- Record: found
- Abstract: found
- Article: found
Correlates of recent nonfatal overdose among people who inject drugs in West Virginia
research-article
Author(s):
N. Jia Ahmad
1 ,
Sean T. Allen
2
,
,
Rebecca Hamilton White
2 ,
Kristin E. Schneider
3 ,
Allison O’Rourke
4 ,
Michelle Perdue
5 ,
Charles Babcock
6 ,
Michael E. Kilkenny
5 ,
Susan G. Sherman
2
Publication date (Electronic):
18 February 2021
Journal:
Harm Reduction Journal
Publisher:
BioMed Central
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Aim
Experiencing a nonfatal overdose (NFOD) is a significant risk factor for a subsequent nonfatal or fatal overdose. Overdose mortality rates in rural Appalachian states are some of the highest in the USA, but little is known about correlates of overdose among rural populations of people who inject drugs (PWID). Our study aimed to identify correlates of experiencing a recent (past 6 months) NFOD among rural PWID in Cabell County, West Virginia.
Methods
Using data from a June–July 2018 cross-sectional survey that was designed to estimate the size and characteristics of the PWID population in Cabell County, West Virginia, we used log binomial regression to identify correlates (e.g., structural vulnerabilities and substance use) of NFOD in the past 6 months.
Results
The majority of our sample of 420 PWID were male (61.2%), White, non-Hispanic (83.6%), and reported recent heroin injection (81.0%). More than two-fifths (42.6%) experienced a recent NFOD. Independent correlates of NFOD included witnessing an overdose in the past 6 months (adjusted prevalence ratio [aPR] = 2.28; 95% CI 1.48–3.50), attempting to quit using drugs in the past 6 months (aPR = 1.54; 95% CI 1.11–2.14), and the number of drugs injected (aPR = 1.16; 95% CI 1.10–1.23)
Conclusions
A large proportion of rural PWID in Appalachia reported having recently overdosed. The associations between witnessing an overdose, attempting to quit using drugs, and number of drugs injected with recent nonfatal overdose underscore the need for expanded access to overdose prevention resources that are tailored to the needs of this population. Expanding access to evidence-based overdose prevention strategies—such as take-home naloxone programs, treatment with methadone or buprenorphine, and harm reduction services—may decrease overdose morbidity and mortality among rural PWID in Appalachia.
Related collections
Most cited references50
- Record: found
- Abstract: found
- Article: found
Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
Spencer L James, Degu Abate, Kalkidan Hassen Abate … (2020)
Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1–4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0–8·4) while the total sum of global YLDs increased from 562 million (421–723) to 853 million (642–1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6–9·2) for males and 6·5% (5·4–7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782–3252] per 100 000 in males vs s1400 [1279–1524] per 100 000 in females), transport injuries (3322 [3082–3583] vs 2336 [2154–2535]), and self-harm and interpersonal violence (3265 [2943–3630] vs 5643 [5057–6302]). Interpretation Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. Funding Bill & Melinda Gates Foundation.
- Record: found
- Abstract: found
- Article: found
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.
- Record: found
- Abstract: found
- Article: not found
Life Expectancy and Mortality Rates in the United States, 1959-2017
Steven H Woolf, Heidi Schoomaker (2019)
US life expectancy has not kept pace with that of other wealthy countries and is now decreasing.
Author and article information
Contributors
N. Jia Ahmad: Jia.ahmad@jhu.edu
Sean T. Allen:
ORCID: http://orcid.org/0000-0002-0540-3824
Sallen63@jhu.edu
Rebecca Hamilton White: rwhite75@jhu.edu
Kristin E. Schneider: Kschne18@jhmi.edu
Allison O’Rourke: orourkea@gwu.edu
Michelle Perdue: Michelle.perdue@chhdwv.gov
Charles Babcock: babcockc@marshall.edu
Michael E. Kilkenny: Michael.Kilkenny@chhdwv.gov
Susan G. Sherman: ssherman@jhu.edu
Journal
Journal ID (nlm-ta): Harm Reduct J
Journal ID (iso-abbrev): Harm Reduct J
Title:
Harm Reduction Journal
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1477-7517
Publication date
(Electronic):
18
February
2021
Publication date PMC-release: 18
February
2021
Publication date Collection: 2021
Volume: 18
Electronic Location Identifier: 22
Affiliations
[1
]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Health Policy and Management, , Johns Hopkins University Bloomberg School of Public Health, ; 624 N. Broadway, Baltimore, MD 21205 USA
[2
]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Health, Behavior and Society, , Johns Hopkins University Bloomberg School of Public Health, ; 624 N. Broadway, Baltimore, MD 21205 USA
[3
]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Mental Health, , Johns Hopkins University Bloomberg School of Public Health, ; 624 N. Broadway, Baltimore, MD 21205 USA
Author information
Sean T. Allen
http://orcid.org/0000-0002-0540-3824
Article
Publisher ID: 470DOI: 10.1186/s12954-021-00470-y
PMC ID: 7890641
PubMed ID: 33602226
SO-VID: 92100a86-88b1-4f24-b793-1ef555104cd5
Copyright © © The Author(s) 2021
License:
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
History
Date
received
: 20
October
2020
Date
accepted
: 11
February
2021
Funding
Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
Award ID: K01DA046234
Award ID: AI117970
Award ID: P30AI094189
Award Recipient
:
Sean T. Allen
Funded by: Bloomberg American Health Initiative
Categories
Subject:
Research
Custom metadata
issue-copyright-statement © The Author(s) 2021
ScienceOpen disciplines: Health & Social care