Of gravest concern is the relationship of SUD with COVID-19 mortality. Our unadjusted results suggested an inverse relationship between SUD and mortality; however, adjustment attenuated all of these associations. Our findings contrast with the prior study showing a positive relationship of SUD to COVID-19 mortality that did not adjust for medical conditions; insufficient detail concerning this sample, design, and measures preclude full explanation of the discrepant results. Three other studies found a positive relationship of SUD with COVID-19 mortality before but not after adjustment for relevant medical conditions. Thus, results may vary depending on the adjustment strategy and population investigated. An additional study, also in veterans, found a protective effect of a non-specific substance use variable on COVID-19 mortality that was attenuated to the null after adjustment for medical conditions . The authors speculated that their results were due to the strong VHA patient social and behavioral support programs, and called for empirical examination of this possibility. We did so, comparing COVID-19 mortality among those with no SUD, untreated SUD, and treated SUD. After adjustment, untreated SUD was unrelated to the odds of mortality, while those with treated SUD had lower odds of mortality, a finding consistent with research showing that among those with SUD, being in treatment reduces mortality risk . In the present study, we therefore speculate that SUD treatment among those with SUD may have been protective against mortality due to greater contact with providers, leading to earlier identification and treatment of COVID-19. In addition to the contact with providers of substance disorder treatment, this speculation would be consistent with other studies showing that those with substance use disorders tend to be greater users of medical/healthcare services than others . Further interrogation of these results was not possible due to small cell sizes, but is warranted as more data become available. We also explored the relationship of SUD severity to mortality, finding that odds of mortality were not elevated if only one SUD was present, but appeared elevated among those with two or more SUDs,rolling flood tables although results were imprecise due to small cell sizes. While number of SUDs is not a direct SUD severity indicator, our results suggest that reported elevations in risk of mortality among those with SUD in other studies are driven by patients with severe SUDs. Future studies should examine this point when more data become available. Study limitations are noted.
Patients diagnosed with COVID-19 after 11/01/20 were not included in order to define a 60-day window for COVID-19 outcomes that occurred before the end of 2020. This may have limited the prevalence of COVID-19+ that was found in the VA , since the last two months of the year were omitted in the numerator but the entire patient population was included in the denominator. In addition, patients who were tested and found to be positive outside the VA but whose test results were never noted in the patient charts would have been missed in the VA dataset. The rate of COVID-19+ that we found was lower than the U.S. rate overall for 2020.This lower rate in VA patients may have been due to missed cases, or, alternatively, due to the fact that VA patients are largely older and have fully-integrated healthcare, and may therefore have been more receptive to the ample messages about COVID-19 mitigation strategies that were disseminated to all VA patients in 2020, helping them to minimize their infection rates. Another limitation is that using the retrospective cohort design, covariates were from 2019; future studies could incorporate diagnoses and care utilization up to the COVID-19 index date. Our analyses of COVID-19 infection did not incorporate information on external circumstances that may have affected infection rates, e.g., state policies and COVID-19 regulations, including preventive measures such as mask mandates. In addition, the VHA SDR did not record negative COVID-19 tests conducted outside the VHA, limiting complete knowledge about those tested and leaving open the possibility of misclassification. Some patients may have had SUDs unknown to providers and not noted in the EHR, or hospitalizations or ICU treatment outside the VHA not noted in the EHR. Environmental variables not included in our study should be examined in future studies. Finally, VHA patients do not represent all veterans or all US adults, limiting generalizability. In contrast, however, the study had several considerable strengths. These included the large sample size, transparent source of patient data, and electronic health records from a nationwide integrated healthcare system that provided a unique opportunity to investigate SUD and COVID-19 in a manner not possible in other studies, and to explore possible explanations of the reasons that SUD was not related to increased mortality risk in the VHA patients. We also provide information from what can be considered an index or reference period in the COVID-19 pandemic, namely, the period in which vaccines were not yet available and the Delta variant was starting to emerge. Future studies will need to incorporate information on vaccine status and subsequent pandemic periods defined by predominant virus strain when evaluating the relationship of SUD to the COVID-19 outcomes. In conclusion, data from over 5.5 million VHA patients suggest that having a substance use disorder increased the odds of a positive COVID- 19 test, and among those infected, inpatient hospitalization. However, SUD was not associated with COVID-19 mortality, perhaps due to the high proportion of patients with SUD who received SUD treatment and hence were likely to have relatively regular contact with providers.
The VHA strongly supports providing evidence-based SUD care to patients who need it, in contrast to the fragmented SUD treatment in much of the rest of the US healthcare system. In an integrated healthcare system with adequate access to SUD treatment, an unanticipated benefit may be closer monitoring of patients’ medical status, ensuring that when patients need it, they receive medical treatment and ultimately survive serious illnesses. One of the more controversial questions in drug policy today is whether the trend toward legalizing marijuana for medicinal and adult recreational use could increase illicit marijuana use among young people . Since 1996, 28 U.S. states and the District of Columbia have legalized the production and sale of marijuana for medicinal use , and eight have legalized marijuana for adult recreational use. Medical marijuana laws could potentially increase the availability of marijuana and reduce perceptions of its harmfulness, leading more young people to try it. But if these restrictions are not carefully enforced, young people could gain increased access to marijuana through the diversion of medical marijuana into illegal markets, which could also lower its price . Marijuana use by young people is associated with lasting detrimental changes in cognitive functioning of the developing brain, and poor educational and occupational outcomes . Use increases the risk of unintentional injuries and auto fatalities, mood and psychotic disorders, and drug dependence, especially when use is initiated at a young age . Long term marijuana smokers have a disproportionate burden of upper respiratory illnesses, including chronic bronchitis and some cancers, and an increased risk of cardiovascular disease . Medical marijuana producers and retailers are promoting new, more potent products such as oils often used as inhalants with tetrahydrocannabinol concentrations ranging from 40 to 70 percent . They are also developing new products that appeal to youth, such as packaged edibles and candies, that may increase the hazard of overdose due to their relatively slow rates of absorption and perceived intoxication . These products could increase the risk of overdose in young people, who tend to be less experienced users with low tolerance levels. Studies have reported little evidence that medical marijuana laws increase marijuana use among young people , although well controlled studies consistently report increased consumption in adults . Using the Youth Risk Behavior Survey, researchers have compared high school students’ consumption before and after medical marijuana laws were enacted, finding no evidence of rising consumption on a national basis .
A national study of 12–17 year old found that medical marijuana laws had no causal impact on consumption , as did a carefully controlled national study of 13–18 year olds . Wen et al. reported a five percent increase in the likelihood of trying marijuana among 12–20 year olds who dwell in states with medical marijuana laws, but the study was limited by the need to pool such a broad range of ages. Prior studies have ignored or been unable to detect age related variation in the impact of medical marijuana laws by pooling children aged 12–17, or even 12–24, or by studying particular age groups in isolation. Age-related variation is important to capture because young peoples’ access to marijuana and their developmental susceptibility to drug related harms differs by age . Most studies have focused on high school students who are likely to have greater access to marijuana and are more susceptible to social pressures than early adolescents . Meanwhile,flood and drain tray young adults different substantially from these younger groups, both in terms of development and access to drugs, being in the peak years of engagement with psychoactive substances during the lifespan . We performed multi-level, serial cross-sectional analyses on 10 annual waves of the U.S. National Survey on Drug Use and Health , from 2004 to 2013. Unlike many prior studies, ours included the key years of 2010–2013—a period of rapid acceleration in the number of states implementing medical marijuana laws , but before state recreational marijuana laws began implementation. In addition, our analyses compared young people across developmentally distinct age groups to account for important age-related heterogeneity in access to marijuana, in the propensity to experiment with psychoactive substances, and in the potential harms of marijuana use.The primary data source was ten annual waves of the NSDUH from 2004 to 2013. Following security clearance and a data use agreement with the U.S. Substance Abuse and Mental Health Services Administration, our team obtained access to individual-level NSDUH data that included the state of residence for each respondent. Each wave of the survey represents the U.S. population in all 50 states and the District of Columbia. During the period studied, no major changes in sampling, data collection, or instruments were made, thus preserving comparability across survey years. Full details of the data collection protocols, informed consent, and the questions asked are available in U.S. Substance Abuse and Mental Health Services Administration methodology reports . This project received an ethics review and was approved by the University of California at San Francisco’s Committee on Human Research. The total sample, pooled over 10 years, includes approximately 450,300 individuals. We stratified young people into three discrete age groups: early adolescents , late adolescents , and young adults . Table 1 provides an overview of sample characteristics. All participant data was provided by the U.S. Substance Abuse and Mental Health Services Administration and is not based upon primary collection of clinical study or patient data requiring individual consent.We examined three dichotomous outcomes at the individual level: self-reports of the accessibility of marijuana, consumption of marijuana within the past month, and initiation or first-time use of marijuana during the past year. The NSDUH framing of the marijuana questions references smoking, edibles, and oils. Individual-level, age-appropriate predictors from the NSDUH dataset were included in the analysis. Across all three age groups, these included sex, race/ethnicity, family income, poor or fair health, and living in an urban area. We included an indicator of poor or fair health status to control for the possibility that participants in medical marijuana states might engage in the legal use of marijuana for health reasons. For early and late adolescents, we also controlled for parental monitoring and participation in group fights, variables that could be indicators of the protective factor of parental involvement and the risk factor of delinquent behavior, respectively. For young adults, additional controls included employment, college attendance, parental status, and marital status. These are strong protective factors mitigating against drug use in this age group . We augmented the NSDUH data with annually updated state-level data on medical marijuana laws and other relevant control variables. For state-level controls, we drew on publicly available sources such as Polidata , including per capita drug courts and whether or not marijuana possession had been decriminalized. We considered a wider range of state-level controls representing demographic, political and religious factors, and aspects of state drug control policies. For the sake of parsimony, we included controls that were most associated with outcome variables.