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      Methadone Access for Opioid Use Disorder During the COVID-19 Pandemic Within the United States and Canada

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          Key Points

          Question

          How does timely methadone access for opioid use disorder compare between the US and Canada during COVID-19?

          Findings

          In this cross-sectional study of methadone clinics during COVID-19 in 13 US states and the District of Columbia and 3 Canadian provinces with the highest rates of opioid overdose deaths, more than 1 in 10 clinics were not accepting patients, one-third of which reported this was due to COVID-19. Canadian clinics offered appointments faster than US clinics.

          Meaning

          These findings suggest that methadone access may be worse than previously estimated and exacerbated by COVID-19 and that Canadian clinics may provide timelier access than US opioid treatment programs.

          Abstract

          This cross-sectional study examines the proportion of clinics accepting new patients with opioid use disorder and time until first appointment in the US and Canada.

          Abstract

          Importance

          Methadone access may be uniquely vulnerable to disruption during COVID-19, and even short delays in access are associated with decreased medication initiation and increased illicit opioid use and overdose death. Relative to Canada, US methadone provision is more restricted and limited to specialized opioid treatment programs.

          Objective

          To compare timely access to methadone initiation in the US and Canada during COVID-19.

          Design, Setting, and Participants

          This cross-sectional study was conducted from May to June 2020. Participating clinics provided methadone for opioid use disorder in 14 US states and territories and 3 Canadian provinces with the highest opioid overdose death rates. Statistical analysis was performed from July 2020 to January 2021.

          Exposures

          Nation and type of health insurance (US Medicaid and US self-pay vs Canadian provincial).

          Main Outcomes and Measures

          Proportion of clinics accepting new patients and days to first appointment.

          Results

          Among 268 of 298 US clinics contacted as a patient with Medicaid (90%), 271 of 301 US clinics contacted as a self-pay patient (90%), and 237 of 288 Canadian clinics contacted as a patient with provincial insurance (82%), new patients were accepted for methadone at 231 clinics (86%) during US Medicaid contacts, 230 clinics (85%) during US self-pay contacts, and at 210 clinics (89%) during Canadian contacts. Among clinics not accepting new patients, at least 44% of 27 clinics reported that the COVID-19 pandemic was the reason. The mean wait for first appointment was greater among US Medicaid contacts (3.5 days [95% CI, 2.9-4.2 days]) and US self-pay contacts (4.1 days [95% CI, 3.4-4.8 days]) than Canadian contacts (1.9 days [95% CI, 1.7-2.1 days]) ( P < .001). Open-access model (walk-in hours for new patients without an appointment) utilization was reported by 57 Medicaid (30%), 57 self-pay (30%), and 115 Canadian (59%) contacts offering an appointment.

          Conclusions and Relevance

          In this cross-sectional study of 2 nations, more than 1 in 10 methadone clinics were not accepting new patients. Canadian clinics offered more timely methadone access than US opioid treatment programs. These results suggest that the methadone access shortage was exacerbated by COVID-19 and that changes to the US opioid treatment program model are needed to improve the timeliness of access. Increased open-access model adoption may increase timely access.

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          Most cited references32

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          Drug and Opioid-Involved Overdose Deaths — United States, 2017–2018

          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.
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            COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States

            The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411–8.997], P < 10−30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107–11.524], P < 10−30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925–8.530], P < 10−30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01–2.349], P < 10−30), with strongest effect for OUD (AOR = 4.162 [3.13–5.533], P < 10−25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.
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              Medication for Opioid Use Disorder After Nonfatal Opioid Overdose and Association With Mortality

              Opioid overdose survivors have an increased risk for death. Whether use of medications for opioid use disorder (MOUD) after overdose is associated with mortality is not known.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                23 July 2021
                July 2021
                23 July 2021
                : 4
                : 7
                : e2118223
                Affiliations
                [1 ]Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
                [2 ]British Columbia Center on Substance Use, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
                [3 ]Yale School of Nursing, Orange, Connecticut
                [4 ]Vassar College, Poughkeepsie, New York
                [5 ]Sections of General Internal Medicine and Infectious Diseases, Department of Medicine, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts
                [6 ]Faculty of Pharmacy, Université de Montréal, Montréal, Canada
                [7 ]VA Connecticut Healthcare System, West Haven
                [8 ]Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, Connecticut
                Author notes
                Article Information
                Accepted for Publication: May 12, 2021.
                Published: July 23, 2021. doi:10.1001/jamanetworkopen.2021.18223
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Joudrey PJ et al. JAMA Network Open.
                Corresponding Author: Paul J. Joudrey, MD, MPH, Department of Internal Medicine, Yale School of Medicine, 367 Cedar St, Harkness Hall A, New Haven, CT 06520 ( paul.joudrey@ 123456yale.edu ).
                Author Contributions : Dr Joudrey 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 Edelman and Wang are co–senior authors.
                Concept and design: Joudrey, Bach, Kimmel, Sung, You Kheang, E. Wang, Edelman.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Joudrey, Guerra, Medley, Sung, You Kheang, E. Wang.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Joudrey, Chaiton, Kimmel, Medley, Zhang.
                Obtained funding: Joudrey.
                Administrative, technical, or material support: Adams, Van Buren, Chaiton, Ehrenfeld, Guerra, Medley, You Kheang, Zhang.
                Supervision: Bach, E. Wang, Edelman.
                Conflict of Interest Disclosures: Dr Bach reported grants from the Michael Smith Foundation for Health Research and grants from the Canadian Institutes of Health Research outside the submitted work. Dr Kimmel reported personal fees from Abt Associates for work as a consultant on access to medications for opioid use disorder in nursing facilities and personal fees from the American Academy of Addiction Psychiatry Fees for lecturing about medications for opioid use disorder and harm reduction as part of the opioid response network outside the submitted work. No other disclosures were reported.
                Funding/Support: Funding for this publication was provided by grant number 5K12DA033312 (Dr Joudrey), L30 DA052056 (Dr Joudrey), and 1UM1DA049412-01 (Dr Kimmel) from the National Institute on Drug Abuse, a component of the National Institutes of Health.
                Role of the Funder/Sponsor: The funders 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.
                Meeting Presentation: Data from this work was presented during the College on the Problems of Drug Dependence COVID-19 Impacts on SUD Research Webinar; October 29, 2020.
                Additional Contributions: We thank Kim Jiang for her completion of standardized patient calls for this project. She was not compensated.
                Article
                zoi210537
                10.1001/jamanetworkopen.2021.18223
                8303098
                34297070
                8dd234ff-cb0f-40a7-85fb-654c05b1ea7e
                Copyright 2021 Joudrey PJ et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 18 March 2021
                : 12 May 2021
                Categories
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                Original Investigation
                Online Only
                Substance Use and Addiction

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