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PERSPECTIVE article

Front. Psychiatry, 21 July 2020
Sec. Addictive Disorders
This article is part of the Research Topic Drug and Behavioral Addictions During Social-Distancing for the COVID-19 Pandemic View all 51 articles

Substance Use Disorders and COVID-19: Multi-Faceted Problems Which Require Multi-Pronged Solutions

Wossenseged Birhane Jemberie,,*Wossenseged Birhane Jemberie1,2,3*Jennifer Stewart Williams,Jennifer Stewart Williams4,5Malin ErikssonMalin Eriksson1Ann-Sofie GrnlundAnn-Sofie Grönlund1Nawi Ng,Nawi Ng4,6Marcus Blom NilssonMarcus Blom Nilsson1Mojgan Padyab,Mojgan Padyab1,2Kelsey Caroline PriestKelsey Caroline Priest7Mikael SandlundMikael Sandlund8Fredrik SnellmanFredrik Snellman1Dennis McCartyDennis McCarty9Lena M. Lundgren,Lena M. Lundgren1,10
  • 1Department of Social Work, Umeå University, Umeå, Sweden
  • 2Centre for Demography and Ageing Research (CEDAR), Umeå University, Umeå, Sweden
  • 3The Swedish National Graduate School for Competitive Science on Ageing and Health (SWEAH), Department of Health Sciences, Faculty of Medicine, Lund University, Lund, Sweden
  • 4Department of Epidemiology and Global Health, Faculty of Medicine, Umeå University, Umeå, Sweden
  • 5Research Centre for Generational Health and Ageing, Faculty of Health, University of Newcastle, Callaghan, NSW, Australia
  • 6School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
  • 7MD/PhD Program, School of Medicine, Oregon Health & Science University, Portland, OR, United States
  • 8Psychiatry Unit, Department of Clinical Science, Umeå University, Umeå, Sweden
  • 9Oregon Health & Science University- Portland State University, School of Public Health, Portland, OR, United States
  • 10Cross-National Behavioral Health Laboratory, Graduate School of Social Work, University of Denver, Denver, CO, United States

COVID-19 shocked health and economic systems leaving millions of people without employment and safety nets. The pandemic disproportionately affects people with substance use disorders (SUDs) due to the collision between SUDs and COVID-19. Comorbidities and risk environments for SUDs are likely risk factors for COVID-19. The pandemic, in turn, diminishes resources that people with SUD need for their recovery and well-being. This article presents an interdisciplinary and international perspective on how COVID-19 and the related systemic shock impact on individuals with SUDs directly and indirectly. We highlight a need to understand SUDs as biopsychosocial disorders and use evidence-based policies to destigmatize SUDs. We recommend a suite of multi-sectorial actions and strategies to strengthen, modernize and complement addiction care systems which will become resilient and responsive to future systemic shocks similar to the COVID-19 pandemic.

Introduction

Persistent use of psychoactive substances increases risk of substance use disorders (SUDs) – biopsychosocial disorders with multiple risk factors interacting at individual and contextual levels resulting in co-morbid health conditions and affecting people from all social and economic backgrounds (1, 2). The health consequences of SUDs (e.g., cardiovascular diseases, respiratory diseases, type-2 diabetes, immune and central nervous system depression, and psychiatric disorders) and the associated environmental challenges (e.g., housing instability, unemployment, and criminal justice involvement) increase risk for COVID-19 (37). COVID-19 adds to the complexity of SUD as it affects the lives of individuals with SUD.

The Intersection of Substance Use Disorder and COVID-19

SUDs and COVID-19 intersect on five dimensions. First, drug and alcohol use are often communal (e.g., sharing blunts, smoking pipes, or syringes) and may contribute to the spread of COVID-19 (8). Second, many individuals with SUD have limited financial resources, unstable housing and limited access to clean water and soap increasing their risk of infection (8, 9). Third, co-morbidities prevalent among people with SUD are associated with more severe COVID-19 symptoms, complications and fatalities and increase vulnerability to COVID-19 (37). Fourth, COVID-19 public health mitigation measures (i.e., physical distancing, quarantine and isolation) may exacerbate loneliness, mental health symptoms, withdrawal symptoms and psychological trauma (1013). Fifth, COVID-19 mitigation measures are likely to inhibit access to SUD treatment services (8). For many patients, the face-to-face interaction with practitioners is a key therapeutic ingredient for their recovery. These collisions between COVID-19 and SUD lead to more severe outcomes, especially among older adults with SUD who already have limited individual and social resources (3).

Finally, because COVID-19 burdens health care and social services, resources may be diverted from addiction services at a time when people with SUD need additional interventions. Lived experience of stigma and discrimination may also deter people with SUD from seeking healthcare during the pandemic (14). It is important that addiction care and social service providers are made aware regarding the vulnerability of the different sub-populations to COVID-19. This will enable providers to treat people with SUD in a non-stigmatizing and nondiscriminatory manner and provide appropriate services (1517).

The COVID-19 pandemic has serious implications for individuals with SUD including long-term socioeconomic and public health effects. Drawing on evidence from previous economic and health disasters, we examine the potential economic, public health and social implications of COVID-19 and SUDs, and provide a short description of efforts to ensure continuity of addiction services during the pandemic. The article closes with recommended policy approaches and solutions for tackling SUD within both the context of COVID-19 and the resulting shock to health and economic systems.

COVID-19 Induced Economic, Public Health, and Social Challenges

Unemployment, Substance Use, and Mental Health Comorbidity

The COVID-19 pandemic impacted the global economy leaving millions of people unemployed, without a social safety net and limited access to healthcare and social services (18, 19). The associations of involuntary or unexpected unemployment with SUD and mental health, and the positive effect of reemployment are well established. When individuals with SUD lose the structure of employment and sense of purpose, substance use and SUD symptom severity may increase (9, 17, 2030). Home foreclosure in the United States (US) was associated with a delayed onset of depression and anxiety after controlling for pre-existing depression and anxiety (31). As pandemic-related unemployment soars, and home foreclosures and housing eviction rises, there may be increases in mental health and SUD problems.

Studies of economic crises, similar to the pandemic-induced recession, suggest that SUD-related mortality and suicide will increase. Unemployment in Sweden during the severe recession in the 1990s was associated with alcohol-attributable hospitalization and mortality (32) and suicide during a 12-year follow-up (33). An analysis of economic changes in 26 European Union (EU) countries over three decades showed that increases in unemployment were associated with a 28% increase in mortality from SUD and a 4.5% increase in suicide (34). During the 2008–2010 financial crisis socioeconomic vulnerability among millennials (compared to older generations) was associated with increased alcohol and drug use disorders in the US (35).

Cuts in Public Expenditures on Healthcare and Social Care: “Where Recession Hurts, Austerity Kills”

Cuts in healthcare and social care expenditures, measures taken in response to the economic impact of COVID-19, may exacerbate the public health effects of acute economic change (20, 3639). These changes, compounded with unemployment and loss of income in the post-COVID-19 period, may affect resource allocation and priority setting, widen socioeconomic disparities, and magnify the marginalization of individuals with SUDs (40, 41).

When an economic crisis worsens and austerity measures are implemented, public health infrastructure can be stressed and the “risk environments” for SUD may expand (42). Poverty drives people to rely on informal economies (e.g., sex work, drug dealing) associated with illicit drug use. Compounded by weakened public health infrastructure, this can lead to a rise in preventable infectious diseases. The rapid increase in the HIV infection rate among persons who inject drugs (PWIDs) after the collapse of the Soviet Union and the formation of newly independent states in Eastern Europe, reflected the dismantling of public health infrastructures and increased unemployment (43). Similarly, the 2008–2010 financial crisis in Greece resulted in ongoing economic depression. Severe austerity measures led to a 40% reduction in hospital budgets by 2013 (44). However, the austerity measures also resulted in a 30% increase in the utilization of public healthcare services (44). Further, one-month prevalence of major depression increased from about 3% in 2008 to 8% in 2011 (45) and suicide mortality increased 56% between 2007 and 2011 (46, 47). The austerity also led to budget cuts for harm reduction and opioid treatment programs. Between 2008 and 2010 the number of people who used drugs increased 12% and was much higher for adults between 35 and 64 years (88%) most likely due to relapse (48). Finally, the number of HIV infected people among PWIDs in Greece increased 16-fold between 2010 (n = 15 cases) and 2011 (n = 260 cases) (49).

The ongoing pandemic is straining healthcare systems across the globe. Data from the Swedish Perioperative Register (SPOR) reflect a 74% decline in elective surgeries in April 2020 compared to April 2019 due to acute reorganization of healthcare to respond to COVID-19 (50). If governments react to the economic crisis through reductions in spending for healthcare and social care, the stress on healthcare may be exacerbated and lead to a resource triage and decline in healthcare quality (51).

People with SUD may be further affected as the COVID-19 impact worsens. This group already faces stigma and discrimination from the general public (52), policy makers (53, 54) and healthcare workers (14, 5558). Resource allocation and clinical practice with embedded stigma and discrimination has a prohibitive effect on healthcare utilization by individuals with SUD (14). Therefore, a reasonable, open and transparent, inclusive, accountable, and responsive process is necessary in priority setting and resource allocation during and after COVID-19.

Changes in Drug Use Patterns During the COVID-19 Induced Systemic Shock

Confinement rules, unemployment and fiscal austerity measures during and following the pandemic period can affect the illicit drug market and drug use patterns. European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and Europol analyses and data from the Global Drug Survey (GDS) suggest that there has been a shift in drug market and drug use patterns during the pandemic (59, 60). While the use of several psychoactive substances increased, use of recreational synthetic drugs, such as MDMA, diminished likely due to closure of clubs and festival avenues in several European countries.

Economic crises in the United States between 1959 and 2003 were associated with increased adolescent cannabis and illicit drug use, and elevated involvement in illicit drug markets (61). As people who use drugs lose income and can no longer afford their primary drug of use, suppliers may adulterate drugs or introduce novel psychoactive substances with unknown risks for overdosing and infectious disease transmission. A Hungarian study reported a shift from heroin and amphetamine injection to synthetic cathinone (bath salt) and reduced availability of heroin after the 2008–2010 financial crisis (62). Synthetic cannabinoids (spice), similarly, became a primary drug of use among the homeless population following a ban on novel psychoactive substances in the United Kingdom (63). Finally, a wastewater analysis from Northern Italy in 2009 noted a reduction in metabolites from expensive drugs (e.g., cocaine and heroin) and increased metabolites from less expensive drugs (e.g., methamphetamine and cannabis) (64).

Bereavement and Loneliness: Lasting Effects of the COVID-19 Pandemic

In addition to the economic peril in the post-COVID-19 period, the pandemic is traumatizing people. Shrinking social networks and deaths from COVID-19 leaves many without coping resources (65). Social isolation, loneliness, death of loved ones, complicated grief, and prolonged bereavement are associated with problematic substance use and relapse both in younger and older adults, and can adversely affect mental health (17, 6675).

Older adults who are living alone are more likely to have SUD when compared to married older adults (5). Living alone is also associated with depression in older adults (76). The current pandemic potentially adds to the already high percentages of older adults living alone (77). For some older adults with depression, the pandemic-related bereavement might also affect their remission (78). Unless socially protective measures are taken, the post-pandemic period will likely exacerbate these risk factors for substance use and mental health disorders.

Current Addiction Care Practice During the COVID-19 Pandemic

Countries differ in legal and regulatory frameworks and the organization of addiction care systems; addiction treatment, however, is recognized internationally as an essential service that should be maintained even in a disaster or pandemic (79). Many countries have national policies guiding the implementation and application of interventions linked to health and social care systems. During the pandemic, psychiatric and addiction care services are making efforts to ensure continuity of care while mitigating the risk for spreading COVID-19 infections (80, 81). In Sweden, the National Board of Health and Welfare posted informational materials on how to prevent the risk of COVID-19 transmission in opioid treatment programs (OTPs); in the United States, the Substance Abuse and Mental Health Services Administration released guidance to allow safer administration of methadone during the pandemic. Most of the measures focus on reducing the number of outpatient treatment visits, increasing the use of telehealth and expanding take-home medication for OTPs (82). While these current actions mitigate the negative impact of COVID-19 on individuals with SUD, there remains a need to adopt proactive policies which support individuals with SUD and strengthen addiction care services.

Policies and Strategies to Prevent and Treat SUD in the COVID-19 Context

SUD is a biopsychosocial disorder with multiple individual risk factors and consequences. SUD and mental health disorders also have distal determinants. Hence, interventions must be multipronged with community involvement and empowerment. It is important to adopt coordinated multi-sector strategies and innovative holistic approaches to benefit individuals with SUD.

Protective Social Policies Can Improve Living Conditions and Access for Addiction Care Services

Social policies impact health, directly and indirectly, through proximal and distal social determinants such as income, housing, employment, education, place of residence and social capital. Outcomes measured at the population level, mask effects on vulnerable groups and individuals with substance use disorders (83). Program evaluations do not always account for unintended consequences although realist evaluation methods take a different approach in seeking to answer what works, for whom, in what respects, to what extent, in what contexts and how.

Strong financial assistance systems can alleviate the negative impact of economic peril on mental health, during COVID-19 pandemic induced recession (22, 41, 84). A study of 26 European countries, with cause-specific mortalities as the outcomes (1970–2007) found that countries with stronger social protection (employment support and welfare systems) fared better compared to their counterparts (34). In a Norwegian study, reemployed individuals were 65% less likely to become harmful alcohol users compared with those who stayed unemployed (85). These studies suggest that public expenditures for labor market programs supporting gainful employment or earning capacity were associated with reductions in alcohol-related mortality and suicide.

Strong public safety nets for health, unemployment and social care insurances, support vulnerable groups such as people with mental health disorders and SUD, and ensure that they have access to treatment despite loss of income or employment related health insurance (63, 86). The number of individuals receiving care for opioid use disorder, for example, increased nearly twofold after Oregon’s Medicaid expansion in 2014 (87). Given the acute reorganization of healthcare during the pandemic and decrease in healthcare utilization, healthcare plans and resources can be redirected to making structural changes to reduce health disparities and promote health in vulnerable populations (88).

Develop and Expand Integrated Primary Care, Addiction, and Mental Health Care Systems

National and local policymakers need to accept that substance use disorders, as any other biopsychosocial disorder (e.g., diabetes), often require several intervention components and multiple treatment episodes. These include services for alcohol and drug, mental health and medical problems plus linkages to unemployment services, housing services, and family support services. In many societies, there is little understanding of the complexities of SUD. Many countries have regressive and punitive national policies which are based on prohibitive and moralistic views rather than evidence-based policies promoting the integration of biopsychosocial services and care for individuals with SUD. The lack of willingness to give up on the legacy of separate health, addiction and mental health care systems, true for many countries, further reduces the likelihood that clients with SUD (who as a result of COVID may have developed a number of co-occurring disorders) will receive integrated care, especially in limited resource settings. Parallel treatment between several care providers means that the patient is responsible for the coordination of treatment between different agencies. An integrated care system, however, reduces this burden and can address coexisting conditions simultaneously (89). Compared to fragmented care, integrated care can increase access to healthcare for individuals with SUD, and may reduce infectious diseases such as COVID-19.

Implement Professional Education About SUD and Co-Occurring Disorders

Health professionals face challenges while using empirically supported screening, assessment, referral treatment, and follow-up for SUD and co-occurring disorders because they lack training about causes and consequences of substance use (including the biomedical aspects), and have limited training with evidence based practices (90, 91). In the United States, medical, nursing, and social work programs are beginning to add SUD curricula to their training (92). Given the likely effects of COVID-19 and other diseases on SUD populations, it is even more critical that physician, nursing, psychology, and social work education programs include addiction and SUD content in their core-curriculum. Rapid training of addiction care professionals, in an emergency situation, (e.g., the current COVID-19 crisis) can help to control rapid outbreaks and provide safe addiction care.

Integrate IT Solutions to Strengthen and Modernize the Addiction Care System

As the current pandemic and the economic crisis threatens health and social care expenditures, information and communication technologies can play vital roles in improving healthcare and social services. New technology solutions that can modernize and strengthen the health and social care systems should be studied, and evaluated for cost-effectiveness.

The Internet of Things has shown effectiveness in monitoring elderly health and medication adherence (9396). OTPs and other medical treatments for individuals with SUD may benefit from similar technology. Individuals with SUD can learn to manage their substance use and self-monitor symptoms. This can lead to reduced outpatient treatment visits and hospitalizations.

Telehealth has been used in some settings during the COVID-19 pandemic to maintain access to treatment (97). A systematic review and meta-analysis reported that telehealth, especially live video interaction with therapists, had significant positive effects on patient mental health (98, 99). A non-randomized trial found that telehealth-delivered treatment for opioid use disorder was associated with better one-year retention compared to in-person delivered treatment (100). Studies have showed that older adults can benefit from telehealth services through reduced visits to emergency departments, increased knowledge of infectious diseases prevention, and improved social functioning and mental health (101, 102). Future studies should investigate how the telehealth services provided during COVID-19, impacted SUD treatment outcomes and stigma.

Concerns related to telehealth services, in addition to scarcity of evidence on their effectiveness, focus on their accessibility (103). Limited access to smartphones and internet services leaves millions of people without access to those services (104). People with SUD may not afford such devices and might not have access to telehealth. One possible solution for this disparity can be mobile health (m-health) technologies. These are less costly and are effective for SUD treatment (105); they might also be utilized for pandemic surveillance in vulnerable groups (106, 107). Social policies focusing on equitable resource allocation and social support (such as health insurance and income insurance) can also address this disparity.

Artificial intelligence (AI), another promising technology that could be used during emergency situations, could support trained clinicians to make treatment decisions. Currently, the research on the potential use and benefits of AI in addiction care and mental health services is in early development and needs to address important scientific, legal and ethical issues (108, 109). Current AI research is focused on assisting addiction care practitioners with treatment for alcohol use disorder (110), identifying and preventing relapse (111), and identifying risk factors (112, 113). Practitioners should, however, be aware that algorithms can be subject to biases (due to misclassification and measurement error, missing data, and small sample size) (108). The implication of such biases can be severe as they might create disparities in addiction care (108, 109). Involving addiction care specialists and patient advocacy groups from the beginning in the development of AI can facilitate innovative, ethical, acceptable, and effective solutions.

Finally, when the technology around unmanned aerial vehicles (drones) improves and becomes cost-effective and ethical and legal issues are addressed, harm reduction kits, and medications could be delivered to individuals with SUD (114116). Drones can deliver medications (e.g., naloxone) and save lives especially in highly congested cities and rural areas. They can also be used as an alternative for take-home medication for OTPs. Drones are already used for medical delivery services in emergency situations (115). However, current policies and views on harm reduction and addiction vary from country to country, and this might influence the acceptability of drones as kit-delivery vehicles.

Mobilization of Community Social Capital

During the COVID-19 pandemic voluntary efforts from community members and non-governmental organizations seek to help vulnerable groups. Mental health hotlines opened so that older adults can talk to professionals if they feel lonely or worried. Mobile apps and chat groups are now available for digital support. Community level coalitions and inclusion will be needed to support individuals with substance use and mental health disorders.

Mobilization of community social capital is an important resource in disaster management (117). A socially cohesive community with strong networks of civic engagement and norms of reciprocity and trust (118) may be better able to prepare for, manage, and recover from systemic shocks such as the COVID-19 pandemic (119). Resources (such as social support) from strong community networks, however, often require adhering to the dominant norms in a particular community. Thus, the same mechanisms that provide support based on reciprocity norms, might lead to increased social exclusion of outsiders who do not conform to the dominant norms (120, 121). For this reason, the focus should be on policies which promote parity for the treatment of substance use disorder to that of other biopsychosocial health conditions, support the development and implementation of community initiatives that complement addiction and mental health care services and can be leveraged during disaster (14, 54).

Strengthening of Cross-National Collaboration

Many illicit substances and their precursors are manufactured and transported through multiple countries, before reaching users. Collaboration between countries can counteract the interplay between SUD and economic crises. After the 2010–2011 HIV outbreak among PWID in Greece, the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and the European Centre for Disease Prevention and Control (ECDC) were instrumental in setting priorities for responding to and controlling the rapid HIV infection rate (122). EMCDDA also provides EU countries with early warning systems for novel psychoactive substances and new drug patterns which can emerge during economic crises.

The World Health Organization and the United Nations Office on Drugs and Crime are international organizations guiding efforts to develop and expand effective, evidence-based and ethical treatment for substance use disorders (79). Hence, national governments should continue funding these organizations, especially during COVID-19 and similar disease outbreaks. Strengthening community treatment capacity is essential during disaster and public health emergencies.

Conclusion

As globalization continues, COVID-19 is unlikely to be the last pandemic, and there will undoubtedly be subsequent global economic crises. These crises, compounded by austerity measures, will disproportionately burden people with SUD due to accumulated social, economic, and health inequities.

Ad hoc measures taken to ensure continuity of care might alleviate some of the challenges these groups face in emergency situations. Evidence-based, collective, and proactive policies and actions are necessary to strengthen and modernize addiction and mental health services.

The acknowledgement of SUD as a biopsychosocial condition and its destigmatization by policy makers and practitioners are essential components for comprehensive multi-sectorial strategies which will protect and address the needs of people with SUD.

COVID-19 presents opportunities to: adopt social protective policies; shift from fragmented health and addiction care systems to integrated care systems; mobilize community social capital; train healthcare and social care professionals on SUD and mental health disorder, and identify and integrate evidence-based information technology and digital tools into addiction care systems. Only then, will it be possible to provide equitable health and social care to people with SUDs and to have addiction care services which are resilient in the face of future systemic shocks.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

Ethics Statement

Ethical approval was not needed for this article as no animal nor human studies are presented, and there are no potentially identifiable human images or data.

Author Contributions

Writing—original draft: WJ, ME, MP, KP, DM, LL, Writing—Review and editing: WJ, JS, ME, A-SG, NN, MB, MP, KP, MS, FS, DM, LL. Conceptualization: WJ, JS, NN, DM, LL. Investigation: WJ. Formal Analysis: WJ. Funding acquisition: LL. All authors contributed to the article and approved the submitted version.

Funding

Grants from The Swedish Research Council for Health, Working Life and Welfare (FORTE) Grant no. 2016-07213 and Grant No. 2019-01453 have supported this study. An award from the National Institute on Drug Abuse (F30 DA044700) supported KP’s participation in the development of the manuscript. The funding organizations were not involved in the design of the study, data collection, data analysis, the interpretation of data, or writing of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

1. Skewes MC, Gonzalez VM. The biopsychosocial model of addiction. Princ Addict (2013) 1:61–70. doi: 10.1016/B978-0-12-398336-7.00006-1

CrossRef Full Text | Google Scholar

2. Griffiths M. A ‘components’ model of addiction within a biopsychosocial framework. J Subst Use (2005) 10(4):191–7. doi: 10.1080/14659890500114359

CrossRef Full Text | Google Scholar

3. CDC C-RT. Severe outcomes among patients with coronavirus disease 2019 (COVID-19)—United States, February 12–March 16, 2020. MMWR Morb Mortal Wkly Rep (2020) 69(12):343–6. doi: 10.15585/mmwr.mm6912e2

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Slaunwhite AK, Gan WQ, Xavier C, Zhao B, A Buxton J, Desai R. Overdose and Risk Factors for Severe Acute Respiratory Syndrome. Drug Alcohol Depend (2020) 212:108047. doi: 10.1016/j.drugalcdep.2020.108047

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Blazer DG, Wu L-T. The epidemiology of substance use and disorders among middle aged and elderly community adults: national survey on drug use and health. Am J Geriatr Psychiatry (2009) 17(3):237–45. doi: 10.1097/JGP.0b013e318190b8ef

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Yancy CW. COVID-19 and African Americans. JAMA (2020) 323(19):1891–2. doi: 10.1001/jama.2020.6548

CrossRef Full Text | Google Scholar

7. Dorn AV, Cooney RE, Sabin ML. COVID-19 exacerbating inequalities in the US. Lancet (2020) 395(10232):1243–4. doi: 10.1016/S0140-6736(20)30893-X

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Volkow N. Collision of the COVID-19 and Addiction Epidemics. Ann Intern Med (2020) 173(1):61–2. doi: 10.7326/m20-1212

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Harris M, Scott J, Hope V, Wright T, McGowan C, Ciccarone D. Navigating environmental constraints to injection preparation: the use of saliva and other alternatives to sterile water among unstably housed PWID in London. Harm Reduct J (2020) 17(1):24. doi: 10.1186/s12954-020-00388-x

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Venkatesh A, Edirappuli S. Social distancing in covid-19: what are the mental health implications? BMJ (2020) 369:m1379. doi: 10.1136/bmj.m1379

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet (2020) 395(10227):912–20. doi: 10.1016/S0140-6736(20)30460-8

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Eurofound. Living, working and COVID-19: First findings. Dublin: European Foundation for the Improvement of Living and Working Conditions (2020). Available from https://www.eurofound.europa.eu/publications/report/2020/living-working-and-covid-19-first-findings-april-2020

Google Scholar

13. Narasimha VL, Shukla L, Mukherjee D, Menon J, Huddar S, Panda UK, et al. Complicated Alcohol Withdrawal—An Unintended Consequence of COVID-19 Lockdown. Alcohol Alcohol (2020) 55(4):350–3. doi: 10.1093/alcalc/agaa042

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Biancarelli DL, Biello KB, Childs E, Drainoni M, Salhaney P, Edeza A, et al. Strategies used by people who inject drugs to avoid stigma in healthcare settings. Drug Alcohol Depend (2019) 198:80–6. doi: 10.1016/j.drugalcdep.2019.01.037

PubMed Abstract | CrossRef Full Text | Google Scholar

15. McNeil R, Small W, Wood E, Kerr T. Hospitals as a ‘risk environment’: An ethno-epidemiological study of voluntary and involuntary discharge from hospital against medical advice among people who inject drugs. Soc Sci Med (2014) 105:59–66. doi: 10.1016/j.socscimed.2014.01.010

PubMed Abstract | CrossRef Full Text | Google Scholar

16. OBHE. Double Jeopardy: COVID-19 and Behavioral Health Disparities for Black and Latino Communities in the U.S. Rockville, MD: The Office of Behavioral Health Equity, SAMHSA (2020). Available from: https://www.samhsa.gov/coronavirus

Google Scholar

17. Jemberie WB, Padyab M, Snellman F, Lundgren L. A Multidimensional Latent Class Analysis of Harmful Alcohol Use Among Older Adults: Subtypes Within the Swedish Addiction Severity Index Registry. J Addict Med (2020). doi: 10.1097/adm.0000000000000636

PubMed Abstract | CrossRef Full Text | Google Scholar

18. ETA. Unemployment Insurance Weekly Claims Data. Washington, DC: U.S. Department of Labor (2020).

Google Scholar

19. IMF. World Economic Outlook, April 2020: The Great Lockdown. Washington, DC: International Monetary Fund (2020).

Google Scholar

20. Corcoran P, Griffin E, Arensman E, Fitzgerald AP, Perry IJ. Impact of the economic recession and subsequent austerity on suicide and self-harm in Ireland: An interrupted time series analysis. Int J Epidemiol (2015) 44(3):969–77. doi: 10.1093/ije/dyv058

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Roelfs DJ, Shor E, Davidson KW, Schwartz JE. Losing life and livelihood: A systematic review and meta-analysis of unemployment and all-cause mortality. Soc Sci Med (2011) 72(6):840–54. doi: 10.1016/j.socscimed.2011.01.005

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Goldman-Mellor SJ, Saxton KB, Catalano RC. Economic Contraction and Mental Health. Int J Ment Health (2010) 39(2):6–31. doi: 10.2753/IMH0020-7411390201

CrossRef Full Text | Google Scholar

23. Jusot F, Khlat M, Rochereau T, Sermet C. Job loss from poor health, smoking and obesity: a national prospective survey in France. J Epidemiol Commun H (2008) 62(4):332–7. doi: 10.1136/jech.2007.060772

CrossRef Full Text | Google Scholar

24. Hammarstrom A. Health Consequences of Youth Unemployment - Review from a Gender Perspective. Soc Sci Med (1994) 38(5):699–709. doi: 10.1016/0277-9536(94)90460-X

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Hammarstrom A, Janlert U. Early unemployment can contribute to adult health problems: results from a longitudinal study of school leavers. J Epidemiol Commun H (2002) 56(8):624–30. doi: 10.1136/jech.56.8.624

CrossRef Full Text | Google Scholar

26. Green KM, Doherty EE, Reisinger HS, Chilcoat HD, Ensminger M. Social integration in young adulthood and the subsequent onset of substance use and disorders among a community population of urban African Americans. Addiction (2010) 105(3):484–93. doi: 10.1111/j.1360-0443.2009.02787.x

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Fergusson DM, Boden JM. Cannabis use and later life outcomes. Addiction (2008) 103(6):969–76. doi: 10.1111/j.1360-0443.2008.02221.x

PubMed Abstract | CrossRef Full Text | Google Scholar

28. McKee-Ryan FM, Song ZL, Wanberg CR, Kinicki AJ. Psychological and physical well-being during unemployment: A meta-analytic study. J Appl Psychol (2005) 90(1):53–76. doi: 10.1037/0021-9010.90.1.53

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Henkel D. Unemployment and substance use: a review of the literature, (1990-2010). Curr Drug Abuse Rev (2011) 4(1):4–27. doi: 10.2174/1874473711104010004

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Dom G, Samochowiec J, Evans-Lacko S, Wahlbeck K, Van Hal G, McDaid D. The Impact of the 2008 Economic Crisis on Substance Use Patterns in the Countries of the European Union. Int J Environ Res Public Health (2016) 13(1):122. doi: 10.3390/ijerph13010122

CrossRef Full Text | Google Scholar

31. McLaughlin KA, Nandi A, Keyes KM, Uddin M, Aiello AE, Galea S, et al. Home foreclosure and risk of psychiatric morbidity during the recent financial crisis. Psychol Med (2012) 42(7):1441–8. doi: 10.1017/S0033291711002613

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Eliason M. Alcohol-related morbidity and mortality following involuntary job loss: evidence from Swedish register data. J Stud Alcohol Drugs (2014) 75(1):35–46. doi: 10.15288/jsad.2014.75.35

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Garcy AM, Vagero D. Unemployment and Suicide During and After a Deep Recession: A Longitudinal Study of 3.4 Million Swedish Men and Women. Am J Public Health (2013) 103(6):1031–8. doi: 10.2105/AJPH.2013.301210

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Stuckler D, Basu S, Suhrcke M, Coutts A, McKee M. The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis. Lancet (Lond Engl) (2009) 374(9686):315–23. doi: 10.1016/S0140-6736(09)61124-7

CrossRef Full Text | Google Scholar

35. Yang JC, Roman-Urrestarazu A, Brayne C. Binge alcohol and substance use across birth cohorts and the global financial crisis in the United States. PLoS One (2018) 13(6):e0199741. doi: 10.1371/journal.pone.0199741

PubMed Abstract | CrossRef Full Text | Google Scholar

36. de Goeij MCM, Suhrcke M, Toffolutti V, van de Mheen D, Schoenmakers TM, Kunst AE. How economic crises affect alcohol consumption and alcohol-related health problems: A realist systematic review. Soc Sci Med (2015) 131:131–46. doi: 10.1016/j.socscimed.2015.02.025

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Stuckler D, Basu S, Suhrcke M, McKee M. The health implications of financial crisis: a review of the evidence. Ulster Med J (2009) 78(3):142–5.

PubMed Abstract | Google Scholar

38. Karanikolos M, Mladovsky P, Cylus J, Thomson S, Basu S, Stuckler D, et al. Financial crisis, austerity, and health in Europe. Lancet (2013) 381(9874):1323–31. doi: 10.1016/S0140-6736(13)60102-6

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Watkins J, Wulaningsih W, Da Zhou C, Marshall DC, Sylianteng GDC, Dela Rosa PG, et al. Effects of health and social care spending constraints on mortality in England: a time trend analysis. BMJ Open (2017) 7(11):e017722. doi: 10.1136/bmjopen-2017-017722

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Van Hal G. The true cost of the economic crisis on psychological well-being: a review. Psychol Res Behav Manag (2015) 8:17–25. doi: 10.2147/PRBM.S44732

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Martin-Carrasco M, Evans-Lacko S, Dom G, Christodoulou NG, Samochowiec J, Gonzalez-Fraile E, et al. EPA guidance on mental health and economic crises in Europe. Eur Arch Psychiatry Clin Neurosci (2016) 266(2):89–124. doi: 10.1007/s00406-016-0681-x

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Collins AB, Boyd J, Cooper HLF, McNeil R. The intersectional risk environment of people who use drugs. Soc Sci Med (2019) 234:112384. doi: 10.1016/j.socscimed.2019.112384

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Rhodes T, Ball A, Stimson GV, Kobyshcha Y, Fitch C, Pokrovsky V, et al. HIV infection associated with drug injecting in the Newly Independent States, eastern Europe: the social and economic context of epidemics. Addiction (1999) 94(9):1323–36. doi: 10.1046/j.1360-0443.1999.94913235.x

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Ifanti AA, Argyriou AA, Kalofonou FH, Kalofonos HP. Financial crisis and austerity measures in Greece: their impact on health promotion policies and public health care. Health Policy (2013) 113(1-2):8–12. doi: 10.1016/j.healthpol.2013.05.017

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Economou M, Madianos M, Peppou LE, Patelakis A, Stefanis CN. Major depression in the era of economic crisis: a replication of a cross-sectional study across Greece. J Affect Disord (2013) 145(3):308–14. doi: 10.1016/j.jad.2012.08.008

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Madianos MG, Alexiou T, Patelakis A, Economou M. Suicide, unemployment and other socioeconomic factors: evidence from the economic crisis in Greece. Eur J Psychiat (2014) 28(1):39–49. doi: 10.4321/s0213-61632014000100004

CrossRef Full Text | Google Scholar

47. Antonakakis N, Collins A. The impact of fiscal austerity on suicide: On the empirics of a modern Greek tragedy. Soc Sci Med (2014) 112:39–50. doi: 10.1016/j.socscimed.2014.04.019

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Kondilis E, Giannakopoulos S, Gavana M, Ierodiakonou I, Waitzkin H, Benos A. Economic crisis, restrictive policies, and the population’s health and health care: the Greek case. Am J Public Health (2013) 103(6):973–9. doi: 10.2105/AJPH.2012.301126

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Paraskevis D, Nikolopoulos G, Fotiou A, Tsiara C, Paraskeva D, Sypsa V, et al. Economic recession and emergence of an HIV-1 outbreak among drug injectors in Athens metropolitan area: a longitudinal study. PLoS One (2013) 8(11):e78941. doi: 10.1371/journal.pone.0078941

PubMed Abstract | CrossRef Full Text | Google Scholar

50. DATAJOURNALISTIK. Så påverkar corona möjligheten att opereras. Stockholm, Sweden: SVT Nyheter (2020). Available from: https://www.svt.se/datajournalistik/corona-uteblivna-operationer/.

Google Scholar

51. Simou E, Koutsogeorgou E. Effects of the economic crisis on health and healthcare in Greece in the literature from 2009 to 2013: a systematic review. Health Policy (2014) 115(2-3):111–9. doi: 10.1016/j.healthpol.2014.02.002

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Barry CL, McGinty EE, Pescosolido BA, Goldman HH. Stigma, discrimination, treatment effectiveness, and policy: public views about drug addiction and mental illness. Psychiatr Serv (2014) 65(10):1269–72. doi: 10.1176/appi.ps.201400140

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Tempalski B, Friedman R, Keem M, Cooper H, Friedman SR. NIMBY localism and national inequitable exclusion alliances: The case of syringe exchange programs in the United States. Geoforum (2007) 38(6):1250–63. doi: 10.1016/j.geoforum.2007.03.012

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Beletsky L, Davis CS. Today’s fentanyl crisis: Prohibition’s Iron Law, revisited. Int J Drug Policy (2017) 46:156–9. doi: 10.1016/j.drugpo.2017.05.050

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Brener L, Von Hippel W, Kippax S, Preacher KJ. The role of physician and nurse attitudes in the health care of injecting drug users. Subst Use Misuse (2010) 45(7-8):1007–18. doi: 10.3109/10826081003659543

PubMed Abstract | CrossRef Full Text | Google Scholar

56. von Hippel W, Brener L, von Hippel C. Implicit prejudice toward injecting drug users predicts intentions to change jobs among drug and alcohol nurses. Psychol Sci (2008) 19(1):7–11. doi: 10.1111/j.1467-9280.2008.02037.x

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Natan MB, Beyil V, Neta O. Nurses’ perception of the quality of care they provide to hospitalized drug addicts: testing the theory of reasoned action. Int J Nurs Pract (2009) 15(6):566–73. doi: 10.1111/j.1440-172X.2009.01799.x

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Simon R, Snow R, Wakeman S. Understanding why patients with substance use disorders leave the hospital against medical advice: A qualitative study. Subst Abuse (2019) 1–7. doi: 10.1080/08897077.2019.1671942

CrossRef Full Text | Google Scholar

59. EMCDDA, Europol. EU Drug Markets: Impact of COVID-19. Publications Office of the European Union, Luxembourg: European Monitoring Centre for Drugs and Drug Addiction and Europol (2020).

Google Scholar

60. Winstock AR, Davies EL, Gilchrist G, Zhuparris A, Ferris JA, Maier LJ, et al. Global Drug Survey special edition on COVID-19 global interim report. London: Global Drug Survey (GDS) (2020).

Google Scholar

61. Arkes J. Does the economy affect teenage substance use? Health Econ (2007) 16(1):19–36. doi: 10.1002/hec.1132

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Peterfi A, Tarjan A, Horvath GC, Csesztregi T, Nyirady A. Changes in patterns of injecting drug use in Hungary: a shift to synthetic cathinones. Drug Test Anal (2014) 6(7-8):825–31. doi: 10.1002/dta.1625

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Alexandrescu L. Streets of the ‘spice zombies’: Dependence and poverty stigma in times of austerity. Crime Media Cult (2020) 16(1):97–113. doi: 10.1177/1741659019835274

CrossRef Full Text | Google Scholar

64. Zuccato E, Castiglioni S, Tettamanti M, Olandese R, Bagnati R, Melis M, et al. Changes in illicit drug consumption patterns in 2009 detected by wastewater analysis. Drug Alcohol Depend (2011) 118(2-3):464–9. doi: 10.1016/j.drugalcdep.2011.05.007

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Kim J, Ng SH, Kim J. Psychological Trauma of Rapid Social Transformations: Korea’s Economic Crisis and Hong Kong after the Reunification. Hist Soc Res / Historische Sozialforschung (2010) 35(2(2 (132):120–50.

Google Scholar

66. Kristensen P, Weisath L, Heir T. Bereavement and mental health after sudden and violent losses: a review. Psychiatry (2012) 75(1):76–97. doi: 10.1521/psyc.2012.75.1.76

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Pilling J, Thege BK, Demetrovics Z, Kopp MS. Alcohol use in the first three years of bereavement: a national representative survey. Subst Abuse Treat Prev Policy (2012) 7:3. doi: 10.1186/1747-597X-7-3

PubMed Abstract | CrossRef Full Text | Google Scholar

68. Masferrer L, Garre-Olmo J, Caparros B. Is there any relationship between drug users’ bereavement and substance consumption. Heroin Addict Related Clin Problems (2015) 17(6):23–30.

Google Scholar

69. Graham K, Zeidman A, Flowers MC, Saunders SJ, White-Campbell M. A Typology of Elderly Persons with Alcohol Problems. Alcohol Treat Q (1993) 9(3-4):79–95. doi: 10.1300/J020v09n03_05

CrossRef Full Text | Google Scholar

70. Courtin E, Knapp M. Social isolation, loneliness and health in old age: a scoping review. Health Soc Care Community (2017) 25(3):799–812. doi: 10.1111/hsc.12311

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Kim S, Spilman SL, Liao DH, Sacco P, Moore AA. Social networks and alcohol use among older adults: a comparison with middle-aged adults. Aging Ment Health (2018) 22(4):550–7. doi: 10.1080/13607863.2016.1268095

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Kuerbis A, Sacco P. The impact of retirement on the drinking patterns of older adults: a review. Addictive Behav (2012) 37(5):587–95. doi: 10.1016/j.addbeh.2012.01.022

CrossRef Full Text | Google Scholar

73. Kuerbis A, Treloar Padovano H, Shao S, Houser J, Muench FJ, Morgenstern J. Comparing daily drivers of problem drinking among older and younger adults: An electronic daily diary study using smartphones. Drug Alcohol Depend (2018) 183:240–6. doi: 10.1016/j.drugalcdep.2017.11.012

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Stahl ST, Schulz R. Changes in routine health behaviors following late-life bereavement: a systematic review. J Behav Med (2014) 37(4):736–55. doi: 10.1007/s10865-013-9524-7

PubMed Abstract | CrossRef Full Text | Google Scholar

75. Sarkar S, Parmar A, Chatterjee B. Substance use disorders in the elderly: A review. J Geriatr Ment Health (2015) 2(2):74–82. doi: 10.4103/2348-9995.174271

CrossRef Full Text | Google Scholar

76. Stahl ST, Beach SR, Musa D, Schulz R. Living alone and depression: the modifying role of the perceived neighborhood environment. Aging Ment Health (2017) 21(10):1065–71. doi: 10.1080/13607863.2016.1191060

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Esteve A, Reher DS, Treviño R, Zueras P, Turu A. Living Alone over the Life Course: Cross-National Variations on an Emerging Issue. Popul Dev Rev (2020) 46(1):169–89. doi: 10.1111/padr.12311

CrossRef Full Text | Google Scholar

78. Ghesquiere AR, Park M, Bogner HR, Greenberg RL, Bruce ML. The effect of recent bereavement on outcomes in a primary care depression intervention study. Am J Geriatr Psychiatry (2014) 22(12):1555–64. doi: 10.1016/j.jagp.2013.12.005

PubMed Abstract | CrossRef Full Text | Google Scholar

79. WHO, UNODC. International standards for the treatment of drug use disorders: revised edition incorporating results of field-testing; License: CC BY-NC-SA 3.0 IGO. Geneva: World Health Organization and United Nations Office on Drugs and Crime (2020).

Google Scholar

80. Starace F, Ferrara M. COVID-19 disease emergency operational instructions for Mental Health Departments issued by the Italian Society of Epidemiological Psychiatry. Epidemiol Psychiatr Sci (2020) 29:e116. doi: 10.1017/S2045796020000372

PubMed Abstract | CrossRef Full Text | Google Scholar

81. Farhoudian A, Baldacchino A, Clark N, Gerra G, Ekhtiari H, Dom G, et al. COVID-19 and Substance Use Disorders: Recommendations to a Comprehensive Healthcare Response. An International Society of Addiction Medicine (ISAM) Practice and Policy Interest Group Position Paper. Basic Clin Neurosci (2020) 11(2):133–46. doi: 10.32598/bcn.11.covid19.1

CrossRef Full Text | Google Scholar

82. Priest KC. The COVID-19 Pandemic: Practice and Policy Considerations For Patients With Opioid Use Disorder. Health Affairs Blog (2020). doi: 10.1377/hblog20200331.557887

CrossRef Full Text | Google Scholar

83. Morgenstern H. Ecologic Studies in Epidemiology - Concepts, Principles, and Methods. Annu Rev Publ Health (1995) 16:61–81. doi: 10.1146/annurev.pu.16.050195.000425

CrossRef Full Text | Google Scholar

84. Uutela A. Economic crisis and mental health. Curr Opin Psychiatry (2010) 23(2):127–30. doi: 10.1097/YCO.0b013e328336657d

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Claussen B. Alcohol disorders and re-employment in a 5-year follow-up of long-term unemployed. Addict (Abingdon Engl) (1999) 94(1):133–8. doi: 10.1046/j.1360-0443.1999.94113310.x

CrossRef Full Text | Google Scholar

86. Wahlbeck K, McDaid D. Actions to alleviate the mental health impact of the economic crisis. World Psychiatry (2012) 11(3):139–45. doi: 10.1002/j.2051-5545.2012.tb00114.x

PubMed Abstract | CrossRef Full Text | Google Scholar

87. McCarty D, Gu YF, McIlveen JW, Lind BK. Medicaid expansion and treatment for opioid use disorders in Oregon: an interrupted time-series analysis. Addict Sci Clin Prac (2019) 14(1):31. doi: 10.1186/s13722-019-0160-6

CrossRef Full Text | Google Scholar

88. Cantor J, Tobey R, Giron N, Kirui T. Medicaid Managed Care Plans Have An Opportunity To Play A Key Role In Recovery. Health Affairs Blog (2020). doi: 10.1377/hblog20200626.418552/full

CrossRef Full Text | Google Scholar

89. Bador K, Kerekes N. Evaluation of an Integrated Intensive Cognitive Behavioral Therapy Treatment Within Addiction Care. J Behav Health Serv Res (2020) 47(1):102–12. doi: 10.1007/s11414-019-09657-5

PubMed Abstract | CrossRef Full Text | Google Scholar

90. Lundgren L, Krull I. Screening, assessment, and treatment of substance use disorders: Evidence-based practices, community and organizational setting in the era of integrated care. New York, NY: Oxford University Press (2018).

Google Scholar

91. Wilkey C, Lundgren L, Amodeo M. Addiction Training in Social Work Schools: A Nationwide Analysis. J Soc Work Pract Addict (2013) 13(2):192–210. doi: 10.1080/1533256X.2013.785872

CrossRef Full Text | Google Scholar

92. Muvvala SB, Schwartz ML, Petrakis I, O’Connor PG, Tetrault JM. Stitching a solution to the addiction epidemic: A longitudinal addiction curricular thread across four years of medical training. Subst Abuse (2020) 1–5. doi: 10.1080/08897077.2019.1709606

CrossRef Full Text | Google Scholar

93. Linn AJ, Vervloet M, van Dijk L, Smit EG, Van Weert JCM. Effects of eHealth interventions on medication adherence: a systematic review of the literature. J Med Internet Res (2011) 13(4):e103. doi: 10.2196/jmir.1738

PubMed Abstract | CrossRef Full Text | Google Scholar

94. Ray PP. ed. Home Health Hub Internet of Things (H3IoT): An architectural framework for monitoring health of elderly people. 2014 International Conference on Science Engineering and Management Research (ICSEMR). IEEE (2014).

Google Scholar

95. Toh X, Tan H, Liang H, Tan H. eds. Elderly medication adherence monitoring with the Internet of Things. 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). IEEE (2016)

Google Scholar

96. de Bruin M, Hospers HJ, van Breukelen GJP, Kok G, Koevoets WM, Prins JM. Electronic monitoring-based counseling to enhance adherence among HIV-infected patients: a randomized controlled trial. Health Psychol (2010) 29(4):421–8. doi: 10.1037/a0020335

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Liu S, Yang L, Zhang C, Xiang Y-T, Liu Z, Hu S, et al. Online mental health services in China during the COVID-19 outbreak. Lancet Psychiatry (2020) 7(4):e17–e8. doi: 10.1016/S2215-0366(20)30077-8

PubMed Abstract | CrossRef Full Text | Google Scholar

98. Dellifraine JL, Dansky KH. Home-based telehealth: a review and meta-analysis. J Telemed Telecare (2008) 14(2):62–6. doi: 10.1258/jtt.2007.070709

PubMed Abstract | CrossRef Full Text | Google Scholar

99. Norman S. The use of telemedicine in psychiatry. J Psychiatr Ment Health Nurs (2006) 13(6):771–7. doi: 10.1111/j.1365-2850.2006.01033.x

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Eibl JK, Gauthier G, Pellegrini D, Daiter J, Varenbut M, Hogenbirk JC, et al. The effectiveness of telemedicine-delivered opioid agonist therapy in a supervised clinical setting. Drug Alcohol Depend (2017) 176:133–8. doi: 10.1016/j.drugalcdep.2017.01.048

PubMed Abstract | CrossRef Full Text | Google Scholar

101. Gellis ZD, Kenaley B, McGinty J, Bardelli E, Davitt J, Ten Have T. Outcomes of a telehealth intervention for homebound older adults with heart or chronic respiratory failure: a randomized controlled trial. Gerontologist (2012) 52(4):541–52. doi: 10.1093/geront/gnr134

PubMed Abstract | CrossRef Full Text | Google Scholar

102. Chan SSC, So WKW, Wong DCN, Lee ACK, Tiwari A. Improving older adults’ knowledge and practice of preventive measures through a telephone health education during the SARS epidemic in Hong Kong: a pilot study. Int J Nurs Stud (2007) 44(7):1120–7. doi: 10.1016/j.ijnurstu.2006.04.019

PubMed Abstract | CrossRef Full Text | Google Scholar

103. Huskamp HA, Busch AB, Souza J, Uscher-Pines L, Rose S, Wilcock A, et al. How Is Telemedicine Being Used In Opioid And Other Substance Use Disorder Treatment? Health Aff (Millwood) (2018) 37(12):1940–7. doi: 10.1377/hlthaff.2018.05134

PubMed Abstract | CrossRef Full Text | Google Scholar

104. Yang Y, Li W, Zhang Q, Zhang L, Cheung T, Xiang Y-T. Mental health services for older adults in China during the COVID-19 outbreak. Lancet Psychiatry (2020) 7(4):e19. doi: 10.1016/S2215-0366(20)30079-1

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Kazemi DM, Borsari B, Levine MJ, Li S, Lamberson KA, Matta LA. A Systematic Review of the mHealth Interventions to Prevent Alcohol and Substance Abuse. J Health Commun (2017) 22(5):413–32. doi: 10.1080/10810730.2017.1303556

PubMed Abstract | CrossRef Full Text | Google Scholar

106. Li J, Ray P. eds. Applications of E-Health for pandemic management. The 12th IEEE International Conference on e-Health Networking, Applications and Services. IEEE (2010).

Google Scholar

107. Li J, Moore N, Akter S, Bleisten S, Ray P. eds. mHealth for Influenza Pandemic Surveillance in Developing Countries. 2010 43rd Hawaii International Conference on System Sciences. IEEE (2010).

Google Scholar

108. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med (2018) 178(11):1544–7. doi: 10.1001/jamainternmed.2018.3763

PubMed Abstract | CrossRef Full Text | Google Scholar

109. Chen IY, Szolovits P, Ghassemi M. Can AI help reduce disparities in general medical and mental health care? AMA J ethics (2019) 21(2):167–79. doi: 10.1001/amajethics.2019.167

CrossRef Full Text | Google Scholar

110. Connor JP, Symons M, Feeney GFX, Young RM, Wiles J. The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment. Subst Use Misuse (2007) 42(14):2193–206. doi: 10.1080/10826080701658125

PubMed Abstract | CrossRef Full Text | Google Scholar

111. Gowin JL, Ball TM, Wittmann M, Tapert SF, Paulus MP. Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse (vol 152, pg 93, 2015). Drug Alcohol Depend (2017) 175:255. doi: 10.1016/j.drugalcdep.2017.03.003

PubMed Abstract | CrossRef Full Text | Google Scholar

112. Ahn WY, Vassileva J. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug Alcohol Depend (2016) 161:247–57. doi: 10.1016/j.drugalcdep.2016.02.008

PubMed Abstract | CrossRef Full Text | Google Scholar

113. Weinstein L, Radano TA, Jack T, Kalina P, Eberhardt JS,3. Application of multivariate probabilistic (Bayesian) networks to substance use disorder risk stratification and cost estimation. Perspect Health Inf Manag (2009) 6(Fall):1b–b.

PubMed Abstract | Google Scholar

114. Buckland DM, Cummings M, Mark DB, Banerjee AG, Snyder K, Starks MA. Design Considerations for UAV-Delivered Opioid Overdose Interventions. Aerosp Conf Proc (2019) 1–7. doi: 10.1109/AERO.2019.8741937

CrossRef Full Text | Google Scholar

115. Lin CA, Shah K, Mauntel LCC, Shah SA. Drone delivery of medications: Review of the landscape and legal considerations. Am J Health-Syst Ph (2018) 75(3):153–8. doi: 10.2146/ajhp170196

CrossRef Full Text | Google Scholar

116. Scalea JR, Restaino S, Scassero M, Blankenship G, Bartlett ST, Wereley N. An Initial Investigation of Unmanned Aircraft Systems (UAS) and Real-Time Organ Status Measurement for Transporting Human Organs. IEEE J Trans Eng Health Med (2018) 6:1–7. doi: 10.1109/JTEHM.2018.2875704

CrossRef Full Text | Google Scholar

117. Koh HK, Cadigan RO. Disaster Preparedness and Social Capital. In: Kawachi I, Subramanian SV, Kim D, editors. Social Capital and Health. New York, NY: Springer New York (2008). p. 273–85.

Google Scholar

118. Putnam RD. Bowling alone: The collapse and revival of American community. New York: Simon and schuster (2000).

Google Scholar

119. Frank C, Davis CG, Elgar FJ. Financial strain, social capital, and perceived health during economic recession: a longitudinal survey in rural Canada. Anxiety Stress Coping (2014) 27(4):422–38. doi: 10.1080/10615806.2013.864389

PubMed Abstract | CrossRef Full Text | Google Scholar

120. Villalonga-Olives E, Kawachi I. The dark side of social capital: A systematic review of the negative health effects of social capital. Soc Sci Med (2017) 194:105–27. doi: 10.1016/j.socscimed.2017.10.020

PubMed Abstract | CrossRef Full Text | Google Scholar

121. Eriksson M, Dahlgren L, Emmelin M. Understanding the role of social capital for health promotion beyond Putnam: A qualitative case study from northern Sweden. Soc Theory Health (2009) 7(4):318–38. doi: 10.1057/sth.2009.6

CrossRef Full Text | Google Scholar

122. Sypsa V, Psichogiou M, Paraskevis D, Nikolopoulos G, Tsiara C, Paraskeva D, et al. Rapid Decline in HIV Incidence Among Persons Who Inject Drugs During a Fast-Track Combination Prevention Program After an HIV Outbreak in Athens. J Infect Dis (2017) 215(10):1496–505. doi: 10.1093/infdis/jix100

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: substance use disorder (SUD), COVID-19, addiction care, integrated care, social capital, pandemic, evidence-based policies and practices, risk environment

Citation: Jemberie WB, Stewart Williams J, Eriksson M, Grönlund A-S, Ng N, Blom Nilsson M, Padyab M, Priest KC, Sandlund M, Snellman F, McCarty D and Lundgren LM (2020) Substance Use Disorders and COVID-19: Multi-Faceted Problems Which Require Multi-Pronged Solutions. Front. Psychiatry 11:714. doi: 10.3389/fpsyt.2020.00714

Received: 19 May 2020; Accepted: 07 July 2020;
Published: 21 July 2020.

Edited by:

Giuseppe Bersani, Sapienza University of Rome, Italy

Reviewed by:

Ruben David Baler, National Institutes of Health (NIH), United States
Domenico De Berardis, Azienda Usl Teramo, Italy

Copyright © 2020 Jemberie, Stewart Williams, Eriksson, Grönlund, Ng, Blom Nilsson, Padyab, Priest, Sandlund, Snellman, McCarty and Lundgren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wossenseged Birhane Jemberie, wossenseged.jemberie@umu.se

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.