Hemp Growing

As expected, they found that illicit dealers were most often victimized and in response mobilized the law least often and retaliated most often. But unexpectedly, the fully licit cafe´ operators reported roughly double the instances of victimization as semi-licit coffee shop operators, and neither mobilized the law nor retaliated often. In the following discussion, I add a few points to the overview of these findings to suggest possible future research.That caf´es selling alcohol experienced more crime than coffee shops selling cannabis would not shock American police. Pharmacology matters, as Jacques et al. suggest, albeit always mediated by culture. At the macro level, there is a well-known correlation between drinking and crime, although it varies significantly across cultures , and different cultures have specific repertoires of intoxication .Culture matters, too, in the micro sense; the normative architecture of the settings of drinking or drug use interact with user expectation sets to affect behavior under the influence.Dutch caf´es, like American bars, tend to be spaces of spirited disinhibition; Dutch coffee shops tend to aspire to a more contemplative ethos, disinhibition in a mellow tone. Future research might usefully extend Jacques et al.’s work by varying pharmacology , type of setting , and culture .The flip side of prohibition creating “zones of statelessness” where law is unavailable is that decriminalization can expand the regulatory capacity of the state. This happened in the Netherlands as its cannabis policy evolved from informal toleration of “house dealers” inside some clubs into formally licensed coffee shops and into subsequent refinements that gave officials greater control, for example, tightening license requirements, raising the minimum age for purchase, and banning advertising . As more U.S. states and other nations legalize cannabis, some are concerned that greater availability could cause greater abuse . The Dutch experience does not support this hypothesis, but instead it supports the counterintuitive argument that legalization can provide more, rather than less, social control. Street dealers generally do not check IDs,cannabis drying racks but as Jacques et al. suggest, Dutch coffee shop operators do because their licenses and incomes are contingent on following the rules. In the United States, by contrast, criminalized cannabis is easier for many high-school students to obtain than tobacco, alcohol, or prescription drugs, which are legal but regulated .

Criminologists well understand that criminalization can amplify inequality. In describing their interviewees, Jacques et al. report that although two thirds of their coffee shop and caf´e operators are White, three fourths of their street dealers are Black, the latter also more often immigrants who reported lower levels of education and a higher frequency of criminal records. Rational choice theory suggests that if criminalization laws are designed to make illicit drug selling as dangerous as possible to deter would-be dealers, we should not be surprised when those who enter that line of work are more desperate. Choices are always made under the constraints of context. Although the Netherlands has substantially less inequality than the United States , immigrants and ethnic minorities there still have fewer licit opportunities. The hypothesis would follow that the marginalized are more likely to find their way into the illicit crevices created by prohibition, where there is often lower cost of entry, higher income, and greater autonomy and dignity than in the legal economy. Moreover, in the United States, well-documented patterns of racially discriminatory drug law enforcement have made minor drug arrests a key gateway to mass incarceration, with all the negative consequences that flow from that. More research is needed to see whether this is the case in other comparable democracies. Future studies would perform a great service if they investigated the degree to which prohibition laws function as an adjunct mechanism of marginalization in other societies. If they do not, it would be even more important to learn how this tendency was avoided.Jacques et al. observe that Dutch decriminalization of cannabis does “not appear to have increased cannabis use by natives.” Indeed, in 2009, the latest year for which national data are available, 25.7% of the Dutch population reported lifetime prevalence of cannabis use, whereas 7% reported last-year prevalence . In the United States, by contrast, where roughly 700,000 citizens are arrested for marijuana possession each year, the latest data available show that 44.2% of the population reported lifetime prevalence of cannabis use, whereas 13.2% reported last-year prevalence . It is worth noting, too, that despite hundreds of coffee shops and decades of claims about cannabis serving as a “gateway” to harder drugs, the Netherlands has lower prevalence of other illicit drug use than the United States and many other European societies.

The Dutch evidence runs counter to the foundational claim of cannabis criminalization; prevalence data indicate that availability is not destiny after all. Although governments committed to criminalization are unlikely to fund such studies, much more research is needed on the relationship between drug policy and drug use prevalence and problems .Jacques et al. rightly argue that the “best way to adjudicate competing claims about the consequences of drug law reform is to conduct research in the settings where the reforms have taken hold.” Their argument centers on the effects of decriminalization on crime and violence in illicit markets. Their findings can be read as mixed. Future researchers will likely generate new findings that support, complicate, and qualify those reported here, showing variation across time, space, cultures, and the complex conjunctures of conditions that shape drug use patterns. But in one sense, the key policy significance of Jacques et al.’s study is simply that it was conducted at all because its core question rests on a consequentialist conceptualization of drug policy: that drug policies must be evaluated on the basis of their actual consequences, not on their intent. Dutch drug policy has opened to empirical examination what has until recently too often remained unquestioned drug war orthodoxy. The Dutch case is complicated, and there is no guarantee that their model could simply be exported to other nations with the same relatively benign results. But the Netherlands provides as good a window as we have on what an alternative drug policy future may look like. As cannabis becomes legalized in more places, its commercialization may yet cause the sky to fall. But the evidence to date, both from the Netherlands and U.S. states, suggests no need to duck for cover just yet. Jacques et al. note that reducing crime and violence in illicit drug markets is not the only objective of Dutch drug policy nor, I would add, the most important. The “other objectives” their study does not directly address include avoiding or reducing the harms of stigma, marginalization, and other negative consequences of criminal punishment . Two odd metaphors catch at the difference between Dutch and U.S. drug policy in this regard. President Lyndon Johnson once famously said of FBI Director J. Edgar Hoover, “better to have him inside the tent pissing out than outside the tent pissing in.” For a century, the United States has pursued drug policies designed to deter use by stigmatizing, punishing,hydroponic cannabis system and ostracizing users. In effect we push them out of the societal tent and then are perplexed when they cause problems, so we pass tougher laws, and so on . Since 1976, drug policy in the Netherlands has been designed to keep illicit drug users inside the societal tent. Compared with the United States, the Netherlands has a stronger welfare state, more social housing, national health care, and greater accessibility of treatment, which result in less poverty, homelessness, addiction, and crime .

In thinking about U.S. drug policy, my Dutch colleagues often use “a stopped-up sink” metaphor: “Americans keep feverishly mopping the floor, but the faucet is still running.” The day I was finishing this article, two stories appeared simultaneously in the New York Times . The first was about an extraordinary letter to UN Secretary General Ban Ki-moon on the eve of the UN General Assembly Special Session on Drugs. The letter urged an end to the war on drugs as a failed public health policy and a human rights disaster. It attracted more than 1,000 signatures, including those of former UN Secretary General Kofifi Anan; former President Jimmy Carter; Hillary Clinton; senators Bernie Sanders, Elizabeth Warren, and Cory Booker; legendary business leaders like Warren Buffett, George Soros, and Richard Branson; former presidents of Switzerland, Brazil, Ireland, and ten other former heads of state; former Federal Reserve Chair Paul Volcker; hundreds of legislators and cabinet ministers from around the world; Nobel Prize winners; university professors; and numerous celebrities. All attendees at the Special Session were given copies of the letter. The UN ordered all copies confiscated . The second article provided vivid testimony as to why such a letter was necessary: The U.S. Supreme Court refused to hear the appeal of a 75-year-old disabled veteran serving a mandatory sentence of life without parole for growing two pounds of cannabis for his own medical use, a fact uncontested by the prosecutor . Such grave injustices have allowed the Drug Policy Alliance and a growing number of other nongovernmental organizations to mount a drug policy reform movement of unprecedented scale. Stopping the drug war and the mass incarceration it helped spawn has become a top priority for the civil rights movement, from the NAACP to Black Lives Matter. Voters in the United States and elsewhere are slowly taking matters into their own hands. Medical marijuana laws have been passed in 24 states, and cannabis has been legalized under state law in Colorado, Washington, Alaska, Oregon, and Washington, DC. Voters in California, Arizona, Massachusetts, and perhaps other states are set to vote on cannabis legalization initiatives in November 2016. Most European countries have embraced at least some harm reduction policies. Portugal, Uruguay, Australia, the Czech Republic, Italy, Germany, and Switzerland have moved toward decriminalization of cannabis in one form or another. Former drug war allies across Latin America are in revolt against U.S.-style prohibition. These are the sounds of the American drug war consensus collapsing. Global drug policy is at an historic inflection point, and it is trending Dutch.ICU patients frequently receive opioid and benzodiazepine medications to treat the pain, anxiety, and agitation experienced during a critical illness. Trauma ICU patients may require high and/or prolonged doses of opioids to manage pain associated with multiple open wounds, fractures, painful procedures, and/or surgery. They may also require benzodiazepines to prevent or manage anxiety and agitation and to facilitate effective mechanical ventilation . Although the effect of different pain and sedative medication regimens on TICU patients is unclear, prior evidence suggests that administration of opioid and benzodiazepine medications in the ICU setting is associated with the development of many complications including delirium and poor patient outcomes . Exposure to high or prolonged use of opioids and benzodiazepines may also contribute to both drug tolerance and drugphysical dependence . Once drug dependence has developed, patients are then at risk for withdrawal syndrome , a group of serious physical and psychologic symptoms that occur upon the abrupt discontinuation of these medications .Unlike in the PICU patient population, physical dependence during drug weaning of adult ICU patients exposed to prolonged doses of opioids and benzodiazepines has received little study. Indeed, there is a large discrepancy in the amount of literature regarding WS in the adult versus PICU populations. There are two descriptive studies with retrospective chart review designs and small samples in adult ICU surgical-trauma patients and burn ICU MV patients . Cammarano et al found that 32% of their sample developed WS after prolonged exposure to high doses of analgesics and sedatives. Brown et al found that all burn MV patients who received opioids and benzodiazepines for more than 7 days developed WS. In a prospective experimental study of major abdominal and cardiothoracic postsurgical ICU patients, 35% who received a combination of opioids and benzodiazepines developed marked withdrawal compared with 28% who received a combination of opioids and propofol . These three studies were reported more than 1 decade ago, prior to the current recommended change in sedative management . A recent prospective study of 54 TICU patients showed a lower occurrence of iatrogenic opioid WS than in previous studies .

Increasing evidence indicates multiple physiologic regulatory systems are dysregulated in frailty, with a greater number of dysregulated systems increasing the odds of frailty. Frailty appears to be the result of a breakdown in the homeostasis that is required for an organism to remain resilient. Despite the growing body of research concerning frailty, questions remain, particularly for older PWH. There is evidence of an interplay between frailty and disease, especially catabolic diseases. The specific system drivers that start the syndrome of frailty, such as inflammation, need to be better understood. It also is unclear if there are shared etiologic factors between frailty and disease and how these interact with one another, especially in the case of HIV. The implications for the prevention and treatment of frailty are complicated by multiple co-occurring events: treatments that can alter multiple pathways, such as physical activity, will be most effective in reducing or preventing frailty. Ultimately, successful frailty prevention will involve interventions that target the underlying physiology and biology, which might be unique for older PWH. Frailty provides a window into the biology of vulnerability by allowing us to examine the resources and resilience in PWH who can be rebound in the face of stressors to the system.A key contributor to the current spectrum of end-organ disease risk among older PWH entails persistent abnormalities in coagulation activation, despite effective ART.Older PWH are well known to be at excess risk for venous thromboembolism ,cannabis indoor greenhouse and atherosclerotic cardiovascular disease is now a leading cause of morbidity and mortality among PWH.Still, beyond these classic manifestations of macrolevel venous or arterial thrombosis, elevations in circulating D-dimer levels also are associated with increased risk for end-stage liver or renal disease, the frailty phenotype, all-cause mortality, and other grade 4 adverse events related to end-organ injury among PWH.

In this context, a central underlying question is how low-level persistent hyperactivation of the coagulation system contributes to excess risk across a wide spectrum of disease, beyond macrolevel thrombosis. When studying potential causal associations between HIVassociated coagulopathy and end-organ disease risk, there are several important considerations and limitations when interpreting data from observational studies. Two examples are potential influence of confounders and mediators on associations between host factors and clinical risk. Confounders are not on the causal pathway, and associate with both the outcome and a biomarker or the exposure of interest.Mediators, however, are more informative in this context as they are directly or partially on the causal pathway, such that they may account, at least in part, for the association between HIV-associated coagulopathy and end-organ disease risk. Mediators specific to HIV disease that may contribute to coagulopathy include direct effects from viral replication as well as persistent immune depletion and loss of a protective barrier at the level of mucosal surfaces.We have previously shown that HIV viremia increases procoagulant factors , and concurrently decreases anticoagulant factors , with the resulting alterations in coagulation factor composition then associated with greater predicted thrombin generation and mortality risk.These HIV-associated changes are very similar to changes in coagulation profiles that occur with advancing age.HIV disease is also characterized by immunologic depletion at effector sites in the gastrointestinal tract, and other secondary lymphatic tissues contribute to the loss of mucosal integrity, which largely persists despite ART.This loss of mucosal integrity contributes to ongoing immune activation and low-level hypercoagulation due, in part, to microbial antigens translocating across mucosal surfaces resulting in endotoxin-mediated activation of tissue factor pathways.Pathologic alterations to coagulation profiles and low-level endotoxemia then represent potential mediators on the causal pathway from HIV disease to coagulopathy. If an HIV-associated coagulopathy increases risk for thrombosis, it follows that risk for ischemic cardiovascular disease and mortality may be increased in this context.

However, other end-organ diseases cannot be explained by macrovessel thrombosis and other explanations must be sought. Additional pathways have therefore been explored, whereby HIV coagulopathy could cause end-organ disease by driving inflammation-associated tissue injury. This hypothesis entails a cross-talk between coagulation and in- flammation that is mediated by clotting factors activating protease-activated receptors , which are expressed on leukocytes and on vascular surfaces.Activation of PAR-1 and/or PAR-2 signaling, in part, drives in- flammation and injury within end-organ tissues. However, this hypothesis has been tested and not supported in two proof-of-concept randomized trials, studying direct acting oral anticoagulants edoxaban and vorapaxar .Alternatively, it is possible that a disease state may contribute to alterations in coagulation , such that the development of end-organ dysfunction, whether HIV related or not, would itself further contribute to a coagulopathy. In summary, current data support that chronic HIV disease contributes to a coagulopathy, but further research is needed to better understand the mechanisms by which this coagulopathy contributes to end-organ pathology and clinical manifestations resembling those of aging among older PWH.There is a long history of medicinal use of cannabis, going back millenia. Political shifts in the early 20th century resulted in the criminalization of cannabis. In the 1990s, there was persistent anecdotal evidence that cannabis mitigated HIV-related symptoms, such as nausea, vomiting, and wasting, with simultaneous political shifts to favor access to medical cannabis. In 1996, the Compassionate Use Act was passed in California, which allowed the use of medicinal cannabis and spurred a call for greater research about the positive and negative effects of cannabis. In 1999, the Medical Marijuana Research Act was passed in California, which provided resources and pathways to conduct rigorous studies on cannabis, including development of the Center for Medicinal Cannabis Research at the University of California, San Diego. In the decade that followed, cannabis researchers identified over 100 different cannabinoids in cannabis, the main two being tetrahydrocannabinol , which is psychoactive, and cannabidiol , which is nonintoxicating.

These are the two compounds most often discussed and studied in cannabis research, including those examining possible medicinal effects. In addition, the plant contains terpenoids, which contribute to the aroma and may act on serotonin, dopamine, and other receptors, and flavonoids, which contribute to the color of the plant and might have antioxidant and anti-inflammatory properties. To date, two primary cannabinoid receptors have been noted: CB1, which is highly prevalent in the brain, as well as other body systems, and CB2. The body also has an endocannabinoid system, with two primary constituents being anandamide and 2-arachidonylglycerol. This system, unlike some other neurotransmitters, can be synthesized on demand and serve as signaling messengers to promote homeostasis. In 2017, The Health Effects of Cannabis and Cannabinoids National Academies Report was released, and stated that in humans there is conclusive evidence that cannabis benefits chronic pain, spasticity associated with multiple sclerosis, and control of nausea; moderate evidence that cannabis helps improve sleep in those with chronic conditions; limited evidence that cannabis helps anxiety disorders and posttraumatic stress disorder ; and no evidence that cannabis is effective as a treatment for diseases such as cancer, epilepsy, or schizophrenia. In most cases, the lack of evidence was based upon a lack of substantive research having been completed, rather than necessarily negative findings. There are a number of studies, typically small, which provide data on the potential benefits of cannabis in PWH. A study by Abrams et al. found evidence that smoking cannabis reduces HIV neuropathic pain and another by Wilsey et al. showed that both low and medium doses of vaporized cannabis were equally effective with neuropathy in other conditions.In a population without HIV, Wallace et al. proposed a ‘‘window’’ for cannabis growing equipment pain relief, such that too low or too high dose of THC may have no effect, or even exacerbate pain, indicating that dosage is very important.Based on a retrospective observational study of PWH, there is a possibility of neuroprotective effects among moderate cannabis users compared to infrequent and frequent users.If true, this is likely a benefit seen in individuals with an inflammatory condition, such as HIV, and in which the anti-inflammatory effects may be helpful, rather than in individuals not with such conditions.

In one study of PWH, having a diagnosis of cannabis use disorder was predictive of higher odds of being a ‘‘superager,’’ an adult who performs better than his or her peers, and at par with younger individuals, on cognitive tests.While there are data supporting positive effects of cannabis use, several challenges to conducting cannabis research remain. First, smoking as a delivery method is a challenge due to concerns regarding the safety of using combustible materials , second hand smoke as an irritant, and difficulties in the standardization of dosing, to name a few. Second, the Drug Enforcement Agency scheduling criteria and access regulations limits access to cannabis for research. Currently, plant-based THC is schedule 1 , and the scheduling of synthetic THC and synthetic and plant-based CBD vary widely . Furthermore, currently the University of Mississippi remains the sole source for plant-based cannabis in the United States. There is promise that the DEA will propose new regulations to expand the policy to allow additional sources. Finally, there has been a proliferation of CBD products ; however, these are not available to researchers because they are federally illegal, and therefore no study can assess their efficacy or safety. Questions regarding HIV, aging, and cannabis remain unanswered, and are of particular importance due to the alterations in body composition, reduction in hepatic and renal drug clearance, cardiovascular and pulmonary changes, and so on, which can occur with both aging and HIV and interact with the effects of cannabis.80 Method of administration will also play a role in evaluating the risks and benefits of cannabis, as there are indications that long-term smoking may result in increased risk of lung-related diseases in PWH.More studies on the potential anti-inflammatory and neuroprotective qualities of cannabis are also needed, to inform their potential uses among older PWH.Loneliness, or the discrepancy between one’s preferred and actual social relationships, is different from social isolation and is hypothesized to serve as an evolutionary cue.Like hunger, the discomfort of loneliness encourages persons to seek out meaningful relationships, ultimately to enhance survival. Loneliness and social isolation are common in the United States, with estimates suggesting that nearly half of Americans report sometimes or always feeling alone and 40% sometimes or always reporting their social relationships are not meaningful.Older PWH may experience slightly higher rates of loneliness than HIV-seronegative persons, with estimates ranging from 39% to 58% depending on the population evaluated.Older PWH who report loneliness are more likely to smoke cigarettes, use alcohol or other substances, and have low social support, depressive symptoms, and poor to fair quality of life.Loneliness and social isolation increase the odds of an early death by 26%–45%, an impact similar to that of smoking 15 cigarettes a day.The effect of loneliness and social isolation on health appears secondary to stress-induced cortisol dysregulation. Persons who are lonely demonstrate higher total peripheral vascular resistance and lower cardiac contractility.Immunologically, persons who are lonely display less natural killer cell activity, poorer immune responses to influenza vaccination, and increased circulating levels of cortisol.It is now apparent that chronic higher than usual levels of cortisol mediate the transcriptional response of glucocorticoid receptor pathways.Clinically, these conserved transcriptional responses to adversity result in a mixed picture of excess inflammation and immunosuppression that moderate the association between loneliness and social isolation and health.Protective factors that mitigate the impact of loneliness also exist and include wisdom, resilience, nostalgia and eudaimonia ,although the prevalence and impact of these factors in older PWH have not been studied. Overall, evaluation of loneliness in older PWH remains a significantly understudied topic. Further work that enhances our understanding of the true impact of loneliness and social isolation on quality of life, health, and function of older PWH is needed to ultimately develop effective interventions for this potentially modifiable condition.Finding a cure for HIV is an important consideration for all PWH. However, there are many unique challenges for cure research within the aging HIV population, including the impact of immuno senescence, increased rates of medical comorbidities, polypharmacy, and frailty. The potential benefits of cure for older PWH are also numerous: curing HIV could reduce stigma, improve psychosocial outcomes, and reduce harm associated with long-term ART toxicity and polypharmacy. An HIV cure could reduce inflammation, immune dysfunction, and tissue fibrosis, resulting in a significant reduction in morbidity for older PWH.At present, PWH older than 65 years are routinely excluded from HIV cure research, potentially limiting advances in the field.

This dark figure exists for two main reasons: victims fail to report crimes , and law enforcement agents are unable to detect crimes . There have been many attempts by law enforcement and criminologists to better estimate crime and diminish this dark figure through improved and new types of surveillance, anonymous reporting systems and victimization surveys, like the National Crime Survey . More recently, law enforcement at international, national and regional levels has attempted to detect crime by using remote sensing technologies. Using imagery collected remotely, from sensors onboard aircraft, unmanned aerial vehicles and satellites, law enforcement agents have been able to assess where and when certain kinds of crimes have taken place. The use of remote sensing, the “observation of earth’s land and water surfaces by means of reflected or emitted electromagnetic energy” or, more simply, a method of “acquiring data about an object without touching it”, for surveillance and analysis has obvious benefits for law enforcement agencies . It greatly expands the supervision of agents of the law in often remote or inaccessible places, reduces the exposure of these agents to dangerous circumstances on the ground and may make up for a lack of manpower . At the same time, using remote sensing has at least three serious limitations. First, and perhaps most obviously, remotely sensed images that are gathered from overflying helicopters, aircraft or satellites can only detect crimes or crime’s impacts that are visible from above and for sustained periods of time. For example, remote sensors can identify illegal logging, large-scale drug production, and trails in the desert but they would be much less likely to detect murder, assault, homicide, robbery,ebb and flow flood table or other small-scale, undercover, rapid actions, though some attempts have been made to capture the lasting effects of these things, for examples, see Pringle and others. Second, remote sensing cannot record the social, political, economic and historical context of landscapes and the actions that take place within them. Crime and criminals are subjective, spatially delineated and historically contingent categories.

They are not, nor ever have been, pre-determined or natural classifications. As laws, land use regulations, as well as national and local power relations shift, so do the definitions of crimes and criminals . Thus, remote sensing cannot detect crime as it might detect a stand of a certain tree species: crimes, their perpetrators and their forms are defined by the dominant forces in society rather than spectral signatures or texture patterns. Because remotely sensed images are collected remotely , they lack detailed or nuanced definitions of crime drawn from the context of the landscapes they seek to analyze; they do not tell us why certain things happened or by whom, specifically. Despite the serious imbalances and problems that may arise from the remote sensing of crime, it continues apace, as we have seen from increasing discussions in the popular press and academic journals about the use of unmanned aircraft systems , increasing availability of micro-satellites and Google Earth images in the detection of crime . The continued and increasing use of remote sensing for these purposes brings us to the third limitation that we will mention here: the issue of validation. As remote sensing scholars, such as Jensen, Congalton and Foody note, validation is a critical part of any remote sensing exercise, and these scholars and others have laid forth strict protocols for validation exercises. Validating that crimes are actually occurring in the places that remote sensing algorithms say they are is not a simple task, however. On the ground, verification of potential illicit drug production, arms and drug smuggling or even illegal logging, activities which are often protected by, or associated with, armed guards or agents, is often dangerous. The lack of validation in the remote sensing of crime is troubling, however, because drastic military or police actions are often used to intervene where crimes are detected with lasting ecological, economic and social impacts: lives, security and livelihoods can be at stake, not to mention law enforcement credibility and resources. In short, classifying an action as a crime or a person as a criminal may have much higher costs than other classification mistakes. Thus, we must be doubly sure of what we classify as crime using remotely sensed images before we act. Further, such validation may add nuance and greater contextual understanding of the images used for analysis, which may allow for a more fair and balanced law enforcement response. Although all three of the above limitations are important to consider, this paper will take a methodological approach to engage with the issue of the validation of remotely sensed crime.

We believe a focus on validation is critical, because as remotely sensed products become increasingly available to our desktops and smartphones, a rising trend of validation-free analysis is emerging. In these circumstances, products, like Google Earth, are used with the assumption that their images portray “the truth”, which should be acted upon. Despite the ease with which these data now flow to us, validation of our findings based on these images remains critical; competing sensors, processing methodologies and the familiarity of analysts with the limitations of the data they are using can present very real challenges to the ethical and accurate use of remote sensing in law enforcement and/or litigation. In this paper, we will first analyze how remote sensing technologies have been used to aid in the detection of crimes that might otherwise go undetected. As other authors have shown, “satellite imagery highlights the spatial footprint of human actors in very real and compelling ways” .Here, we review the literature that discusses how satellite and airborne technologies have been used in the active detection of felony cases of drug production, smuggling and extra-legal migrations. We use the term “extra-legal” here, rather than “illegal,” in order to highlight the fact that though these acts are prohibited by USA or international law, the prohibition of these actions is often highly political and may not be deemed illegal in all cultures or by all groups. Forensic remote sensing has also been critical in the detection of environmental crimes, such as extra-legal mining and timber extraction, as well as in detecting oil spills and hazardous waste dumping. While the use of remote sensing in environmental forensics of this kind are important, many of the articles on these topics are embedded in larger land-clearance, deforestation and oceanographic literatures that deal with licit, illicit and accidental extraction or pollution, making the attribution of legality associated with the event difficult. Forensic remote sensing can also be used to identify the location of single and mass grave sites, but because most of these studies are experimental or historically oriented, we excluded them from our review. Remote sensing has also been used to find bodies, munitions and toxic waste that may have drifted based on water-current analysis.

While our scope is narrower than that of forensic remote sensing, we do draw upon the advances in crime detection and validation that these studies have advanced in our analysis. Second, building on this literature review, we consider what kinds of validation protocols for the remote sensing of crime have been attempted and what the limitations to these protocols are, geographically, financially, as well as in terms of personnel and time. Third, we seek to generate a discussion on new and less traditional ways that crime may be sensed remotely or validated. While “first order” validation protocols, such as the collection of ground reference data, over flights and the use of higher spectral or spatial resolution images, are critical to assessing the accuracy of remotely sensed processes, they may not always be useful, possible or sufficient in the context of criminal investigations. Here, we propose going beyond the “first order” validation protocols that are standard in remote sensing to ensure accurate assessments of remotely sensed crime are occurring in ethical and contextually-situated ways. Here, we define the remote sensing of crime as the use of airborne and satellite remote imagery to detect crimes that have heretofore gone unreported or undetected. Lein describes forensic remote sensing as considering “the investigative use of image processing technology to support policy decisions regarding the environment and the regulation of human activities that interact with environmental process and amenities.” In this definition the term “forensic” refers to detailed investigation rather than a criminological one . As Lien points out,hydroponic drain table forensic remote sensing seeks to generate information pertaining to a specific event rather than “provide a broad thematic explanation”. As we note above, not all crimes are well suited to detection by remote sensing, however. Those crimes that have been most successfully detected using remote sensing technologies generally have the following three characteristics: first, they occur over relatively large geographic areas, so that their patterns may be easily detected, even with moderate or low spatial resolution imagery, like Landsat or MODIS ; second, the crimes or their evidence are generally visible for extended periods of time, allowing for their detection by satellites or airborne sensors over the length of a day, week or month; and third, they generally have characteristic spatial or spectral patterns that can be recognized from above using object-based analysis or spectral analysis. This paper focuses on the utility of remote sensing in detecting crimes that are deemed a felony offense under U.S. federal law and are recognized as crimes internationally: arms, drug and human trafficking, repeat extra-legal migration and drug production/possession . While there exists a plethora of academic papers that test methods that could theoretically be used for the remote sensing of crime—testing algorithms, detection techniques or spectral reflectances of illicit crops and smuggling trails—there are relatively few studies that document the use of remote sensing in the active reconnaissance of criminal activities. In this section, we review studies of active reconnaissance that exist in peer reviewed journals, as well as in gray literature in relationship to drug production, smuggling and extra-legal migrations. The characteristics of these activities fit those described above: they often occur in large geographic and temporal scales and may be uniquely identifiable from the surrounding landscape using aerial images. Because of these attributes, they represent the most common examples in papers regarding remote sensing used in the active detection of crimes.

We reviewed 61 papers, reports from the United Nations Office on Drugs and Crime and master’s theses on these topics that were found through searches in the Google Scholar, Web of Science and Jstor search databases using a number of combined words and phrases . Some of these reports involved multiple case studies. Though, as Figure 1 shows, there were thousands of results that came from these combinations of search terms, very few of these results dealt with the active reconnaissance of crimes using remote sensing. We do acknowledge that there are probably many more reports and papers available on this topic in the law enforcement literature that are not available to the public. Government agencies, like Homeland Security, the Federal Bureau of Investigation and the Central Intelligence Agency, as well as international law enforcement agencies, like Interpol, may have extensive documentation on these topics that we were unable to access. Most prevalent in literature involving remote sensing of crime were studies on the detection of the cultivation of illicit substances. While the criminalization of each of these plants and their use is fraught with important political, cultural, economic and militaristic implications, an in-depth discussion of the reasoning behind these criminalizations and their ethics is beyond the purview of this article. Rather, we narrow our focus to the application of remote sensing products to actively detect “crime”, as it is construed by international or national governing powers. The use of remote sensing to detect the cultivation of illicit crops is a trend that has increased over time, perhaps because of the opening of the Landsat archives in 2008, and perhaps because of interest in opium growing in Afghanistan and South East Asia . We gathered the publicly available literature on the remote sensing of drug production in Afghanistan, Myanmar, Thailand, Laos, Bolivia, Colombia and Peru, countries targeted for drug production monitoring both by the UN’s Office of Drugs and Crime and academic researchers, due to these countries’ historically high exports of illicit substances .

Once a patient is deemed stable for discharge from the ED by the trauma service, the rest of the patient’s care is up to the discretion of the emergency physician, which includes any and all medication prescriptions and ultimate disposition decisions. Lastly, as a supplementary analysis to look more specifically into potential associations with THC use we compared opioid prescriptions against three separate groups that included patients with negative toxicology screens for THC, patients with positive screens for THC, and patients without a toxicology screen.The study population was divided into five subgroups that included the following: negative urine and serum toxicology screen ; depressants; stimulants; mixed; and no toxicology screens. The median total MME for the five separate subgroups was as follows: none ; depressant ; stimulants ; mixed ; and no toxicology screens . The median total number of pills for the five separate subgroups was as follows: none ; depressant ; stimulants ; mixed ; and no toxicology screen . When comparing the 103 patients from whom toxicology screens were obtained to the 255 patients without toxicology screens, we found no statistically significant differences in the total prescribed MME or in the number of pills prescribed . Notably, none of the 103 patients who had toxicology screens were prescribed naloxone upon discharge. We also looked into whether the type of injury had any association with opioid prescriptions. Our data, shown in Table 2 below, indicates there was no statistically significant difference in total prescribed MME or amount of pills prescribed when comparing patients with fractures, dislocations,flood tray or amputations. As a supplementary analysis we aimed to determine whether or not the presence of THC on urine toxicology screens was associated with an increase or decrease in the amount and total MME prescribed . The median total prescribed MME for patients with urine toxicology screens positive for THC was 87.5.

The median for patients with urine toxicology screens negative for THC was 75.0, and there was no statistically significant difference between the two groups . The median total number of pills for patients with urine toxicology screens positive for THC was 15.0. The median total number of pills for patients with urine toxicology screens negative for THC was 15.0, and there was no statistically significant difference between the two groups .At our Level I trauma center it is routine to obtain urine and serum toxicology screens for trauma activations. Most often, the results of these toxicology screens are not pertinent and will not significantly affect the patient’s disposition. However, previous reports have suggested that in some circumstances the urine drug screen is of utility in improving patient care by identifying patients who are at risk for diversion and mismanagement of controlled substances. 33 Our results did not substantiate these reports. For context, providers in California must consult the Controlled Substance Utilization Review and Evaluation System , the state’s prescription drug monitoring program, prior to prescribing Schedules II-IV controlled substances for the first time and at least once every four months thereafter if the patient continues to use the controlled substances.34 However, if prescribed in the ED, providers do not have to consult CURES if the quantity of controlled substance does not exceed a nonrefillable seven-day supply. In fact, it is common practice to prescribe less than one week’s supply and to consult CURES only if the prescriber has suspicion of diversion, misuse, or abuse. For these reasons we suspect CURES reports likely had limited to no effect on prescribing habits. A large-scale study based upon Medicaid States Drug Utilization Data found an associated decrease in the number of opioid prescriptions, dosages, and Medicaid spending in states that have legalized medical cannabis. 30 A similar study found that in states that have legalized recreational marijuana, there was a notable decrease in opioid prescriptions of about 6.38%. 35 Since then, several studies have failed to demonstrate similar findings in actual clinical practice, and many have actually found that cannabis use was associated with an increased risk of opioid use disorder and opioid misuse. 36-39 In our study, we found no statistically significant difference in opioid prescriptions in terms of either total MME or number of pills prescribed between groups. Thus, we do not see that emergency physicians reduce or significantly change the quantity of prescribed opioids when urine toxicology screens are noted to be positive for THC. This was consistently true even when our study population was divided into different classes of toxicology results .There was also no difference in opioid prescriptions between these four separate groups. Thus, physician knowledge of prior drug use was not associated with a decrease in the total quantity of opioid prescriptions.

This may be explained in part by the legal status of cannabis in the state of California and may portend an overall reduction in the stigma that was previously endured by patients who used cannabis medicinally or recreationally. Another salient finding within this data was the absence of naloxone prescriptions for any patient in this study. In the state of California, Assembly Bill No. 2760 was passed on September 10, 2018, and took effect January 1 2019. This bill mandates that opioid prescribers must offer a prescription of naloxone hydrochloride when the prescription dosage is 90 MME or more per day, when an opioid is prescribed concurrently with a benzodiazepine, and when the patient is at increased risk for overdose, which includes patients with a history of overdose, patients with substance use disorder, or patients at risk for returning to a high dose of opioid medications.We collected the data for our study prior to the enactment of this law. However, it is prudent to recognize that even within this law, there is no clear mandate on prescribing naloxone based upon toxicology results that imply higher risk of illicit drug use, such as urine drug screens that are positive for both opioids and benzodiazepines. We also found that of the 103 patients who had toxicology screens performed, were prescribed a total MME <90, and 46 were prescribed a total MME >90. Thus, had the law been in effect, 44.7% of these patients should have received a prescription for naloxone regardless of their drug screens, strictly due to the total MME prescribed. While this study was performed at an academic tertiary care center, if it were repeated at other community-based institutions, we could see similar patterns regarding the lack of naloxone prescriptions. Furthermore, we undertook this study in Orange County, California, a densely populated setting in Southern California that was ranked 17th out of 58 counties in the state for rates of prescription opioid deaths and unintentional injuries. Drug overdose was the largest contributor and the number 1 cause of death in patients between the ages of 15-44 years old.One study that surveyed emergency providers at an academic, urban, Level I trauma center found that the factors most commonly influencing providers’ willingness to prescribe naloxone were the prevalence of prescribing these medications in their institution, or if there was a strong mortality benefit.Sixty-two percent of prescribers endorsed that lack of training was a barrier to prescribing, and 52% cited lack of knowledge as a barrier. Thus, it is pertinent that as a medical community, we focus on methods to improve research and education on naloxone so that prescribing can become a more common practice.

Several initiatives have been developed and described in the literature aimed at improving naloxone prescription rates. Some examples include screening questionnaires for patients, pharmacy-led opioid overdose risk assessments, and multi-disciplinary teams with clinical nurse specialists for overdose education and naloxone distribution. In one study a program was implemented within the electronic health record system to search for keywords within nursing assessment notes to identify patients who were at high risk for opioid overdose. This then prompted the physician to consider naloxone prescriptions. Overall, the study found that since implementation of this integrated EHR programming, there was an associated increase in the rate of take-home naloxone prescriptions. Implementation of similar programming in EHRs could be used to flag patients with toxicology results positive for high-risk illicit drug use such as benzodiazepines, other opiates,grow table supplier and alcohol. These flagged patients could then trigger a prompt to consider prescribing naloxone if the clinician attempts to prescribe an opioid. Given that some states have implemented mandates requiring the prescription of naloxone when prescribing opioid regimens greater than 90 MME, an additional prompt from the EHR recommending naloxone in these situations may prove useful to ensure compliance with local laws and practice guidelines.39Despite the health risks and societal costs of cigarette smoking, the prevalence of smoking in the USA remains high at ∼19 % . Roughly 44 % of cigarettes are used by smokers with substance abuse/dependence and/or mental illness , and people with almost all substance abuse and mental illness diagnoses have elevated rates of cigarette smoking .Roughly half of smokers drink coffee and report drinking almost twice as much coffee per day as nonsmokers . Similarly, among smokers, 57.9 % have ever used marijuana, and smokers are about 8 times more likely than non-smokers to have a marijuana use disorder , with cigarette smoking and marijuana use being associated even after controlling for potential confounding variables, such as depression, alcohol use, and stressful life events . Given the high comorbidity of smoking and both caffeine and marijuana use, it is important to better understand biological factors that may be associated with these co-occurrences. One of the most well-established effects of chronic cigarette smoking on the human brain is widespread upregulation of α4β2* nicotinic acetylcholine receptors . Recent studies using single-photon emission computed tomographyand positron emission tomographyhave consistently demonstrated significant upregulation of these receptors in smokers compared to nonsmokers. These in vivo studies were an extension of much prior research, including human postmortem brain tissue studies, demonstrating that chronic smokers have increased nAChR density compared to non-smokers and former smokers . Additionally, many studies of laboratory animals have demonstrated upregulation of markers of nAChR density in response to chronic nicotine administration . In a previous study by our group comparing nAChR availability between smokers and nonsmokers , we explored the effect of many variables, including caffeine and marijuana use. Both heavy caffeine and marijuana use were exclusionary, such that participants drank an average of 1.3 coffee cup equivalents per day and only 12 % of the study sample reported occasional marijuana use. PET results indicated that caffeine and marijuana use had significant relationships with α4β2* nAChR availability in this group with low levels of usage. Based on these preliminary findings, we undertook a study of the effect of heavy caffeine or marijuana usage on α4β2* nAChR density in cigarette smokers.

One hundred and one otherwise healthy male adults completed the study and had usable data. Participants were recruited and screened using the same methodology as in our prior reports , with the exception that this study only included Veterans. For smokers, the central inclusion criteria were current nicotine dependence and smoking 10 to 40 cigarettes per day, while for non-smokers, the central inclusion criterion was no cigarette usage within the past year. Heavy caffeine use was defined as the equivalent of ≥3 cups of coffee per day, and heavy marijuana use was defined as ≥4 uses of at least 1 marijuana cigarette per week. Exclusion criteria for all participants were as follows: use of a medication or history of a medical condition that might affect the central nervous system at the time of scanning, any history of mental illness, or any substance abuse/dependence diagnosis within the past year other than caffeine or marijuana diagnoses. Occasional use of alcohol or illicit drugs was not exclusionary. There was no overlap between this study and prior research by our group. During an initial visit, screening data were obtained to verify participant reports and characterize smoking history. Rating scales obtained were as follows: the Smoker’s Profile Form , Fagerström Test for Nicotine Dependence , Beck Depression Inventory , Hamilton Depression Rating Scale , and Hamilton Anxiety Rating Scale . An exhaled carbon monoxide level was determined using a Micro Smokerlyzer to verify smoking status. A breathalyzer test and urine toxicology screen were obtained at the screening visit to support the participant’s report of no current alcohol abuse or other drug dependencies. This study was approved by the local institutional review board , and participants provided written informed consent.

It is particularly surprising that we see no effect on narcotics, considering most medical marijuana patients specifically use cannabis as a substitute for narcotics. An explanation for this can be that some medical marijuana users do not use for medical reasons many of the MMIC holders in this particular data base may only use for recreational purposes. To observe any further substitution effects, I used Equation 5.7 to regress alcohol induced crude rates, drug-induced crude rates, and all other crude rates on MMICs and unemployment still controlling for county and year fixed effects. Unlike the arrest rate data, no substitution effects were found. Referring to the regression output in Table 5.9 for alcohol-induced deaths, MMICs actually had a statistically significant positive effect on alcohol related deaths. The interpretation is that for every new medical marijuana user, the alcohol crude rate increases by 0.0068 deaths per 100,000. However, observing that zero is in the confidence interval and that the t-statistic is borderline significant, it is likely that there is no effect at all. While this is still a positive number, its suggested effect is so small, it becomes negligible. This can be determined by looking at the average crude rate for alcohol related deaths, which is 15.8. There would have to be an additional 147 MMICs per 100,000 to increase this crude rate by 1 death per 100,000. This is a highly unlikely scenario,plant grow table and could therefore be dismissed. By applying this same model to drug-related deaths, we again get a statistically significant positive effect on the crude rate, shown in Table 5.10.

While this would typically suggest that marijuana is a complement drug to other drugs, the effect is again, miniscule. With the average drug-induced crude rate of 13.4 deaths per 100,000, the number of medical marijuana cardholders would have to increase by 142 to cause 1 drug-related death. Similar to the effect on alcohol-induced mortality rates, this is a very unlikely event, and can be disregarded. While the drug and alcohol related deaths were affected slightly by medical marijuana, all other crude rates did not. There was no statistically significant effect when applying Equation 5.7 to all other crude rates. Fatty acid ethanolamides are a family of endogenous lipid mediators, whose chemical structures consist of a fatty acid moiety bound to ethanolamine by an amide linkage. These compounds are synthesized by cells throughout the body and control inflammation, appetite and food intake, learning and memory, and pain among other functions.1 Palmitoylethanolamide and oleoylethanolamide suppress inflammation by activating the ligand-operated transcription factor, peroxisome proliferator-activated receptor-a.Anandamide acts as a partial agonist at cannabinoid receptor type 1 and 2 receptors and, therefore, belongs to the diverse family of lipid signaling molecules called endocannabinoids .Due to their similar physicochemical properties, FAEs and other ECBs, such as 2-arachidonoyl-snglycerol , are usually coextracted from biological samples.The procedure for their analysis includes extraction with organic solvents followed by purification through solid-phase extraction and subsequent quantitation by liquid chromatography–mass spectrometry or gas chromatography–mass spectrometry .FAEs are present in blood serum or plasma in the pmol per mL scale and in biological tissues in concentration ranging from the pmol to nmol per gram scale. A review of the literature, however, reveals that data from different laboratories, reporting concentration of FAEs in human serum from healthy subjects, often do not corroborate one another. In particular, reported levels of PEA and OEA in serum or plasma of healthy human subjects differ by up to two orders of magnitude, from 5 to 30 pmol per mL6–17 up to 200 pmol per mL of serum or plasma.

During the validation process of a new method for LC/MS analysis of FAEs and ECBs in human serum extracts, we observed unexpectedly high levels of PEA, as compared with data previously obtained in our laboratory.We suspected that these abnormal levels could be due to a recurrent contamination. We found that 5”Pasteur pipettes of most, if not all, commercial brands, contain multiple contaminants detectable by LC/MS, including readily detectable quantities of a compound indistinguishable from PEA.A contaminant that is undistinguishable from PEA is present in glass Pasteur pipettes in amounts that are sufficient to interfere with analysis of biological samples. The contaminant was identified based on its LC retention time, accurate mass, and MS/MS fragmentation pattern, which were identical to those of authentic PEA. By contrast, only a negligible PEA contamination was found in 9” Pasteur pipettes. Furthermore, we isolated the PEA contamination to the polyurethane foam used to package the pipettes, which is transferred to glass pipettes by contact. In line with this finding, Oddi et al.22 recently reported that FAEs can be absorbed by plastic materials during laboratory assays. It is therefore conceivable that FAEs incidentally absorbed by plastics during industrial processes can be released later in organic solvents. Lastly, no other commonly analyzed FAEs or monoacylglycerols were found to be present in the pipettes. We published GC/MS23 and LC/MS5 analytical methods for the quantitation of ECBs and other related FAEs and monoacylglycerols in biological samples, including human serum.Prompted by the need for a novel quantitative LC/MS method to analyze ECBs in blood, we reviewed the literature and noticed discrepancies in the reported concentrations of FAEs and ECBs in human blood serum and plasma . The EC50 for anandamide and 2AG vary depending upon assay and tissue; however, it is important to note that levels reported in Table 1 for both compounds in plasma/serum are below the apparent biologically active concentrations required to activate CB receptors . Regarding relative levels of PEA and OEA, a number of studies reported very similar concentrations for both compounds,whereas others reported PEA approximately twice higher than OEA.Regarding absolute values, two separate laboratories reported levels of PEA and OEA in plasma that were excessively high,which reached or exceeded the concentrations needed by these ligands to engage PPAR-a as agonists. PEA and OEA are, in fact, considered high-potency ligands of PPAR-a; in heterologous expression systems, these FAEs engage the receptor with median effective concentration values of 0.12 lM for OEA and 3 lM for PEA.In the abovementioned reports,although PEA levels did not exceed the EC50, levels of PEA were high relative to other reports.The steady-state concentrations of FAEs in plasma/serum of healthy individuals possibly reflect an equilibrium of ECBs released by peripheral tissues and their enzymatic degradation in the blood stream. In animal tissues , levels of PEA and OEA are present in the same order of magnitude; therefore, it was predictable to find a similar pattern in human serum or plasma, as also shown by the literature reports in Table 1. Surprisingly, in our preliminary experiments, the measured level of OEA was in agreement with most literature reports, whereas PEA was one order of magnitude higher than expected . This finding prompted us to carefully screen all possible sources of contamination, including solvents, reagents, and glassware used for lipid extraction and quantitative analysis. In this study, we identify glass Pasteur pipettes used to transfer solvents and lipid extracts as the source of PEA contamination. The contaminant was identified as PEA by its exact mass and RT in three similar but different chromatographic systems, as well as by its MS2 fragmentation pattern, which were identical to those of standard PEA. Furthermore, we show that PEA is present in the polyurethane foam that manufacturers use to wrap the pipettes before packing, from which it leaks onto the glass pipettes. Moreover, accurate exact mass measurements with ppm deviation lower than five unambiguously confirmed the identity of the contaminant as PEA. Quantitative assessment showed that the content of PEA is 33.4 – 4.02 pmol per pipette. Unfortunately, none of the various manufacturers, whose pipettes were tested, provides 5”3 4 glass Pasteur pipette that are contaminant free . Only 9” pipettes from one vendor were free of PEA traces , allowing the use of these consumables in the overall procedure. The field of lipidomics is rapidly developing; however, reproducible standard procedures across laboratories are not established. Therefore, it is not uncommon for lipidomics data to differ among from independent laboratories.It is generally thought that these discrepancies are a result of the use of different instruments for lipid analysis, as well as differing extraction and separation protocols. In this study, however, all results were confirmed by two independent laboratories using different LC systems and QQQ mass spectrometers . Furthermore, accurate mass data were acquired on a third Shimadzu IT-TOF High-Resolution Mass Spectrometer for definitive confirmation that the contaminant was indeed PEA.Lipids, especially fatty acids, are common contaminants in detergents, mineral oils, greases, and plasticizers; hence, they are often present in laboratory equipment, including glassware and solvents. As shown in this study, assessment of FAEs, a group of lipids with diverse signaling properties, is not sheltered from this pitfall. We have shown that glass Pasteur pipettes,hydroponic table commonly used in lipidomic laboratories to transfer lipid extracts and organic solvents, can contain PEA as contaminant. This contamination gives rise to artifacts in the measurement of PEA in biological samples, especially when the procedure for sample preparation includes fractionation of the lipid extract, which concentrates the contaminant. The scope of this study is an alert to the ECB and FAE scientific community about possible PEA analytical artifacts and thus, great care is needed to exclude the possibility of contaminants when analyzing endogenous PEA levels in biological tissues.Heavy and problematic alcohol use is highly prevalent among HIV-infected individuals, with estimates of use documented as high as 63 % among HIV clinic patients. Indeed, the rate of alcohol use disorders among HIV infected individuals is markedly higher than that observed in the general population.Importantly, both HIV and problematic alcohol use have significant implications for memory functioning, which is vital to successful adherence to complicated medication regimens and effectiveness of cognitive and behavioral interventions for HIV. Although previous research has shown that heavy alcohol use is associated with a host of negative HIV health outcomes, including poor medication adherence, increased immune suppression, reduced effectiveness of therapeutic regimens, faster HIV disease progression, lower survival rates, and worse health-related quality of life, less is known about its impact on self-reported memory functioning and HIV symptom severity. Further, greater HIV symptom burden is associated with reduced health-related quality of life, an outcome that has gained increased significance as treatments for HIV infection have improved. Thus, in order to provide a richer clinical conceptualization to inform intervention and treatment efforts, it is important to determine how problematic alcohol use impacts these domains in this already vulnerable population. HIV disease progression poses significant risk for compromised cognitive efficiency and memory functioning, and although antiretroviral therapy can reduce neurocognitive impairment, mild forms still persist in a large proportion of individuals with HIV. Of note, cognitive impairment, and memory dysfunction more specifically, is associated with worse treatment outcomes among HIV-infected individuals and is known to reduce the effectiveness of interventions aimed at optimizing adherence and reducing risk behavior. Memory dysfunction has also been observed among individuals with heavy drinking and alcohol use disorders and these changes can persist even following an extended period of abstinence. The effects of alcohol on memory varies substantially across social drinkers and chronic alcoholics, and mild neurocognitive deficits are more notable at heavier drinking levels . These independent bodies of research suggest that the combination of problematic alcohol use and HIV may exert a negative additive or synergistic effect on neuropsychological indices of memory functioning. However, the literature speaking to these associations is mixed. Compared to healthy controls and participants with a single diagnosis, individuals with co-occurring HIV and alcohol dependence or abuse have been shown to perform worse on measures of immediate and delayed memory [WMS-R;], and on selective memory processes . In contrast, Rothlind et al. did not observe differences on measures of verbal and visual learning and memory in a comparison of light/non-drinking and heavy drinking HIV-infected individuals. Similarly, no differences in verbal or non-verbal memory emerged in a comparison of HIV-infected and HIV-uninfected African Americans with no drinking and light, moderate and heavy drinking. Finally, no differences in learning and memory were observed in a comparison of HIV positive and HIV negative males with and without a history of alcohol abuse.

Although children do spend a substantial period of time at school, which may or may not be in a similar geographical location to that of their primary residence. Lastly, there is not a direct correlation between external environmental exposures to chemicals and internal exposure doses. For some environmental toxins, internal biomarkers exist to determine internal dose , whereas others, like air pollution, do not. Nonetheless, these geospatial factors can lead to misclassification, or information bias, which can severely affect observed associations between the exposure and the outcome. Therefore, given these limitations, it is important to note that while the current LED Environment measures may help provide a snapshot as to the built and natural environment surrounding ABCD participants’ residential homes, the current data fall short of fully characterizing participant exposomes. Thus, while continued efforts by the LED Environment Working Group aim to mitigate these challenges, findings should be interpreted considering these potential pitfalls, and misclassification should be acknowledged and discussed when necessary. Another potential challenge for researchers using these data is conceptual and/or statistical collinearity and potential confounders. Environmental variables included from various databases can greatly overlap in terms of theoretical construct. For example, various factors may represent broad constructs of economic advantage, and many variables from the same databases may be highly collinear. It is also important to note that although some estimates may draw from similar linked databases , they may implement any number of transformations or operations when computing measures. In addition to considering the exposure of interest from these data, a number of spatial contextual variables may also be important to consider as source of confounding. For example, ecological variables, such as air pollution,indoor hydroponic system may be an important spatial confounder in examining associations between neighborhood socioeconomic factors and child health outcomes in ABCD.

Some models of exposures may also include other important geospatial or socioeconomic factors in establishing estimates of exposure, such as temperature and humidity in estimating ambient air pollution, or age of housing in compiling a metric for lead risk. Therefore, it is vital in the early stages of planning analyses with these data to consider the choice of which variables to use for a given construct, identifying potential ecological or spatial confounders, and understanding the raw datasets that were utilized in calculating various environmental and societal variables included in the ABCD Study. Additional sensitivity analyses should always be considered to evaluate the impact of potential confounds and the specificity of the tested environments. Lastly, researchers should note that the environmental estimates do not represent the ‘lived’ or subjective experience of these exposures, with careful consideration given to the potential interpretation of any effects seen between these variables and brain and cognitive outcomes of interest. For example, these data are derived from outside databases that may capture an objective perspective of a given geospatial location, as they do not rely on the subjective report of the participants. However, these objective constructs do not necessarily reflect any individual’s subjective experience in a given state, census tract, or even residential neighborhood. Further, neighborhood socioeconomic factors, environmental exposures, and potential health and behavioral outcomes should also be considered in light of local, state, and federal policies of racism, segregation, and inequality that has resulted in persistent inequalities in social, economic, and educational opportunities . For these reasons, socioeconomic and other family-level factors are likely to also be highly correlated to various built and natural exposure variables. Thus, thoughtful consideration is vital in reporting on potential exposure and outcome associations but also the nexus of neighborhoods, communities, and environmental justice and equity. The legal landscape around marijuana in the USA is changing rapidly. Currently, medical marijuana is legal in 25 states and Washington DC, with retail marijuana legalised in four states and Washington DC. On 1 January 2014, Colorado became the first state to legally sell retail marijuana to people 21 years or older. Shifting regulations have been accompanied by technological innovations, including electronic vaporisers for tobacco and marijuana.

These developments are likely to transform use of these substances, especially among young adults. Nationally, young adults have the highest rates of current marijuana use, with 18.9% aged 18–25 years using in 2013, compared to 7.1% aged 12–17 years and 5.5% aged ≥26 years.According to 2014 data, almost 30% of young adults in Colorado reported current marijuana use.Young adults also have the highest current rates of tobacco smoking .More young adults have ever tried e-cigarettes compared to other age groups.Rates of dual and poly use are also high: in 2013, among US young adults who smoked cigarettes in the past 30 days , 47% were current marijuana users.Given high rates of co-use of tobacco and marijuana among young adults,as well as transformations in the realm of policy and technology, tobacco, marijuana and vaporisers are most effectively studied in relationship to one another.Referred to as ‘the triangulum’ , this approach reflects interest in the intersection of tobacco, marijuana and electronic vaporiser use, with implications for surveillance , policy and treatment.Several quantitative studies examined some aspects of the triangulum, including co-use of combustible tobacco and marijuana,perceptions of comparative harm of tobacco and marijuana,prevalence of vaporiser use among marijuana users and reasons for use of marijuana vaporisers.Two qualitative studies examined the intersection of tobacco and marijuana by interviewing youth in Scotland.The data in the studies, however, were collected over a decade ago and do not reflect changing legal and normative environments around marijuana or the proliferation of vaporising devices. Several quantitative studies have addressed marijuana vaporisers use by adults, including Lee et al and Etter,but neither was designed to explore in depth why users choose to vaporise marijuana, or the social or policy contexts shaping vaporiser use. To the best of our knowledge, this is the first in-depth, qualitative investigation of the triangulum in the ‘natural laboratory’ of Colorado. We interviewed young adults in Colorado to understand how they use, perceive and ascribe meaning to various tobacco, marijuana and vaporiser products.As part of the State and Community Tobacco Control research initiative , this project was developed in strategic partnership with Denver Public Health and Jefferson County Public Health departments in Colorado.

Beginning in early 2014, we worked with local agencies to identify research questions that would advance policy solutions and practice. These questions were further refined iteratively throughout the data collection period. Local agencies provided staff to recruit participants, assisted with interviewing, provided space for interviews and engaged key stakeholders in reviewing early findings.Participants were recruited using flyers placed in marijuana dispensaries, vape shops, cafes, stores and on bulletin boards at community colleges in the Denver Metro area. Online recruitment was conducted through Craigslist and posting on Facebook. Inclusion criteria included being 18–26 years old and current use of at least one of the three products . Prospective participants were screened and enrolled in the study via telephone by trained research staff. We attempted to interview all 32 enrolled participants twice, in order to allow conversations to develop more deeply. Twenty-four completed both interviews. Participants were compensated $35 for the first interview and $65 for the second. Each participant gave written consent. All study protocols were approved by the Committee on Human Research at the University of California, San Francisco.Semi-structured interviews were conducted between January and August 2015 by six trained interviewers , following a standard interview guide. Interviews were conducted individually in public places or in meeting rooms in local health departments. Before each interview, participants completed brief questionnaires with demographic information and past tobacco and marijuana use history. Discussion topics included definitions of smoking, experiences with tobacco, e-cigarettes, marijuana, marijuana vaporisers and other products, perceived benefits and risks of products and experiences with marijuana legalisation in Denver. Interviews lasted between 60 and 90 min, and were audio recorded.Audio recordings were professionally transcribed. Data were coded using Dedoose software. Researchers McDonald and Popova independently blind-coded a subset of transcripts, which were then compared to develop coding guidelines. Researchers created code definitions and developed a consistent coding scheme to ensure that codes were applied consistently. The larger set of transcripts was divided and coded independently. Themes were generated iteratively during review of coded transcripts. Memos summarising each theme with illustrative quotes were reviewed by authors and discussed iteratively to reach consensus and theme saturation. Pseudonyms are used for all participants quoted in this article and no real names have been used.Participants highlighted fluidity between use of tobacco,microgreen flood table marijuana and vaporisers. Reflecting this fluidity, the terms ‘smoke’ and to be a ‘smoker’ were used to describe either tobacco or marijuana use in ways that left unclear which substance was referred to. While dual and poly use was our primary focus, some participants also reported co-use through merging products, including use of tobacco wraps or little cigars/ cigarillos to smoke marijuana and the use of tobacco cigarettes to ‘extend’ the effects of marijuana. Vaporising devices were used to consume either nicotine or marijuana concentrates, with such devices nearly indistinguishable in appearance. Participants remarked upon the increasing popularity of ‘vaping’, expanded interest in vaporisers for nicotine and marijuana products and the convenience of vaporisers for use in public spaces. In some contexts, participants clearly distinguished between tobacco, marijuana and vaping, as they did when discussing the risks of secondhand smoke. Participants viewed secondhand tobacco smoke as potentially dangerous, often limiting or prohibiting use of combustible tobacco in homes or cars.

Marijuana secondhand smoke, in sharp contrast, was widely considered safer and more pleasant smelling than tobacco smoke, with few participants restricting combustible marijuana indoors.Our question ‘Do you smoke?’ was frequently met with the question: ‘smoke what?’ The term ‘smoking’ was used interchangeably to refer to the use of marijuana or tobacco, with this ambiguity only uncovered through conversation: when a researcher asked ‘Ethan’, ‘In terms of your social circle in Colorado, do many people smoke?’ ‘Ethan’ responded, ‘everyone that I work with under the age of 30 smokes. I have five roommates and they all smoke. Just about everyone I know in Denver smokes. I have one friend that doesn’t, just because he gets panic attacks’. When the researcher asked whether these friends were regular or occasional smokers, ‘Ethan’ responded, ‘Much more regular marijuana smokers…[pause] are we still talking about tobacco smoking? When I hear “smoking” now, I associate it more with marijuana than tobacco smoking’. ‘Ethan’ clarified that among his friends, only five were regular tobacco smokers, whereas the majority smoke marijuana. In Colorado, he elaborated, the term ‘smoke’ primarily indicates use of marijuana, but added, ‘If I go back to Texas, and somebody says, “I’m going to go for a smoke,” I know [they mean] cigarettes— tobacco’. When asked if he ever smoked while drinking, ‘Owen’, 20, commented, ‘Yeah… if I have one drink I’ll probably be smoking before, you know?’ When the researcher asked him to clarify whether he meant smoking marijuana or tobacco, he responded, ‘Marijuana. I don’t really smoke tobacco products like that. The only reason why I put 20 times [of tobacco use per month on the questionnaire] is because [of ] Swisher Sweets[cigarillos]. I’d have to get a Swisher Sweet to roll up the marijuana, you know?’. He added that he would not smoke cigarillos ‘straight’, but only as a wrap for marijuana.The emerging issues uncovered in this qualitative study highlight the need to reconsider the traditional silo-based approach to tobacco control and marijuana research. It is particularly important to consider the triangulum of tobacco, marijuana and vaporisers, and we believe this is the first study to address this intersection in the context of legalised marijuana. We found widespread ambiguity about whether ‘to smoke’ referred to the use of tobacco or marijuana products. While not unique to Colorado, this linguistic equivalence between tobacco and marijuana use may signal increasing normalisation of marijuana. Researchers should be aware of this ambiguity in designing precisely worded research instruments. Additionally, antitobacco messaging that focus on ‘smoke’ or ‘smoker’ identity may be diluted in this context, as combustible marijuana moves towards legality and widespread availability. Participants reported the use of tobacco products as part of the consumption of marijuana. This points to several key issues.

Screening and assessment for unhealthy substance use offers clinicians the opportunity to identify harmful substance use or disorders and provides the opportunity to address such use. However, few studies have reported on screening for substance use as part of HIV primary care. The goal of our study was to characterize patterns and severity of substance use through two different screening and assessment approaches in a large, urban public HIV clinic providing primary care to PLWH and to describe gender and racial differences in alcohol, tobacco, and other substance use. Descriptive statistics, including frequencies, percentages, means, and standard deviations were performed to characterize the sample. Pearson’s chi-square and Fischer’s exact test analyses were used to determine differences in risk stratification of SSIS scores between male and female genders, and across the four race/ethnicity categories and substances detected via urine toxicology. All analyses were conducted with STATA 14. Urine toxicology screens provides information that will tell you if a substance is present in the urine or not, however, urine tests cannot be used for diagnosis of a substance use disorder. We enrolled 208 HIV primary care clinic patients from an urban public clinic. The analysis presented here is based on the 168 participants who completed both the ASSIST questionnaire and urine drug screening procedure. The participants were primarily male ; and more than one third were African American . There were no significant demographic differences between the entire sample of 208 and the analytic sample of 168. The average age was 45.66 years with an average of 12.40 years living with HIV. The majority of the participants had an undetectable HIV viral load . More than two thirds of the study sample reported using tobacco or other non-prescribed substances in the previous 3 months. Forty-one percent of our participants reported alcohol use for the same time period.

As described in the Methods section, we determined Single Substance Involvement Scores for each substance reported and stratified these scores into low , moderate , and high risk for all substances except alcohol ,flood table following the validated ASSIST scoring guidelines. More than half of our participants’ SSIS scores indicated moderate risk for tobacco, and cannabis . The three drug classes with the greatest number of participants exhibiting high-risk scores were for tobacco , cocaine , and amphetamine . The SSIS for alcohol use indicated that over one-third of study participants reported a moderate risk level for alcohol and 7.7% had a high-risk score for alcohol use. When comparing the SSIS score for each substance by gender and race, we observed differences in reported substance use. Compared to females, males in this sample reported greater levels of moderate risk cannabis use and moderate risk amphetamine use . There were also significant differences for cocaine use with Hispanic or Latino/a participants reporting lower risk use than African American, White/Anglo, or Other race participants . Finally, more African American participants reported low or no risk amphetamine use as compared to Hispanic or Latino/a, White/Anglo, or Other race participants . More than half of the sample submitted urine specimens that tested positive for cannabis , nearly one third tested positive for cocaine, and almost a quarter tested positive for benzodiazepines. Significant gender differences in urine toxicology were also present . Male gender was significantly associated with positive urine toxicology for amphetamine and methamphetamine . Female gender was significantly associated with positive urine toxicology for cocaine , methadone , and opiates . Significant racial differences were also observed in urinetoxicology. Those of Other race or ethnicity screened positive for cannabis use more frequently . Both Hispanic or Latino/a participants and White/Anglo participants screened positive for cocaine less frequently. African American race or ethnicity was associated with lower levels of positive urine toxicology for both amphetamine and methamphetamine . In this study of patients in an HIV primary care clinic-based urban population, we found high rates of self-reported substance use, which were confirmed by urine toxicology testing. The SSIS risk scores for all substances, excepting inhalants and hallucinogens, demonstrated that moderate and high-risk substance use was highly prevalent in this sample of patients.

Reported substance use in this HIV clinic sample was higher than in other studies of both HIV and non-HIV primary care patient samples for most substances reported except for tobacco use. In the United States, approximately 19% of the adult population smokes cigarettes . When compared to the U.S. general population, a number of studies have documented considerably higher rates of smoking in PLWH , which is of grave concern given the now well-documented increased mortality associated with smoking among PLWH due to cardiovascular disease and non-AIDS related cancers . For other substances such as cannabis, our sample exhibited levels of use similar to other primary care settings where the ASSIST measure was used. However, in another study of an HIV clinic-based sample, the reported use of cannabis was 18% , which was considerably lower than what we found in our study. When examining other substances reported by participants in our study, we saw similarities compared to other clinic samples of HIV-infected and uninfected patients, for example with stimulant use . A large number of participants in our sample reported moderate or higher ASSIST scores for cocaine and amphetamine-type stimulants . There have been a multitude of studies on stimulant use and HIV, ranging from stimulants as a risk factor for HIV transmission and as a method of managing mental health symptoms and the experience of discrimination, to the manner in which they impacted adherence to ART; however, very few of these samples were drawn solely from clinic settings where HIV care was delivered. In the studies that have been conducted in HIV primary care settings, a range of stimulant use has been reported. Skeer et al. studied HIV-infected men who have sex with men in a large primary care setting in Boston, MA. and reported that 21% of their sample used amphetamines. In an earlier study of a nationally representative probability sample of PLWH, 40% of the subjects reported using an illicit drug other than cannabis. In a more recent study of the Women’s Interagency HIV Study, investigators did not solely recruit samples from HIV primary clinics; however, nearly one third of the HIV infected women in the sample reported crack cocaine use within the previous 3 months . The participants in our study also reported a high prevalence of moderate-severe SSIS for alcohol . In comparison, the 2013 National Survey on Drug Use and Health determined the national rate of alcohol use disorders was 7% . While in studies conducted in general outpatient settings site the prevalence of unhealthy alcohol use ranging from 7 and 20% . The methods used in these studies vary, however, the prevalence of alcohol use in general medical settings is much lower than what we measured in this sample.

Alcohol like other substance use can complicate HIV care and treatment outcomes and continues to be a major driver of HIV acquisition. Substance use patterns can differ between women and men. In the literature, many studies of HIV and substance use conducted with MSM have focused on alcohol or amphetamine use , while studies of HIV-infected women have been more focused on crack cocaine and heroin use . In our study, we observed gender differences in SSIS scores and in urine toxicology results. Males in our sample had a significantly higher proportion of moderate or high-risk SSIS scores for amphetamine and for cannabis , while women had significantly higher levels of cocaine, methadone, and opiate positive urines when compared to men . This differed from what we observed in the self-report SSIS scores. While women were marginally more likely than men to report moderate or high-risk cocaine use, this difference was not statistically significant. Many studies in the HIV literature have focused on men, MSM, or women and substance use. To our knowledge, however, no studies analyzed gender differences between men and women in an HIV-infected sample. One more general study found that women were more likely to have a substance use disorder combined with other mental illness compared to men; however, there were no gender differences in the presence of a substance use disorder in the absence of mental illness . Urine toxicology in our study looked different from self-report responses using the ASSIST. Urine drug screening is limited to the detection of drug use within a few days before the test and, as in most tests,indoor plant table false positives and false negatives as well as technical problems can occur. Although objective, the use of biomarkers is not without limitation. The literature has indicated that, in some persons who use drugs, self-report, when compared to urine toxicology verifies under reporting of illicit substance use, although it is not known how widespread this is. Also, some clinicians may conduct urine screening as evidence of therapeutic adherence and evidence of use or non-use of illicit drugs. . In our sample, women had more methadone and opiates in their urine when compared with men; however, opiates and methadone are both commonly prescribed in medical settings for both pain management and opiate agonist therapy and we did not systematically ask participants if they were being prescribed opiates. As reported by Robinson-Papp, Elliott, Simpson, and Morgello , singular reliance on self-reports for implementation of substance use screening and brief interventions has limitations. In addition, more stigmatized drugs, such as cocaine, methamphetamine, or heroin, may be under-reported using self-report but could be documented with urine toxicology tests . In this study participants were paid for urine testing which might not happen in a primary care setting so motivation to provide a urine sample may be different. While we are not advocating urine screening as the initial step for screening in a clinical setting, some clinicians may use it as a tool to work with patients with a history of substance use to validate their reported use and not as a test, which could penalize the patient . Although substance use levels differed by screening modality in our study, the evidence clearly pointed to high levels of substance use in this HIV clinic sample.

High amounts of reported substance use found in our study and others highlights a critical problem that HIV clinicians may be overlooking and that could be addressed by universal substance use screening. Based on the evidence of efficacy for screening and offering a brief intervention for alcohol and tobacco use, the U.S. Preventive Services Task Force has recommended universal preventive substance use screening in primary carefor adolescents and adults . While screening and brief intervention has shown promise for harmful alcohol use and smoking , the efficacy of universal BI for illicit drug use and prescription drug misuse has not been universally recommended for primary care settings . However, because of the overwhelming evidence that illicit drug use negatively impacts health, research to determine the efficacy of screening and brief intervention for drug use is ongoing. SBIRT has emerged as an important model for identifying and addressing substance use problems in health care settings . Brief intervention approaches are typically delivered on site, and individuals with more severe substance use problems also may be offered referrals to specialized treatment. Brief intervention for non-treatment seeking samples has strong support in the alcohol literature and some promising effects have been observed with respect to other substance use . Substance use screening followed by a brief intervention conducted by an individual trained in motivational interviewing has been extensively examined in adolescents and young adults using drugs and alcohol. These studies have revealed significant reductions in marijuana use ; decreases in alcohol use, binge drinking, and days of drug use ; lower alcohol, tobacco, and cannabis use ; and reductions in illicit drug use . To our knowledge, few studies of SBIRT have been conducted in HIV settings. Cropsey et al. conducted an SBIRT feasibility and acceptability study in an HIV primary care clinic to address the high rates of smoking by PLWH; the findings of Cropsey’s study indicated that SBIRT was feasible and acceptable to staff and patients in the HIV primary care setting. Using SBIRT as an approach for SBI was feasible and acceptable for many participants in our study .

False negative reports of a general substance-related problem can include statements that the person did not take the substance when he or she had been using, admissions of use but denial of high levels of consumption or associated problems that occurred, or a person admitting to substance related difficulties but denying an overarching problem with the substance . For alcohol, the focus of the current analyses, the latter might be a form of denial that is especially problematic for clinicians who only ask general questions about substance use and problems rather than using standardized screening questionnaires, like the Alcohol Use Disorders Identification Test . In such situations, the clinician might ask questions like “How much do you drink?” or “How would you describe your drinking pattern?”. The answer they might receive from many individuals who fit the definition of an AUD could be something like “I’m a moderate social drinker”. Much of the literature on denial has focused on underlying mechanisms that contribute to false negative reports regarding SUDs. Possible mechanisms include deliberate and conscious lies to avoid negative views from others or consequences of the behaviors and false negative reports from cognitive difficulties in correctly appraising the dangers of the substance use . Other theories reflect psychodynamic defense mechanisms where persons facing substance-related psychological stressors subconsciously “defend” themselves by denying that the substance problem or adverse event occurred . It is likely that multiple factors contribute simultaneously to denial ,trimming tray and the literature suggests that the underlying mechanisms might differ with different drugs and for different situations . The current analyses focus on inaccurate denial of current AUDs in individuals who report themselves as light or moderate social drinkers. To prepare for the study we searched the literature for specific characteristics of individuals who evidence denial.

Regarding demography, the most consistent data were seen for race/ethnicity where a relatively scant literature indicated that a range of denial-related behaviors were more common for African American and Hispanic American subjects than for European Americans . Marital status and education level did not consistently relate to the probability of denial , although one study suggested more denial among lower educated individuals . Even more inconsistent results were seen for the relationship to denial for sex, age, socioeconomic status or income . We found no published studies regarding whether an individual’s report of specific AUD criteria items were more likely to relate to inaccurate recognition or reluctance to admit to an overall alcohol problem. Optimally, the impact of specific criteria should be evaluated while also considering the relationship of denial to drinking quantities, the number of alcohol problems, and whether an individual has alcohol abuse or dependence in DSM-IV. Our group recently reported a phenomenon that might overlap with denial. That paper searched for characteristics of San Diego Prospective Study probands with AUDs whose young-adult offspring erroneously reported no significant alcohol problems in that parent . The attributes of the person who denies their own overarching alcohol problem might be similar to characteristics related to lack of recognition of his alcohol-related difficulties by his offspring. Items associated with an offspring’s incorrect report of their father’s problems included the lack of endorsement of four specific AUD criterion items. These included probands denying spending a great deal of time to obtain, use, and/or recover from alcohol , not endorsing decreasing important activities due to alcohol , and not admitting to continuing to use alcohol despite physical and/or psychological problems or despite social and/or interpersonal problems . This paper uses data from two SDPS generations to evaluate characteristics associated with denial of global ratings of problem drinking in individuals who admitted to specific abuse or dependence criteria.

The analyses test five hypotheses: 1) Based on clinical experience and the literature we estimate 30%–50% of SDPS AUD subjects will not rate themselves as falling into problem drinking categories; 2) The lower the number of AUD criteria endorsed the greater the chance of denying having a general problem with alcohol; 3) The lower the maximum drinks endorsed the greater the probability of denying having a general problem with alcohol; 4) Individuals with alcohol abuse will be more likely than those with alcohol dependence to deny having a general problem with alcohol; and 5) The absence of the four criterion items that related to false negative reports by offspring of their proband father’s AUD will also relate to that father’s own denial of a general problem with alcohol including D5 ; D6 ; D7 ; and A4 . Following University of California, San Diego Institutional Review Board approval, randomly mailed questionnaires were used to recruit 453 SDPS probands as drinking 18-to-25-year-old male UCSD students who never met criteria for an AUD, SUD, bipolar disorder or schizophrenia and did not currently have a major depressive or anxiety disorder.Beginning in 1988, the 453 probands began participation in every five-year personal follow-ups using a semi-structured interview reviewing substance use and problems based on the Third-Revised and Fourth Diagnostic and Statistical Manuals . The questions were extracted from the Semi-Structured Assessment for the Genetics of Alcoholism. Fifteen-year follow-ups included the Self Report of the Effects of alcohol questionnaire, the Impulsiveness Subscale of the Karolinska Scales of Personality and the Zuckerman Sensation Seeking Scale . The SRE records numbers of standard drinks required for up to four effects including a first effect, feeling dizzy or slurring speech, unstable standing, and unplanned falling asleep. SRE-5 scores for the first five times of drinking and is generated by the total drinks in that period needed across effects divided by the number of effects endorsed. SRE-T scores reflect the average across first five, heaviest drinking period, and recent 3-month drinking. Higher average drinks needed for effects indicates lower response per drink and higher future risk for alcohol problems . As probands’ biological children reached age 18, they were personally interviewed every five-years using SSAGA-based questions. The first interview following their 18th birthday included the impulsivity and sensation seeking questionnaires, and, for those with experience with drinking, the SRE.

Analyses include all 94 AUD male probands and all 176 offspring who met AUD criteria in the five-years prior to the index interview and these participants were not chosen as proband-off spring pairs. Their SSAGA-like interviews queried their recent five-year quantities, frequencies and problems associated with substances, including all 11 DSM-IV substance-related criterion items. We added a final question to the alcohol section which asked: “Since your prior evaluation , how would you label your own drinking pattern overall?” The options included: 1) nondrinker/abstainer; 2) infrequent/occasional light social drinker; 3) moderate social drinker; 4) frequent/heavy social drinker; 5) problem drinker/alcoholic; and 6) recovering alcoholic. The follow-up rate in the SDPS was over 90 %, and maximum likelihood procedures were used to address missing data with Little’s MCAR test showing data missing completely at random . Tables 1,2,3, respectively, describe AUD proband and AUD offspring demography, personality, and substance-related variables for all relevant participants combined and then separately for subjects who rated themselves as falling into categories 1–3 regarding their drinking pattern overall versus those who rated themselves as categories 4–6 . The deniers were reporting categories that might indicate to clinicians that a patient does not have problems with alcohol. The first step, univariate comparisons of Groups 1 versus 2, used F-tests for continuous variables and x2 for categorical data. Tables 2 and 4 present our key results involving backwards elimination logistic regression analyses using variables that significantly differentiated between deniers and non-deniers in Tables 1 and 3. Finally regarding methods, for both probands and offspring data, multicollinearity was assed using both simple correlation matrixes among the variables and evaluating for variance inflation factors . For correlations, values greater than or equal to 0.80 and for VIF values greater than 5 indicate possible multicollinearity .Table 1 for probands and Table 3 for offspring each first present data for the entire relevant sample and then separately for Group 1 denier and Group 2 non-denier participants. Self-ratings of their general alcohol status among AUD probands included 0% nondrinkers, 12 % infrequent/occasional light social drinkers, 55 % moderate social drinkers, 25 % frequent/heavy social drinkers, 6% problematic drinkers/alcoholics and 2% recovering alcoholics. AUD offspring self-ratings were 0% non-drinkers, 24 % infrequent/occasional light social drinkers, 58 % moderate social drinkers, 13 % frequent/heavy social drinkers, 2% problematic drinkers/alcoholics and 3% recovering alcoholics. Table 1 demonstrates that overall most AUD probands were European American, had ever married, 70 % had children, and their average education was 17 years. On average, probands endorsed 2.5 AUD criteria and 52 % were alcohol dependent with the remainder meeting alcohol abuse. Thirty-one percent had used cannabis in the recent five-years, 4% met cannabis use disorder criteria, 17 % smoked cigarettes,10 % used other illicit drugs, including 2% who met SUD criteria on that substance. Among AUD probands, 67 % were classified as deniers of problematic drinking . Significant alcohol-related univariate comparisons between probands in Groups 1 and 2 revealed that deniers were less likely to have alcohol dependence, reported lower average maximum drinks,grow tent kit and were less likely to endorse five AUD criteria, including dependence criteria D4, D5, and D7, along with abuse criteria A1 and A4. These included three of the four criteria predicted in Hypothesis 5.

Deniers were also less likely to have SUDs for noncannabis drugs. While not noted in the table, the correlation between a false negative family report of a father with an AUD in the prior paper and an AUD father being a denier in the current analysis was 0.28 . Table 2 presents results predicting AUD proband denier status using a backwards elimination logistic regression analysis that included variables that differed significantly across deniers and non-deniers in Table 1. Four variables contributed significantly to the analysis including three of the criteria predicted in Hypothesis 5 along with a SUD on illicit drugs other than cannabis. Tables 3 and 4 focus on 176 AUD offspring who were primarily European American, 40 % of whom were women, 29 % had ever been married, and individuals who reported on average 15 years of education. Sixty-two percent met interval criteria for alcohol dependence, they reported on average 11 maximum drinks per occasion and endorsed an average of four AUD criteria. One in five smoked cigarettes in the prior 5 years, 80 % used cannabis,19 % had a cannabis use disorder, and 37 % had used other illicit drugs, including 3% who developed a SUD on those substances. Comparisons of Groups 1 and 2 revealed that the 82 % who were deniers were slightly younger and had lower proportions with alcohol dependence, lower average maximum drinks, and fewer AUD criteria endorsed compared to non-deniers. Group 1 deniers were also less likely to endorse every specific AUD criterion except for D3 . AUD offspring in Group 1 on average reported fewer drinks required for effects across the time frames , were less involved with other drugs and had lower scores on sensation seeking. Group 1 and 2 offspring comparisons were repeated for the 106- male offspring, 84 of whom were deniers. Here, results were generally consistent with those in Table 3. Analyses using the 70 female offspring alone could not be adequately interpreted because there were only 9 non-deniers. Table 4 describes the backwards elimination regression analysis predicting denial in AUD offspring using variables that differed significantly across Groups 1 and 2 in Table 3. Like Table 2, significant predictors of denial involved indicators of less intense alcohol involvement and less use and/or problems with other drugs. The five specific variables in Table 4 included only one that contributed to Table 2 and one variable noted in Hypothesis 5 , but D6 had not entered the regression analysis for probands. The three other variables included lower proportions of deniers who smoked, reported alcohol withdrawal, or met criteria for alcohol dependence. If regression analyses were limited to the 106 AUD males, denial remained associated with lower levels of both alcohol and drug related problems, but the specific items for male offspring included a lower average maximum drinks per occasion, lower cannabis use, and deniers had a lower average age. Within the same interview session 67 % of SDPS probands with current AUDs and 82 % of current AUD offspring endorsed enough alcohol problems to meet DSM-IV AUD criteria but denied having a general alcohol problem.

Two waves of surveys were administered at varying times by each cohort between May 11th, 2020 and February 15th 2021. Given our objective to explore associations of alcohol and other drug use, we conducted a cross-sectional analysis of survey respondents who had complete information on alcohol use and the covariates of interest. If people responded to more than one wave of the survey, we used their first survey. We explored incorporating alcohol consumption prior to the start of the COVID-19 pandemic but 75% of the respondents were missing this information which limited this analysis. The primary outcome was alcohol use, categorized into three groups based on participant responses to the United States Alcohol Use Disorders Identification Test – Consumption questions , modified to ask about drinking in the past 30 days rather than past year . We denote the three groups as; no alcohol use , low-risk alcohol use, and hazardous alcohol use based on terminology best practices .The third question asks about heavy episodic drinking . Heavy episodic drinking was defined as reporting drinking ≥ 4 drinks for women or ≥ 5 drinks for men in a single occasion , 2016. Hazardous alcohol use was defined as averaging ≥ 7 drinks per week for females and males ≥ 65 years of age, ≥ 14 drinks per week for males < 65 years of age or reporting heavy episodic drinking on any occasion in the past month. Low risk alcohol use was defined as reporting any alcohol use below the thresholds for hazardous use. No alcohol use was defined as reporting no alcohol use in the past 30 days. Demographic covariates included age, sex assigned at birth , and self-reported race/ethnicity. We also included two indicators of socio-economic status: employment status and current food insecurity. Respondents were classified as employed if they endorsed being employed full time,roll bench employed but with a reduction in hours, furloughed, or working “without formal employment” . Current food insecurity was defined as endorsing either not having enough money for food, or rationing food.

Mental health variables considered were anxiety measured on the Generalized Anxiety Disorder-7 categorized as none-low anxiety , resilience as classified by the Brief Resiliency Scale categorized as low , normal , or high resiliency , self-reported disruptions to mental health care during the pandemic, and level of worry about the pandemic. Patients reported their level of worry about COVID-19 on a scale of 1–10, and patients who reported a level of ≥ 5 were classified as having substantial worry. HIV status was self-reported at the time of the survey. Current drug use was ascertained using the second question of the Alcohol, Smoking and Substance Involvement Screening Test modified to ask about the past-month frequency of use . ASSIST provides 5 levels of frequency of use and participants werecategorized as self-reported past month use for each of the following substances: tobacco, stimulant , non-prescribed opioid , and cannabis. Participants also reported whether they were currently receiving any substance use disorder treatment . Participants who reported being in substance use disorder treatment also reported whether there had been any disruptions to their treatment which could include missed in-person or telemedicine visits with clinicians or disruption in medications. Overdose was defined as self-reporting an overdose event in the past 30 days at the time of the survey. Lastly, because of broad differences in the six cohorts’ geographic and social profiles due to different study goals, cohort was included as an adjustment variable, as was survey wave . We did not adjust for calendar month in which the survey was completed due to high collinearity with cohort. We determined the proportion of individuals who reported no alcohol use, low-risk use, and hazardous use in the past 30 days. We examined associations between the covariates listed above with low-risk alcohol use and with hazardous alcohol use using multi-nomial logistic regression, with no alcohol use as the reference category. Age was included as a linear covariate in the model. On visual inspection of the relationship of age and prevalence of hazardous or low-risk alcohol use relative to no alcohol use, the rate of prevalence decrease for each increasing year of age was different for those ages < 50 and ≥ 50.

We accounted for this by adding a knot at age 50 which allows for separate slopes to be calculated for ages < 50 and ≥ 50. We report the results of both crude and fully adjusted models . A post-hoc secondary analysis was conducted to measure the association between opioid and stimulant use patterns and alcohol use. A total of 2121 participants completed a survey. Of these, 14 participants were excluded due to missing alcohol use information on their survey. We excluded 123 participants that were missing information on any covariates of interest. Table 1 provides the study population characteristics. The median age of the study sample was 42 years, and a majority were male and non-Hispanic Black . Current employment was reported by 43% of participants and 28% reported some limitations to their access to food. At the time of the survey, 42% reported having HIV. Overall, 45% of the sample reported no alcohol use in the past 30 days, 33% reported low-risk alcohol use, and 22% reported hazardous alcohol use. Current tobacco use and cannabis use were relatively common and there was substantial use of stimulants and opioids as well. Of 351 participants who used either opioid or stimulants, 224 used stimulants only, 77 used opioids only, and 50 used both opioids and stimulants. Of the 17% of participants who were receiving substance use disorder treatment, 69% experienced disruptions to their treatment. Ten participants reported recent overdose. Table 2 shows the proportion of participants who reported current drug use and recent overdose by alcohol use category. Compared to participants with no alcohol use, participants with low-risk alcohol use had higher prevalence of stimulant use and cannabis use and similar levels of tobacco use , opioid use , and recent overdose . Among participants with hazardous alcohol use, there was a higher prevalence of tobacco , stimulant , opioid , cannabis use and overdose compared to participants with no alcohol use. Multinomial logistic regression estimates a ratio of prevalence ratios . In the crude analyses adjusted only for cohort, the prevalence of low-risk alcohol use relative to no alcohol use decreased with each year of age before the age of 50 and also after the age of 50 . The prevalence of low-risk alcohol use relative to no use was statistically higher among males and among employed participants , and lower among participants with HIV .

With respect to drug use, cannabis had the largest association with low-risk alcohol use relative to no alcohol use , but any drug use was associated with a higher prevalence of low-risk versus no use: tobacco , stimulants , and opioids . Participants receiving substance use treatment had a lower prevalence of low-risk alcohol use relative to no use . Among those in substance use treatment, there was a non-significant increase in the relative prevalence of low-risk alcohol use among those whose treatment was interrupted . Participants with recent overdose had a non-significant increased prevalence of low risk alcohol use relative to no use . Self reported race, food insecurity, and survey round were not significantly associated with the prevalence of low-risk relative to no alcohol use, nor were the mental health indicators of low-risk-to-severe anxiety, resilience, interruptions to mental healthcare, or worry about the pandemic. In the fully adjusted model , the prevalence of low-risk alcohol use relative to no use decreased with each year of age before 50 but not after 50 . The prevalence of low-risk alcohol use relative to no use remained higher among males and employed participants , and remained lower among participants with HIV . White participants had a lower prevalence of low-risk alcohol use relative to no use compared to non-Hispanic, Black participants. After adjustment, opioid use and tobacco use were no longer significantly associated with higher prevalence of low-risk alcohol use relative to no use ,drying rack cannabis but cannabis maintained the strongest association followed by stimulants . Receiving substance use treatment was still associated with a lower prevalence of low-risk alcohol use compared to no use . In our multi-cohort study of people with and at risk for HIV with high prevalence of drug use, we found that nearly a quarter of participants reported drinking above recommended levels set by NIAAA. As expected , drug use was relatively common and higher compared to the general population . However, the significant relationship between hazardous alcohol use and stimulant use is notable. Stimulant use in the last month was reported by 13% of all participants, while one-in-four participants with hazardous alcohol use reported stimulant use; when compared to people who did not report stimulant use, stimulant use was associated with a nearly 3-fold increase in prevalence of hazardous alcohol use compared to no use. Overdose deaths involving stimulants is rising and recognizing the strong relationship of hazardous alcohol use with stimulants should lead clinicians to screen for both alcohol and stimulant use when patients report using one those substances. Additional studies examining the temporal relationship of alcohol and stimulant use are needed to understand this relationship. Alcohol sales surged at the start of the COVID-19 pandemic with a 54% increase in sales in March 2020 . Multiple nationally representative surveys showed that alcohol spending and consumption increased . The prevalence of hazardous alcohol use in our study is comparable to U.S. general public which potentially suggests that, when considering alcohol use alone, these cohorts are similar to the broader community . However, we believe this finding should be a cause for specific concern for the End the HIV Epidemic plan . Alcohol use is associated with behaviors which increase the risk of HIV transmission, less adherence to anti-retroviral treatment, and lower retention of care among people with HIV which could hinder the national goal of stopping the HIV epidemic . At the same time that alcohol use is increasing, surveillance data shows that drug overdoses are now at the highest levels ever recorded . In both US and Canada, most overdose deaths involve heroin tainted by illicitly manufactured fentanyl and represent a continuation and worsening of the opioid overdose epidemic. However, stimulant use, including cocaine and methamphetamines, was rising prior to the COVID-19 pandemic, and stimulants are now involved in nearly half of overdose deaths .

In our study, stimulant use was strongly associated with both low-risk and hazardous alcohol use. Understanding the context and patterns of people’s use of alcohol and stimulants could inform harm reduction approaches as simulant use becomes more widespread. Other drug use including tobacco, cannabis, stimulants, and opioids was associated with increased prevalence of low-risk or hazardous alcohol use relative to no use. This result is consistent with previous studies demonstrating an association between hazardous alcohol use and other drug use . The drug most strongly associated with low-risk or hazardous alcohol use was cannabis, indicating the rarity of cannabis use in the absence of alcohol use. Opioid use alone was only weakly and not statistically significantly associated with hazardous alcohol use; the association between opioid use and stimulant use together and low-risk alcohol use was weaker and not statistically significant. People often mix opioids and stimulants, specifically cocaine, and combining both drugs could be a marker of more intense drug use and thus also more intense alcohol use. The co-use of opioids and alcohol raises the risk of overdose ; one-in-seven opioid overdose deaths involved alcohol . Given the association between these three substances in our study, further public health surveillance of hazardous alcohol use and its identification and treatment when caring for people who use opioids and stimulants could inform harm reduction approaches as simulant use becomes more widespread. For participants who had current substance use treatment and for those who have HIV, there was a lower prevalence of hazardous alcohol use. Given the cross-sectional nature of the study, we consider several potential explanations. For participants undergoing substance use treatment, they could be more motivated to not drink just as they are motivated to engage in substance use treatment .