The dispensary provided electrically heated cannabis flower vaporizers and dab rigs for use.Smoking of cannabis and tobacco were not permitted.We monitored PM2.5 in the dispensary for 38 days and 16 hours.During business hours, the average PM2.5 concentration was 84 µg/m3,with a standard deviation of ± 124 µg/m3 , an inter quartile range of 16-111 µg/m3 and a median of 47 µg/m3.When the business was closed, the average PM2.5 concentration was 3 ± 7 µg/m3, the IQR of 1-4 µg/m3 and the median was 2 µg/m3.When examined in two-hour intervals, the median PM2.5concentration was highest between 5:00 and 7:00 PM, at 76 µg/m3.The average PM2.5 concentration outdoors was 6 ± 4 µg/m3 during business hours and 6 ± 5 µg/m3 when the business was closed.The dispensary gravimetric data yielded a photometer calibration factor of 0.57.Our data show a clear association between the consumption of cannabis and elevated PM2.5 concentrations in the dispensary.The average PM2.5 concentration when the business was open was 28 times higher than when the business was closed, the median concentration was 23.5 times higher and peak daily particle concentrations corresponded with the busiest hours.The PM2.5 concentrations in this cannabis dispensary are similar to those observed in indoor spaces where smoking is permitted.These findings are some of the first field measurements of PM2.5 emissions from cannabis flower vaporizers and dabbing of cannabis concentrates.In a space with similar ventilation and consumption activity, it is likely that dabbing and vaporizing would create lower PM2.5 concentrations than smoking, because smoking decomposes the cannabis more completely, creating more side stream smoke.Most of our data are from TSI Sidepak laser photometers, which are factory-calibrated to NIST standard A1 test dust.To deliver accurate measurements of any other aerosol, a specific calibration factor is required.As of this writing, there are no published calibration factors for aerosols created by vaporizing cannabis flower or dabbing cannabis grow set up concentrates and little is known of their properties.

The gravimetric data from the dispensary yielded a calibration factor of 0.57, but variation was high because there were only seven daylong samples.We therefore used the well-validated calibration factor for secondhand cigarette smoke to adjust our data.It is unlikely to yield inflated values and if the true calibration factor is higher, that does not affect our finding that on-site consumption was associated with strong and consistent increases in PM2.5.As of February, 2022, over 78 million Americans are known to have been infected with COVID-19, and over 925,000 have died . Many remain unvaccinated, and highly contagious variants of the virus have appeared since the start of the pandemic, so greater knowledge of risk factors for COVID-19 infection and poor outcomes remains critical. Demographic risk factors for COVID-19 infection include non-Hispanic Black or Hispanic race/ethnicity ; males may also be at higher risk of infection . Risk factors for hospitalization or death include older age , obesity , non-Hispanic Black or Hispanic race/ethnicity , male sex , and medical conditions such as diabetes, neoplasms, cardiovascular, respiratory and kidney disease . Substance use disorder is also a potential risk factor for COVID-19 infection and poor outcome. SUD may increase risk for infection due to homelessness, or to SUD-associated decision-making traits that could increase COVID-19 exposure through preference for in-person interactions over maintaining social distance. Among those infected, SUD could increase the risk for poor outcomes through effects on the immune, respiratory, or cardiovascular systems , indirect effects of medical conditions commonly co-occurring with SUD , or delays in seeking medical care . To study the relationship of SUDs and COVID-19 outcomes, large-scale electronic health record databases are needed to evaluate if SUD is associated with COVID-19 outcomes over and above the potentially confounding effects of other medical conditions that are associated with both SUD and COVID-19 outcomes. To our knowledge, five US studies have examined SUD and COVID- 19 in EHR databases . Two studies addressed infection risk. One found that SUD was associated with a positive COVID-19 test after adjustment for demographics, but did not further adjust for medical conditions. The other found an unadjusted association of SUD with COVID-19+, but not after adjustment for medical illnesses that could confound the relationship by increasing the likelihood of COVID-19 testing among those in treatment for other conditions. Four studies examined SUD and hospitalization among COVID-19+ patients . Across several studies, SUD was associated with hospitalization before and after adjustment for medical conditions. Three of these studies also found associations of SUD with aspects of intensive care, e.g., ICU admissions.

These studies also addressed mortality , as did a study of social, behavioral and substance use risk factors for mortality in COVID-19+ veterans . In the study without adjustment for medical conditions, SUD predicted increased COVID-19 mortality . Three studies found that SUD increased the unadjusted but not adjusted risk of COVID-19 mortality . In the study of veterans , substance use was inversely related to COVID-19 mortality, although the association was attenuated after adjusting for medical conditions . Therefore, findings were mixed. The range in SUD prevalence in these studies did not appear to account for their findings. The Veterans Health Administration is the largest U.S. integrated healthcare system, treating approximately 5.5 million veterans each year. EHR data are aggregated across the entire VHA system. Using a retrospective cohort design, we investigated relationships of SUD to COVID-19+ and COVID-19 outcomes among veterans treated at the VHA in 2019, including whether SUD was related to COVID-19+ up to 11/1/2020, and among the COVID-19+ sub-sample, whether SUD was related to hospitalization, ICU admission, and death. SDR COVID-19 diagnoses came from two sources. One was a record in the EHR of a VHA SARS-CoV-2 real-time reverse transcription polymerase chain reaction or antigen test. The other source was electronic chart notes. To provide COVID-19 testing as widely as possible to VHA-eligible veterans , the VHA covered the costs of COVID-19 rRT-PCR or antigen test conducted by outside providers. Coverage of community care generally requires advance VHA authorization, resulting in a chart note in the electronic medical record. “Natural language processing of these notes was used to detect positive COVID-19 tests conducted outside the VHA . Natural language processing involves computerized analysis of human language to extract information, for example, from text notations in electronic records. Note that natural language processing is a widely-used strategy in medical research . The SDR thus included positive and negative tests conducted at the VHA, and information about positive outside tests. To create a consistent time frame, an index date variable was created to indicate the date of the first positive COVID-19 test or inpatient admission date closest to the first test within the 15 days prior to the test. We included COVID-19 tests from 02/20/20–11/01/2020.Substance disorders examined separately included cannabis , cocaine , opioids , stimulants and sedatives . We also created an ‘any SUD’ variable by combining these, two additional categories too rare to examine separately , and ‘other’ SUD , used when the specific substance is unknown. ICD-10-CM codes indicating remission were excluded. For exploratory post hoc analyses of the mortality results, we created 2 additional SUD exposure variables. First, since SUD treatment could mitigate SUD effects on mortality through increased clinical monitoring and hospitalization at an early stage of COVID-19 illness that prevented later mortality , we created a 3-level SUD variable incorporating SUD treatment: no SUD ; any-SUD without SUD specialty treatment; and any-SUD with SUD specialty treatment. Treatment was indicated by any 2019 visits to a VHA SUD specialty care setting. Second, since SUD could affect mortality only in patients with severe SUD and having multiple poly substance problems is an indicator of severity , we created a 3-level variable indicating no SUD , 1 SUD, or ≥ 2 SUDs. ICD-10-CM medical conditions associated with poor COVID-19 prognosis included cardiovascular disease , respiratory disease , diabetes , HIV , neoplasms , and kidney disease . Obesity is not associated with SUD but is associated with COVID-19 mortality and is prevalent in VHA patients , so we included body mass index , calculated from height and weight measured at the date closest to the index date. We also included ICD-10-CM mental disorders, as these may be related to SUD and to COVID-19 infection or poor prognosis , including bipolar , depressive , psychotic , and post traumatic stress disorders . Mental disorders in remission were excluded. Heavy drinking can impair lung functioning and increase risk for respiratory disease ,grow rack systems while smoking elevates risk for poor COVID-19 outcomes . Because both are associated with SUD,we also controlled for alcohol and nicotine use disorders . All diagnoses were from the 2019 EHR. Demographic characteristics included sex , age and race/ethnicity . The ‘other’ category included American Indian/Alaskan Natives and patients with multiple race/ethnicities.

Multi-variable logistic regression modeled the association of 2019 any SUD diagnoses with 2020 COVID-19 outcomes, producing odds ratios , adjusted odds ratios and 95% confidence intervals . We also modeled the association between 2019 substance-specific disorders and 2020 COVID-19 outcomes. When analyzing the substance specific disorders, to account for multiple testing and potential increased risk of Type I error , we used an alpha level of 1%, calculating and reporting 99% confidence intervals. We evaluated any SUD and substance-specific disorders in separate models. We conducted analyses in 4 stages. The first was unadjusted for covariates, providing simple associations. The second adjusted for demographics . The third further adjusted for medical conditions . The fourth further adjusted for mental, alcohol and nicotine use disorders to identify the unique effect of these additional, fully-legal SUDs on COVID-19 outcomes. We also conducted 2 sets of post hoc exploratory analyses of mortality of mortality. In one, we replaced the binary any-SUD exposure variable with the 3-level variable incorporating information on SUD treatment. In the other, we replaced the binary any-SUD exposure variable with the 3-level SUD severity variable. These models were run using the same four adjustment stages described above. In over 5.5 million veterans treated by the VHA in 2019, we investigated the relationship of substance use disorders to testing positive for COVID-19, and among those positive, the association of SUD with hospitalization, ICU admission, and mortality. Analyses were conducted without adjustment, and with adjustment for demographic characteristics and medical conditions that increase the risk of poor COVID-19 outcome.Among COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to an ICU, and 5.84% died. SUDs were robustly associated with increased odds of hospitalization regardless of adjustments. After adjustment, no SUDs were associated with ICU admission. In contrast, in unadjusted results, any SUD and cannabis, cocaine, and stimulant disorders were inversely associated with mortality. However, associations became attenuated after adjustment and were no longer significant. Previous studies of SUD and COVID-19 infection yielded conflicting results: one study without adjustment for medical conditions found a strong positive association , while another study with such adjustments found no association . While our results varied somewhat depending on the adjustments made in each model, in the final fully-adjusted models, all substance-specific SUDs were positively associated with COVID-19+ except cannabis use disorder. In studies of this relationship using EHR data, results may depend on factors influencing whether a COVID-19 test is conducted. VHA patients with SUDs other than cannabis use disorder may have been more likely to be tested because many of them were in SUD treatment, potentially providing closer monitoring of their medical status than others. Such monitoring and testing could help detect disease early and prevent further spread, but it complicates interpretation of findings on risk factors for infection. Since 2020, the VHA has greatly expanded its COVID-19 testing capacity . Future studies should re-examine the relationship of SUD to infection using data from a broader sector of the VHA patient population. Among VHA patients who tested positive for COVID-19, any SUD and all substance-specific SUDs were robustly associated with inpatient hospitalization before and after adjustments. These findings are consistent with several other studies . Taken as a whole, SUD appears to be associated with more severe COVID-19 requiring hospitalization even accounting for patients’ co-existing medical and psychiatric conditions. This is also consistent with our findings on ICU admission and with other studies showing that SUD is associated with greater intensity of treatment among those hospitalized .