By Christopher Shea
From the people who brought you the study that found that legalizing medical marijuana was associated with fewer deaths on the highways — possibly because people substitute marijuana for drinking — comes new contentious research involving pot. Its title is “High on Life? Medical Marijuana Laws and Suicide,” and this is the gist:
Using state-level data for the period 1990 through 2007, we estimate the effect of legalizing medical marijuana on suicide rates. Our results suggest that the passage of a medical marijuana law is associated with an almost 5 percent reduction in the total suicide rate, an 11 percent reduction in the suicide rate of 20- through 29-year-old males, and a 9 percent reduction in the suicide rate of 30- through 39-year-old males. Estimates of the relationship between legalization and female suicides are less precise and are sensitive to functional form.
It’s an IZA Discussion Paper, from January. http://www.iza.org/en/webcontent/publications/papers/viewAbstract?dp_id=6280
D I S C U S S I O N P A P E R S E R I E S Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor High on Life? Medical Marijuana Laws and Suicide IZA DP No. 6280 January 2012 D. Mark Anderson Daniel I. Rees Joseph J. Sabia High on Life? Medical Marijuana Laws and Suicide D. Mark Anderson Montana State University Daniel I. Rees University of Colorado Denver and IZA Joseph J. Sabia San Diego State University Discussion Paper No. 6280 January 2012 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: email@example.com Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. IZA Discussion Paper No. 6280 January 2012 ABSTRACT High on Life? Medical Marijuana Laws and Suicide Using state-level data for the period 1990 through 2007, we estimate the effect of legalizing medical marijuana on suicide rates. Our results suggest that the passage of a medical marijuana law is associated with an almost 5 percent reduction in the total suicide rate, an 11 percent reduction in the suicide rate of 20- through 29-year-old males, and a 9 percent reduction in the suicide rate of 30- through 39-year-old males. Estimates of the relationship between legalization and female suicides are less precise and are sensitive to functional form. JEL Classification: I10, I18 Keywords: medical marijuana laws, marijuana, alcohol, suicide Corresponding author: Daniel I. Rees University of Colorado Denver Department of Economics Campus Box 181 P.O. Box 173364 Denver, CO 80217-3364 USA E-mail: Daniel.Rees@ucdenver.edu 1 1. INTRODUCTION Under the Controlled Substances Act of 1970, marijuana was classified as a Schedule I drug because it was considered to have no “accepted medical use in treatment in the United States” (Eddy 2010). Since then, 16 states and the District of Columbia have legalized the use of medical marijuana; six more state legislatures have recently considered medical marijuana bills. The relationship between marijuana and mental health has received a great deal of attention from both proponents and opponents of legalization. Proponents argue that marijuana can be an effective treatment for bipolarism, depression, and other mood disorders (Rosenthal et al. 1996; Grinspoon and Bakalar 1997; Zimmerman 1999).1 They also argue that medical marijuana patients are able to reduce their use of pain killers, tranquilizers, and psychiatric medicines because of marijuana (Lucido 2004).2 Opponents, on the other hand, argue that marijuana use increases the likelihood of depression, anxiety, psychosis, and schizophrenia (Zammit et al. 2002; Henquet et al. 2004; Goldberg 2006; Shulman 2008). They also argue that the negative effects of marijuana are long-lasting and that users are at risk of suffering from decreased psychological well-being later in life (Green and Ritter 2000; McGee et al. 2000). This paper examines the relationship between medical marijuana laws (hereafter MMLs) and suicides. While the majority of people who suffer from mental illness do not commit suicide, over 90 percent of those who do commit suicide have a diagnosable mental or substance use disorder (Moscicki 2001). The relationship between marijuana 1 Tetrahydrocannabinol (THC), the primary psychoactive ingredient in marijuana, produces mild euphoria and has analgesic effects (Elphick and Egertova 2001). 2 Among some advocates, marijuana is referred to as “green Prozac” (Chaboya-Hembree 2011). 2 use and suicide-related outcomes (e.g., depression, suicidal ideation, and suicide attempts) has been studied extensively (Petronis et al. 1990; Felts et al. 1992; Borowsky et al. 2001), but there have been no previous attempts to estimate the effect of medical marijuana laws on completed suicides, the tenth leading cause of death in the United States (National Institute of Mental Health 2010). This study begins by detailing the channels through which MMLs could impact the risk of suicide. We cite research from epidemiological and medical studies showing that marijuana use is associated with depression and suicidal ideation, although we note that any observed association between marijuana use and outcomes such as these could be driven by difficult-to-observe factors (Van Ours and Williams 2011) or simply reflect self-medication (Gruber et al. 1996; Rey et al. 2002). We also note that previous studies have found evidence to support the hypothesis that marijuana and alcohol are substitutes (Chaloupka and Laixuthai 1997; Saffer and Chaloupka 1999; DiNardo and Lemieux 2001; Anderson et al. 2011; Crost and Guerrero 2011), and cite research linking alcohol policies and suicides (Birckmayer and Hemenway 1990; Jones et al. 1992; Markowitz et al. 2003; Carpenter 2004; Carpenter and Dobkin 2009). The empirical analysis draws on data from the Centers for Disease Control from 1990 through 2007 to estimate the effect of legalization on suicide rates. Our results suggest that the passage of a MML is associated with an almost 5 percent reduction in the total suicide rate. When we examine the relationship between legalization and suicides by gender and age, we find evidence that MMLs are associated with decreased suicides among 20- through 29-year-old males and among 30- through 39-year-old males. This result is 3 consistent with registry data from Arizona, Colorado, and Montana showing that most medical marijuana patients are male, and that roughly half are under the age of 40 (Anderson et al. 2011). Estimates of the relationship between legalization and suicides among females are less precise, and are sensitive to functional form. We conclude that the legalization of medical marijuana leads to an improvement in the psychological wellbeing of young adult males, an improvement that is reflected in fewer suicides. 2. BACKGROUND 2.1. MMLs and substance use MMLs remove criminal penalties for using, possessing, and cultivating marijuana for medicinal purposes. Because it is prohibitively expensive for the government to ensure that all marijuana ostensibly grown for the medicinal market ends up in the hands of registered patients, diversion to the illegal market likely occurs.3 Only a handful of MMLs permit individuals with mental health problems to obtain medical marijuana. California includes anxiety as a qualifying condition, while Delaware and New Mexico both allow the use of medical marijuana for post traumatic stress disorder (Marijuana Doctors 2011). However, without exception, MMLs allow patients to register based on medical conditions that cannot be objectively confirmed (e.g., chronic pain and nausea), and in some states it is possible to petition for access to medical marijuana based on ailments not included on the official list of qualifying conditions (Marijuana Doctors 2011). 3 All MMLs enacted during the period 1990-2007 allowed for home cultivation. Since 2007, Delaware, New Jersey and Washington D.C. have passed MMLs that do not permit home cultivation (Marijuana Policy Project 2011). 4 Surprisingly little is known about the relationship between MMLs and marijuana use. Gorman and Huber (2007) examined data on adult arrestees for the period 1995– 2002 from Denver, Los Angeles, Portland, San Diego and San Jose. They found little evidence that marijuana use by arrestees increased as a result of legalization. In contrast, Wall et al. (2011) found that rates of marijuana use among 12- through 17-year-olds were higher in states that had legalized medical marijuana than in states that had not, but noted that “in the years prior to MML passage, there was already a higher prevalence of use and lower perceptions of risk” in states that had legalized medical marijuana. Perhaps the best evidence on the relationship between MMLs and marijuana use comes from Anderson et al. (2011). These authors used a difference-in-differences estimation strategy and data from the National Survey on Drug Use and Health (NSDUH) from three states that legalized medical marijuana in the mid-2000s: Montana, Rhode Island, and Vermont. They found that marijuana use among 18- through 25-year-olds living in Montana increased by 19 percent as a result of legalization. Marijuana use among 18- through 25-year-olds living in Rhode Island increased by 21 percent, and marijuana use by Rhode Island residents ages 26 and older increased by 44 percent. However, there was no evidence that legalization in Vermont led to an increase in consumption.4 Anderson et al. (2011) also examined the relationship between MMLs and alcohol consumption. Using data from the Fatal Accident Report System (FARS) for the period 1990-2009, they found that legalization of medical marijuana led to a sharp reduction in 4 Vermont passed a MML in 2004, but did not allow for medical marijuana dispensaries until 2011. This likely explains Anderson et al.’s (2011) null finding for Vermont. 5 fatal crashes involving alcohol. In addition, using data from the Behavioral Risk Factor Surveillance System (BRFSS) for the same period, they found evidence that legalization led to a 25 percent reduction in self-reported alcohol consumption among 20- through 29- year-olds; using data from the Beer Institute, they found that legalization led to a 5.3 percent decrease in per capita beer sales, the most popular alcoholic beverage among young adults (Jones 2008).5 Anderson et al. (2011) concluded that their results were “consistent with the hypothesis that marijuana and alcohol are substitutes” (p. 18).6 2.2. Stressful life events and suicide Hamermesh and Soss (1974) were the first economists to model suicidal behavior. According to their “economic theory of suicide”, negative shocks to happiness can reduce expected lifetime utility to the point where an individual will decide to take his or her own life. Even if a shock is perceived to be temporary, an individual with “timeinconsistent preferences” may commit suicide at the prospect of having to cope with an acutely “painful state in the present” (Cutler et al. 2001, p. 235).7 In fact, there is evidence that suicide among adolescents and young adults is often triggered by “stressful life events”, or SLEs. SLEs include the breakup of a romantic 5 Estimates from Anderson et al. (2011) on the relationship between MMLs and alcohol consumption are reproduced in Appendix Tables 2 and 3. 6 Anderson et al. (2011) noted that a consensus has not been reached with regard to the relationship between marijuana and alcohol consumption. A number of studies have found evidence of complementarity between marijuana and alcohol (Pacula 1998; Farrelly et al. 1999; Williams et al. 2004; Yörük and Yörük 2011), while others studies lend support to the hypothesis that marijuana and alcohol are substitutes (Chaloupka and Laixuthai 1997; Saffer and Chaloupka 1999; DiNardo and Lemieux 2001; Crost and Guerrero 2011). 7 There is evidence to support the hypothesis that preferences are time inconsistent (O’Donoghue and Rabin 2001, p. 41). Non-economists have exerted considerable effort to understand the rationality (or lack thereof) of suicide. See, for instance, Henry and Short (1954), Brandt (1975) and Graber (1981). 6 relationship (De Wilde et al. 1992; Brent et al. 1993; Beautrais et al. 1997; Johnson et al. 2002), conflict with a parent or sibling (Brent et al. 1993; Johnson et al. 2002), an abortion (Gissler, Hemminki, and Lonnqvist 1996; Gissler et al. 2005), and legal/disciplinary problems (Brent et al. 1993). Among older adults, problems at work, financial difficulties, unemployment and separation/divorce are common triggers of suicide (Heikkinen et al. 1994; Johansson and Sundquist 1997; Cavanagh et al. 1999; Preti and Miotto1999; Duberstein et al. 2004; Kõlves et al. 2006). Among the elderly, suicide is often associated with physical illness and functional impairment (Whitlock 1986; Mackenzie and Popkin 1987; Harris and Barraclough 1994; Conwell et al 2000; Waern et al. 2002). A number of researchers have concluded that the symptoms of depression (or self-reported depression) mediate the relationship between SLEs and suicidal behavior (Fergusson et al. 2000; Conwell et al. 2002; Hardt and Johnson 2010). 2.3. Marijuana use and mental health Could marijuana use help individuals cope with the negative shocks to happiness that, according to Hamermesh and Soss (1974), lead to suicide? Alternatively, could marijuana use magnify the impact of SLEs? Animal studies provide evidence that, at low doses, synthetic cannabinoid injections can have a potent anti-depressant effect (Jiang et al. 2005; Bambico et al. 7 2007). However, higher dosages can reduce serotonin transmission and lead to “depression-like behavior” (Bambico et al. 2007; Bambico et al. 2010).8 The majority of epidemiological studies have found that marijuana use is positively associated with both the symptoms of depression and suicidal ideation (Rey et al. 2002; Moore et al. 2007).9 However, any association between marijuana use and outcomes such as these could be driven by difficult-to-observe factors (Van Ours and Williams 2011) or simply reflect self-medication (Gruber et al. 1996; Rey et al. 2002). Although there have been attempts to account for these sources of endogeneity, none have been particularly convincing. Moore et al. (2007, p. 325) concluded their review of the literature by noting that “the majority of studies for affective outcomes [such as depression and suicidal ideation] did not adequately address the problem of reverse causation as a possible alternative explanation for any association observed.” 2.4. Alcohol use and mental health Alcohol consumption represents an alternative route through which legalization of medical marijuana could impact suicides. Numerous studies have shown that alcoholism is more common among individuals with major depression (Sullivan et al. 2004). 8 Deficiency of the neurotransmitter serotonin, a chemical produced by the brain, is linked to depression (Karg et al. 2011). 9 For instance, Bovasso (2001) found that marijuana use was positively associated with these outcomes among Baltimore residents who participated in the Epidemiologic Catchment Area Study; Fergusson et al. (2002) found similar results among teenagers and young adults in New Zealand. In contrast, Denson and Earleywine (2006) found a negative association between marijuana use and the symptoms of depression. Beautrais et al. (1999) found that “much of the association between cannabis abuse/dependence and suicide attempt risk arose because: (a) individuals who develop cannabis abuse/dependency tend to come from disadvantaged socio-demographic and childhood backgrounds…, or (b) because cannabis abuse/dependence is co-morbid with other mental disorders which are independently associated with suicidal behaviour.” Moore (2007) described the case of one woman who “without the relief that marijuana delivered to her…killed herself at home”. 8 Moreover, alcoholism is associated with suicidal ideation as well as attempted and completed suicides (Cornelious et al. 1995; Kessing 1999; Chatterji et al. 2004; Rodriguez Andres 2005). Policy studies provide further evidence of a link between alcohol use and suicide. For instance, Birckmayer and Hemenway (1990) and Carpenter and Dobkin (2009) provide evidence that prohibiting young adults from drinking protects against suicide. In a similar vein, Carpenter (2004) found that the adoption of stricter drunk driving laws lead to fewer suicides among young males. Markowitz et al. (2003) found a strong negative relationship between the excise tax on beer and male suicides. 10 Interestingly, neither Carpenter (2004) nor Markowitz et al. (2003) found consistent evidence that alcohol policies had an impact on female suicides. 3. SUICIDE, TRENDS AND DATA Data on suicides come from the National Center for Health Statistics Mortality Detail Files, produced by the Centers for Disease Control.11 Table 1 shows the number of suicides per 100,000 population by age and gender for the period under study, 1990- 2007. Females suffer from depression and attempt suicide at higher rates than males (Murphy 1998).12 Nevertheless, males are approximately four times more likely to 10 See also Son and Topyan (2011), who examined the relationship between alcoholic beverage excise taxes and suicides using data from the United States for the period 1995-2004. 11 As pointed out by Birckmayer and Hemenway (1999) and Carpenter (2004), suicides are likely underreported in these data. However, there is no reason to believe that this underreporting is systematically related to the legalization of medical marijuana. 12 Murphy (1998) argued that females are less likely to commit suicide than males because they are more likely to seek treatment for depression. 9 commit suicide. Since 1990, the male suicide rate has decreased slightly, while the female suicide rate has remained fairly constant (Figure 1). Fifteen- through 19-year-olds were less likely to commit suicide than older individuals during the period under study; males over the age of 59 were the most likely to commit suicide (Figures 2 and 3). Canetto (1992) argued that, as they age, men are especially prone to suicide because they have unrealistic expectations and an inadequate range of coping strategies. Clarke (1993, p. 21) hypothesized that “elderly persons who die by suicide have a lifelong character fault that remains invisible until aging lifechanges force the issue into the open.” Figures 4 through 14 illustrate suicide trends by legalization status.13 For states that legalized medical marijuana, we report the suicide rate for the three years prior to legalization, the year in which the law changed (year 0), and the three years following legalization. For states that did not legalize medical marijuana during the period under study, the year in which the law changed was randomly assigned. Figure 4 presents pre- and post-legalization trends in the total suicide rate. The total suicide rate falls smoothly during the pre-legalization period in both MML and non- MML states. However, beginning in year 0, the trends diverge: the suicide rate in MML states continues to fall, while the suicide rate in states that never legalized medical marijuana begins to climb gradually. Comparing Figures 5 and 6, it is clear that the postlegalization downward trend in male suicides is much more pronounced than the postlegalization downward trend in female suicides. 13 In each figure, a vertical line separates the years before legalization from the year of the law change and the post- legalization period. 10 Consistent with the hypothesis that marijuana can be an effective treatment for depression and other mood disorders, there appears to be a sharp decrease in the suicide rate of 15- through 19-year olds males in the treatment states as compared to the control states approximately two years after legalization (Figure 7). A similar decrease can be seen for 20- through 59-year-old males (Figures 8 and 9), and there is a modest decrease in the suicide rate of males 60 years of age and older approximately three years after legalization (Figure 10). Among females under the age of 40, there is little evidence that legalization lead to a decrease in the suicide rate (Figures 11 and 12). However, among older females, there appears to be a small decrease in the suicide rate two to three years after legalization (Figures 13 and 14). 4. THE EMPIRICAL MODEL The trends discussed above could be driven by a number of factors such as statelevel economic conditions or alcohol policies. In order to address this shortcoming, we use a difference-in-differences (DD) regression framework to estimate the relationship between MMLs and suicide rates. This framework allows us to control for both unobserved state-level heterogeneity and national trends. Our baseline estimating equation is: (1) ln(Suicides Totalst) = β0 + β1MMLst + Xstβ2 + vs + wt + εst, where s indexes states, t indexes years, and vs and wt represent state and year fixed effects, respectively. The variable MMLst is an indicator for whether medical marijuana 11 was legal in state s and year t. The coefficient of interest, β1, represents the effect of medical marijuana legislation. In alternative specifications we replace Suicides Totalst with the other dependent variables listed in Table 1 and add state-specific linear time trends to the right-hand side.14 The state-specific trends are intended to control for the influence of difficult-to-measure factors at the state level that evolve smoothly over time such as attitudes.15 The vector Xst includes indicators for whether Zero Tolerance drunk driving and 0.08 BAC laws were enforced in state s and year t, and the state beer tax. Previous studies provide evidence that stricter alcohol policies and higher beer taxes can reduce suicides (e.g., Jones et al. 1992; Birckmayer and Hemenway 1999; Markowitz et al. 2003; Carpenter 2004).16 In addition, the vector Xst includes the state unemployment rate and income per capita.17 Previous studies have shown that suicide rates are sensitive to economic conditions (Gerdtham and Johannesson 2003; Andres 2005; Minoiu and Rodriguez Andres 2008; Chung 2009). Finally, we control for whether marijuana use and possession was decriminalized.18 14 In other words, we include an interaction between the state dummies and a variable equal to 1 in 1990, 2 in 1991, 3 in 1993, and so forth. 15 DeCicca et al. (2008) found that attitudes toward cigarette smoking were an important determinant of youth smoking participation. 16 Data on the Zero Tolerance laws, 0.08 BAC laws, and beer taxes are from the National Highway Traffic Safety Administration, Freeman (2007), and the Beer Institute, respectively. Jones et al. (1992) and Birckmayer and Hemenway (1999) both found that higher minimum legal drinking ages (MLDA) were associated with fewer youth suicides. Carpenter and Dobkin (2009) used a regression discontinuity design to show that granting legal access to alcohol at age 21 leads to a large and immediate increase in suicides. But, because all states had a minimum legal drinking age of 21 by 1990, we do not control for the MLDA. 17 The unemployment and income data are from the Bureau of Labor Statistics and the Bureau of Economic Analysis, respectively. 18 During the period 1990-2007, the decriminalization indicator only captures one policy change: Nevada decriminalized marijuana in 2001. The majority of these laws were passed prior to 1990. 12 5. THE RESULTS 5.1. The baseline results Table 2 presents OLS estimates of the relationship between MMLs and suicides. All regressions are weighted by the relevant population in state s and year t, and the standard errors are corrected for clustering at the state level (Bertrand et al. 2004). The estimated relationship between MMLs and the total suicide rate is presented in the first column of Table 2. Controlling for year and state fixed effects, the legalization of medical marijuana is associated with an 8.1 percent decrease in the suicide rate (e-0.084 – 1 = -0.081). When the controls listed in Table 1 are included, legalization is associated with a 6.9 percent decrease in the suicide rate; when state-specific linear time trends are included, legalization is associated with a 4.8 percent decrease in the suicide rate. In the remaining columns of Table 2, we allow the estimated relationship between legalization and suicides to differ by gender. MMLs are associated with a 4.6 to 7.0 percent decrease in the male suicide rate. MMLs are negatively associated with the female suicide rate, but these estimates are not statistically significant at conventional levels. 5.2. Estimates by gender and age group Next, we examine the relationship between the legalization of medical marijuana and suicides by age group (Table 3). Our estimates suggest that legalization leads to a sharp reduction in suicides among young adult males. For instance, in the fully-specified model, legalization is associated with a 10.8 percent decrease in the suicide rate of 20- through 29-year-old males, and a 9.4 percent decrease in the suicide rate of 30- through 13 39-year-old males. These results are consistent with those of Anderson et al. (2011), who found that MMLs have the greatest impact on the substance use of young adults, and are also consistent with registry data from Arizona, Colorado, and Montana showing that most medical marijuana patients are male and that roughly half of medical marijuana patients are under the age of 40 (Anderson et al. 2011). Although the estimates of β1 are negative for males between the ages of 15 and 19, they are never significantly different from zero. Likewise, the estimates of β1 for males ages 40 through 59 are consistently negative, but not precisely estimated. For males 60 years of age and older, estimates are negative in two of the three specifications, but none are precisely estimated. Table 4 presents the results for females by age group. There is little evidence that legalization of medical marijuana impacted suicides among females under the age of 30. The estimated relationship between legalization and suicides is stronger among older females, although it is sensitive to model specification. Controlling for state and year fixed effects and the covariates listed in Table 1, the legalization of medical marijuana is associated with a 10.4 percent decrease in the suicide rate of 30- through 39-year-old females. However, the estimated impact of legalization becomes much smaller in magnitude and is no longer statistically significant when we add state-specific linear time trends. Although consistently negative, the estimated impact of legalization for females ages 40 through 49 is never statistically significant. However, when we control for statespecific linear time trends, legalization is associated with large and statistically 14 significant decreases in the suicide rate of 50-through 59-year-old females and the suicide rate of females ages 60 and older. 5.3. Adding leads and lags to the model In Table 5 we examine pre- and post-legalization trends in suicides using the regression framework introduced above. Specifically, we include three lead indicators, an indicator for the year of the law change, and four lag indictors. The omitted category is 3+ years before a MML was passed. The results are consistent with the hypothesis that MMLs have a causal impact on male suicide rates. Controlling for year and state fixed effects, the covariates listed in Table 1 and state-specific linear trends, none of the lead dummies are statistically distinguishable from zero. However, once a MML is passed, there is a strong negative relationship between legalization and the male suicide rate. In contrast, the evidence that legalization reduces suicides among females is much weaker. The estimated coefficients of the three lead indicators are large, negative, and statistically significant, suggesting that there was a systematic change in the suicidal behavior of females preceding legalization. Although the estimated coefficients of the lag indicators are two to three times larger than those of the lead indicators, we cannot reject the hypothesis that they are equal.19 5.4. A placebo test 19 As noted above, Carpenter (2004) found evidence that Zero Tolerance drunk driving laws were negatively associated with male suicides. He also found evidence of an implausibly large (and statistically significant) relationship between Zero Tolerance drunk driving laws and female suicides. 15 Lastly, we performed a robustness check based on Luallen (2006).20 Specifically, we generated a placebo MML indicator using a random number generator based on the continuous uniform distribution. Because 12 states passed a MML during our sample period, we assigned 12 placebo policies for each trail run.21 In total, 50 trials were run. Table 6 illustrates the average coefficient estimates for the placebo MML on total, male and female suicide rates. In all three sets of regressions, the average estimate is quite small in magnitude. Moreover, in 50 trials, only four estimates were negative and statistically significant at the 5 percent level for the total suicide rate. For male and female suicide rates, only three and four of the estimates were negative and statistically significant at the 5 percent level, respectively. This pattern of results provides evidence that our estimates cannot be readily reproduced through random assignment of legalization. 6. CONCLUSION Numerous epidemiological studies have found a positive association between marijuana and suicide-related outcomes such as depression and suicidal ideation. However, this association could be driven by difficult-to-observe factors or simply reflect self-medication. In fact, a recent review of the epidemiological literature concluded that most studies had not adequately addressed the problem of reverse causation (Moore et al. 2007). 20 Luallen (2006) examined the relationship between teacher strike days and juvenile crime. 21 A year of legalization between 1990 and 2007 was randomly selected. Randomly assigning a legalization year between 1999 and 2007 produced qualitatively similar results. 16 The current study avoids the problems of reverse causality and unobserved heterogeneity at the individual level by examining the relationship between the legalization of medical marijuana and completed suicides. Specifically, we use a difference-in-differences empirical strategy to estimate the impact of MMLs on statelevel suicide rates. Our results suggest that the legalization of medical marijuana is associated with a 5 percent decrease in the total suicide rate, an 11 percent decrease in the suicide rate of 20- through 29-year-old males, and a 9 percent decrease in the suicide rate of 30- through 39 year-old-males. Estimates for female suicide rates are generally measured with less precision and are sensitive to functional form. In an often-cited article, Hamermesh and Soss (1974) argued that negative shocks to happiness may reduce expected lifetime utility to the point where an individual will decide to take his or her own life. The negative relationship between legalization and suicides among young adult males is consistent with the argument that marijuana can be used to cope with such shocks. However, estimates provided by Anderson et al. (2011) provide an alternative explanation. These authors found that the passage of MMLs led to sharp decreases in alcohol-related traffic fatalities, self-reported alcohol use, and per capita beer sales. The strong association between alcohol consumption and suiciderelated outcomes found by previous researchers (Markowitz et al. 2003; Carpenter 2004; Sullivan et al. 2004; Rodriguez Andres 2005; Carpenter and Dobkin 2009) raises the possibility that medical marijuana laws reduce the risk of suicide by decreasing alcohol consumption. Although 16 states have passed medical marijuana laws, and others are considering legalization, very little is known about the effects of these laws. 17 Policymakers weighing the pros and cons of legalization should consider the possibility that medical marijuana laws may lead to fewer suicides among young adult males. However, the exact mechanism through which suicides are reduced remains a topic for future study. REFERENCES Anderson, D. Mark, Daniel I. 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National Suicide Rates by Age, Males Males 15 to 19 Males 20 to 39 Males 40 to 59 Males 60+ 0 1 2 3 4 5 6 7 8 9 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Mean Suicides per 100,000 Figure 3. National Suicide Rates by Age, Females Females 15 to 19 Females 20 to 39 Females 40 to 59 Females 60+ 12 13 14 15 16 17 18 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 4. Pre- and Post-Legalization Trends in Suicide Rates, Total MML Non-MML 22 23 24 25 26 27 28 29 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 5. Pre- and Post-Legalization Trends in Suicide Rates, Males MML Non-MML 4 4.5 5 5.5 6 6.5 7 7.5 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 6. Pre- and Post-Legalization Trends in Suicide Rates, Females MML Non-MML 8 10 12 14 16 18 20 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 7. Pre- and Post-Legalization Trends in Suicide Rates, Males 15 to 19 years-old MML Non-MML 19 20 21 22 23 24 25 26 27 28 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 8. Pre- and Post-Legalization Trends in Suicide Rates, Males 20 to 39 years-old MML Non-MML 23 23.5 24 24.5 25 25.5 26 26.5 27 27.5 28 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 9. Pre- and Post-Legalization Trends in Suicide Rates, Males 40 to 59 years-old MML Non-MML 25 27 29 31 33 35 37 39 41 43 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 10. Pre- and Post-Legalization Trends in Suicide Rates, Males 60+ years-old MML Non-MML 2 2.5 3 3.5 4 4.5 5 5.5 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 11. Pre- and Post-Legalization Trends in Suicide Rates, Females 15 to 19 years-old MML Non-MML 3 3.5 4 4.5 5 5.5 6 6.5 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 12. Pre- and Post-Legalization Trends in Suicide Rates, Females 20 to 39 years-old MML Non-MML 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 13. Pre- and Post-Legalization Trends in Suicide Rates, Females 40 to 59 years-old MML Non-MML 3 4 5 6 7 8 9 -3 -2 -1 Year of law change 1 2 3 Mean Suicides per 100,000 On the horizontal axis, 0 represents the year in which medical marijuana was legalized. It was randomly assigned to states that did not legalize medical marijuana during the period under study (1990-2007). Figure 14. Pre- and Post-Legalization Trends in Suicide Rates, Females 60+ years-old MML Non-MML Table 1. Weighted Means of Dependent and Independent Variables Dependent Variables Mean (SD) Suicide rates per 100,000 Suicides Total 14.3 (3.47) Suicides Male 23.7 (5.72) Ages 15-19 14.4 (6.13) Ages 20-29 22.9 (6.92) Ages 30-39 22.9 (6.27) Ages 40-49 24.0 (6.19) Ages 50-59 23.3 (6.10) Ages 60+ 30.3 (8.81) Suicides Female 5.51 (1.49) Ages 15-19 3.09 (1.82) Ages 20-29 4.13 (1.53) Ages 30-39 5.79 (2.00) Ages 40-49 7.35 (2.59) Ages 50-59 6.97 (2.43) Ages 60+ 4.89 (1.90) Independent Variables Medical Marijuana Law 0.111 (0.311) Marijuana Decriminalization Law 0.327 (0.469) Ln (State Income) 10.3 (0.153) Unemployment Rate 5.43 (1.36) Blood Alcohol Content 0.08 0.509 (0.491) Zero Tolerance Law 0.721 (0.440) Real Beer Tax (2000 dollars) 0.252 (0.214) N 918 Table 2. Estimates of Relationship between Medical Marijuana Laws and the Natural Log of the Suicide Rate Suicides Total Suicides Male Suicides Female Year/State FEs -0.084* (0.050) [0.94] -0.073* (0.043) [0.94] -0.106 (0.070) [0.82] Year/State FEs and Covariates -0.072** (0.036) [0.94] -0.065*** (0.031) [0.94] -0.080 (0.050) [0.83] Year/State FEs, Covariates, and State Trends -0.049* (0.025) [0.96] -0.047** (0.021) [0.96] -0.060 (0.042) [0.87] Observations 918 918 918 ***Statistically significant at the 1% level; **at the 5% level; *at the 1% level. Each cell represents the results from a separate regression. The dependent variable is equal to the natural log of the suicide rate per 100,000 population, and is based on information collected from the Centers for Disease Control for the period 1990-2007; the covariates are listed in Table 1. Regressions are weighted using the relevant state-by-sex populations. Standard errors, corrected for clustering at the state level, are in parentheses and R2s are in brackets. Table 3. Estimates of Relationship between Medical Marijuana Laws and the Natural Log of the Male Suicide Rate by Age Group 15-19 yr.-olds 20-29 yr.-olds 30-39 yr.-olds 40-49 yr.-olds 50-59 yr.-olds 60+ yr.-olds Year/State FEs -0.077 (0.077) [0.72] -0.096*** (0.025) [0.85] -0.147*** (0.048) [0.82] -0.129 (0.083) [0.78] -0.030 (0.062) [0.72] -0.008 (0.037) [0.89] Year/State FEs and Covariates -0.091 (0.080) [0.72] -0.096*** (0.022) [0.85] -0.129*** (0.032) [0.83] -0.113** (0.054) [0.80] -0.010 (0.045) [0.73] -0.010 (0.034) [0.90] Year/State FEs, Covariates, and State Trends -0.118 (0.114) [0.75] -0.114*** (0.077) [0.87] -0.099*** (0.037) [0.86] -0.060 (0.043) [0.84] -0.015 (0.041) [0.77] 0.036 (0.028) [0.91] Observations 918 918 918 918 918 918 ***Statistically significant at the 1% level; **at the 5% level; *at the 1% level. Each cell represents the results from a separate regression. The dependent variable is equal to the natural log of the suicide rate per 100,000 population, and is based on information collected from the Centers for Disease Control for the period 1990-2007; the covariates are listed in Table 1. Regressions are weighted using the relevant state-by-age populations. Standard errors, corrected for clustering at the state level, are in parentheses and R2s are in brackets. Table 4. Estimates of Relationship between Medical Marijuana Laws and the Natural Log of the Female Suicide Rate by Age Group 15-19 yr.-olds 20-29 yr.-olds 30-39 yr.-olds 40-49 yr.-olds 50-59 yr.-olds 60+ yr.-olds Year/State FEs -0.069 (0.159) [0.29] -0.062 (0.061) [0.40] -0.125** (0.059) [0.53] -0.120 (0.106) [0.64] -0.068 (0.103) [0.50] -0.082 (0.062) [0.63] Year/State FEs and Covariates -0.105 (0.165) [0.30] -0.044 (0.065) [0.40] -0.110** (0.054) [0.53] -0.078 (0.066) [0.65] -0.019 (0.072) [0.51] -0.082 (0.057) [0.63] Year/State FEs, Covariates, and State Trends 0.083 (0.235) [0.34] -0.008 (0.065) [0.45] -0.035 (0.088) [0.59] -0.041 (0.055) [0.71] -0.104* (0.060) [0.55] -0.121** (0.059) [0.65] Observations 918 918 918 918 918 918 ***Statistically significant at the 1% level; **at the 5% level; *at the 1% level. Each cell represents the results from a separate regression. The dependent variable is equal to the natural log of the suicide rate per 100,000 population, and is based on information collected from the Centers for Disease Control for the period 1990-2007; the covariates are listed in Table 1. Regressions are weighted using the relevant state-by-age populations. Standard errors, corrected for clustering at the state level, are in parentheses and R2s are in brackets. Table 5. Estimates of Relationship between Medical Marijuana Laws and the Natural Log of the Suicide Rate: Adding Leads and Lags to the Model Suicides Total Suicides Male Suicides Female 2-3 years before MML -0.020 (0.015) -0.006 (0.018) -0.066* (0.033) 1-2 years before MML -0.023 (0.021) -0.002 (0.010) -0.106*** (0.036) 0-1 year before MML -0.045** (0.017) -0.018 (0.019) -0.140** (0.053) Year of law change -0.085*** (0.029) -0.066** (0.022) -0.154** (0.058) 0-1 year after MML -0.063* (0.034) -0.043 (0.027) -0.138* (0.070) 1-2 years after MML -0.098*** (0.029) -0.080*** (0.021) -0.160** (0.062) 2-3 years after MML -0.154*** (0.035) -0.124*** (0.023) -0.257*** (0.094) 3+ years after MML -0.174*** (0.052) -0.129*** (0.032) -0.327** (0.124) Year FE yes yes yes State FE yes yes yes Covariates yes yes yes State Trends yes yes yes R2 0.97 0.96 0.87 Observations 918 918 918 ***Statistically significant at the 1% level; **at the 5% level; *at the 1% level. Each cell represents the results from a separate regression. The dependent variable is equal to the natural log of the suicide rate per 100,000 population, and is based on information collected from the Centers for Disease Control for the period 1990-2007; the covariates are listed in Table 1. Regressions are weighted using the relevant state-by-sex populations. Standard errors, corrected for clustering at the state level, are in parentheses and R2s are in brackets. Table 6: The Average Effect of Randomly Assigned MMLs on Suicide Rates Suicides Total Suicides Male Suicides Female Average MML estimate -0.002 -0.001 -0.009 (0.020) (0.017) (0.038) Number of trials 50 50 50 Number of negative MML 30 29 31 coefficients Number of MML estimates 4 3 4 that were negative and significant at 5% level Year FE Yes Yes Yes State FE Yes Yes Yes Covariates Yes Yes Yes State Trends Yes Yes Yes *Statistically significant at 10% level; ** at 5% level; *** at 1% level. Notes: Each column represents the results from a series of regressions. Average standard errors, corrected for clustering at the state-level, are in parentheses. Appendix Table 1. Medical Marijuana Laws, 1990-2007 Effective date Alaska March 4, 1999 California November 6, 1996 Colorado June 1, 2001 Hawaii December 28, 2000 Maine December 22, 1999 Montana November 2, 2004 Nevada October 1, 2001 New Mexico July 1, 2007 Oregon December 3, 1998 Rhode Island January 3, 2006 Vermont July 1, 2004 Washington November 3, 1998 Notes: Arizona, D.C., Delaware, Michigan, and New Jersey have passed medical marijuana laws since 2007. Source: Eddy (2010). Appendix Table 2. Alcohol Consumption and Medical Marijuana Laws: Summary of Results from Anderson et al. (2011) (1) (2) (3) (4) (5) (6) 18-19 20-29 30-39 40-49 50-59 60 + yr.-olds yr.-olds yr.-olds yr.-olds yr.-olds yr.-olds Number of Drinks (Males) MML -18.7% -21.6%** -2.53% -4.70% -3.74% -3.07% Observations 852 852 852 852 852 852 Number of Drinks (Females) MML 5.26% -21.0%** -10.3%* -12.6%** -5.43% -12.2% Observations 855 855 855 855 855 855 Year FE Yes Yes Yes Yes Yes Yes State FE Yes Yes Yes Yes Yes Yes Covariates Yes Yes Yes Yes Yes Yes State-specific trends Yes Yes Yes Yes Yes Yes *Statistically significant at 10% level; **at 5% level; ***at 1% level. Notes: Each column represents the results from a separate regression. The dependent variable is the mean number of drinks consumed in the past 30 days in state s and year t, and is based on information collected from the Behavioral Risk Factor Surveillance System (BRFSS) for the period 1990-2009. Controls include the state unemployment rate, per capita income, the relevant population size (e.g., the number of males or females in state s and year t), the state beer tax, and indicators for marijuana decriminalization, BAC 0.08, administrative license revocation, and Zero Tolerance. Regressions are weighted using sample sizes from the BRFSS. Standard errors, corrected for clustering at the state level, are in parentheses. Appendix Table 3. Alcohol Sales and Medical Marijuana Laws: Summary of Results from Anderson et al. (2011) (1) (2) (3) State-level per State-level per State-level per capita beer sales capita wine sales capita spirits sales (1990-2009) (1994-2009) (1994-2009) MML -5.3%*** -1.2% -0.2% Observations 1020 816 816 Year FE Yes Yes Yes State FE Yes Yes Yes State covariates Yes Yes Yes State-specific trends Yes Yes Yes *Statistically significant at 10% level; **at 5% level; ***at 1% level. Notes: Each column represents the results from a separate regression. The dependent variable is equal to the natural log of per capita sales (measured in gallons) and is based on data from the Brewers Almanac, published by the Beer Institute. Controls include the state unemployment rate, per capita income, the state beer tax, and indicators for marijuana decriminalization, BAC 0.08, administrative license revocation, and Zero Tolerance. Regressions are weighted using state populations. Standard errors, corrected for clustering at the state level, are in parentheses.
Yesterday we revealed The Top Ten “Reefer Madness” Stories of 2011. Today we continue our Year-End Retrospective with a look at the biggest news stories of scientific research into cannabis, public opinion polls on legalization, and statistical research on cannabis consumers. We call it The Top Ten Cannabis Science Stories of 2011. Tomorrow we’ll continue with The Top Ten “Stupid Stoner Stories” of 2011 and Friday we conclude with the The Top Ten People in Cannabis of 2011.
The Top Ten Cannabis Science Stories of 2011
Cannabis Karri reported on a study that measured just how much electricity we’re using to grow cannabis indoors.
A new report conducted and published by Even Mills, PhD, a respected and long time energy analyst along with Staff Scientists at the Lawrence Berkley National Laboratory has concluded that Americans spend an amazing 1% of the entire national electricity consumption, or the equivalent of the output of seven large power plants on growing cannabis.
Since medical marijuana use has become so much more popular, and most of those states do not have a dispensary program, many more people are learning to grow marijuana indoors. The 20 terawatt-hours per year that marijuana growers use is due to the bright, often 24 hours a day lighting and an air change rate 60 times higher than a norml home. Even a modest indoor garden can have the same energy consumption rate of an entire data center. Since indoor cultivation of cannabis is a necessity to hide operations from authorities and others the energy bill to growers is about $5 billion each year. That extra energy to produce American cannabis is equal to the energy consumption of an extra 2 million average US homes. It also, unfortunately, produces greenhouse gas pollution equal to 3 million cars according to the new research.
We have all suffered through jokes about cannabis consumers being fat, stupid couch potatoes. So it was a joy in 2011 when two international studies found us to be thinner than our non-toking counterparts…
“We found that cannabis users are less likely to be obese than non-users,” [researchers said]. “We were so surprised, we thought we had [made] a mistake. Or that our results were due to the sample we studied. So we turned to another completely independent sample and found exactly the same association.”
…and smarter, too!
A new British study finds … men with high childhood IQs were up to two times more likely to use illegal drugs than their lower-scoring counterparts. Girls with high IQs were up to three times more likely to use drugs as adults. A high IQ is defined as a score between 107 and 158. An average IQ is 100. The study appears in the Journal of Epidemiology and Community Health.
None of this means taking up pot smoking is going to shed points and boost IQ. It does mean that some popular stereotypes about us are completely unfounded.
Many a medical marijuana activist can tell anecdotes of patients who’ve reduced or eliminated their need for opiate pain killers by substituting cannabis. This year, Berkeley Patients Group surveyed their patients and found two-out-of-three had done just that.
In an anonymous survey, 66% of 350 clients at the Berkeley (Calif.) Patients Group, a medical marijuana dispensary, said that they use marijuana as a prescription drug substitute. Their reasons: Cannabis offered better symptom control with fewer side effects than did prescription drugs.
Those with pain symptoms said that marijuana has less addiction potential than do opioids. Others said marijuana helped to reduce the dose of other medications.
Speaking of the addiction potential of opioids…
Prohibitionists have been using the “Gateway Drug” scare for years to frighten the public about legalization. Despite every study blowing the concept out of the water, it still resonates with a large segment of the voters. So I decided to take a look at the data to find out which drug is really the one with the greatest correlation to hard drug use, and it definitely wasn’t cannabis!
We cross-referenced the NSDUH numbers based on whether someone had ever tried marijuana. We found that only 1.5% of people who have toked became monthly cocaine users. For ecstasy, crack, meth, heroin, LSD, and PCP, less than 1% of the people who’ve tried pot are using those drugs regularly. Meanwhile, 2.9% of the people who’ve ever tried an legal analgesic (pain reliever) are regular cocaine users. For ecstasy, crack, and meth, more than 1% of who tried analgesics are regular users. People who tried analgesics are more than twice as likely as people who tried pot to use heroin regularly and three times more likely to use LSD regularly.
But if opponents want to cling to the idea that we should do everything in our power to stop someone from smoking that first marijuana joint, lest they become illegal drug addicts, then it is time to prohibit Vicodin, Lortab, Lorcet, and Oxycontin, those powerful legal opioid pain killers. The first Vicodin/Lortab/Lorcet leads to almost three times the risk of becoming a non-pot illegal drug user than the first joint and almost the same risk as smoking a joint every month. That first Oxycontin is more than five times the risk for drug abuse than the first joint.
6. Drug testing is still unreliable, inaccurate, unnecessary,invasive, and counter-productive
We drug test our citizens when we suspect they’re committing a crime, when they’re applying for a job, when they’re going to school, and when they’re in an accident. Yet drug detection for marijuana is so unreliable and unscientific that its use is an affront to all free people.
First it is the “drug dog” that police and courts believe are akin to infallible scientific instruments instead of animals with instincts to please their human masters.
The accuracy of drug- and explosives-sniffing dogs is affected by human handlers’ beliefs, possibly in response to subtle, unintentional cues, UC Davis researchers have found.
The study, published in the January issue of the journal Animal Cognition, found that detection-dog teams erroneously “alerted,” or identified a scent, when there was no scent present more than 200 times — particularly when the handler believed that there was scent present.
Next it is the “drug lab” that may mishandle as many as one in ten tests.
An Indiana state lab wrongly reported 1 in 10 marijuana cases as positive, including some that were deliberately manipulated, an audit report indicated.
The audit’s findings showed errors in about 200 of 2,000 marijuana tests reported to law enforcement as having positive results, the Star said. This includes about 50 results the report said were consciously manipulated by lab workers.
Part of the justification for testing us for employment is workplace safety. Yet, in medical marijuana states where tens or hundreds of thousands of citizens are legally using cannabis, we’ve seen drastic declines in workplace danger.
Prior to the beginning of the medical marijuana program [in Oregon], workplace injuries and illnesses that contributed to a lost workday stood at 3.4 per 100 full-time workers; in 2009 that rate is 2.3 per 100, a decline of 32%. No-time-lost injuries and illnesses declined 40%, from 3.5 to 2.1 per 100. Fatalities are down from 3.3 to 1.9 per 100, a drop of 42%.
These declines occurred while the medical marijuana patient registry grew by an average of a little more than 50% per year.
Another egregious use of drug testing is to make it a requirement of citizens seeking welfare assistance. Florida’s law to do just that has been blocked while its (un-)constitutionality is determined, but in the time it was in effect, it cost Florida more than it saved. It also found that welfare recipients were less likely to turn up positive than the general public.
The Department of Central Florida’s (DCF) region tested 40 applicants and only two tested positive for drugs, officials said. One of the tests is being appealed.
DCF said it has been referring applicants to clinics where drug screenings cost between $30 and $35. The applicant pays for the test out of his or her own pocket and then the state reimburses him if they test comes back negative.
Therefore, the 38 applicants in the Central Florida area, who tested negative, were reimbursed at least $30 each and cost taxpayers $1,140.
Meanwhile, the state is saving less than $240 a month by refusing benefits to those two applicants who tested positive.
Finally, the unintended consequences of drug testing became more apparent. When marijuana is the drug that is the hardest to conceal on a drug test, people will turn to drugs that are easier to conceal.
As I looked at the data, I noticed that in the span from 2005 to 2011, the positive test rate for marijuana for all workplace drug tests (pre-employment, random, and post-accident) declined 20%, from 2.5% of approximately 2.4 million tests to 2.0%. That’s about 12,000 fewer cannabis consumers who were caught by a pee test.
Meanwhile, oxycodone positives have increased 96% for all urine testing, although these tests are administered about one tenth as often (280,000) for oxycodone as for cannabis (2,400,000). This despite the facts that while marijuana metabolites may be detected in urine for weeks, oxycodone metabolites are flushed from one’s system in two or three days. Furthermore, random positives for oxycodone (1.20%) are almost twice as great and post-accident positives for oxycodone (1.80%) are nearly three-times greater than pre-employment positives for oxycodone (0.65%), which suggests to me that the pre-employment screens don’t work very well at keeping oxycodone users out of the workplace.
5. For past two years, more Americans arrested for marijuana than all other drugs combined despite arrest protection for America’s One Million Legal Marijuana Users
When somebody mentions “The War on Drugs”, remind them what we’re really talking about is a “War on Marijuana”.
Nationally, there were 1,638,846 drug arrests reported to the FBI, with 52.1% of those arrests for marijuana charges. Last year, 51.6% of all drug arrests were for marijuana, showing a slight increase in marijuana as the majority of all drug arrests. The last time marijuana made up a majority of the “War on Drugs” was 1985, when 55.6% of all drug arrests were for marijuana.
Keep in mind that these annual marijuana arrests continue to climb even as we reduce the number of marijuana users eligible for arrest in the medical marijuana state, users who grow and use the most marijuana.
Between one to one-and-a-half million people are legally authorized by their state to use marijuana in the United States, according to data compiled by NORML from state medical marijuana registries and patient estimates. Assuming usage of one-half to one gram of cannabis medicine per day per patient and an average retail price of $320 per ounce, these legal consumers represent a $2.3 to $6.2 billion dollar market annually.
4. Drug Czar claims medical marijuana makes more young people smoke pot, despite fewer teens smoking pot
A popular refrain of the Drug Czar is that by calling marijuana “medicine”, we lead young people to think it is less dangerous, and therefore, use goes up.
“Emerging research reveals potential links between state laws permitting access to smoked medical marijuana and higher rates of marijuana use,” said Gil Kerlikowske, Director of National Drug Control Policy. “In light of what we know regarding the serious harm of illegal drug use, I urge every family – but particularly those in states targeted by pro-drug political campaigns – to redouble their efforts to shield young people from serious harm by educating them about the real health and safety consequences caused by illegal drug use.”
Except that medical marijuana’s been around on the West Coast for over a dozen years. Between 2003 and 2009, as more states have adopted medical marijuana, nationally the rate of monthly teen use is on the decline.
In fact, eleven of the thirteen states that had medical marijuana as of 2009 saw declines in teen marijuana use, and the five that added it after 2003 saw double-digit declines.
From 2003 to 2009 in California, monthly teen use is up only 0.26%. In Colorado, teen use is up 3.77% in that time frame. Yet Wyoming, a state without medical marijuana, saw the greatest increase of 5.18%. Furthermore, looking back before 2003, to 1996 and 1998 when the West Coast legalized medical marijuana, teen use is lower now than then.
3. The people really, really want to ask the President about the legalization of marijuana that half of them support
This year, the esteemed Gallup Poll finally recorded half of the US population in support of legalizing marijuana.
Gallup reports that the 50% nationwide support for legalization also represents the first time support has outweighed opposition. Only 46% of Americans believe marijuana should remain criminalized, with 4% undecided.
Support for marijuana legalization remains greatest in the Western states (55%) and majorities support legalization in the Midwest (54%) and East (51%). Only voters in the South still oppose marijuana legalization (44%). Men still support legalization at a much greater rate than women (55% vs. 46%).
Support is also greatest among younger Americans (62%), Democrats (57%), and liberals (69%). However, support for legalization has increased even in demographics generally opposed to legalization. Compared to Gallup’s poll last year, support increased 4% points in the South, 12% points in the Midwest, and 6% points among 50-64, but fell 1% among 65+. Support rose 6% points among Republicans, and 4% points among conservatives. Marijuana legalization is becoming more popular with just about everyone.
President Obama, seeking input from the people on policy questions, was stunned once again to find…
On the “We the People” petitions site of Whitehouse.gov, as of this writing, NORML’s “Regulate Marijuana Like Alcohol” petition is #1 by a long shot. It has garnered over 42,000 signatures. It needed 5,000 signatures in 30 days to generate an official response from the administration, a figure it had topped in just over three hours.
And when he asked for videos from citizens on policy issues, another stunning result…
The top question, submitted by Law Enforcement Against Prohibition, garnered 13,842 votes – over 1% of all votes cast (people could vote for more than one question).
As a police officer, I saw how waging the war on drugs has cost a trillion dollars and thousands of lives but does nothing to reduce drug use. Should we discuss legalizing marijuana and other drugs, which would eliminate the violent criminal market?
Of the 193,060 people who voted more than 7% voted for the LEAP question. That’s about one in fourteen people who took the time to Ask Obama.
2. National Cancer Institute drama over anti-tumoral effects of cannabis
A very high-profile battle over scientific integrity played itself out on the webpage of Cancer.gov, the government’s site for the National Cancer Institute. It began when the site surprisingly updated its summary page on cannabis and cannabinoids.
The potential benefits of medicinal Cannabis for people living with cancer include antiemetic effects, appetite stimulation, pain relief, and improved sleep. In the practice of integrative oncology, the health care provider may recommend medicinal Cannabis not only for symptom management but also for its possible direct antitumor effect.
Cannabinoids may cause antitumor effects by various mechanisms, including induction of cell death, inhibition of cell growth, and inhibition of tumor angiogenesis and metastasis. [9-11] Cannabinoids appear to kill tumor cells but do not affect their nontransformed counterparts and may even protect them from cell death. These compounds have been shown to induce apoptosis in glioma cells in culture and induce regression of glioma tumors in mice and rats.
Then it appeared that somebody pressured NCI to revise its update to better align with the government’s prohibition of cannabis. The paragraphs above were removed and replaced with:
The potential benefits of medicinal Cannabis for people living with cancer include antiemetic effects, appetite stimulation, pain relief, and improved sleep. Though no relevant surveys of practice patterns exist, it appears that physicians caring for cancer patients who prescribe medicinal Cannabis predominantly do so for symptom management.
Then NCI updated the “clinical studies” portion of the website to again highlight the anti-tumoral effects:
One study in mice and rats suggested that cannabinoids may have a protective effect against the development of certain types of tumors.
Cannabinoids appear to kill tumor cells but do not affect their nontransformed counterparts and may even protect them from cell death.
1. Colorado’s 5ng/mL per se DUID bill dies again as new research backs higher thresholds for regular users
We tackled drug testing above in #6, but this story takes #1 for showing how science and the scientific method can actually beat back prohibition. Colorado had proposed a 5ng of THC per milliliter of blood (5ng/mL) per se DUID, meaning: if you test positive on a drug test above 5ng/mL, you’re automatically guilty of DUI, whether you were impaired or not.
Naturally, many medical marijuana patients in Colorado complained that they are such frequent and heavy users of cannabis that they would never be under such a threshold. Furthermore, most of them have developed a tolerance to cannabis’ effects that allows them to drive under its influence without impairment, much as we understand an “until you know how [Pill X] affects you, do not drive or operate heavy machinery” warning on a pharmaceutical.
The “pot critic” of Denver’s WestWord, William Breathes, decided to become the experiment by abstaining from cannabis use under controlled conditions. After sixteen hours and a night’s sleep, upon awakening, presumably clean and sober, Breathes was tested at 13ng/mL. This anecdotal report, splashed all over the Denver media, was also backed up by the latest scientific research:
It concludes: “A threshold of 2-3ng/ml THC as an indicator of recent drug use (i.e, smoking within the previous 6 hours) as recommended by Huestis et al appears to be valid only for occasional users. Heavy users might exhibit measurable cannabinoid concentrations in blood, even if the last cannabis use was more than 24 hours ago…. Therefore, cannabinoid concentrations in heavy users’ blood from a later elimination phase might not be distinguished from an acute use of an occasional user.”
Cross-posted from The NORML Daily Stash Blog
November 1st, 2011 By: Paul Armentano, NORML Deputy Director
Alcohol consumption causes far greater harms to the individual user and to society than does the use of cannabis, according to a new review published online in the Journal of Psychopharmacology, the journal of the British Association of Psychopharmacology.
Investigators at the Imperial College of London assessed “the relative physical, psychological, and social harms of cannabis and alcohol.” Authors reported that cannabis inhalation, particularly long-term, contributes to some potential adverse health effects, including harms to the lungs, circulatory system, as well as the exacerbation of certain mental health risks. By contrast, authors described alcohol as “ a toxic substance” that is responsible for nearly five percent “of the total global disease burden.”
Researchers determined, “A direct comparison of alcohol and cannabis showed that alcohol was considered to be more than twice as harmful as cannabis to [individual] users, and five times more harmful as cannabis to others (society). … As there are few areas of harm that each drug can produce where cannabis scores more [dangerous to health] than alcohol, we suggest that even if there were no legal impediment to cannabis use, it would be unlikely to be more harmful than alcohol.”
They concluded, “The findings underline the need for a coherent, evidence-based drugs policy that enables individuals to make informed decisions about the consequences of their drug use.”
The researchers’ findings should hardly come as a revelation. Last week, a just-published study that was completely ignored by the mainstream media reported that alcohol consumption increased lung cancer risk by 30 percent.
Surprised? You shouldn’t be. After all, a February 2011 World Health Organization report concluded that alcohol consumption causes a staggering four percent of all deaths worldwide, more than AIDS, tuberculosis or violence. A just-published analysis in the American Journal of Preventive Medicine finds that in the United States alone, an estimated 79,000 lives are lost annually due to excessive drinking. The study further estimates that the overall economic cost of excessive drinking by Americans is $223.5 billion annually.
Naturally, any health costs related to cannabis use pale in comparison. A 2009 review published in the British Columbia Mental Health and Addictions Journal estimated that health-related costs per user are eight times higher for drinkers of alcoholic beverages than they are for those who use cannabis, and are more than 40 times higher for tobacco smokers. “In terms of [health-related] costs per user: tobacco-related health costs are over $800 per user, alcohol-related health costs are much lower at $165 per user, and cannabis-related health costs are the lowest at $20 per user,” investigators concluded.
In an op/ed I wrote last year entitled “Pot Versus Alcohol: Experts Say Booze Is the Bigger Danger,” I cited the findings of numerous independent commissions, all of which pronounced that the risks of marijuana were nominal compared to those associated with booze. You can read these findings here and much of this evidence is discussed in even greater detail in my book, Marijuana Is Safer: So Why Are We Driving People to Drink?
Nevertheless, despite its enormous societal toll, alcohol remains celebrated in this country — American Craft Beer Week is now endorsed by the U.S. Congress — while cannabis remains arbitrarily criminalized and demonized. It’s a situation illogical enough to drive most anyone to drink.
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