[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.
Growth and Level of the Opioid Epidemic, 2016
Growth and Level of the Opioid Epidemic, 2016

For each state and opioid type (A-D), we categorized the 2016 mortality rate as low (0-4.9 per 100 000 people), medium (5.0-10.0 per 100 000 people), or high (>10.0 per 100 000 people). We categorized the current annual percent change (APC) of the 2016 mortality rate as slow (0%-25.9% increase per year), moderate (26.0%-41.0% increase per year), or rapid (>41% increase per year). An annual growth rate of 26% reflects a mortality rate that is doubling every 3 years, and an annual growth rate of 41% reflects a mortality rate that is doubling every 2 years. States in white have APCs with P > .05. Interactive plots, which allow for specifying different breakpoints and years, are available online at https://sanjaybasu.shinyapps.io/opioid_geographic/.

Figure 2.
Number of Years of Life Expectancy Lost at Age 15 Years by State and Opioid Type
Number of Years of Life Expectancy Lost at Age 15 Years by State and Opioid Type

The number of years of life expectancy lost at age 15 years is the number of life-years lost, after the age of 15 years, if all deaths from that specific cause were removed. For reference, the national number of years of life expectancy lost at age 15 years is 0.30 years for motor vehicle crashes and 0.34 years for deaths involving firearms. Additional disaggregated results for other years, ages, and reference outcomes are available online at https://sanjaybasu.shinyapps.io/opioid_geographic/.

Table.  
Hot Spots of the Opioid Epidemic, 2016a
Hot Spots of the Opioid Epidemic, 2016a
1.
Davis  JH. Trump declares opioid crisis a ‘health emergency’ but requests no funds. New York Times. https://www.nytimes.com/2017/10/26/us/politics/trump-opioid-crisis.html. Published October 26, 2017. Accessed November 23, 2018.
2.
Jalal  H, Buchanich  JM, Roberts  MS, Balmert  LC, Zhang  K, Burke  DS.  Changing dynamics of the drug overdose epidemic in the United States from 1979 through 2016.  Science. 2018;361(6408):eaau1184. doi:10.1126/science.aau1184PubMedGoogle ScholarCrossref
3.
Havens  JR, Walker  R, Leukefeld  CG.  Prescription opioid use in the rural Appalachia: a community-based study.  J Opioid Manag. 2008;4(2):63-71. doi:10.5055/jom.2008.0010PubMedGoogle ScholarCrossref
4.
Kolodny  A, Courtwright  DT, Hwang  CS,  et al.  The prescription opioid and heroin crisis: a public health approach to an epidemic of addiction.  Annu Rev Public Health. 2015;36(1):559-574. doi:10.1146/annurev-publhealth-031914-122957PubMedGoogle ScholarCrossref
5.
Compton  WM, Jones  CM, Baldwin  GT.  Relationship between nonmedical prescription-opioid use and heroin use.  N Engl J Med. 2016;374(2):154-163. doi:10.1056/NEJMra1508490PubMedGoogle ScholarCrossref
6.
Alexander  MJ, Kiang  MV, Barbieri  M.  Trends in black and white opioid mortality in the United States, 1979-2015.  Epidemiology. 2018;29(5):707-715. doi:10.1097/EDE.0000000000000858PubMedGoogle ScholarCrossref
7.
Ciccarone  D.  Fentanyl in the US heroin supply: a rapidly changing risk environment.  Int J Drug Policy. 2017;46:107-111. doi:10.1016/j.drugpo.2017.06.010PubMedGoogle ScholarCrossref
8.
Bonnie RJ, Ford MA, Phillips JK, eds; National Academies of Sciences and Engineering Medicine.  Pain Management and the Opioid Epidemic: Balancing Societal and Individual Benefits and Risks of Prescription Opioid Use. Washington, DC: National Academies of Science; 2017. doi:10.17226/24781
9.
Doleac  JL, Mukherjee  A. The moral hazard of lifesaving innovations: naloxone access, opioid abuse, and crime. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3135264. Revised October 30, 2018. Accessed November 22, 2018.
10.
Lurigio  AJ, Andrus  J, Scott  CK.  The opioid epidemic and the role of law enforcement officers in saving lives.  Victims Offenders. 2018;13(8):1055-1076. doi:10.1080/15564886.2018.1514552Google ScholarCrossref
11.
Soelberg  CD, Brown  RE  Jr, Du Vivier  D, Meyer  JE, Ramachandran  BK.  The US opioid crisis: current federal and state legal issues.  Anesth Analg. 2017;125(5):1675-1681. doi:10.1213/ANE.0000000000002403PubMedGoogle ScholarCrossref
12.
National Center for Health Statistics, Centers for Disease Control and Prevention. NCHS data release and access policy for micro-data and compressed vital statistics files. https://www.cdc.gov/nchs/nvss/dvs_data_release.htm. Accessed September 13, 2018.
13.
National Center for Health Statistics, Centers for Disease Control and Prevention. US census populations with bridged race categories. https://www.cdc.gov/nchs/nvss/bridged_race.htm. Accessed September 5, 2018.
14.
Injury Surveillance Workgroup (ISW7). Consensus recommendations for national and state poisoning surveillance. https://cdn.ymaws.com/www.cste.org/resource/resmgr/injury/ISW7.pdf. Published April 2017. Accessed September 5, 2018.
15.
Kim  HJ, Fay  MP, Feuer  EJ, Midthune  DN.  Permutation tests for joinpoint regression with applications to cancer rates.  Stat Med. 2000;19(3):335-351. doi:10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-ZPubMedGoogle ScholarCrossref
16.
Storey  JD.  The positive false discovery rate: a Bayesian interpretation and the q-value.  Ann Stat. 2003;31(6):2013-2035. doi:10.1214/aos/1074290335Google ScholarCrossref
17.
Chiang  C.  Introduction to Stochastic Processes in Biostatistics. New York, NY: John Wiley & Sons; 1968.
18.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2018. https://www.R-project.org/. Accessed November 22, 2018.
19.
National Cancer Institute. Joinpoint trend analysis software. https://surveillance.cancer.gov/joinpoint/. Accessed September 5, 2018.
20.
Ruhm  CJ.  Geographic variation in opioid and heroin involved drug poisoning mortality rates.  Am J Prev Med. 2017;53(6):745-753. doi:10.1016/j.amepre.2017.06.009PubMedGoogle ScholarCrossref
21.
Slavova  S, Costich  JF, Bunn  TL,  et al.  Heroin and fentanyl overdoses in Kentucky: epidemiology and surveillance.  Int J Drug Policy. 2017;46:120-129. doi:10.1016/j.drugpo.2017.05.051PubMedGoogle ScholarCrossref
22.
Unick  G, Rosenblum  D, Mars  S, Ciccarone  D.  Intertwined epidemics: national demographic trends in hospitalizations for heroin- and opioid-related overdoses, 1993-2009. 2013;8(2):e54496. doi:10.1371/journal.pone.0054496PubMed
23.
Arias  E, Xu  J. United States life tables, 2015. National Center for Health Statistics. https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_07-508.pdf. Published November 13, 2018. Accessed January 15, 2019.
24.
Arias  E, Heron  M, Xu  J. United States life tables, 2014. National Center for Health Statistics. https://www.cdc.gov/nchs/products/life_tables.htm. Published August 14, 2017. Accessed January 15, 2019.
25.
Barbieri  M.  The contribution of drug-related deaths to the US disadvantage in mortality  [published online December 28, 2018].  Int J Epidemiol. PubMedGoogle Scholar
26.
Ahmad  FB, Rossen  LM, Spencer  MR, Warner  M, Sutton  P. Provisional drug overdose death counts. National Center for Health Statistics. 2018. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm. Accessed November 22, 2018.
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    2 Comments for this article
    EXPAND ALL
    Complexities of opioid deaths
    Frederick Rivara, MD, MPH | University of Washington
    This article, combined with other that we have recently published, shows the complexities of the current problem of opioid-related mortality. It again emphasizes the problem of synthetic opioids like fentanyl, and the unequal distribution of the problem across the US. The federal government needs to be involved, but so do states and local jurisdictions in solving this problem.
    CONFLICT OF INTEREST: Editor in Chief, JAMA Network Open
    Overdose Data Assumptions: Spatial & Temporal
    Brian Piper, PhD MS | Geisinger Commonwealth School of Medicine
    The authors are commended for careful analysis and for clear data representation. However, the discussion section only partially addresses the limitations of their data source from the National Center for Health Statistics. In order to meaningfully interpret Figure 1 and 2, the reader is expected to make two assumptions.

    First, the policies that underlie death determination are assumed to be similar across states. There are pronounced variability in who fills out the autopsy reports and their credentials (medical or non-medical) which may involve medical examiners or coroners depending on the state or county. The Centers for Disease Control and
    Prevention [1] acknowledges the inconsistencies across states. Half of the US (24 states and DC) were listed as “very good or excellent” based on > 90% of death certificates listing a specific drug. Seven states (AZ, CO, HI, MN, MO, TX, WI) were categorized as “good” based on > 80% of death certificates listing a specific drug. Eighteen states (AL, AK, CA, DE, FL, ID, IN, KS, KY, LO, MI, MI, MO, ND, NE, NJ, PA, SD) were not listed in either of those groups but may be categorized as “not good” based on < 80% of death certificates listing a specific drug. For example, over half of death certificates did not list a specific drug in Pennsylvania [2]. Many of the states with the highest depicted levels of synthetic opioids (New England: ME, NH, VT, CT, MA, RI, also MD, OH and WV) in Figure 1D also have good/excellent reporting. At the very least, increased attention to these methodological details and then a nod to citation(s) like [3] by the Substance Abuse Mental Health Services Administration that recognize these concerns would be extremely informative to readers.

    Second, in order to interpret the mortality rate change in Figure 1, one assumes that the policies in death determination were consistent over time. What percentage states or counties were uniformly testing for fentanyl analouges over the past decade? New York City was transparent that they inconsistently tested for fentanyl between 2013 and mid-2016 [4]. Were screens used or the more expensive gas-chromatography mass-spectrometry? How many of the 200 fentanyl analogues were tested? Even this level of information was not contained in this manuscript. A comparison of these policies in 1999 and 2016 would greatly improve on the reader’s ability to untangle how many of these changes, beautifully depicted, represent changes in opioid overdoses versus changes (improvements) in overdose determinations.

    The authors indicate, repeatedly, that this data can be used to inform state-level policies. If the results were more uniformly obtained, this would better support their conclusion.

    1. Seth P, et al. Overdose deaths involving opioids, cocaine, and psychostimulants - United States, 2015-2016. MMWR Morb Mortal Wkly Rep. 2018; 67(12): 349-358. doi: 10.15585/mmwr.mm6712a1.
    2. Buchanich JM, . The effect of incomplete death certificates on estimates of unintentional opioid-related overdose deaths in the United States, 1999-2015. Public Health Rep. 2018; 133(4):423-431. doi: 10.1177/0033354918774330.
    3. Goldberger BA, et al. Uniform standards and case definitions for classifying opioid-related deaths: Recommendations by a SAMSA consensus panel. J Addict Dis 2013; 32:231-243.
    4. Colon-Berezin C, et al. Overdose deaths Involving fentanyl and fentanyl analogs - New York City, 2000-2017. MMWR Morb Mortal Wkly Rep. 2019; 68(2):37-40. doi: 10.15585/mmwr.mm6802a3.
    CONFLICT OF INTEREST: BJP receives research support from the Fahs-Beck Fund for Research & Experimentation (a non-profit organization).
    READ MORE
    Original Investigation
    Substance Use and Addiction
    February 22, 2019

    Assessment of Changes in the Geographical Distribution of Opioid-Related Mortality Across the United States by Opioid Type, 1999-2016

    Author Affiliations
    • 1Center for Population Health Sciences, Stanford University School of Medicine, Stanford, California
    • 2Center for Primary Care, Harvard Medical School, Boston, Massachusetts
    • 3Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
    • 4Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
    • 5Department of Sociology, University of Toronto, Toronto, Ontario, Canada
    JAMA Netw Open. 2019;2(2):e190040. doi:10.1001/jamanetworkopen.2019.0040
    Key Points español 中文 (chinese)

    Question  How has opioid-related mortality changed over time across the United States, and how have the types of opioids associated with these deaths changed?

    Findings  In this cross-sectional study of 351 564 US residents who died from opioid-related causes, the age-standardized mortality rate from opioids increased more than 2-fold every 2 years in 24 eastern states, reflecting an expansion from lower-income, rural states. The life expectancy lost at age 15 years from opioids is now greater than that lost from deaths due to firearms or motor vehicle crashes in most of the United States.

    Meaning  Opioid-related mortality, driven by the use of synthetic opioids, has rapidly increased in all of the eastern states.

    Abstract

    Importance  As the opioid epidemic evolves, it is vital to identify changes in the geographical distribution of opioid-related deaths, and the specific opioids to which those deaths are attributed, to ensure that federal and state public health interventions remain appropriately targeted.

    Objective  To identify changes in the geographical distribution of opioid-related mortality across the United States by opioid type.

    Design, Setting, and Participants  Cross-sectional study using joinpoint modeling and life table analysis of individual-level data from the National Center for Health Statistics on 351 630 US residents who died from opioid-related causes from January 1, 1999, to December 31, 2016, for all of the United States and the District of Columbia. The analysis was conducted from September 6 to November 23, 2018.

    Exposures  Deaths involving any opioid, heroin, synthetic opioids, and natural and semisynthetic opioids.

    Main Outcomes and Measures  Opioid-related mortality rate, annual percent change in the opioid-related mortality rate, and life expectancy lost at age 15 years by state and opioid type.

    Results  From 1999 to 2016, a total of 231 264 men and 120 366 women died from opioid-related causes across the whole United States. Sixty-six observations were removed owing to missing data on age; therefore, 351 564 US residents were included in this study. The mean (SD) age at death was 39.8 (12.5) years for men and was 43.5 (12.9) years from women. Opioid-related mortality rates, especially from synthetic opioids, rapidly increased in all of the eastern United States. In most states, mortality associated with natural and semisynthetic opioids (ie, prescription painkillers) remained stable. In contrast, 28 states had mortality rates from synthetic opioids that more than doubled every 2 years (ie, annual percent change, ≥41%), including 12 with high mortality rates from synthetic opioids (>10 per 100 000 people). Among these 28 states, the mortality rate from natural and semisynthetic opioids ranged from 2.0 to 18.7 per 100 000 people (with a mean mortality rate of 6.0 per 100 000 people). The District of Columbia had the fastest rate of increase in mortality from opioids, more than tripling every year since 2013 (annual percent change, 228.3%; 95% CI, 169.7%-299.6%; P < .001), and a high mortality rate from synthetic opioids in 2016 (18.8 per 100 000 people); the mortality rate from natural and semisynthetic opioids was 6.9 per 100 000 people. Nationally, overall opioid-related mortality resulted in 0.36 years of life expectancy lost in 2016, which was 14% higher than deaths due to firearms and 18% higher than deaths due to motor vehicle crashes; 0.17 years of the life expectancy lost was due specifically to synthetic opioids. In 2016, New Hampshire and West Virginia lost more than 1 year of life expectancy due to opioid-related mortality.

    Conclusions and Relevance  Opioid-related mortality, particularly mortality associated with synthetic opioids, has increased in the eastern United States. These findings indicate that policies focused on reducing opioid-related deaths may need to prioritize synthetic opioids and rapidly expanding epidemics in northeastern states and consider the potential for synthetic opioid epidemics outside of the heroin supply.

    Introduction

    The opioid epidemic is one of the largest public health crises facing the United States.1 Opioid-related deaths in the United States have increased more than 4-fold during an 18-year period, from 2.9 (95% CI, 2.8-2.9) per 100 000 people in 1999 to 13.2 (95% CI, 13.1-13.3) per 100 000 people in 2016. This increase corresponds to more than 42 000 opioid-related deaths in 2016, many of which occurred among young adults.2

    Although opioid-related mortality has increased steadily and exponentially since 1999, the types of opioids involved, and the places and people most affected, have changed over time. Opioid-related deaths were previously thought to be concentrated in the white population, in the Appalachian and midwestern states, and particularly induced by natural and semisynthetic prescription opioids, such as oxycodone hydrochloride.3-5 However, emerging research suggests that the opioid epidemic is not a single epidemic but multiple co-occurring epidemics marked by different types of opioids and diverse geographical, temporal, and sociodemographic patterns. Specifically, research suggests the opioid epidemic has evolved as a series of 3 intertwined but distinct epidemics, or waves, based on the types of opioids associated with mortality. In the first wave, opioid-related deaths were associated with prescription painkillers from the 1990s until about 2010. From 2010 until the present, the second wave was associated with a large increase in heroin-related deaths. In the third and current wave, which started around 2013, the rapid increase is associated with illicitly manufactured synthetic opioids.2,6,7 The evolution has also seen a wider range of populations being affected, with the spread of the epidemic from rural to urban areas2 and considerable increases in opioid-related mortality observed in the black population.6

    To combat the evolving epidemic, states have enacted policies ranging from restricting the supply of prescription opioids to expanding treatment and access to naloxone.8-11 To ensure that state policies are relevant and appropriately targeted, it is vital to identify changes in the geographical distribution of opioid-related deaths and the types of opioids associated with those deaths. Given the rapid evolution of the epidemic, it is also important to identify which areas have high mortality rates due to historical trends and which areas have newly established high mortality rates likely due to changing illicit opioid markets. The identification and characterization of opioid “hot spots”—in terms of both high mortality rates and increasing trends in mortality—may allow for better-targeted policies that address the current state of the epidemic and the needs of the population.

    Previous research has highlighted the spatial differences in the opioid epidemic across broad year ranges. We systematically quantify the current rate of increase in opioid-related deaths by state and opioid type. We focus on the state level because this is the geographical level at which most control policies are enacted. In addition to analyzing current rates and trends in opioid-related mortality, we compare the number of years of life expectancy lost (LEL) due to opioid-related deaths with the number of years of LEL due to other external causes to contextualize the population-level effect of these trends. We additionally provide an online interactive tool to allow visualization of opioid-related mortality over time by state and opioid type.

    Methods

    This study was reviewed by the Harvard T.H. Chan School of Public Health Institutional Review Board and was deemed exempt from full review because it uses retrospective, deidentified data on deceased individuals. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    Data Source and Study Population

    We used multiple cause of death data from January 1, 1999, through December 31, 2016, from the National Center for Health Statistics,12 and we used corresponding population estimates (denominators for death rates) from the US Census.13 We identified opioid-related deaths through recommended guidelines14 (eAppendix 1 in the Supplement).

    Statistical Analysis

    We calculated age-standardized mortality for the total resident population for all opioids, for natural and semisynthetic opioids, for heroin, and for synthetic opioids by state and for the overall nation. We standardized mortality rates by age using 5-year age groups (ie, 0-4 years, 5-9 years, …, ≥85 years) with the direct method, using the age distribution of the US standard population in 2000. We calculated the SEs and corresponding 95% CIs of the mortality rates using Poisson approximation.8

    Quantifying Trends

    We analyzed trends in mortality rates by state and opioid type using joinpoint regression.15 The coefficient of each segment is expressed as the annual percent change (APC). Annual percent changes are described as “increasing” or “decreasing” only if they were statistically significantly different from zero at the P < .05 level. The current rate of increase or decrease is the APC of the most recent segment, regardless of the starting year. False discovery rate–adjusted P values (ie, Q values)16 are provided in an online interactive results viewer. Throughout the text, we present P values, which were more conservative than the corresponding Q values (eAppendix 2 in the Supplement). Further details of the joinpoint analysis are available in eAppendix 3 and the eFigure in the Supplement.

    Quantifying LEL

    We estimated the number of years of LEL due to opioid overdoses by comparing all-cause and cause-deleted life tables using the Chiang method.17 Specifically, we estimated the implied number of years of LEL at age 15 years as the difference in life expectancy at age 15 years from the all-cause and cause-deleted life tables. For reference comparisons, we calculated the number of years of LEL at age 15 years for 2 additional external causes of death: firearms and motor vehicle crashes (eAppendix 4 in the Supplement).

    Joinpoint modeling and life table analysis of individual-level data from the National Center for Health Statistics on 351 630 US residents who died from opioid-related causes from January 1, 1999, to December 31, 2016, for all of the United States and the District of Columbia were conducted from September 6 to November 23, 2018, using R, version 3.5.018 and the Joinpoint Regression Program, version 4.6.0.0.19 Links to the reproducible code as well as an online interactive results viewer are available in eAppendix 5 in the Supplement.

    Results

    From 1999 to 2016, there were more than 44.9 million deaths among US residents. We identified a total of 231 264 men and 120 366 women who died from opioid-related causes across the United States (351 630 US residents). We removed 66 observations (0.02%) owing to missing data on age (eTable 1 in the Supplement); therefore, 351 564 US residents were included in this cross-sectional study. The mean (SD) age at death was 39.8 (12.5) years for men and 43.5 (12.9) years for women. In 2016, there were 42 249 opioid-related deaths (28 498 men and 13 751 women) in the United States. This number corresponded to an age-standardized, opioid-related mortality rate of 13.2 (95% CI, 13.1-13.3) that was increasing by 18.5% (95% CI, 13.7%-23.5%; P < .001) per year since 2014. Higher rates of opioid-related mortality and more rapid increases in mortality were observed in the eastern United States (Figure 1). Specifically, 8 states (Connecticut, Illinois, Indiana, Massachusetts, Maryland, Maine, New Hampshire, and Ohio) had opioid-related mortality rates that were at least doubling every 3 years (APC ≥26%), and 2 states (Florida and Pennsylvania) and the District of Columbia had opioid-related mortality rates that were at least doubling every 2 years (APC ≥41%) (Figure 1). Among these 10 states and the District of Columbia with rapidly increasing mortality rates, the opioid-related mortality rate ranged from 12.6 (95% CI, 11.7-13.4) in Indiana to 35.8 (95% CI, 32.3-39.2) in New Hampshire. Only Montana and Oregon had decreasing opioid-related mortality rates (eTables 2-5 in the Supplement).

    The increased mortality rates in the eastern United States were driven by synthetic opioids. Twenty-eight eastern states had mortality rates from synthetic opioids that were at least doubling every 2 years (ie, APC ≥41%), with half of those states experiencing a doubling in mortality rates from synthetic opioids every year. Among these 28 states, the mortality rate from natural and semisynthetic opioids ranged from 2.0 to 18.7 per 100 000 people (mean, 6.0 per 100 000 people). Of these 28 states, 11 (Connecticut, Kentucky, Massachusetts, Maryland, Maine, New Hampshire, Ohio, Pennsylvania, Rhode Island, Vermont, and West Virginia) and the District of Columbia had mortality rates from synthetic opioids greater than 10 per 100 000 people and had seen a rapid increase in the mortality rate, which more than doubled every 2 years (APC ≥41%) (Figure 1). Among these states with a rapid increase in mortality from opioids, the District of Columbia had a mortality rate from synthetic opioids of 18.8 (95% CI, 15.5-22.1) per 100 000 people in 2016 that was more than tripling every year (APC, 228.3%; 95% CI, 169.7%-299.6%; P < .001), with a mortality rate from natural and semisynthetic opioids of 6.9 per 100 000. In contrast, the national mortality rate from natural and semisynthetic opioids was lower (4.4 per 100 000 people; 95% CI, 4.3-4.5 per 100 000 people) and increased more slowly (APC, 7.4%; 95% CI, 2.3%-12.7%; P = .004).

    Nationally, the number of years of LEL at age 15 years due to all fatal opioid overdoses in 2016 was 0.36 years, which was 18% higher than the number of years of LEL at age 15 years due to motor vehicle crashes (0.30 years) and 14% higher than the years of LEL at age 15 years due to firearms (0.32 years); 0.17 years of LEL was due specifically to synthetic opioids. Most states had a higher number of years of LEL at age 15 years due to opioids than due to deaths by motor vehicle accidents (N = 29 states) or firearm deaths (N = 27 states) (Table). The highest numbers for years of LEL at age 15 years were observed in the eastern states (mean, 0.49 years), while the numbers of years of LEL at age 15 years were relatively low in the western states (mean, 0.23 years) (Figure 2). In 2016 in 2 states, New Hampshire and West Virginia, the number of years of LEL at age 15 years due to opioids was greater than 1 year. The substantial geographical variation was reflected by differences in LEL from synthetic opioid deaths, which ranged from 0.02 years (Texas and Hawaii) to 0.90 years (New Hampshire).

    Additional disaggregated results are available via the online interactive results viewer, which allows users to explore 5 sets of analyses corresponding to our results. First, we display raw opioid-related mortality rates. Second, we enable viewers to perform joinpoint analysis of epidemic hot spots. Third, we provide a national overview plot, which shows the results from all joinpoint regression models, as well as the mean change in mortality over the entire period. Fourth, for each state, we provide state-specific joinpoint results, model fit statistics, and observed rates. Finally, we allow users to view estimates of LEL at other ages, for all years, and relative to other external causes of death.

    Discussion

    Although opioid-related mortality has been stereotyped as a rural, low-income phenomenon concentrated among Appalachian or midwestern states, it has spread rapidly, particularly among the eastern states. The increase in mortality has been driven primarily by synthetic opioids, which shows a distinct geographical patterning from east to west. Twenty-eight eastern states had synthetic opioid–related mortality rates that are at least doubling every 2 years, with half of those states experiencing a doubling in mortality rates every year. Of these 28 states, 12 had mortality rates from synthetic opioids greater than 10 per 100 000.

    Limitations

    This study has some limitations. Our analysis assumes the accurate classification of deaths; however, opioid-related mortality may be underreported, and this underreporting may vary geographically.20 Data from 2014 indicate that the spatial patterning of misclassification is such that the eastern states actually have more underreporting than other states, which suggests that our results would not be driven by differential geographical underreporting.20 For example, in 2014, the states with the highest levels of underreporting of opioid deaths (ie, underreporting by at least 4.0 per 100 000 people) were Pennsylvania, Indiana, Louisiana, Alabama, Kentucky, Mississippi, Michigan, and Wyoming. To our knowledge, no study to date has tracked the state-level changes in underreporting over time. In addition, the presence of fentanyl requires an additional toxicology test to be requested by the coroner. Therefore, mortality rates from synthetic opioids are likely biased downward (ie, underreported), and the increase in deaths from synthetic opioids reported in recent years may be owing in part to increased detection during postmortem investigations.21

    Despite these limitations, we believe several key points may inform health policy and public health interventions. Importantly, substantial acceleration in mortality from synthetic opioids is occurring in most eastern states regardless of past trends in deaths associated with natural and semisynthetic prescription opioids. For example, West Virginia and Kentucky, 2 states that were initial epicenters of the opioid epidemic because of high mortality rates from prescription opioids, are now experiencing rapid increases in mortality from synthetic opioids. Conversely, the District of Columbia, which did not historically have high levels of mortality from prescription opioids, has a mortality rate from synthetic opioids greater than 18 per 100 000 people and has experienced a tripling of the rate every year since 2013. By contrast, western states, such as Utah and New Mexico, that have relatively high mortality associated with prescription opioids have not experienced the same acceleration in mortality from synthetic opioids. This finding suggests that, while a “triple-wave”2,22 opioid epidemic has been observed at the national level and in some states, it has not been the case in all areas.

    Expressing the burden of the opioid epidemic in terms of LEL highlights the fact that most opioid-related deaths are occurring among young and middle-aged adults. The results of the analysis of the numbers of years of LEL at age 15 years suggest that eliminating all deaths associated with opioid overdoses would lead to greater increases in life expectancy than would eliminating other external causes of death, such as motor vehicle crashes or deaths due to firearms. The national LEL at age 15 years due to opioids is 3 times higher than recent noted decreases in the overall life expectancy at age 15 years.23,24 The states with the greatest burden in terms of LEL in 2016 were no longer only West Virginia or Ohio but also included Connecticut, Maryland, Massachusetts, Rhode Island, and the District of Columbia. In 2 states, the number of years of LEL is more than 1 year. These findings are consistent with new research that suggests that the high toll of drug overdoses on life expectancy is unique to the United States among high-income countries.25

    There is already a wide range of state policies in place to try curb the opioid epidemic. Although fatal opioid overdoses are generally increasing everywhere, there is recent evidence of decreases in some states, such as in Ohio.26 The multifaceted policy approach to reducing opioid-related deaths in Ohio—which includes increased access to naloxone, needle exchange programs, and increased support for those with mental health and addiction problems—may therefore serve as guidance to other states. The results presented in this article, which highlight the large heterogeneity in how the opioid epidemic has evolved across the country, suggest that policies may need to be additionally targeted, however, to take into consideration the experience of the population in terms of the prior established or unestablished nature of the opioid epidemic and the degree to which synthetic opioids are a major driver of current deaths.

    Conclusions

    Opioid-related mortality, particularly mortality associated with synthetic opioids, has increased in the eastern United States. Our findings indicate that policies focused on reducing opioid-related deaths may need to prioritize synthetic opioids and rapidly expanding epidemics in northeastern states and consider the potential for synthetic opioid epidemics outside of the heroin supply.

    Back to top
    Article Information

    Accepted for Publication: January 4, 2019.

    Published: February 22, 2019. doi:10.1001/jamanetworkopen.2019.0040

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Kiang MV et al. JAMA Network Open.

    Corresponding Author: Mathew V. Kiang, ScD, Center for Population Health Sciences, Stanford University School of Medicine, 1070 Arastradero Rd, Room 2C-2516, Palo Alto, CA 94304 (mkiang@stanford.edu).

    Author Contributions: Dr Kiang had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: Kiang, Chen.

    Drafting of the manuscript: Kiang, Chen, Alexander.

    Critical revision of the manuscript for important intellectual content: Kiang, Basu, Chen.

    Statistical analysis: All authors.

    Supervision: Basu.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Drs Kiang and Basu were supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under award DP2MD010478.

    Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication

    Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

    References
    1.
    Davis  JH. Trump declares opioid crisis a ‘health emergency’ but requests no funds. New York Times. https://www.nytimes.com/2017/10/26/us/politics/trump-opioid-crisis.html. Published October 26, 2017. Accessed November 23, 2018.
    2.
    Jalal  H, Buchanich  JM, Roberts  MS, Balmert  LC, Zhang  K, Burke  DS.  Changing dynamics of the drug overdose epidemic in the United States from 1979 through 2016.  Science. 2018;361(6408):eaau1184. doi:10.1126/science.aau1184PubMedGoogle ScholarCrossref
    3.
    Havens  JR, Walker  R, Leukefeld  CG.  Prescription opioid use in the rural Appalachia: a community-based study.  J Opioid Manag. 2008;4(2):63-71. doi:10.5055/jom.2008.0010PubMedGoogle ScholarCrossref
    4.
    Kolodny  A, Courtwright  DT, Hwang  CS,  et al.  The prescription opioid and heroin crisis: a public health approach to an epidemic of addiction.  Annu Rev Public Health. 2015;36(1):559-574. doi:10.1146/annurev-publhealth-031914-122957PubMedGoogle ScholarCrossref
    5.
    Compton  WM, Jones  CM, Baldwin  GT.  Relationship between nonmedical prescription-opioid use and heroin use.  N Engl J Med. 2016;374(2):154-163. doi:10.1056/NEJMra1508490PubMedGoogle ScholarCrossref
    6.
    Alexander  MJ, Kiang  MV, Barbieri  M.  Trends in black and white opioid mortality in the United States, 1979-2015.  Epidemiology. 2018;29(5):707-715. doi:10.1097/EDE.0000000000000858PubMedGoogle ScholarCrossref
    7.
    Ciccarone  D.  Fentanyl in the US heroin supply: a rapidly changing risk environment.  Int J Drug Policy. 2017;46:107-111. doi:10.1016/j.drugpo.2017.06.010PubMedGoogle ScholarCrossref
    8.
    Bonnie RJ, Ford MA, Phillips JK, eds; National Academies of Sciences and Engineering Medicine.  Pain Management and the Opioid Epidemic: Balancing Societal and Individual Benefits and Risks of Prescription Opioid Use. Washington, DC: National Academies of Science; 2017. doi:10.17226/24781
    9.
    Doleac  JL, Mukherjee  A. The moral hazard of lifesaving innovations: naloxone access, opioid abuse, and crime. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3135264. Revised October 30, 2018. Accessed November 22, 2018.
    10.
    Lurigio  AJ, Andrus  J, Scott  CK.  The opioid epidemic and the role of law enforcement officers in saving lives.  Victims Offenders. 2018;13(8):1055-1076. doi:10.1080/15564886.2018.1514552Google ScholarCrossref
    11.
    Soelberg  CD, Brown  RE  Jr, Du Vivier  D, Meyer  JE, Ramachandran  BK.  The US opioid crisis: current federal and state legal issues.  Anesth Analg. 2017;125(5):1675-1681. doi:10.1213/ANE.0000000000002403PubMedGoogle ScholarCrossref
    12.
    National Center for Health Statistics, Centers for Disease Control and Prevention. NCHS data release and access policy for micro-data and compressed vital statistics files. https://www.cdc.gov/nchs/nvss/dvs_data_release.htm. Accessed September 13, 2018.
    13.
    National Center for Health Statistics, Centers for Disease Control and Prevention. US census populations with bridged race categories. https://www.cdc.gov/nchs/nvss/bridged_race.htm. Accessed September 5, 2018.
    14.
    Injury Surveillance Workgroup (ISW7). Consensus recommendations for national and state poisoning surveillance. https://cdn.ymaws.com/www.cste.org/resource/resmgr/injury/ISW7.pdf. Published April 2017. Accessed September 5, 2018.
    15.
    Kim  HJ, Fay  MP, Feuer  EJ, Midthune  DN.  Permutation tests for joinpoint regression with applications to cancer rates.  Stat Med. 2000;19(3):335-351. doi:10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-ZPubMedGoogle ScholarCrossref
    16.
    Storey  JD.  The positive false discovery rate: a Bayesian interpretation and the q-value.  Ann Stat. 2003;31(6):2013-2035. doi:10.1214/aos/1074290335Google ScholarCrossref
    17.
    Chiang  C.  Introduction to Stochastic Processes in Biostatistics. New York, NY: John Wiley & Sons; 1968.
    18.
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2018. https://www.R-project.org/. Accessed November 22, 2018.
    19.
    National Cancer Institute. Joinpoint trend analysis software. https://surveillance.cancer.gov/joinpoint/. Accessed September 5, 2018.
    20.
    Ruhm  CJ.  Geographic variation in opioid and heroin involved drug poisoning mortality rates.  Am J Prev Med. 2017;53(6):745-753. doi:10.1016/j.amepre.2017.06.009PubMedGoogle ScholarCrossref
    21.
    Slavova  S, Costich  JF, Bunn  TL,  et al.  Heroin and fentanyl overdoses in Kentucky: epidemiology and surveillance.  Int J Drug Policy. 2017;46:120-129. doi:10.1016/j.drugpo.2017.05.051PubMedGoogle ScholarCrossref
    22.
    Unick  G, Rosenblum  D, Mars  S, Ciccarone  D.  Intertwined epidemics: national demographic trends in hospitalizations for heroin- and opioid-related overdoses, 1993-2009. 2013;8(2):e54496. doi:10.1371/journal.pone.0054496PubMed
    23.
    Arias  E, Xu  J. United States life tables, 2015. National Center for Health Statistics. https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_07-508.pdf. Published November 13, 2018. Accessed January 15, 2019.
    24.
    Arias  E, Heron  M, Xu  J. United States life tables, 2014. National Center for Health Statistics. https://www.cdc.gov/nchs/products/life_tables.htm. Published August 14, 2017. Accessed January 15, 2019.
    25.
    Barbieri  M.  The contribution of drug-related deaths to the US disadvantage in mortality  [published online December 28, 2018].  Int J Epidemiol. PubMedGoogle Scholar
    26.
    Ahmad  FB, Rossen  LM, Spencer  MR, Warner  M, Sutton  P. Provisional drug overdose death counts. National Center for Health Statistics. 2018. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm. Accessed November 22, 2018.
    ×