It is notable that we found that those who used marijuana more frequently prior to 2018 reported greater increases in use from 2018 onward. On one hand this is encouraging in that it suggests that lighter and non-users of marijuana were not necessarily encouraged to use as a result of legalization. On the other hand, it appears that those who were already more regular users may have tended to increase consumption, potentially increasing vulnerability to the risks associated with marijuana use. In contrast to previous studies , we found participants who endorsed greater frequency of marijuana use had greater frequency of use of tobacco products. Following legalization this was particularly true for e-cigarettes. The specific mechanism for this association is uncertain, but there are multiple possibilities. First, it may be that relaxing restrictions on a specific substance reduces substance-specific concerns about harm , which then generalizes to other drugs. Alternatively, the association can be explained by use of products that deliver both drugs at the same time , or newer vaporizing devices that may do so separately. It is plausible that innovations in nicotine vaping devices encourages marijuana vaping, promoting diversified marijuana product use and synergistically increasing use of both products. This is consistent with the strengthening association between marijuana and e-cigarette use frequencies post legalization. The possibility that lessening marijuana barriers increases tobacco use is concerning given evidence that co-use is associated with psychosocial distress , health problems , nicotine dependence , and tobacco cessation failure . The present study has several limitations. It is a secondary analysis of a naturalistic study of young adult tobacco users, which limited the specificity of marijuana-related measures and may have yielded a sample with disproportionately frequent marijuana use. There is a strong need for additional studies that include outcomes beyond simply quantity,growers equipment frequency or prevalence of use . The design may limit generalizability to other young adult samples.

Another limitation is reliance on self-reported substance use data, though evidence suggests self-report tends to be accurate in observational studies, given the lack of strong demand characteristics . Additionally, self-reported data include only some days during 2015–2019 and may not be representative of use during the entirety of this period. Finally, while the study captured self-reported use of marijuana and nicotine/tobacco products before and after legalized sales of recreational marijuana began in California, we did not directly evaluate access to marijuana retail outlets or other methods of product acquisition. In examining marijuana use before and after legalization of recreational sales in California, we found that frequency of use did not change significantly overall, including following legalization. We also found that increases in marijuana frequency tended to coincide with increased tobacco use, and a specific post-legalization association with e-cigarette use. Finally, we found that the most frequent users of marijuana after legalization were those who had used most often prior to 2018. Findings suggest loosening of marijuana restrictions could lead to negative health consequences for young adults. Strengths of the study include the sample size, and the repeated evaluation of a cohort of young adults before and after legalization. Further research is needed to confirm these findings, to understand how risks associated with changes in marijuana policy can be attenuated, and to identify surveillance targets. The continuously evolving marijuana and tobacco landscape also indicates the importance of ongoing evaluation of co-use. Correlation coefficients between environmental and biomarker measurements are widely used in environmental health assessments and epidemiology to explain the exposure associations between environmental media and human body burdens. As a result considerable attention and effort have been given to interpretation of these coefficients. However, there is limited information available on how the variance in environmental measurements, the relative contribution of exposure sources, and the elimination half-life affect the reliability of the resulting correlation coefficients.

To address this information gap, we conducted a simulation study for various exposure scenarios of home-based exposure to explore the impacts of pathway-specific scales of exposure variability on the resulting correlation coefficients between environmental and biomarker measurements. Bio-monitoring data, including those from blood, urine, hair, etc., have been used extensively to identify and quantify human exposures to environmental and occupational contaminants. However, because the measured levels in biologic samples result from multiple sources, exposure routes, and environmental media, the levels mostly fail to reveal how the exposures are linked to the source or route of exposure . Thus, comparison of biologic samples with measurements from a single environmental medium results in weak correlations and lacks statistically significance. In addition, cross-sectional biological sample sets that track a single marker have large population variability and do not capture longitudinal variability, especially for compounds with relatively short biologic half-lives, which can be on the order of days such as pesticides and phthalates. Therefore, in the case where the day-to-day variability of biological sample measurements is large, the use of biomarker samples with a low number of biological measurements in epidemiologic studies as a dependent variable can result in a misclassification of exposure as well as questions of reliability. For chemicals frequently found at higher levels in indoor residential environments than in outdoor environments, it is common to assume that major contributions to cumulative intake are home-based exposure and/or food ingestion. This simplification can be further justified because people generally spend more than 70 percent of their time indoors. Compounds with significant indoor sources and long half-lives in the human body– on the order of years for chemicals such as polybrominateddiphenyl ethers –have been found to have positive associations between indoor dust or air concentrations and serum concentrations in U.S. populations. On the other hand, extant research has not reported significant associations between indoor samples and biomarkers for chemicals primarily associated with food-based exposures, for example, bisphenol-A and perfluorinated compounds.

For chemicals with both home and food-based exposure pathways and short body half-lives , as is the case for many pesticides, a significant association between indoor samples and biomarkers is found less frequently or relatively weak compared to PBDEs. To better interpret these types of findings, we provide here a simulation study for various exposure scenarios to explore the role of the chemical properties and exposure conditions that are likely to give rise to a significant contribution from indoor exposures. We then assess for these situations the magnitude and variance of the associated correlation coefficients between biomarker and indoor levels. The objectives of this study are to generate simulated correlation coefficients between environmental measurements and biomarkers with different contributions of home-based exposure to total exposure and different day-to-day and population variability of intake from both residential environments and food, to interpret the contribution of home-based exposure to human body burden for two hypothetical compounds whose half-lives are on the order of days and years, and to determine how the pattern of variability in exposure attributes impacts the resulting correlation coefficients linking biomarker levels to exposure media concentrations.Because some indoor contaminants are considered potential threats to human health, many studies have applied significant resources to examine the relationship between exposure to indoor pollutants and adverse health effects. However, these studies are potentially limited by the use of a single or a few environmental and biological samples. The significant implications of this situation are reflected in our results. Multi-day, multi-person sample analyses are costly and labor-intensive. In addition, the resulting R2 values from these studies are not interpreted or poorly interpreted in terms of variability and contribution of exposure sources and the biological half-life of a compound. In this regard, the simulation study in this paper provides an important step towards interpreting the relative contribution of home-based exposure to human body burden for two compounds whose biological half-lives are significantly different . Although these two compounds do not cover the full range of chemical substances, bracketing half lives allows us to quantify the significance of source, measurement,plant benches and exposure pattern variability for disaggregating body burden. In particular, it shows that exposure variability and different contributions of exposure sources are more interconnected than commonly considered in many experimental studies. The work also brings to attention the need to understand the impact of a chemical half-life on the relationship between environmental exposures and bio-monitoring data. The sensitivity of day-to-day variability of wipe concentrations and food exposures on the resulting R2 values also points to the importance of understanding variability and contribution of exposure sources. Finally, future work includes computing the relative number of samples needed for various levels of confidence to disaggregate body burden for various types of compounds , environments, and exposure pathways. Despite the lack of experimental data, the simulated results provide key insights on the role of the variability and contribution of exposure sources and biological half-lives in quantifying a relationship between indoor exposure and human body burden. This approach will be useful for designing future exposure and epidemiologic studies that includes indoor environmental samples and bio-monitoring data.In 1996, California became the first state to legalize medical marijuana. Known officially as the Compassionate Use Act, Proposition 215 allowed patients and caregivers to cultivate and possess marijuana for medical use. The campaign in favor of Proposition 215 focused on the benefits for seriously ill patients. Claiming that the Proposition “sends our children the false message that marijuana is safe and healthy,” the campaign against the Proposition focused on anti-drug education .

Neither side addressed potential public health consequences. If Proposition 215 led to an increase in marijuana use, for example, might it also lead to higher rates of all injury deaths , including deaths from assault , deaths from motor vehicle crashes , and—the subject of the present study—deaths from suicide ? Such consequences assume that medical and recreational users are similar. With one exception, the evidence supports this assumption. Since most California medical users were introduced to marijuana as recreational users, for example, it is reasonable to assume that the user-types have similar socioeconomic backgrounds . Compared with recreational users, however, California’s medical users were more likely to report early health problems or disabilities that would warrant medical use . Although Proposition 215 was drafted so loosely that it effectively legalized all uses of marijuana , marijuana use by California juveniles, who were not eligible for medical marijuana certificates, did not increase following Proposition 215 . Nevertheless, at the national level, during a 15-year period when a majority of states loosened their control of medical marijuana, the U.S. suicide rate rose by 24 percent , prompting many to question how legalization and suicide might be linked. The systematic evidence connecting this trend to the availability of medical marijuana is ambiguous, however. Rylander, Valdez, and Nussbaum , for example, find no correlation between a state’s suicide rate and the number of medical marijuana cardholders in the state. Similarly, comparing suicide before and after a state enacts a medical marijuana law, Grucza et al. find no change in a state’s suicide rate. In contrast, Anderson, Rees, and Sabia report a 10.8 percent reduction in suicides averaged across all medical marijuana states. Attributing a suicide trend to the availability of medical marijuana raises questions about the potential mechanisms at play. What theoretical mechanisms could lead us to expect a relationship between the availability of medical marijuana and suicide? Could these mechanisms be more salient for certain types of suicides than others? If the expected relationship is observed, what methodological rules could be used to support a causal interpretation of the relationship? We address these questions in order. responds to changes in opportunity. Holding opportunity constant, risk responds to changes in motivation. Chew and McCleary use motivation/ opportunity mechanisms to explain life course changes in suicide. Kubrin and Wadsworth use motivation/opportunity mechanisms to explain the effects of socioeconomic factors and firearms availability on race-specific suicide. Wadsworth, Kubrin, and Herting use motivation/opportunity mechanisms to explain suicide trends for young Black males. Consistent with this literature, we argue that if medical marijuana affects suicide risk, it must do so through one or both pathways. Mental health theories operate through a motivation pathway. The psychiatric consensus is that suicide is related to depression, anxiety, and other treatable disorders . If marijuana alleviates the acute stress associated with these disorders, then we expect suicide risk to decrease following legalization of medical marijuana.