors presented only AAMRs but do not report the total number of opioid-related deaths - this is a key omission. 6. Readers cannot contextualize the burden or interpret the scale of disparities without raw counts. Moreover, Standard epidemiologic practice like STROBE, GATHER requires inclusion of both counts and rates. 7. Potential overlapping: "Data were selected from 1999–2020 and 2018–2022.” This introduces data overlap between 2018–2020. Authors did not justified how the overlapping data were adjusted. This raises concern of duplicate inclusion or inconsistent data treatment. 8. Authors report AAMRs for those aged ≥15 years but do not describe, but the reference population used (e.g., 2000 U.S. standard population), the standardization method (direct or indirect) and how age weights were recalculated for a 15+ population subset were not clearly explained. For example, If the authors are selecting age group from 15-24 or 15-19 the population weights are different and need more clarification on how AAMR are calculated. 9. Some interpretations, particularly those relating to Native American “cultural resilience” or “community fragmentation” during COVID-19, are speculative and unsupported by direct evidence in the manuscript. 10. State-level differences are presented as meaningful, but no adjustment is made for Data quality variability - eg., in toxicology testing) or Population characteristics (e.g., urbanicity, socioeconomic status). 11. Grammatical errors: Data was obtained- data were obtained. 12. The authors omitted T40.6 (unspecified narcotic) — please clarify whether this was excluded intentionally and why. 13. Joinpoint analysis: The results section should provide the number and timing of Joinpoints and model diagnostics (like BIC/AIC, p-values). It is not clearly described how joinpoints were selected and which criteria was applied. Is the work clearly and accurately presented and does it cite the current literature? No Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Epidemiology, Opioids, Biostatistics, disparities, and Bigdata anlytics. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Bhagavathula AS. Peer Review Report For: Evolving regional trends in U.S. opioid-related mortality: Disparities and the impact of COVID-19 (1999-2023) [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :72 ( https://doi.org/10.5256/f1000research.175765.r370946) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-72/v1#referee-response-370946 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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