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Receipt of Telehealth Services, Receipt and Retention of Medications for Opioid Use Disorder, and Medically Treated Overdose Among Medicare Beneficiaries Before and During the COVID-19 Pandemic
research-article
Author(s):
Christopher M. Jones , PharmD, DrPH
1
,
,
Carla Shoff , PhD
2 ,
Kevin Hodges , BS
2 ,
Carlos Blanco , MD, PhD
3 ,
Jan L. Losby , PhD, MSW
1 ,
Shari M. Ling , MD
4 ,
Wilson M. Compton , MD, MPE
3
Publication date (Electronic):
31 August 2022
Journal:
JAMA Psychiatry
Publisher:
American Medical Association
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Key Points
Question
How were federal emergency authorities to expand telehealth use for substance use disorder treatment and facilitate provision of medications for opioid use disorder (MOUD) used during the COVID-19 pandemic among Medicare beneficiaries with opioid use disorder (OUD)?
Abstract
This cohort study examines receipt of telehealth services, medications for opioid use disorder (MOUD: methadone, buprenorphine, and extended-release naltrexone) receipt and retention, and medically treated overdose before and during the COVID-19 pandemic among Medicare beneficiaries.
Abstract
Importance
Federal emergency authorities were invoked during the COVID-19 pandemic to expand use of telehealth for new and continued care, including provision of medications for opioid use disorder (MOUD).
Objective
To examine receipt of telehealth services, MOUD (methadone, buprenorphine, and extended-release [ER] naltrexone) receipt and retention, and medically treated overdose before and during the COVID-19 pandemic.
Design, Setting, and Participants
This exploratory longitudinal cohort study used data from the US Centers for Medicare & Medicaid Services from September 2018 to February 2021. Two cohorts (before COVID-19 pandemic from September 2018 to February 2020 and during COVID-19 pandemic from September 2019 to February 2021) of Medicare fee-for-service beneficiaries 18 years and older with an International Statistical Classification of Diseases, Tenth Revision, Clinical Modification OUD diagnosis.
Exposures
Pre–COVID-19 pandemic vs COVID-19 pandemic cohort demographic characteristics, medical and substance use, and psychiatric comorbidities.
Main Outcomes and Measures
Receipt and retention of MOUD, receipt of OUD and behavioral health-related telehealth services, and experiencing medically treated overdose.
Results
The pre–COVID-19 pandemic cohort comprised 105 240 beneficiaries; of these, 61 152 (58.1%) were female, 71 152 (67.6%) were aged 45 to 74 years, and 82 822 (79.5%) non-Hispanic White. The COVID-19 pandemic cohort comprised 70 538 beneficiaries; of these, 40 257 (57.1%) were female, 46 793 (66.3%) were aged 45 to 74 years, and 55 510 (79.7%) were non-Hispanic White. During the study period, a larger percentage of beneficiaries in the pandemic cohort compared with the prepandemic cohort received OUD-related telehealth services (13 829 [19.6%] vs 593 [0.6%]; P < .001), behavioral health-related telehealth services (28 902 [41.0%] vs 1967 [1.9%]; P < .001), and MOUD (8854 [12.6%] vs 11 360 [10.8%]; P < .001). The percentage experiencing a medically treated overdose during the study period was similar (18.5% [19 491 of 105 240] in the prepandemic cohort vs 18.4% [13 004 of 70 538] in the pandemic cohort; P = .65). Receipt of OUD-related telehealth services in the pandemic cohort was associated with increased odds of MOUD retention (adjusted odds ratio [aOR], 1.27; 95% CI, 1.14-1.41) and lower odds of medically treated overdose (aOR, 0.67; 95% CI, 0.63-0.71). Among beneficiaries in the pandemic cohort, those receiving MOUD from opioid treatment programs only (aOR, 0.54; 95% CI, 0.47-0.63) and those receiving buprenorphine from pharmacies only (aOR, 0.91; 95% CI, 0.84-0.98) had lower odds of medically treated overdose compared with beneficiaries who did not receive MOUD.
Conclusions and Relevance
Emergency authorities to expand use of telehealth and provide flexibilities for MOUD provision during the pandemic were used by Medicare beneficiaries initiating an episode of OUD-related care and were associated with improved retention in care and reduced odds of medically treated overdose. Strategies to expand provision of MOUD and increase retention in care are urgently needed.
Related collections
Most cited references29
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Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic — United States, June 24–30, 2020
Mark Czeisler, Rashon Lane, Emiko Petrosky … (2020)
The coronavirus disease 2019 (COVID-19) pandemic has been associated with mental health challenges related to the morbidity and mortality caused by the disease and to mitigation activities, including the impact of physical distancing and stay-at-home orders.* Symptoms of anxiety disorder and depressive disorder increased considerably in the United States during April–June of 2020, compared with the same period in 2019 ( 1 , 2 ). To assess mental health, substance use, and suicidal ideation during the pandemic, representative panel surveys were conducted among adults aged ≥18 years across the United States during June 24–30, 2020. Overall, 40.9% of respondents reported at least one adverse mental or behavioral health condition, including symptoms of anxiety disorder or depressive disorder (30.9%), symptoms of a trauma- and stressor-related disorder (TSRD) related to the pandemic † (26.3%), and having started or increased substance use to cope with stress or emotions related to COVID-19 (13.3%). The percentage of respondents who reported having seriously considered suicide in the 30 days before completing the survey (10.7%) was significantly higher among respondents aged 18–24 years (25.5%), minority racial/ethnic groups (Hispanic respondents [18.6%], non-Hispanic black [black] respondents [15.1%]), self-reported unpaid caregivers for adults § (30.7%), and essential workers ¶ (21.7%). Community-level intervention and prevention efforts, including health communication strategies, designed to reach these groups could help address various mental health conditions associated with the COVID-19 pandemic. During June 24–30, 2020, a total of 5,412 (54.7%) of 9,896 eligible invited adults** completed web-based surveys †† administered by Qualtrics. §§ The Monash University Human Research Ethics Committee of Monash University (Melbourne, Australia) reviewed and approved the study protocol on human subjects research. Respondents were informed of the study purposes and provided electronic consent before commencement, and investigators received anonymized responses. Participants included 3,683 (68.1%) first-time respondents and 1,729 (31.9%) respondents who had completed a related survey during April 2–8, May 5–12, 2020, or both intervals; 1,497 (27.7%) respondents participated during all three intervals ( 2 , 3 ). Quota sampling and survey weighting were employed to improve cohort representativeness of the U.S. population by gender, age, and race/ethnicity. ¶¶ Symptoms of anxiety disorder and depressive disorder were assessed using the four-item Patient Health Questionnaire*** ( 4 ), and symptoms of a COVID-19–related TSRD were assessed using the six-item Impact of Event Scale ††† ( 5 ). Respondents also reported whether they had started or increased substance use to cope with stress or emotions related to COVID-19 or seriously considered suicide in the 30 days preceding the survey. §§§ Analyses were stratified by gender, age, race/ethnicity, employment status, essential worker status, unpaid adult caregiver status, rural-urban residence classification, ¶¶¶ whether the respondent knew someone who had positive test results for SARS-CoV-2, the virus that causes COVID-19, or who had died from COVID-19, and whether the respondent was receiving treatment for diagnosed anxiety, depression, or posttraumatic stress disorder (PTSD) at the time of the survey. Comparisons within subgroups were evaluated using Poisson regressions with robust standard errors to calculate prevalence ratios, 95% confidence intervals (CIs), and p-values to evaluate statistical significance (α = 0.005 to account for multiple comparisons). Among the 1,497 respondents who completed all three surveys, longitudinal analyses of the odds of incidence**** of symptoms of adverse mental or behavioral health conditions by essential worker and unpaid adult caregiver status were conducted on unweighted responses using logistic regressions to calculate unadjusted and adjusted †††† odds ratios (ORs), 95% CI, and p-values (α = 0.05). The statsmodels package in Python (version 3.7.8; Python Software Foundation) was used to conduct all analyses. Overall, 40.9% of 5,470 respondents who completed surveys during June reported an adverse mental or behavioral health condition, including those who reported symptoms of anxiety disorder or depressive disorder (30.9%), those with TSRD symptoms related to COVID-19 (26.3%), those who reported having started or increased substance use to cope with stress or emotions related to COVID-19 (13.3%), and those who reported having seriously considered suicide in the preceding 30 days (10.7%) (Table 1). At least one adverse mental or behavioral health symptom was reported by more than one half of respondents who were aged 18–24 years (74.9%) and 25–44 years (51.9%), of Hispanic ethnicity (52.1%), and who held less than a high school diploma (66.2%), as well as those who were essential workers (54.0%), unpaid caregivers for adults (66.6%), and who reported treatment for diagnosed anxiety (72.7%), depression (68.8%), or PTSD (88.0%) at the time of the survey. TABLE 1 Respondent characteristics and prevalence of adverse mental health outcomes, increased substance use to cope with stress or emotions related to COVID-19 pandemic, and suicidal ideation — United States, June 24–30, 2020 Characteristic All respondents who completed surveys during June 24–30, 2020 weighted* no. (%) Weighted %* Conditions Started or increased substance use to cope with pandemic-related stress or emotions¶ Seriously considered suicide in past 30 days ≥1 adverse mental or behavioral health symptom Anxiety disorder† Depressive disorder† Anxiety or depressive disorder† COVID-19–related TSRD§ All respondents 5,470 (100) 25.5 24.3 30.9 26.3 13.3 10.7 40.9 Gender Female 2,784 (50.9) 26.3 23.9 31.5 24.7 12.2 8.9 41.4 Male 2,676 (48.9) 24.7 24.8 30.4 27.9 14.4 12.6 40.5 Other 10 (0.2) 20.0 30.0 30.0 30.0 10.0 0.0 30.0 Age group (yrs) 18–24 731 (13.4) 49.1 52.3 62.9 46.0 24.7 25.5 74.9 25–44 1,911 (34.9) 35.3 32.5 40.4 36.0 19.5 16.0 51.9 45–64 1,895 (34.6) 16.1 14.4 20.3 17.2 7.7 3.8 29.5 ≥65 933 (17.1) 6.2 5.8 8.1 9.2 3.0 2.0 15.1 Race/Ethnicity White, non-Hispanic 3,453 (63.1) 24.0 22.9 29.2 23.3 10.6 7.9 37.8 Black, non-Hispanic 663 (12.1) 23.4 24.6 30.2 30.4 18.4 15.1 44.2 Asian, non-Hispanic 256 (4.7) 14.1 14.2 18.0 22.1 6.7 6.6 31.9 Other race or multiple races, non-Hispanic** 164 (3.0) 27.8 29.3 33.2 28.3 11.0 9.8 43.8 Hispanic, any race(s) 885 (16.2) 35.5 31.3 40.8 35.1 21.9 18.6 52.1 Unknown 50 (0.9) 38.0 34.0 44.0 34.0 18.0 26.0 48.0 2019 Household income (USD) <25,000 741 (13.6) 30.6 30.8 36.6 29.9 12.5 9.9 45.4 25,000–49,999 1,123 (20.5) 26.0 25.6 33.2 27.2 13.5 10.1 43.9 50,999–99,999 1,775 (32.5) 27.1 24.8 31.6 26.4 12.6 11.4 40.3 100,999–199,999 1,301 (23.8) 23.1 20.8 27.7 24.2 15.5 11.7 37.8 ≥200,000 282 (5.2) 17.4 17.0 20.6 23.1 14.8 11.6 35.1 Unknown 247 (4.5) 19.6 23.1 27.2 24.9 6.2 3.9 41.5 Education Less than high school diploma 78 (1.4) 44.5 51.4 57.5 44.5 22.1 30.0 66.2 High school diploma 943 (17.2) 31.5 32.8 38.4 32.1 15.3 13.1 48.0 Some college 1,455 (26.6) 25.2 23.4 31.7 22.8 10.9 8.6 39.9 Bachelor's degree 1,888 (34.5) 24.7 22.5 28.7 26.4 14.2 10.7 40.6 Professional degree 1,074 (19.6) 20.9 19.5 25.4 24.5 12.6 10.0 35.2 Unknown 33 (0.6) 25.2 23.2 28.2 23.2 10.5 5.5 28.2 Employment status†† Employed 3,431 (62.7) 30.1 29.1 36.4 32.1 17.9 15.0 47.8 Essential 1,785 (32.6) 35.5 33.6 42.4 38.5 24.7 21.7 54.0 Nonessential 1,646 (30.1) 24.1 24.1 29.9 25.2 10.5 7.8 41.0 Unemployed 761 (13.9) 32.0 29.4 37.8 25.0 7.7 4.7 45.9 Retired 1,278 (23.4) 9.6 8.7 12.1 11.3 4.2 2.5 19.6 Unpaid adult caregiver status§§ Yes 1,435 (26.2) 47.6 45.2 56.1 48.4 32.9 30.7 66.6 No 4,035 (73.8) 17.7 16.9 22.0 18.4 6.3 3.6 31.8 Region ¶¶ Northeast 1,193 (21.8) 23.9 23.9 29.9 22.8 12.8 10.2 37.1 Midwest 1,015 (18.6) 22.7 21.1 27.5 24.4 9.0 7.5 36.1 South 1,921 (35.1) 27.9 26.5 33.4 29.1 15.4 12.5 44.4 West 1,340 (24.5) 25.8 24.2 30.9 26.7 14.0 10.9 43.0 Rural-urban classification*** Rural 599 (10.9) 26.0 22.5 29.3 25.4 11.5 10.2 38.3 Urban 4,871 (89.1) 25.5 24.6 31.1 26.4 13.5 10.7 41.2 Know someone who had positive test results for SARS-CoV-2 Yes 1,109 (20.3) 23.8 21.9 29.6 21.5 12.9 7.5 39.2 No 4,361 (79.7) 26.0 25.0 31.3 27.5 13.4 11.5 41.3 Knew someone who died from COVID-19 Yes 428 (7.8) 25.8 20.6 30.6 28.1 11.3 7.6 40.1 No 5,042 (92.2) 25.5 24.7 31.0. 26.1 13.4 10.9 41.0 Receiving treatment for previously diagnosed condition Anxiety Yes 536 (9.8) 59.6 52.0 66.0 51.9 26.6 23.6 72.7 No 4,934 (90.2) 21.8 21.3 27.1 23.5 11.8 9.3 37.5 Depression Yes 540 (9.9) 52.5 50.6 60.8 45.5 25.2 22.1 68.8 No 4,930 (90.1) 22.6 21.5 27.7 24.2 12.0 9.4 37.9 Posttraumatic stress disorder Yes 251 (4.6) 72.3 69.1 78.7 69.4 43.8 44.8 88.0 No 5,219 (95.4) 23.3 22.2 28.6 24.2 11.8 9.0 38.7 Abbreviations: COVID-19 = coronavirus disease 2019; TSRD = trauma- and stressor-related disorder. * Survey weighting was employed to improve the cross-sectional June cohort representativeness of the U.S. population by gender, age, and race/ethnicity according to the 2010 U.S. Census with respondents in which gender, age, and race/ethnicity were reported. Respondents who reported a gender of “Other” or who did not report race/ethnicity were assigned a weight of one. † Symptoms of anxiety disorder and depressive disorder were assessed via the four-item Patient Health Questionnaire (PHQ-4). Those who scored ≥3 out of 6 on the Generalized Anxiety Disorder (GAD-2) and Patient Health Questionnaire (PHQ-2) subscales were considered symptomatic for each disorder, respectively. § Disorders classified as TSRDs in the Diagnostic and Statistical Manual of Mental Disorders (DSM–5) include posttraumatic stress disorder (PTSD), acute stress disorder (ASD), and adjustment disorders (ADs), among others. Symptoms of a TSRD precipitated by the COVID-19 pandemic were assessed via the six-item Impact of Event Scale (IES-6) to screen for overlapping symptoms of PTSD, ASD, and ADs. For this survey, the COVID-19 pandemic was specified as the traumatic exposure to record peri- and posttraumatic symptoms associated with the range of stressors introduced by the COVID-19 pandemic. Those who scored ≥1.75 out of 4 were considered symptomatic. ¶ 104 respondents selected “Prefer not to answer.” ** The Other race or multiple races, non-Hispanic category includes respondents who identified as not being Hispanic and as more than one race or as American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or “Other.” †† Essential worker status was self-reported. The comparison was between employed respondents (n = 3,431) who identified as essential vs. nonessential. For this analysis, students who were not separately employed as essential workers were considered nonessential workers. §§ Unpaid adult caregiver status was self-reported. The definition of an unpaid caregiver for adults was a person who had provided unpaid care to a relative or friend aged ≥18 years to help them take care of themselves at any time in the last 3 months. Examples provided included helping with personal needs, household chores, health care tasks, managing a person’s finances, taking them to a doctor’s appointment, arranging for outside services, and visiting regularly to see how they are doing. ¶¶ Region classification was determined by using the U.S. Census Bureau’s Census Regions and Divisions of the United States. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. *** Rural-urban classification was determined by using self-reported ZIP codes according to the Federal Office of Rural Health Policy definition of rurality. https://www.hrsa.gov/rural-health/about-us/definition/datafiles.html. Prevalences of symptoms of adverse mental or behavioral health conditions varied significantly among subgroups (Table 2). Suicidal ideation was more prevalent among males than among females. Symptoms of anxiety disorder or depressive disorder, COVID-19–related TSRD, initiation of or increase in substance use to cope with COVID-19–associated stress, and serious suicidal ideation in the previous 30 days were most commonly reported by persons aged 18–24 years; prevalence decreased progressively with age. Hispanic respondents reported higher prevalences of symptoms of anxiety disorder or depressive disorder, COVID-19–related TSRD, increased substance use, and suicidal ideation than did non-Hispanic whites (whites) or non-Hispanic Asian (Asian) respondents. Black respondents reported increased substance use and past 30-day serious consideration of suicide in the previous 30 days more commonly than did white and Asian respondents. Respondents who reported treatment for diagnosed anxiety, depression, or PTSD at the time of the survey reported higher prevalences of symptoms of adverse mental and behavioral health conditions compared with those who did not. Symptoms of a COVID-19–related TSRD, increased substance use, and suicidal ideation were more prevalent among employed than unemployed respondents, and among essential workers than nonessential workers. Adverse conditions also were more prevalent among unpaid caregivers for adults than among those who were not, with particularly large differences in increased substance use (32.9% versus 6.3%) and suicidal ideation (30.7% versus 3.6%) in this group. TABLE 2 Comparison of symptoms of adverse mental health outcomes among all respondents who completed surveys (N = 5,470), by respondent characteristic* — United States, June 24–30, 2020 Characteristic Prevalence ratio ¶ (95% CI¶) Symptoms of anxiety disorder or depressive disorder † Symptoms of a TSRD related to COVID-19 § Started or increased substance use to cope with stress or emotions related to COVID-19 Serious consideration of suicide in past 30 days Gender Female vs. male 1.04 (0.96–1.12) 0.88 (0.81–0.97) 0.85 (0.75–0.98) 0.70 (0.60–0.82)** Age group (yrs) 18–24 vs. 25–44 1.56 (1.44–1.68)** 1.28 (1.16–1.41)** 1.31 (1.12–1.53)** 1.59 (1.35–1.87)** 18–24 vs. 45–64 3.10 (2.79–3.44)** 2.67 (2.35–3.03)** 3.35 (2.75–4.10)** 6.66 (5.15–8.61)** 18–24 vs. ≥65 7.73 (6.19–9.66)** 5.01 (4.04–6.22)** 8.77 (5.95–12.93)** 12.51 (7.88–19.86)** 25–44 vs. 45–64 1.99 (1.79–2.21)** 2.09 (1.86–2.35)** 2.56 (2.14–3.07)** 4.18 (3.26–5.36)** 25–44 vs. ≥65 4.96 (3.97–6.20)** 3.93 (3.18–4.85)** 6.70 (4.59–9.78)** 7.86 (4.98–12.41)** 45–64 vs. ≥65 2.49 (1.98–3.15)** 1.88 (1.50–2.35)** 2.62 (1.76–3.9)** 1.88 (1.14–3.10) Race/Ethnicity†† Hispanic vs. non-Hispanic black 1.35 (1.18–1.56)** 1.15 (1.00–1.33) 1.19 (0.97–1.46) 1.23 (0.98–1.55) Hispanic vs. non-Hispanic Asian 2.27 (1.73–2.98)** 1.59 (1.24–2.04)** 3.29 (2.05–5.28)** 2.82 (1.74–4.57)** Hispanic vs. non-Hispanic other race or multiple races 1.23 (0.98–1.55) 1.24 (0.96–1.61) 1.99 (1.27–3.13)** 1.89 (1.16–3.06) Hispanic vs. non-Hispanic white 1.40 (1.27–1.54)** 1.50 (1.35–1.68)** 2.09 (1.79–2.45)** 2.35 (1.96–2.80)** Non-Hispanic black vs. non-Hispanic Asian 1.68 (1.26–2.23)** 1.38 (1.07–1.78) 2.75 (1.70–4.47)** 2.29 (1.39–3.76)** Non-Hispanic black vs. non-Hispanic other race or multiple races 0.91 (0.71–1.16) 1.08 (0.82–1.41) 1.67 (1.05–2.65) 1.53 (0.93–2.52) Non-Hispanic black vs. non-Hispanic white 1.03 (0.91–1.17) 1.30 (1.14–1.48)** 1.75 (1.45–2.11)** 1.90 (1.54–2.36)** Non-Hispanic Asian vs. non-Hispanic other race or multiple races 0.54 (0.39–0.76)** 0.78 (0.56–1.09) 0.61 (0.32–1.14) 0.67 (0.35–1.29) Non-Hispanic Asian vs. non-Hispanic white 0.62 (0.47–0.80)** 0.95 (0.74–1.20) 0.64 (0.40–1.02) 0.83 (0.52–1.34) Non-Hispanic other race or multiple races vs. non-Hispanic white 1.14 (0.91–1.42) 1.21 (0.94–1.56) 1.05 (0.67–1.64) 1.24 (0.77–2) Employment status Employed vs. unemployed 0.96 (0.87–1.07) 1.28 (1.12–1.46)** 2.30 (1.78–2.98)** 3.21 (2.31–4.47)** Employed vs. retired 3.01 (2.58–3.51)** 2.84 (2.42–3.34)** 4.30 (3.28–5.63)** 5.97 (4.20–8.47)** Unemployed vs. retired 3.12 (2.63–3.71)** 2.21 (1.82–2.69)** 1.87 (1.30–2.67)** 1.86 (1.16–2.96) Essential vs. nonessential worker§§ 1.42 (1.30–1.56)** 1.52 (1.38–1.69)** 2.36 (2.00–2.77)** 2.76 (2.29–3.33)** Unpaid caregiver for adults vs. not¶¶` 2.55 (2.37–2.75)** 2.63 (2.42–2.86)** 5.28 (4.59–6.07)** 8.64 (7.23–10.33)** Rural vs. urban residence*** 0.94 (0.82–1.07) 0.96 (0.83–1.11) 0.84 (0.67–1.06) 0.95 (0.74–1.22) Knows someone with positive SARS-CoV-2 test result vs. not 0.95 (0.86–1.05) 0.78 (0.69–0.88)** 0.96 (0.81–1.14) 0.65 (0.52–0.81)** Knew someone who died from COVID-19 vs. not 0.99 (0.85–1.15) 1.08 (0.92–1.26) 0.84 (0.64–1.11) 0.69 (0.49–0.97) Receiving treatment for anxiety vs. not 2.43 (2.26–2.63)** 2.21 (2.01–2.43)** 2.27 (1.94–2.66)** 2.54 (2.13–3.03)** Receiving treatment for depression vs. not 2.20 (2.03–2.39)** 1.88 (1.70–2.09)** 2.13 (1.81–2.51)** 2.35 (1.96–2.82)** Receiving treatment for PTSD vs. not 2.75 (2.55–2.97)** 2.87 (2.61–3.16)** 3.78 (3.23–4.42)** 4.95 (4.21–5.83)** Abbreviations: CI = confidence interval; COVID-19 = coronavirus disease 2019; PTSD = posttraumatic stress disorder; TSRD = trauma- and stressor-related disorder. * Number of respondents for characteristics: gender (female = 2,784, male = 2,676), age group in years (18–24 = 731; 25–44 = 1,911; 45–64 = 1,895; ≥65 = 933), race/ethnicity (non-Hispanic white = 3453, non-Hispanic black = 663, non-Hispanic Asian = 256, non-Hispanic other race or multiple races = 164, Hispanic = 885). † Symptoms of anxiety disorder and depressive disorder were assessed via the four-item Patient Health Questionnaire (PHQ-4). Those who scored ≥3 out of 6 on the Generalized Anxiety Disorder (GAD-2) and Patient Health Questionnaire (PHQ-2) subscales were considered to have symptoms of these disorders. § Disorders classified as TSRDs in the Diagnostic and Statistical Manual of Mental Disorders (DSM–5) include PTSD, acute stress disorder (ASD), and adjustment disorders (ADs), among others. Symptoms of a TSRD precipitated by the COVID-19 pandemic were assessed via the six-item Impact of Event Scale (IES-6) to screen for overlapping symptoms of PTSD, ASD, and ADs. For this survey, the COVID-19 pandemic was specified as the traumatic exposure to record peri- and posttraumatic symptoms associated with the range of stressors introduced by the COVID-19 pandemic. Persons who scored ≥1.75 out of 4 were considered to be symptomatic. ¶ Comparisons within subgroups were evaluated on weighted responses via Poisson regressions used to calculate a prevalence ratio, 95% CI, and p-value (not shown). Statistical significance was evaluated at a threshold of α = 0.005 to account for multiple comparisons. In the calculation of prevalence ratios for started or increased substance use, respondents who selected “Prefer not to answer” (n = 104) were excluded. ** P-value is statistically significant (p<0.005). †† Respondents identified as a single race unless otherwise specified. The non-Hispanic, other race or multiple races category includes respondents who identified as not Hispanic and as more than one race or as American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or ‘Other’. §§ Essential worker status was self-reported. The comparison was between employed respondents (n = 3,431) who identified as essential vs. nonessential. For this analysis, students who were not separately employed as essential workers were considered nonessential workers. ¶¶ Unpaid adult caregiver status was self-reported. The definition of an unpaid caregiver for adults was having provided unpaid care to a relative or friend aged ≥18 years to help them take care of themselves at any time in the last 3 months. Examples provided included helping with personal needs, household chores, health care tasks, managing a person’s finances, taking them to a doctor’s appointment, arranging for outside services, and visiting regularly to see how they are doing. *** Rural-urban classification was determined by using self-reported ZIP codes according to the Federal Office of Rural Health Policy definition of rurality. https://www.hrsa.gov/rural-health/about-us/definition/datafiles.html. Longitudinal analysis of responses of 1,497 persons who completed all three surveys revealed that unpaid caregivers for adults had a significantly higher odds of incidence of adverse mental health conditions compared with others (Table 3). Among those who did not report having started or increased substance use to cope with stress or emotions related to COVID-19 in May, unpaid caregivers for adults had 3.33 times the odds of reporting this behavior in June (adjusted OR 95% CI = 1.75–6.31; p<0.001). Similarly, among those who did not report having seriously considered suicide in the previous 30 days in May, unpaid caregivers for adults had 3.03 times the odds of reporting suicidal ideation in June (adjusted OR 95% CI = 1.20–7.63; p = 0.019). TABLE 3 Odds of incidence* of symptoms of adverse mental health, substance use to cope with stress or emotions related to COVID–19 pandemic, and suicidal ideation in the third survey wave, by essential worker status and unpaid adult caregiver status among respondents who completed monthly surveys from April through June (N = 1,497) — United States, April 2–8, May 5–12, and June 24–30, 2020 Symptom or behavior Essential worker† vs. all other employment statuses (nonessential worker, unemployed, retired) Unpaid caregiver for adults§ vs. not unpaid caregiver Unadjusted Adjusted¶ Unadjusted Adjusted** OR (95% CI)†† p-value†† OR (95% CI)†† p-value†† OR (95% CI)†† p-value†† OR (95% CI)†† p-value†† Symptoms of anxiety disorder§§ 1.92 (1.29–2.87) 0.001 1.63 (0.99–2.69) 0.056 1.97 (1.25–3.11) 0.004 1.81 (1.14–2.87) 0.012 Symptoms of depressive disorder§§ 1.49 (1.00–2.22) 0.052 1.13 (0.70–1.82) 0.606 2.29 (1.50–3.50) <0.001 2.22 (1.45–3.41) <0.001 Symptoms of anxiety disorder or depressive disorder§§ 1.67 (1.14–2.46) 0.008 1.26 (0.79–2.00) 0.326 1.84 (1.19–2.85) 0.006 1.73 (1.11–2.70) 0.015 Symptoms of a TSRD related to COVID–19¶¶ 1.55 (0.86–2.81) 0.146 1.27 (0.63–2.56) 0.512 1.88 (0.99–3.56) 0.054 1.79 (0.94–3.42) 0.076 Started or increased substance use to cope with stress or emotions related to COVID–19 2.36 (1.26–4.42) 0.007 2.04 (0.92–4.48) 0.078 3.51 (1.86–6.61) <0.001 3.33 (1.75–6.31) <0.001 Serious consideration of suicide in previous 30 days 0.93 (0.31–2.78) 0.895 0.53 (0.16–1.70) 0.285 3.00 (1.20–7.52) 0.019 3.03 (1.20–7.63) 0.019 Abbreviations: CI = confidence interval, COVID–19 = coronavirus disease 2019, OR = odds ratio, TSRD = trauma– and stressor–related disorder. * For outcomes assessed via the four-item Patient Health Questionnaire (PHQ–4), odds of incidence were marked by the presence of symptoms during May 5–12 or June 24–30, 2020, after the absence of symptoms during April 2–8, 2020. Respondent pools for prospective analysis of odds of incidence (did not screen positive for symptoms during April 2–8): anxiety disorder (n = 1,236), depressive disorder (n = 1,301) and anxiety disorder or depressive disorder (n = 1,190). For symptoms of a TSRD precipitated by COVID–19, started or increased substance use to cope with stress or emotions related to COVID–19, and serious suicidal ideation in the previous 30 days, odds of incidence were marked by the presence of an outcome during June 24–30, 2020, after the absence of that outcome during May 5–12, 2020. Respondent pools for prospective analysis of odds of incidence (did not report symptoms or behavior during May 5–12): symptoms of a TSRD (n = 1,206), started or increased substance use (n = 1,408), and suicidal ideation (n = 1,456). † Essential worker status was self–reported. For Table 3, essential worker status was determined by identification as an essential worker during the June 24–30 survey. Essential workers were compared with all other respondents, not just employed respondents (i.e., essential workers vs. all other employment statuses (nonessential worker, unemployed, and retired), not essential vs. nonessential workers). § Unpaid adult caregiver status was self–reported. The definition of an unpaid caregiver for adults was having provided unpaid care to a relative or friend 18 years or older to help them take care of themselves at any time in the last 3 months. Examples provided included helping with personal needs, household chores, health care tasks, managing a person’s finances, taking them to a doctor’s appointment, arranging for outside services, and visiting regularly to see how they are doing. ¶ Adjusted for gender, employment status, and unpaid adult caregiver status. ** Adjusted for gender, employment status, and essential worker status. †† Respondents who completed surveys from all three waves (April, May, June) were eligible to be included in an unweighted longitudinal analysis. Comparisons within subgroups were evaluated via logit–linked Binomial regressions used to calculate unadjusted and adjusted odds ratios, 95% confidence intervals, and p–values. Statistical significance was evaluated at a threshold of α = 0.05. In the calculation of odds ratios for started or increased substance use, respondents who selected “Prefer not to answer” (n = 11) were excluded. §§ Symptoms of anxiety disorder and depressive disorder were assessed via the PHQ–4. Those who scored ≥3 out of 6 on the two–item Generalized Anxiety Disorder (GAD–2) and two-item Patient Health Questionnaire (PHQ–2) subscales were considered symptomatic for each disorder, respectively. ¶¶ Disorders classified as TSRDs in the Diagnostic and Statistical Manual of Mental Disorders (DSM–5) include posttraumatic stress disorder (PTSD), acute stress disorder (ASD), and adjustment disorders (ADs), among others. Symptoms of a TSRD precipitated by the COVID–19 pandemic were assessed via the six–item Impact of Event Scale (IES–6) to screen for overlapping symptoms of PTSD, ASD, and ADs. For this survey, the COVID–19 pandemic was specified as the traumatic exposure to record peri– and posttraumatic symptoms associated with the range of potential stressors introduced by the COVID–19 pandemic. Those who scored ≥1.75 out of 4 were considered symptomatic. Discussion Elevated levels of adverse mental health conditions, substance use, and suicidal ideation were reported by adults in the United States in June 2020. The prevalence of symptoms of anxiety disorder was approximately three times those reported in the second quarter of 2019 (25.5% versus 8.1%), and prevalence of depressive disorder was approximately four times that reported in the second quarter of 2019 (24.3% versus 6.5%) ( 2 ). However, given the methodological differences and potential unknown biases in survey designs, this analysis might not be directly comparable with data reported on anxiety and depression disorders in 2019 ( 2 ). Approximately one quarter of respondents reported symptoms of a TSRD related to the pandemic, and approximately one in 10 reported that they started or increased substance use because of COVID-19. Suicidal ideation was also elevated; approximately twice as many respondents reported serious consideration of suicide in the previous 30 days than did adults in the United States in 2018, referring to the previous 12 months (10.7% versus 4.3%) ( 6 ). Mental health conditions are disproportionately affecting specific populations, especially young adults, Hispanic persons, black persons, essential workers, unpaid caregivers for adults, and those receiving treatment for preexisting psychiatric conditions. Unpaid caregivers for adults, many of whom are currently providing critical aid to persons at increased risk for severe illness from COVID-19, had a higher incidence of adverse mental and behavioral health conditions compared with others. Although unpaid caregivers of children were not evaluated in this study, approximately 39% of unpaid caregivers for adults shared a household with children (compared with 27% of other respondents). Caregiver workload, especially in multigenerational caregivers, should be considered for future assessment of mental health, given the findings of this report and hardships potentially faced by caregivers. The findings in this report are subject to at least four limitations. First, a diagnostic evaluation for anxiety disorder or depressive disorder was not conducted; however, clinically validated screening instruments were used to assess symptoms. Second, the trauma- and stressor-related symptoms assessed were common to multiple TSRDs, precluding distinction among them; however, the findings highlight the importance of including COVID-19–specific trauma measures to gain insights into peri- and posttraumatic impacts of the COVID-19 pandemic ( 7 ). Third, substance use behavior was self-reported; therefore, responses might be subject to recall, response, and social desirability biases. Finally, given that the web-based survey might not be fully representative of the United States population, findings might have limited generalizability. However, standardized quality and data inclusion screening procedures, including algorithmic analysis of click-through behavior, removal of duplicate responses and scrubbing methods for web-based panel quality were applied. Further the prevalence of symptoms of anxiety disorder and depressive disorder were largely consistent with findings from the Household Pulse Survey during June ( 1 ). Markedly elevated prevalences of reported adverse mental and behavioral health conditions associated with the COVID-19 pandemic highlight the broad impact of the pandemic and the need to prevent and treat these conditions. Identification of populations at increased risk for psychological distress and unhealthy coping can inform policies to address health inequity, including increasing access to resources for clinical diagnoses and treatment options. Expanded use of telehealth, an effective means of delivering treatment for mental health conditions, including depression, substance use disorder, and suicidal ideation ( 8 ), might reduce COVID-19-related mental health consequences. Future studies should identify drivers of adverse mental and behavioral health during the COVID-19 pandemic and whether factors such as social isolation, absence of school structure, unemployment and other financial worries, and various forms of violence (e.g., physical, emotional, mental, or sexual abuse) serve as additional stressors. Community-level intervention and prevention efforts should include strengthening economic supports to reduce financial strain, addressing stress from experienced racial discrimination, promoting social connectedness, and supporting persons at risk for suicide ( 9 ). Communication strategies should focus on promotion of health services §§§§ , ¶¶¶¶ , ***** and culturally and linguistically tailored prevention messaging regarding practices to improve emotional well-being. Development and implementation of COVID-19–specific screening instruments for early identification of COVID-19–related TSRD symptoms would allow for early clinical interventions that might prevent progression from acute to chronic TSRDs. To reduce potential harms of increased substance use related to COVID-19, resources, including social support, comprehensive treatment options, and harm reduction services, are essential and should remain accessible. Periodic assessment of mental health, substance use, and suicidal ideation should evaluate the prevalence of psychological distress over time. Addressing mental health disparities and preparing support systems to mitigate mental health consequences as the pandemic evolves will continue to be needed urgently. Summary What is already known about this topic? Communities have faced mental health challenges related to COVID-19–associated morbidity, mortality, and mitigation activities. What is added by this report? During June 24–30, 2020, U.S. adults reported considerably elevated adverse mental health conditions associated with COVID-19. Younger adults, racial/ethnic minorities, essential workers, and unpaid adult caregivers reported having experienced disproportionately worse mental health outcomes, increased substance use, and elevated suicidal ideation. What are the implications for public health practice? The public health response to the COVID-19 pandemic should increase intervention and prevention efforts to address associated mental health conditions. Community-level efforts, including health communication strategies, should prioritize young adults, racial/ethnic minorities, essential workers, and unpaid adult caregivers.
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Collision of the COVID-19 and Addiction Epidemics
Nora D. Volkow (2020)
People with substance use disorder may be especially susceptible to COVID-19, and compromised lung function from COVID-19 could also put at risk those who have opioid use disorder and methamphetamine use disorder. This commentary describes the risks of the collision of the COVID-19 and addiction epidemics.
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Drug and Opioid-Involved Overdose Deaths — United States, 2017–2018
Nana O. Wilson, Mbabazi Kariisa, Puja Seth … (2020)
Of the 70,237 drug overdose deaths in the United States in 2017, approximately two thirds (47,600) involved an opioid ( 1 ). In recent years, increases in opioid-involved overdose deaths have been driven primarily by deaths involving synthetic opioids other than methadone (hereafter referred to as synthetic opioids) ( 1 ). CDC analyzed changes in age-adjusted death rates from 2017 to 2018 involving all opioids and opioid subcategories* by demographic characteristics, county urbanization levels, U.S. Census region, and state. During 2018, a total of 67,367 drug overdose deaths occurred in the United States, a 4.1% decline from 2017; 46,802 (69.5%) involved an opioid ( 2 ). From 2017 to 2018, deaths involving all opioids, prescription opioids, and heroin decreased 2%, 13.5%, and 4.1%, respectively. However, deaths involving synthetic opioids increased 10%, likely driven by illicitly manufactured fentanyl (IMF), including fentanyl analogs ( 1 , 3 ). Efforts related to all opioids, particularly deaths involving synthetic opioids, should be strengthened to sustain and accelerate declines in opioid-involved deaths. Comprehensive surveillance and prevention measures are critical to reducing opioid-involved deaths, including continued surveillance of evolving drug use and overdose, polysubstance use, and the changing illicit drug market; naloxone distribution and outreach to groups at risk for IMF exposure; linkage to evidence-based treatment for persons with substance use disorders; and continued partnerships with public safety. Drug overdose deaths were identified in National Vital Statistics System multiple cause-of-death mortality files † using the International Classification of Diseases, Tenth Revision (ICD-10) underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Among deaths with drug overdose as the underlying cause, the opioid subcategory was determined by the following ICD-10 multiple cause-of-death codes: all opioids (T40.0, T40.1, T40.2, T40.3, T40.4, or T40.6) § ; prescription opioids (T40.2 or T40.3); heroin (T40.1); and synthetic opioids other than methadone (T40.4). Some deaths involved more than one opioid subcategory and were included in the rates for each; subcategories are not mutually exclusive. ¶ Changes from 2017 to 2018 in age-adjusted overdose death rates** were examined for all opioids, prescription opioids, heroin, and synthetic opioids. Death rates were stratified by age, sex, race/ethnicity, urbanization level, †† U.S. Census region, §§ and state. State-level analyses included 38 states and the District of Columbia (DC) with adequate drug specificity ¶¶ for 2017 and 2018.*** The drug or drugs involved in the drug overdose death were not specified on 12% of drug overdose death certificates in 2017 and on 8% of those from 2018. The percentage of 2018 death certificates with at least one drug specified ranged from 54.1% to 100% among states. Changes in death rates from 2017 to 2018 were compared using z-tests when deaths were ≥100 and nonoverlapping confidence intervals based on a gamma distribution when <100. ††† Changes presented in the text represent statistically significant findings, unless otherwise specified. During 2018, drug overdoses resulted in 67,367 deaths in the United States, a 4.1% decrease from 2017. Among these drug overdose deaths, 46,802 (69.5%) involved an opioid. From 2017 to 2018, opioid-involved death rates decreased 2.0%, from 14.9 per 100,000 population to 14.6 (Table 1); decreases occurred among females; persons aged 15–34 years and 45–54 years; non-Hispanic whites; and in small metro, micropolitan, and noncore areas; and in the Midwest and South regions. Rates during 2017–2018 increased among persons aged ≥65 years, non-Hispanic blacks, and Hispanics, and in the Northeast and the West regions. Rates decreased in 11 states and DC and increased in three states, with the largest relative (percentage) decrease in Iowa (–30.4%) and the largest absolute decrease (difference in rates) in Ohio (–9.6); the largest relative and absolute increase occurred in Missouri (18.8%, 3.1). The highest opioid-involved death rate in 2018 was in West Virginia (42.4 per 100,000). TABLE 1 Annual number and age-adjusted rate of drug overdose deaths* involving all opioids † and prescription opioids, § , ¶ by sex, age, race/ethnicity,** urbanization level, †† U.S. Census region, §§ and selected states ¶¶ — National Vital Statistics System, United States, 2017 and 2018 Decedent characteristic All opioids Prescription opioids 2017 2018 Rate change from 2017 to 2018*** 2017 2018 Rate change from 2017 to 2018*** No. (rate) No. (rate) Absolute change Relative change No. (rate) No. (rate) Absolute change Relative change All 47,600 (14.9) 46,802 (14.6) −0.3††† −2.0††† 17,029 (5.2) 14,975 (4.5) −0.7††† −13.5††† Sex Male 32,337 (20.4) 32,078 (20.1) −0.3 −1.5 9,873 (6.1) 8,723 (5.3) −0.8 ††† −13.1 ††† Female 15,263 (9.4) 14,724 (9.0) −0.4 ††† −4.3 ††† 7,156 (4.2) 6,252 (3.7) −0.5 ††† −11.9 ††† Age group (yrs) 0–14 79 (0.1) 65 (0.1) 0.0 0.0 50 (0.1) 36 (0.1) 0.0 0.0 15–24 4,094 (9.5) 3,618 (8.4) −1.1 ††† −11.6 ††† 1,050 (2.4) 790 (1.8) −0.6 ††† −25.0 ††† 25–34 13,181 (29.1) 12,839 (28.1) −1.0 ††† −3.4 ††† 3,408 (7.5) 2,862 (6.3) −1.2 ††† −16.0 ††† 35–44 11,149 (27.3) 11,414 (27.7) 0.4 1.5 3,714 (9.1) 3,350 (8.1) −1.0 ††† −11.0 ††† 45–54 10,207 (24.1) 9,565 (23.0) −1.1 ††† −4.6 ††† 4,238 (10.0) 3,490 (8.4) −1.6 ††† −16.0 ††† 55–64 7,153 (17.0) 7,278 (17.2) 0.2 1.2 3,509 (8.4) 3,291 (7.8) −0.6 ††† −7.1 ††† ≥65 1,724 (3.4) 2,012 (3.8) 0.4 ††† 11.8 ††† 1,055 (2.1) 1,152 (2.2) 0.1 4.8 Sex and age group (yrs) Male 15–24 2,885 (13.0) 2,527 (11.5) −1.5 ††† −11.5 ††† 728 (3.3) 548 (2.5) −0.8 ††† −24.2 ††† Male 25–44 17,352 (40.0) 17,240 (39.4) −0.6 −1.5 4,516 (10.4) 3,895 (8.9) −1.5 ††† −14.4 ††† Male 45–64 11,061 (26.9) 10,986 (26.8) −0.1 −0.4 4,089 (9.9) 3,637 (8.9) −1.0 ††† −10.1 ††† Female 15–24 1,209 (5.7) 1,091 (5.2) −0.5 ††† −8.8 ††† 322 (1.5) 242 (1.2) −0.3 ††† −20.0 ††† Female 25–44 6,978 (16.3) 7,013 (16.2) −0.1 −0.6 2,606 (6.1) 2,317 (5.4) −0.7 ††† −11.5 ††† Female 45–64 6,299 (14.6) 5,857 (13.6) −1.0 ††† −6.8 ††† 3,658 (8.5) 3,144 (7.3) −1.2 ††† −14.1 ††† Race/Ethnicity** White, non-Hispanic 37,113 (19.4) 35,363 (18.6) −0.8 ††† −4.1 ††† 13,900 (6.9) 12,085 (6.0) −0.9 ††† −13.0 ††† Black, non-Hispanic 5,513 (12.9) 6,088 (14.0) 1.1 ††† 8.5 ††† 1,508 (3.5) 1,444 (3.3) −0.2 −5.7 Hispanic 3,932 (6.8) 4,370 (7.5) 0.7 ††† 10.3 ††† 1,211 (2.2) 1,122 (2.0) −0.2 ††† −9.1 ††† American Indian/Alaska Native, non-Hispanic 408 (15.7) 373 (14.2) −1.5 −9.6 187 (7.2) 125 (4.7) −2.5 ††† −34.7 ††† Asian/Pacific Islander, non-Hispanic 348 (1.6) 345 (1.5) −0.1 −6.3 130 (0.6) 115 (0.5) −0.1 −16.7 County urbanization level†† Large central metro 14,518 (13.9) 14,767 (14.1) 0.2 1.4 4,945 (4.7) 4,394 (4.1) −0.6 ††† −12.8 ††† Large fringe metro 13,594 (17.2) 13,476 (17.0) −0.2 −1.2 4,273 (5.2) 3,791 (4.6) −0.6 ††† −11.5 ††† Medium metro 10,561 (16.2) 10,328 (15.8) −0.4 −2.5 3,951 (5.9) 3,539 (5.2) −0.7 ††† −11.9 ††† Small metro 3,560 (12.9) 3,379 (12.2) −0.7 ††† −5.4 ††† 1,479 (5.2) 1,278 (4.5) −0.7 ††† −13.5 ††† Micropolitan (nonmetro) 3,462 (13.9) 3,162 (12.7) −1.2 ††† −8.6 ††† 1,440 (5.6) 1,240 (4.7) −0.9 ††† −16.1 ††† Noncore (nonmetro) 1,905 (11.2) 1,690 (10.1) −1.1 ††† −9.8 ††† 941 (5.3) 733 (4.1) −1.2 ††† −22.6 ††† U.S. Census region of residence§§ Northeast 11,784 (21.3) 12,467 (22.8) 1.5 ††† 7.0 ††† 3,047 (5.3) 2,991 (5.3) 0.0 0.0 Midwest 12,483 (19.1) 11,268 (17.2) −1.9 ††† −9.9 ††† 3,702 (5.5) 2,965 (4.4) −1.1 ††† −20.0 ††† South 16,999 (14.1) 16,413 (13.5) −0.6 ††† −4.3 ††† 6,929 (5.6) 5,936 (4.7) −0.9 ††† −16.1 ††† West 6,334 (8.0) 6,654 (8.3) 0.3 ††† 3.8 ††† 3,351 (4.1) 3,083 (3.8) −0.3 ††† −7.3 ††† States with very good to excellent reporting (n = 29)¶¶ Alaska 102 (13.9) 68 (8.8) −5.1 −36.7 51 (7.0) 38 (4.9) −2.1 −30.0 Arizona 928 (13.5) 1,106 (15.9) 2.4 ††† 17.8 ††† 414 (5.9) 362 (5.0) −0.9 ††† −15.3 ††† Connecticut 955 (27.7) 948 (27.5) −0.2 −0.7 273 (7.7) 231 (6.4) −1.3 −16.9 District of Columbia 244 (34.7) 191 (26.7) −8.0 ††† −23.1 ††† 58 (8.4) 41 (5.7) −2.7 −32.1 Georgia 1,014 (9.7) 866 (8.3) −1.4 ††† −14.4 ††† 568 (5.4) 440 (4.1) −1.3 ††† −24.1 ††† Illinois 2,202 (17.2) 2,169 (17.0) −0.2 −1.2 623 (4.8) 539 (4.2) −0.6 ††† −12.5 ††† Iowa 206 (6.9) 143 (4.8) −2.1 ††† −30.4 ††† 104 (3.4) 64 (2.1) −1.3 ††† −38.2 ††† Maine 360 (29.9) 282 (23.4) −6.5 ††† −21.7 ††† 100 (7.6) 69 (5.1) −2.5 −32.9 Maryland 1,985 (32.2) 2,087 (33.7) 1.5 4.7 711 (11.5) 576 (9.2) −2.3 ††† −20.0 ††† Massachusetts 1,913 (28.2) 1,991 (29.3) 1.1 3.9 321 (4.6) 331 (4.7) 0.1 2.2 Missouri 952 (16.5) 1,132 (19.6) 3.1 ††† 18.8 ††† 253 (4.1) 265 (4.4) 0.3 7.3 Nevada 412 (13.3) 372 (11.5) −1.8 −13.5 276 (8.7) 235 (7.2) −1.5 ††† −17.2 ††† New Hampshire 424 (34.0) 412 (33.1) −0.9 −2.6 62 (4.8) 43 (3.1) −1.7 −35.4 New Mexico 332 (16.7) 338 (16.7) 0.0 0.0 171 (8.5) 176 (8.2) −0.3 −3.5 New York 3,224 (16.1) 2,991 (15.1) −1.0 ††† −6.2 ††† 1,044 (5.1) 998 (4.9) −0.2 −3.9 North Carolina 1,953 (19.8) 1,783 (17.9) −1.9 ††† −9.6 ††† 659 (6.5) 489 (4.7) −1.8 ††† −27.7 ††† Ohio 4,293 (39.2) 3,237 (29.6) −9.6 ††† −24.5 ††† 947 (8.4) 571 (5.0) −3.4 ††† −40.5 ††† Oklahoma 388 (10.2) 308 (7.8) −2.4 ††† −23.5 ††† 251 (6.7 172 (4.3) −2.4 ††† −35.8 ††† Oregon 344 (8.1) 339 (8.0) −0.1 −1.2 154 (3.5) 151 (3.4) −0.1 −2.9 Rhode Island 277 (26.9) 267 (25.9) −1.0 −3.7 99 (8.8) 85 (7.7) −1.1 −12.5 South Carolina 749 (15.5) 835 (17.1) 1.6 10.3 345 (7.1) 375 (7.4) 0.3 4.2 Tennessee 1,269 (19.3) 1,307 (19.9) 0.6 3.1 644 (9.6) 550 (8.2) −1.4 ††† −14.6 ††† Utah 456 (15.5) 437 (14.8) −0.7 −4.5 315 (10.8) 306 (10.5) −0.3 −2.8 Vermont 114 (20.0) 127 (22.8) 2.8 14.0 40 (6.3) 27 (4.4) −1.9 −30.2 Virginia 1,241 (14.8) 1,193 (14.3) −0.5 −3.4 404 (4.7) 326 (3.8) −0.9 ††† −19.1 ††† Washington 742 (9.6) 737 (9.4) −0.2 −2.1 343 (4.3) 301 (3.8) −0.5 −11.6 West Virginia 833 (49.6) 702 (42.4) −7.2 ††† −14.5 ††† 304 (17.2) 234 (13.1) −4.1 ††† −23.8 ††† Wisconsin 926 (16.9) 846 (15.3) −1.6 ††† −9.5 ††† 362 (6.4) 301 (5.3) −1.1 ††† −17.2 ††† Wyoming 47 (8.7) 40 (6.8) −1.9 −21.8 31 (6.0) 28 (4.6) −1.4 −23.3 States with good reporting (n = 10)¶¶ California 2,199 (5.3) 2,410 (5.8) 0.5 ††† 9.4 ††† 1,169 (2.8) 1,084 (2.6) −0.2 −7.1 Colorado 578 (10.0) 564 (9.5) −0.5 −5.0 300 (5.1) 268 (4.4) −0.7 −13.7 Florida 3,245 (16.3) 3,189 (15.8) −0.5 −3.1 1,272 (6.0) 1,282 (6.0) 0.0 0.0 Hawaii 53 (3.4) 59 (4.1) 0.7 20.6 40 (2.5) 33 (2.3) −0.2 −8.0 Indiana 1,176 (18.8) 1,104 (17.5) −1.3 −6.9 425 (6.6) 370 (5.6) −1.0 ††† −15.2 ††† Kentucky 1,160 (27.9) 989 (23.4) −4.5 ††† −16.1 ††† 433 (10.2) 315 (7.2) −3.0 ††† −29.4 ††† Michigan 2,033 (21.2) 2,011 (20.8) −0.4 −1.9 633 (6.5) 556 (5.6) −0.9 ††† −13.8 ††† Minnesota 422 (7.8) 343 (6.3) −1.5 ††† −19.2 ††† 195 (3.6) 136 (2.5) −1.1 ††† −30.6 ††† Mississippi 185 (6.4) 173 (6.1) −0.3 −4.7 96 (3.2) 92 (3.1) −0.1 −3.1 Texas 1,458 (5.1) 1,402 (4.8) −0.3 −5.9 646 (2.3) 547 (1.9) −0.4 −17.4 * Deaths were classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug overdose deaths were identified using underlying cause-of-death codes X40–X44, X60–X64, X85, and Y10–Y14. Rates are age-adjusted using the direct method and the 2000 U.S. standard population, except for age-specific crude rates. All rates are per 100,000 population. † Drug overdose deaths, as defined, that have opium (T40.0), heroin (T40.1), natural and semisynthetic opioids (T40.2), methadone (T40.3), synthetic opioids other than methadone (T40.4) or other and unspecified narcotics (T40.6) as a contributing cause. § Drug overdose deaths, as defined, that have natural and semisynthetic opioids (T40.2) or methadone (T40.3) as a contributing cause. ¶ Categories of deaths are not exclusive as deaths might involve more than one drug category. Summing of categories will result in more than the total number of deaths in a year. ** Data for Hispanic origin should be interpreted with caution; studies comparing Hispanic origin on death certificates and on Census surveys have shown inconsistent reporting on Hispanic ethnicity. Potential race misclassification might lead to underestimates for certain categories, primarily American Indian/Alaska Native non-Hispanic and Asian/Pacific Islander non-Hispanic decedents. https://www.cdc.gov/nchs/data/series/sr_02/sr02_172.pdf. †† By the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. https://www.cdc.gov/nchs/data_access/urban_rural.htm. §§ Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. ¶¶ Analyses were limited to states meeting the following criteria. States with very good to excellent reporting had ≥90% of drug overdose deaths mention at least one specific drug in 2017, with the change in drug overdose deaths mentioning of at least one specific drug differing by <10 percentage points from 2017 to 2018. States with good reporting had 80% to <90% of drug overdose deaths mention at least one specific drug in 2017, with the change in the percentage of drug overdose deaths mentioning at least one specific drug differing by <10 percentage points from 2017 to 2018. States included also were required to have stable rate estimates (i.e., based on ≥20 deaths in at least two of the following drug categories: opioids, prescription opioids, synthetic opioids other than methadone, and heroin). *** Absolute rate change is the difference between 2017 and 2018 rates. Relative rate change is the absolute rate change divided by the 2017 rate, multiplied by 100. Nonoverlapping confidence intervals based on the gamma method were used if the number of deaths was <100 in 2017 or 2018, and z-tests were used if the number of deaths was ≥100 in both 2017 and 2018. ††† Statistically significant (p-value <0.05). Prescription opioid-involved death rates decreased by 13.5% from 2017 to 2018. Rates decreased in males and females, persons aged 15–64 years, non-Hispanic whites, Hispanics, non-Hispanic American Indian/Alaska Natives, and across all urbanization levels. Prescription opioid–involved death rates remained stable in the Northeast and decreased in the Midwest, South, and the West. Seventeen states experienced declines in prescription opioid–involved death rates, with no states experiencing significant increases. The largest relative decrease occurred in Ohio (–40.5%), whereas the largest absolute decrease occurred in West Virginia (–4.1), which also had the highest prescription opioid-involved death rate in 2018 (13.1 per 100,000). Heroin-involved death rates decreased 4.1% from 2017 to 2018; reductions occurred among males and females, persons aged 15–34 years, non-Hispanic whites, and in large central metro and large fringe metro areas (Table 2). Rates decreased in the Midwest and increased in the West. Rates decreased in seven states and DC and increased in three states from 2017 to 2018. The largest relative decrease occurred in Kentucky (50.0%), and the largest absolute decrease occurred in DC (–7.1); the largest relative and absolute increase was in Tennessee (18.8%, 0.9). The highest heroin-involved death rate in 2018 was in Vermont (12.5 per 100,000). TABLE 2 Annual number and age-adjusted rate of drug overdose deaths* involving heroin † and synthetic opioids other than methadone, § , ¶ by sex, age, race/ethnicity,** urbanization level, †† U.S. Census region, §§ and selected states ¶¶ — National Vital Statistics System, United States, 2017 and 2018 Decedent characteristic Heroin Synthetic opioids other than methadone 2017 2018 Rate change from 2017 to 2018*** 2017 2018 Rate change from 2017 to 2018*** No. (rate) No. (rate) Absolute change Relative change No. (rate) No. (rate) Absolute change Relative change All 15,482 (4.9) 14,996 (4.7) −0.2††† −4.1††† 28,466 (9.0) 31,335 (9.9) 0.9††† 10.0††† Sex Male 11,596 (7.3) 11,291 (7.1) −0.2 ††† −2.7 ††† 20,524 (13.0) 22,528 (14.2) 1.2 ††† 9.2 ††† Female 3,886 (2.5) 3,705 (2.3) −0.2 ††† −8.0 ††† 7,942 (5.0) 8,807 (5.5) 0.5 ††† 10.0 ††† Age group (yrs) 0–14 —§§§ —§§§ —§§§ —§§§ 33 (0.1) 29 (0.1) 0.0 0.0 15–24 1,454 (3.4) 1,160 (2.7) −0.7 ††† −20.6 ††† 2,655 (6.1) 2,640 (6.1) 0.0 0.0 25–34 4,890 (10.8) 4,642 (10.2) −0.6 ††† −5.6 ††† 8,825 (19.5) 9,568 (20.9) 1.4 ††† 7.2 ††† 35–44 3,713 (9.1) 3,740 (9.1) 0.0 0.0 7,084 (17.3) 8,070 (19.6) 2.3 ††† 13.3 ††† 45–54 3,043 (7.2) 2,922 (7.0) −0.2 −2.8 5,762 (13.6) 6,132 (14.7) 1.1 ††† 8.1 ††† 55–64 2,005 (4.8) 2,077 (4.9) 0.1 2.1 3,481 (8.3) 4,018 (9.5) 1.2 ††† 14.5 ††† ≥65 368 (0.7) 445 (0.8) 0.1 14.3 620 (1.2) 871 (1.7) 0.5 ††† 41.7 ††† Sex and age group (yrs) Male 15–24 1,031 (4.7) 821 (3.7) −1.0 ††† −21.3 ††† 1,877 (8.5) 1,841 (8.4) −0.1 −1.2 Male 25–44 6,428 (14.8) 6,305 (14.4) −0.4 −2.7 11,693 (27.0) 12,810 (29.2) 2.2 ††† 8.1 ††† Male 45–64 3,830 (9.3) 3,778 (9.2) −0.1 −1.1 6,524 (15.8) 7,195 (17.6) 1.8 ††† 11.4 ††† Female 15–24 423 (2.0) 339 (1.6) −0.4 ††† −20.0 ††† 778 (3.7) 799 (3.8) 0.1 2.7 Female 25–44 2,175 (5.1) 2,077 (4.8) −0.3 −5.9 4,216 (9.8) 4,828 (11.2) 1.4 ††† 14.3 ††† Female 45–64 1,218 (2.8) 1,221 (2.8) 0.0 0.0 2,719 (6.3) 2,955 (6.9) 0.6 ††† 9.5 ††† Race/Ethnicity** White, non-Hispanic 11,293 (6.1) 10,756 (5.8) −0.3 ††† −4.9 ††† 21,956 (11.9) 23,214 (12.6) 0.7 ††† 5.9 ††† Black, non-Hispanic 2,140 (4.9) 2,145 (4.9) 0.0 0.0 3,832 (9.0) 4,780 (11.0) 2.0 ††† 22.2 ††† Hispanic 1,669 (2.9) 1,768 (3.1) 0.2 6.9 2,152 (3.7) 2,766 (4.7) 1.0 ††† 27.0 ††† American Indian/Alaska Native, non-Hispanic 136 (5.2) 133 (5.1) −0.1 −1.9 171 (6.5) 191 (7.3) 0.8 12.3 Asian/Pacific Islander, non-Hispanic 119 (0.5) 85 (0.4) −0.1 −20.0 189 (0.8) 214 (1.0) 0.2 ††† 25.0 ††† County urbanization level†† Large central metro 5,820 (5.6) 5,467 (5.2) −0.4 ††† −7.1 ††† 8,511 (8.2) 9,804 (9.4) 1.2 ††† 14.6 ††† Large fringe metro 4,526 (5.8) 4,321 (5.5) −0.3 ††† −5.2 ††† 8,991 (11.6) 9,871 (12.7) 1.1 ††† 9.5 ††† Medium metro 2,973 (4.6) 3,091 (4.8) 0.2 4.3 6,254 (9.8) 6,750 (10.5) 0.7 ††† 7.1 ††† Small metro 972 (3.6) 949 (3.5) −0.1 −2.8 1,878 (7.0) 2,050 (7.6) 0.6 ††† 8.6 ††† Micropolitan (nonmetro) 801 (3.3) 780 (3.3) 0.0 0.0 1,860 (7.7) 1,925 (8.0) 0.3 3.9 Noncore (nonmetro) 390 (2.4) 388 (2.4) 0.0 0.0 972 (6.0) 935 (5.8) −0.2 −3.3 U.S. Census region of residence§§ Northeast 4,310 (7.8) 4,363 (8.0) 0.2 2.6 8,861 (16.2) 10,351 (19.1) 2.9 ††† 17.9 ††† Midwest 4,228 (6.5) 3,575 (5.5) −1.0 ††† −15.4 ††† 8,234 (12.8) 8,348 (12.9) 0.1 0.8 South 4,776 (4.0) 4,718 (3.9) −0.1 −2.5 9,906 (8.3) 10,443 (8.6) 0.3 ††† 3.6 ††† West 2,168 (2.8) 2,340 (3.0) 0.2 ††† 7.1 ††† 1,465 (1.9) 2,193 (2.8) 0.9 ††† 47.4 ††† States with very good to excellent reporting (n = 29)¶¶ Alaska 36 (4.9) 29 (3.8) −1.1 −22.4 37 (4.9) 18 –§§§ –§§§ –§§§ Arizona 334 (5.0) 352 (5.2) 0.2 4.0 267 (4.0) 522 (7.7) 3.7 ††† 92.5 ††† Connecticut 425 (12.4) 338 (9.9) −2.5 ††† −20.2 ††† 686 (20.3) 767 (22.5) 2.2 10.8 District of Columbia 127 (18) 79 (10.9) −7.1 ††† −39.4 ††† 182 (25.7) 162 (22.6) −3.1 −12.1 Georgia 263 (2.6) 299 (2.9) 0.3 11.5 419 (4.1) 349 (3.4) −0.7 ††† −17.1 ††† Illinois 1,187 (9.2) 1,050 (8.3) −0.9 ††† −9.8 ††† 1,251 (9.8) 1,568 (12.4) 2.6 ††† 26.5 ††† Iowa 61 (2.1) 37 (1.3) −0.8 −38.1 92 (3.2) 80 (2.8) −0.4 −12.5 Maine 76 (6.2) 71 (6.0) −0.2 −3.2 278 (23.5) 229 (19.8) −3.7 −15.7 Maryland 522 (8.6) 356 (5.9) −2.7 ††† −31.4 ††† 1,542 (25.2) 1,825 (29.6) 4.4 ††† 17.5 ††† Massachusetts 466 (7.0) 475 (7.0) 0.0 0.0 1,649 (24.5) 1,806 (26.8) 2.3 ††† 9.4 ††† Missouri 299 (5.3) 351 (6.1) 0.8 15.1 618 (10.9) 868 (15.3) 4.4 ††† 40.4 ††† Nevada 94 (3.1) 108 (3.5) 0.4 12.9 66 (2.2) 85 (2.8) 0.6 27.3 New Hampshire 28 (2.4) 12 –§§§ –§§§ –§§§ 374 (30.4) 386 (31.3) 0.9 3.0 New Mexico 144 (7.4) 130 (6.6) −0.8 −10.8 75 (3.7) 105 (5.4) 1.7 45.9 New York 1,356 (6.8) 1,243 (6.3) −0.5 −7.4 2,238 (11.3) 2,195 (11.2) −0.1 −0.9 North Carolina 537 (5.6) 619 (6.3) 0.7 12.5 1,285 (13.2) 1,272 (13.0) −0.2 −1.5 Ohio 1,000 (9.2) 721 (6.6) −2.6 ††† −28.3 ††† 3,523 (32.4) 2,783 (25.7) −6.7 ††† −20.7 ††† Oklahoma 61 (1.6) 84 (2.2) 0.6 37.5 102 (2.6) 79 (2.0) −0.6 −23.1 Oregon 124 (3.0) 154 (3.7) 0.7 23.3 85 (2.1) 97 (2.4) 0.3 14.3 Rhode Island 14—§§§ 24 (2.2) –§§§ –§§§ 201 (20.1) 213 (21.0) 0.9 4.5 South Carolina 153 (3.2) 183 (3.8) 0.6 18.8 404 (8.5) 510 (10.8) 2.3 ††† 27.1 ††† Tennessee 311 (4.8) 369 (5.7) 0.9 ††† 18.8 ††† 590 (9.3) 827 (12.8) 3.5 ††† 37.6 ††† Utah 147 (4.8) 156 (5.1) 0.3 6.3 92 (3.1) 83 (2.9) −0.2 −6.5 Vermont 41 (7.3) 68 (12.5) 5.2 71.2 77 (13.8) 106 (19.3) 5.5 39.9 Virginia 556 (6.7) 532 (6.4) −0.3 −4.5 829 (10.0) 852 (10.3) 0.3 3.0 Washington 306 (4.0) 328 (4.2) 0.2 5.0 143 (1.9) 221 (2.9) 1.0 ††† 52.6 ††† West Virginia 244 (14.9) 195 (12.3) −2.6 −17.4 618 (37.4) 551 (34.0) −3.4 −9.1 Wisconsin 414 (7.8) 327 (6.0) −1.8 ††† −23.1 ††† 466 (8.6) 506 (9.4) 0.8 9.3 Wyoming —§§§ —§§§ —§§§ —§§§ —§§§ —§§§ —§§§ —§§§ States with good reporting (n = 10)¶¶ California 715 (1.7) 778 (1.9) 0.2 ††† 11.8 ††† 536 (1.3) 865 (2.2) 0.9 ††† 69.2 ††† Colorado 224 (3.9) 233 (3.9) 0.0 0.0 112 (2.0) 134 (2.2) 0.2 10.0 Florida 707 (3.6) 689 (3.5) −0.1 −2.8 2,126 (11.0) 2,091 (10.7) −0.3 −2.7 Hawaii 10 —§§§ —§§§ —§§§ —§§§ —§§§ —§§§ —§§§ —§§§ Indiana 327 (5.3) 311 (5.0) −0.3 −5.7 649 (10.5) 713 (11.5) 1.0 9.5 Kentucky 269 (6.6) 140 (3.3) −3.3 ††† −50.0 ††† 780 (19.1) 744 (17.9) −1.2 −6.3 Michigan 783 (8.2) 633 (6.5) −1.7 ††† −20.7 ††† 1,368 (14.4) 1,531 (16.0) 1.6 ††† 11.1 ††† Minnesota 111 (2.0) 93 (1.7) −0.3 −15.0 184 (3.5) 202 (3.7) 0.2 5.7 Mississippi 34 (1.3) 39 (1.4) 0.1 7.7 81 (2.9) 72 (2.6) −0.3 −10.3 Texas 569 (2.0) 668 (2.3) 0.3 ††† 15.0 ††† 348 (1.2) 358 (1.2) 0.0 0.0 * Deaths were classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug overdose deaths were identified using underlying cause-of-death codes X40–X44, X60–X64, X85, and Y10–Y14. Rates are age-adjusted using the direct method and the 2000 U.S. standard population, except for age-specific crude rates. All rates were per 100,000 population. † Drug overdose deaths, as defined, that have heroin (T40.1) as a contributing cause. § Drug overdose deaths, as defined, that have semisynthetic opioids other than methadone (T40.4) as a contributing cause. ¶ Categories of deaths are not exclusive as deaths might involve more than one drug category. Summing of categories will result in more than the total number of deaths in a year. ** Data on Hispanic origin should be interpreted with caution; studies comparing Hispanic origin on death certificates and on Census surveys have shown inconsistent reporting on Hispanic ethnicity. Potential race misclassification might lead to underestimates for certain categories, primarily American Indian/Alaska Native non-Hispanic and Asian/Pacific Islander non-Hispanic decedents. https://www.cdc.gov/nchs/data/series/sr_02/sr02_172.pdf. †† By the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. https://www.cdc.gov/nchs/data_access/urban_rural.htm. §§ Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia. West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. ¶¶ Analyses were limited to states meeting the following criteria. States with very good to excellent reporting had ≥90% of drug overdose deaths mention at least one specific drug in 2017, with the change in drug overdose deaths mentioning of at least one specific drug differing by <10 percentage points from 2017 to 2018. States with good reporting had 80% to <90% of drug overdose deaths mention at least one specific drug in 2017, with the change in the percentage of drug overdose deaths mentioning at least one specific drug differing by <10 percentage points from 2017 to 2018. States included also were required to have stable rate estimates (i.e., based on ≥20 deaths in at least two of the following drug categories: opioids, prescription opioids, synthetic opioids other than methadone, and heroin). *** Absolute rate change is the difference between 2017 and 2018 rates. Relative rate change is the absolute rate change divided by the 2017 rate, multiplied by 100. Nonoverlapping confidence intervals based on the gamma method were used if the number of deaths was <100 in 2017 or 2018, and z-tests were used if the number of deaths was ≥100 in both 2017 and 2018. ††† Statistically significant (p-value <0.05). §§§ Cells with nine or fewer deaths are not reported. Rates based on <20 deaths are not considered stable rate estimates and are not reported. Death rates involving synthetic opioids increased from 9.0 per 100,000 population in 2017 to 9.9 in 2018 and accounted for 67.0% of opioid-involved deaths in 2018. These rates increased from 2017 to 2018 among males and females, persons aged ≥25 years, non-Hispanic whites, non-Hispanic blacks, Hispanics, non-Hispanic Asian/Pacific Islanders, and in large central metro, large fringe metro, medium metro, and small metro counties. Synthetic opioid–involved death rates increased in the Northeast, South and West and remained stable in the Midwest. Rates increased in 10 states and decreased in two states. The largest relative increase occurred in Arizona (92.5%), and the largest absolute increase occurred in Maryland and Missouri (4.4 per 100,000 in both states); the largest relative and absolute decrease was in Ohio (–20.7%, –6.7). The highest synthetic opioid–involved death rate in 2018 occurred in West Virginia (34.0 per 100,000). Discussion During 1999–2018, opioids were involved in 446,032 deaths in the United States. §§§ From 2017 to 2018, relative decreases occurred in death rates involving all drug overdoses (–4.1%), all opioids (–2.0%), prescription opioids (–13.5%), and heroin (–4.1%); a relative increase occurred in the rate of overdose deaths involving synthetic opioids (10.0%). Decreases in all opioid-involved death rates were largely driven by those involving prescription opioids. The number of filled opioid prescriptions peaked in 2012 and decreased thereafter ( 4 ). Efforts to reduce high-dose opioid prescribing ¶¶¶ ( 4 ) have increased and have contributed to decreases in prescription opioid–involved deaths. Factors that might be contributing to the decrease in heroin-involved deaths include fewer persons initiating heroin use ( 5 ), shifts from a heroin-based market to a fentanyl-based market ( 6 ), increased treatment provision for persons using heroin, and expansion of naloxone access ( 5 , 7 ). Increases in synthetic opioid–involved deaths are likely driven by proliferation of IMF or fentanyl analogs in the illicit drug supply ( 3 , 5 , 6 ). According to the Drug Enforcement Administration, fentanyl was the most identified synthetic opioid found during drug seizures in the first half of 2017 ( 6 ); in addition, fentanyl reports in all regions increased during 2014–2018.**** This is consistent with current findings indicating recent increases in synthetic opioid–involved death rates in all regions except the Midwest. The findings in this report are subject to at least five limitations. First, postmortem toxicology testing varies by jurisdiction; improvements in testing might account for some reported increases. Second, the percentage of 2017 and 2018 death certificates with at least one drug specified varied among states and over time, limiting opioid subcategory rate comparisons. Third, because heroin is metabolized to morphine ( 8 ), some heroin deaths might have been misclassified as morphine deaths, resulting in an underreporting of heroin deaths. Fourth, potential race misclassification might have led to underestimates for certain categories, particularly American Indian/Alaska Natives and Asian/Pacific Islanders. †††† Finally, adequate drug specificity data were available from only 38 states and DC, which might limit generalizability of state-based analyses. From 2017 to 2018, small decreases occurred in all overdose deaths and in deaths involving all opioids, prescription opioids, and heroin; however, deaths involving synthetic opioids continued to increase in 2018 and accounted for two thirds of opioid-involved deaths. Findings also highlight increases in deaths among non-Hispanic blacks and Hispanics, indicating the need for culturally tailored interventions that address social determinants of health and structural-level factors. In addition, changing substance use patterns, including the resurgence of methamphetamine use, particularly among persons using opioids ( 9 ) and the mixing of opioids with methamphetamine and cocaine in the illicit drug supply ( 6 ), have continued to make the drug overdose landscape more complicated and surveillance and prevention efforts more challenging. To sustain decreases and prevent continued increases, continued urgent action is needed. Overdose Data to Action §§§§ is a 3-year cooperative agreement through which CDC funds health departments in 47 states, DC, two territories, and 16 cities and counties for surveillance and prevention efforts. These measures include obtaining more timely data on all drug overdoses, improving toxicology to better identify polysubstance-involved deaths, enhancing linkage to treatment for persons with opioid use disorder and risk for opioid overdose, improving prescription drug monitoring programs, implementing health systems interventions, partnering with public safety, and implementing other innovative surveillance and prevention activities. Because of the reductions observed in deaths involving prescription opioids, continued efforts to encourage safe prescribing practices, such as following the CDC Guideline for Prescribing Opioids for Chronic Pain ( 10 ) might be enhanced by increased use of nonopioid and nonpharmacologic treatments for pain. Additional public health efforts to reduce opioid-involved overdose deaths include expanding the distribution of naloxone, addressing polysubstance use, and increasing the provision of medication-assisted treatment. Enhanced and coordinated multisectoral surveillance of the illicit drug supply ¶¶¶¶ to track emerging threats, including the type and amount of specific drugs, could also help prevent overdoses. A comprehensive, multisectoral surveillance, prevention, and response approach remains critical for sustaining and expanding preliminary successes in reducing opioid-involved overdose deaths and specifically curtailing synthetic opioid–involved deaths and other emerging threats. Summary What is already known about this topic? In 2017, 68% of the 70,237 U.S. drug overdose deaths involved an opioid. During 2016–2017, deaths involving all opioids and synthetic opioids increased; deaths involving prescription opioids and heroin remained stable. What is added by this report? Opioids were involved in approximately 70% (46,802) of drug overdose deaths during 2018, representing decreases from 2017 in overdose death rates involving all opioids (2% decline), prescription opioids (14%), and heroin (4%); rates involving synthetic opioids increased 10%. What are the implications for public health practice? Surveillance of overdose and polysubstance use trends and the illicit drug supply to track emerging threats, enhancing linkage to treatment, and a multisectoral response are critical to sustaining and accelerating declines in opioid-involved deaths.
Author and article information
Journal
Journal ID (nlm-ta): JAMA Psychiatry
Journal ID (iso-abbrev): JAMA Psychiatry
Title:
JAMA Psychiatry
Publisher:
American Medical Association
ISSN
(Print):
2168-622X
ISSN
(Electronic):
2168-6238
Publication date
(Electronic):
31
August
2022
Publication date
(Print):
October
2022
Publication date
(Corrected, electronic):
5
October
2022
Publication date PMC-release: 31
August
2022
Volume: 79
Issue: 10
Pages: 981-992
Affiliations
[1
]National Center for Injury Prevention and Control, US Centers for Disease Control
& Prevention, Atlanta, Georgia
Author notes
Article Information
Correction: This article was corrected on October 5, 2022, to change the article to open access
status under the CC-BY License.
Open Access: This is an open access article distributed under the terms of the
CC-BY License. © 2022 Jones CM et al.
JAMA Psychiatry.
Corresponding Author: Christopher M. Jones, PharmD, DrPH, National Center for Injury Prevention and Control,
US Centers for Disease Control & Prevention, 4770 Buford Hwy NE, Atlanta, GA 30341
(
fjr0@
123456cdc.gov
).
Author Contributions: Dr Shoff had full access to all of the data in the study and takes responsibility
for the integrity of the data and the accuracy of the data analysis. Drs Ling and
Compton jointly share senior authorship.
Critical revision of the manuscript for important intellectual content: Shoff, Hodges, Blanco, Losby, Ling, Compton.
Conflict of Interest Disclosures: Dr Compton reports long-term stock holdings in General Electric Co, 3M Companies,
and Pfizer outside the submitted work. No other disclosures were reported.
Funding/Support: This study was sponsored by the
US Centers for Disease Control & Prevention, the
US Centers for Medicare & Medicaid Services, and the
National Institutes of Health.
Role of the Funder/Sponsor: The sponsors reviewed and approved the manuscript. The sponsors had no role in the
design and conduct of the study; collection, management, analysis, and interpretation
of the data; preparation of the manuscript; and decision to submit the manuscript
for publication.
Disclaimers: The findings and conclusions of this study are those of the authors and do not necessarily
reflect the views of the National Institute on Drug Abuse of the National Institutes
of Health, the US Centers for Disease Control & Prevention, and the US Department
of Health and Human Services.
Additional Contributions: We thank the following staff from the US Centers for Medicare & Medicaid Services
for their assistance with this study: Doug Olson, MD, Ellen Blackwell, MSW, Stephanie
Bartee, BS, Gloria Wheatcroft, MPH, and Leah Durbak, MPH. None of the contributors
were compensated for their work.
Article
Publisher ID: yoi220048DOI: 10.1001/jamapsychiatry.2022.2284
PMC ID: 9434479
PubMed ID: 36044198
SO-VID: 2a52dce2-bb65-48d1-b67e-551801c236c7
Copyright © Copyright 2022 Jones CM et al.
JAMA Psychiatry.
License:
This is an open access article distributed under the terms of the CC-BY License.
History
Date
received
: 20
April
2022
Date
accepted
: 17
June
2022
Related
Funding
Funded by: US Centers for Disease Control & Prevention
Funded by: US Centers for Medicare & Medicaid Services
Funded by: National Institutes of Health
Categories
Subject:
Research
Subject:
Research
Subject:
Original Investigation
Subject:
Online First
Subject:
Comments
Data availability: