| Literature DB >> 34959077 |
Navin Kumar1, Kamila Janmohamed2, Kate Nyhan3, Silvia S Martins4, Magdalena Cerda5, Deborah Hasin4, Jenny Scott6, Afia Sarpong Frimpong2, Richard Pates7, Lilian A Ghandour8, Mayyada Wazaify9, Kaveh Khoshnood10.
Abstract
BACKGROUND: We conducted a scoping review focused on various forms of substance use amid the pandemic, looking at both the impact of substance use on COVID-19 infection, severity, and vaccine uptake, as well as the impact that COVID-19 has had on substance use treatment and rates.Entities:
Keywords: COVID-19; Lockdown; Substance use
Mesh:
Year: 2021 PMID: 34959077 PMCID: PMC8684053 DOI: 10.1016/j.addbeh.2021.107213
Source DB: PubMed Journal: Addict Behav ISSN: 0306-4603 Impact factor: 3.913
Fig. 1Flow diagram of search strategy used during the scoping review of substance use in relation to COVID-19.
Study characteristics related to design of study, setting, and sample size.
| Author, Year | Study (Cite/State, location Country) | Study type | Description of sample: N (% male) |
|---|---|---|---|
| Baghdad, Iraq | Cross-sectional Observational study | 284 (66%) | |
| Iran | Retrospective Observational study | N/A | |
| ( | United States | Retrospective Observational study | 22,024 (53%) |
| ( | Iran | Cross-sectional Observational study | N/A |
| ( | New Delhi, India | Cross-sectional Observational study | 73 (100%) |
| ( | United States | Cross-sectional Observational study | 16 (N/A) |
| ( | Spain | Retrospective Observational study | 362 (69%) |
| ( | Canada | Prospective observational study | 70 (34%) |
| ( | United States | Qualitative study | 16 (0%) |
| ( | Netherlands | Cross-sectional observational study | 957 (56%) |
| ( | United States | Retrospective Observational study | 585 (59%) |
| ( | N/A | Qualitative study | N/A |
| ( | Australia | Retrospective Observational study | 2307 (50%) |
| ( | Lombardy and Piedmont, Italy | Cross-sectional Observational study | 490 (16%) |
| ( | United States | Cross-sectional Observational study | N/A |
| ( | United States | Retrospective Observational study | 5850 (47%) |
| ( | Italy | Cross-sectional observational study | 1825 (38%) |
| ( | France | Retrospective study observational | 325 (67%) |
| ( | Observational study | 254 (16%) | |
| ( | Poland | Prospective observational study | 443 (21.4%) |
| ( | Cote d’Or, France | Cross-sectional observational study | 195 (61%) |
| ( | United States | Mixed-methods study | N/A |
| ( | United States | Cross-sectional observational study | 5470 (49%) |
| ( | Netherlands | Qualitative study | 15 (73%) |
| ( | United States | Cross-sectional observational study | 6391 (42%) |
| ( | Canada | Cross-sectional observational study | 1054 (22%) |
| ( | Worldwide | Cross-sectional observational study | 185 (63%) |
| ( | United States | Cross-sectional study observational | 4351 (33%) |
| ( | India | Qualitative study | N/A |
| ( | United States, NewZealand, India, South Africa | Cross-sectional observational study | N/A |
| ( | United States | Retrospective observational | 572 (39%) |
| ( | Australia | Cross-sectional observational study | 100 (65%) |
| ( | Belarus, Russia | Retrospective Observational study | 939 (19%) |
| ( | Worldwide | Retrospective Observational study | N/A |
| ( | Wisconsin, United States | Retrospective Observational study | 64 (75%) |
| ( | United States | Cross-sectional Observational study | N/A |
| ( | United Kingdom | Prospective observational study | 2,401,982 (37%) |
| ( | United States | Cross-sectional Observational study | 1008 (52%) |
| ( | United States | Mixed-methods study | 24 (0%) |
| ( | Brazil | Retrospective Observational study | 993 (N/A) |
| ( | United Kingdom | Cross-sectional Observational study | 53,002 (49%) |
| ( | United Kingdom | Cross-sectional observational study | 691 (39%) |
| ( | United States | Cross-sectional Observational study | N/A |
| ( | Canada | Cross-sectional observational study | 508 (0%) |
| ( | United States | Qualitative study | 22 (NA) |
| ( | Ardabil, Iran | Cross-sectional Observational study | 193 (64%) |
| ( | United Kingdom | Cross-sectional observational Study | 2791 (48%) |
| ( | Worldwide | Cross-sectional Observational study | 33,890 (N/A) |
| ( | Malatya, Turkey | Cross-sectional Observational study | 357 (66.9%) |
| ( | United States | Cross-sectional Observational study | N/A |
| ( | United States | Retrospective Observational study | 5931 (43%) |
| ( | United Kingdom | Cross-sectional Observational study | 182 (73%) |
| ( | United States | Cross-sectional Observational study | 366 (69%) |
| ( | Germany | Cross-sectional Observational study | 2102 (N/A) |
| ( | United States | Cross-sectional Observational study | 777 (50%) |
| ( | Japan | Retrospective Observational study | 5120 (NA/) |
| ( | United States | Qualitative study | 1000 (N/A) |
| United States | Cross-sectional Observational study | N/A | |
| United States | Cross-sectional Observational study | N/A | |
| ( | Ohio, United States | Retrospective Observational study | 1958 (20%) |
| ( | United States | Retrospective Observational study | 71,502 (NA) |
| ( | United States | Modeling study | N/A |
| ( | China | Retrospective Observational study | 954 (49%) |
| ( | Hong Kong | Cross-sectional Observational study | 1501 (48%) |
| ( | United States | Retrospective Observational study | 2391 (60%) |
| ( | United States | Cross-sectional Observational study | 2008 |
| ( | United States | Cross-sectional Observational study | 1127(65%) |
| ( | United States | Cross-sectional Observational study | 1118 (32%) |
| ( | Los Angeles, United States | Retrospective Observational study | 112 (66%) |
| ( | Bangalore, India | Retrospective Observational study | 96 (100%) |
| ( | Australia | Retrospective Observational study | 5068 (17%) |
| ( | United Kingdom | Prospective observational study | 27,141 (48%) |
| ( | New York, United States | Cross-sectional Observational study | 128 (38%) |
| ( | Greece | Cross-sectional Observational study | 705 (25%) |
| ( | United Kingdom | Retrospective Observational study | 164,986 (N/A) |
| ( | United Kingdom, Australia, New Zealand | Cross-sectional Observational study | 1509 (50%) |
| ( | United States | Retrospective Observational study | 1540 (42%) |
| ( | N/A | Cross-sectional Observational study | 177 (58%) |
| ( | United States | Cross-sectional Observational study | 754 (100%) |
| ( | United States | Cross-sectional Observational study | 160 (57%) |
| ( | France | Cross-sectional Observational study | 11,391 (22%) |
| ( | United States | Retrospective Observational study | 82,049 |
| ( | et al, | United States Cross-sectional study | observational |
| ( | Kyiv, Ukraine | Qualitative study | 123 (56%) |
| ( | India | Cross-sectional Observational study | 153 (69%) |
| ( | India | Cross-sectional Observational study | 911 (64%) |
| ( | Pakistan | Retrospective Observational study | 6014 |
| ( | Poland | Cross-sectional Observational study | 1097 (54%) |
| ( | Poland | Cross-sectional Observational study | 117 (45%) |
| ( | Kentucky, United States | Retrospective Observational study | N/A |
| ( | United States | Cross-sectional Observational study | 312 (49%) |
| ( | Australia | Cross-sectional Observational study | 1491 (33%) |
| ( | United States | Cross-sectional Observational study | 910 (100%) |
| ( | United States | Cross-sectional Observational study | 222 (53%) |
| ( | China | Retrospective Observational study | 6416 (47%) |
| ( | United Kingdom | Cross-sectional Observational study | 3179 (46%) |
| ( | United States, Canada | Cross-sectional Observational study | 3075 (49%) |
| ( | Europe | Cross-sectional Observational study | N/A |
| ( | United States | Cross-sectional Observational study | 90 (72%) |
| ( | United States | Cross-sectional Observational study | 269 (56%) |
| ( | United States | Qualitative study | 18 (N/A) |
| ( | India | Retrospective Observational study | N/A |
| ( | Spain | Cross-sectional Observational study | 27 (74%) |
| ( | United States | Retrospective Observational study | 7,510,380 (53%) |
| ( | Canada | Retrospective Observational study | 320 (55%) |
| ( | Australia | Cross-sectional Observational study | 2365 (19%) |
| ( | United States | Cross-sectional Observational study | 776 (59%) |
| ( | Italy, India, South Africa, United Kingdom, United States | Retrospective Observational study | 6800 (N/A) |
| ( | Austria | Cross-sectional Observational study | 127 (67%) |
| ( | Israel, Russia | Mixed-methods study | 291 (46%) |
Note: Studies reporting (N/A) for sample characteristics have not been conducted e.g. trials.
Synthesis of results organized by substance studied.
| Individuals who smoked presented with a lower COVID-19 mortality rate compared to diabetic patients and diabetic+ smoking. | |
| Individuals who smoke and were somewhat stressed were more likely to have increased their smoking during the pandemic. | |
| ( | Among the 105 individuals who smoked habitually, 38% reported an increase in the TC consumption during lockdown. |
| ( | Individuals who both smoked and vaped reported a slight decrease in daily tobacco consumption during the pandemic. |
| Among current individuals who smoke (n = 43), 13 (30.3%) declared increased tobacco consumption during the pandemic. | |
| ( | 26% of individuals who smoke increased their tobacco consumption by |
| ( | COVID-19 diagnosis was five times more likely among ever-users of EC only. |
| ( | There was no increase in the number of searches for smoking cessation on Google in the first months of the COVID-19 pandemic. |
| Current individuals who smoke were more likely to report COVID-19 symptoms. | |
| Compared with individuals who have never smoked, prevalence of confirmed COVID-19 was higher among current but not individuals who previously smoked. | |
| ( | COVID-19-induced environmental changes had mixed effects, facilitating quitting for some and impeding quitting for others. |
| ( | A topic related to CBD product preference emerged after COVID-19 was first reported. |
| The average time between the onset of COVID-19 symptoms and hospitalization was approximately 4 days for individuals who smoke WPs, 3 days for individuals who smoke TC, and 5 days for never--individuals who smoke. | |
| Among current vapers (n = 397), 9.7% self-reported vaping less than usual since COVID-19. | |
| Of a total of 2,250 tweets posted by tobacco industry accounts, 58 (2.6%) mentioned COVID-19. | |
| There was a difference in the success of those who quit smoking before the pandemic and those who quit during it. | |
| There were positive correlations between perceived risk of harm from COVID-19 due to TC or EC use and motivation to quit for both TCs and ECs. | |
| ( | Majority of respondents (70.80%) reported intentions to quit smoking in the next six months due to COVID-19. |
| ( | There was a 32.1% increase in the number of cigarettes smoked and 11.9% of respondents quit smoking. |
| ( | Tobacco usage was associated with a large increase in the severity of COVID-19 symptoms. |
| ( | In Australia, New Zealand, and the United Kingdom, tobacco users appeared to be receptive to smoking-cessation messaging about how tobacco use increased COVID-19 susceptibility. |
| ( | Participants (n = 42) reported that COVID-19 had made it easier to quit tobacco usage |
| ( | Current tobacco usage appeared to reduce the risk of contracting COVID-19 (n = 911). |
| ( | During the pandemic, many have ceased or initiated tobacco use. |
| ( | Campus closures prompted many young adults to use tobacco less frequently. |
| ( | Nations with a higher number of individuals who smoked showed fewer COVID-19 cases. |
| ( | Perceptions of risk related to tobacco use and COVID-19 have variable impacts on tobacco usage. |
| EC use marginally increased during lockdown. | |
| COVID-19 news events may be related to perceptions around vaping. | |
| The online vaping environment may be affected by the pandemic. | |
| ( | A 1% increase in weighted proportion of vapers in each state was associated with a 0.31 increase in the number of COVID-19 cases. |
| ( | Former and current smoking status were associated with invasive mechanical ventilation due to COVID-19. |
| ( | There was no evidence for changes in smoking cessation app downloads attributable to the start of the pandemic. |
| Odds of self-reported COVID-19 were greater among current individuals who smoke (20.90%). | |
| Alcohol | |
| ( | From March 7 to April 8, 2020, all Iranian provinces reported methanol poisoning cases. |
| ( | Five patients (6.60%) reported alcohol withdrawal during the pandemic. |
| ( | Most (73%) respondents consumed alcohol, followed by smoking tobacco (25%). |
| ( | 20% of individuals increased or decreased their normal alcohol consumption during lockdown. |
| ( | 37.70% reported no change in their alcohol drinking behavior, 19.40% reported drinking less or much less and 34.70% reported drinking more or much more alcohol since the begin of the lockdown. |
| ( | Alcohol consumption increased as the pandemic progressed. |
| ( | Frequency of solitary drinking increased post-social-distancing relative to pre-social-distancing. |
| ( | The average number of alcohol withdrawal cases reported increased following lockdown. |
| ( | Heavier drinking prepandemic, middle age, and average or higher income, were associated with increased drinking during the pandemic. |
| ( | Psychological distress and perceived threat related to COVID-19 were associated with a greater number of heavy drinking episodes. |
| ( | Over 53% of screened doctors indicated that alcohol consumption escalated during the pandemic. |
| ( | Past 30-day coping motives predicted increased past 30-day alcohol use during the pandemic. |
| Opioids | |
| ( | Ten of 16 systems reduced the scale of their OAT during the pandemic. |
| ( | While OPWH maintained HIV and substance use therapy throughout the lockdown, there was great anxiety about the availability of treatment services. |
| ( | After the COVID-19 state of emergency declaration, there was a 17% increase in the number of EMS opioid overdose runs. |
| ( | Most participants reported changing their typical clinical care patterns to help patients remain at home and minimize COVID-19 exposure. |
| ( | N/A Croff et al, 2020 |
| ( | 13.30% of respondents increased substance use to cope with COVID-19 stress. |
| ( | During the pandemic, the use of psychoactive substances differed between the patients in treatment compared to those in stable recovery. |
| ( | Most adolescents (49.30%) were engaging in solitary substance use during the pandemic. |
| ( | Participants (69%) reported a decrease in drug supply during the pandemic. |
| ( | Those who needed substance use treatment during lockdown faced multiple problems. |
| ( | N/A |
| ( | 34% of participants reported that the main drug they used in the past month was different to the main drug they used in February 2020. |
| ( | During the pandemic, there was no difference in the proportion of people using alcohol (41%) or cannabis (32%) compared to before the pandemic. |
| ( | Of participating mothers, 54.90% did not change their substance use during the pandemic. |
| ( | Exposure to claims that smoking/drinking alcohol can protect against COVID-19 were associated with increased tobacco use for current individuals who smoke (N = 280)). |
| ( | Older participants and those reporting past-year use of more drugs were more likely to use drugs during virtual raves. |
| ( | An increase in alcohol consumption during the pandemic was seen in 14.60% of participants. |
| ( | Those who reported an increase in smoking during the pandemic were more likely to have higher depression, anxiety, and stress symptoms. |
| ( | The proportion reporting cannabis use declined (34.50% versus 45.70% pre-COVID-19). |
| Interest in the search term ‘how to quit smoking’ showed an increase early in the pandemic. | |
| ( | Patients who engaged in substance use were at increased risk for COVID-19. |
| ( | During the pandemic period parents reported higher alcohol consumption. |
| ( | Secular more than religious students reported higher levels of TC (45.70% vs. 31.60%), alcohol (83% vs. 57.40%), and cannabis (29.70% vs. 14.70%) use. |
| ( | During the pandemic, the opioid Twitter network had less disinformation. |
Note: N/A refers to in-progress studies. AOR: adjusted odds ratio; EC: e-cigarette; CI: confidence interval; EMS: emergency medical services; HR: hazard ratio; OAT: opioid agonist therapy; OPWH: older people with HIV; PRR: prevalence risk ratio; RSV: relative search value; TC: traditional cigarette; WP: water pipe.
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