A murine model also suggested inhaled VEA may cause EVALI-like lung injury,but the underlying mechanism remains to be determined. The age range of cases and deaths is broad, and the e-cigarette use patterns are diverse, although 75% of EVALI patients were young Caucasian males and an overwhelming majority admitted to THC vaping. While VEA from THC vaping has been most commonly and consistently linked to EVALI cases, the spectrum of usage patterns and clinical manifestations suggest a possible role of multiple toxicants from unregulated products. Chemical analysis of counterfeit cartridges obtained from EVALI patients demonstrated the presence of several toxicants including volatile organic compounds, semi-volatile hydrocarbons, silicon conjugated compounds, terpenes, pesticides, and metals, which were not found in medical-grade THC cartridges.The typical symptoms of EVALI include dyspnea, chest pain, cough, fever, and fatigue. Additionally, many of the EVALI patients also presented with nausea and vomiting and other gastrointestinal symptoms. Chest radiography of most cases was abnormal; images typically showed ground-glass opacities in both lungs.Four radiographic patterns were identified in EVALI patients including acute eosinophilic pneumonia, diffuse alveolar damage, organizing pneumonia, and lipoid pneumonia.Histological analysis of lung biopsies showed patterns of acute fibrinous pneumonitis, diffuse alveolar damage, or organizing pneumonia.EVALI patients may have slightly different phenotypes and have been diagnosed with acute respiratory distress syndrome, lipoid pneumonia, and pneumonitis. Patients have been treated with antibiotics and glucocorticoids,and the steroidal treatment has been shown to improve symptoms and lung function.
Although this is a new field,cannabis flood table where initial cross-sectional epidemiological studies have demonstrated several limitations, adolescents who vaped have been found to be more likely to try cigarettes than non-smoking non-vaping youth.For example, a cross-sectional analysis of PATH study data indicated an association between e-cigarette use and self-reported wheeze,and an analysis of data from 402 822 never-smoking participants in the behavioral risk factor surveillance system indicated an association between self-reported asthma and e-cigarette use intensity.It is important to recognize that the above studies were observational in nature, and the chronology of e-cigarette use and disease development are often not clear, so more evidence is needed that will further clarify the cause-effect relationship between e-cigarette use, cardiopulmonary disease, and cerebrovascular events. Regardless, these publications serve as an impetus for future research into the causative and mechanistic relationships between e-cigarette use and cardiopulmonary disease risk.E-cigarettes have been proposed as an effective strategy to quit conventional cigarette smoking, but they have not been approved for this purpose in the USA or elsewhere. To date, the clinical trials that have been carried out do not address the question of effectiveness in the “real world”, that is, does the availability of e-cigarettes in the marketplace decrease smoking at the population level. Instead, clinical trials have compared the delivery of nicotine by an e-cigarette to other modalities of nicotine delivery. The most recent review on this topic concluded: “The evidence is inadequate to infer that e-cigarettes, in general, increase smoking cessation. However, the evidence is suggestive but not sufficient to infer that the use of e-cigarettes containing nicotine is associated with increased smoking cessation compared to the use of e-cigarettes not containing nicotine, and the evidence is suggestive but not sufficient to infer that more frequent use of e-cigarettes is associated with increased smoking cessation compared with less frequent use of ecigarettes”.
To predict the relative dangers of second- and third-hand e-cigarette exposures, an understanding of the degree to which ecigarette use might lead to an increase in ambient nicotine and particulate matter, and the degree to which nicotine and other e-cigarette constituents deposit on surfaces, will be critical. Since there are no side stream aerosols from e-cigarettes, unlike combustible cigarettes, secondhand e-cigarette exposure is almost exclusively from user exhalation. Thus, it remains unclear and somewhat controversial as to what level of additional particulate matter, vapor phase, and nicotine emissions are released into the environment from e-cigarettes. Some of this uncertainty may relate to variability in device design and liquid composition. However, several studies have demonstrated ecigarette use by individuals can contribute to worse indoor air quality, including release of toxicants and particulate matter.For example, indoor e-cigarette use can generate fine particulate matter in high concentrations during natural use conditions in indoor environments, as well as an increase in particle numbers and concentrations of 1,2-propanediol, glycerin, and nicotine.Increased levels of 1,2-propanediol, diacetin,cannabis grow supplier and nicotine were also measured by gas chromatography from one exhaled e-cigarette puff. E-cigarettes containing nicotine-free solutions may have higher particulate levels than those containing nicotine. However, these particles dissipate much more quickly than cigarette smoke particles and further studies will be needed to fully understand the risk of second and third-hand exposures. Measurable nicotine levels have been detected in samples from hard surfaces and cotton surfaces exposed to e-cigarette emissions.Recent developments in detection strategies by use of auto fluorescence have further elucidated e-liquid deposition topography. One study found that for each 70-mL aerosol puff, 0.019% of the aerosolized e-liquid was deposited on hard surfaces.
These studies may also be an overestimate when compared to real-life scenarios because aerosol puffs were directly administered to the observed surfaces and were not inhaled and exhaled prior to surface deposition. However, in an attempt to provide a better model of surface deposition, deposition as a result of inhaled and exhaled e-cigarette aerosol was performed.This study found no significant increase in surface nicotine levels following 80 puffs per participant. The authors noted these results may not indicate a lack of risk for third-hand exposure, since they did not account for gradual accumulation on surfaces over time. Together, these results indicate that potentially hazardous e-cigarette emissions, including PG/VG, nicotine, and heavy metals may be deposited on household surfaces as a result of typical vaping behavior. Furthermore, they suggest a potential risk for third-hand exposure which could serve as a public health concern. However, more studies are needed to better understand the risk of vaping for second and third-hand exposures.In assessing the public health impact of e-cigarette use, there is an implicit comparison to alternative or counterfactual scenarios; in the case of e-cigarettes, the comparison is to the hypothetical situation of a world lacking e-cigarettes. There are established methods for quantitative risk assessment that are widely used for public health decision-making, such as the four-element paradigm set out in the 1983 National Research Council Report generally referred to as “The Red Book”. The elements include: hazard identification, that is, is there a risk?; exposure assessment, that is, what is the pattern of exposure?; dose-response assessment, that is, how does risk vary with dose?These four elements have general applicability to characterizing the impact of ecigarettes in terms of the prevalence of nicotine addiction and its profile across groups in the population and the associated additional burden of disease. Population impact is quantitatively assessed using conceptual models that capture an understanding of the relationships between independent and modifying factors and their outcomes. Models are implemented using statistical approaches and evidence-based estimates of the values of parameters at key steps in the model, for example, the rate of initiation of use of tobacco products with e-cigarettes present . This approach was used by the FDA’s Tobacco Products Scientific Advisory Committee to estimate the impact of menthol-containing tobacco products.
The overall approach was to formulate a conceptual framework, conduct systematic reviews around the framework, and implement an evidence-based statistical model for making estimates related to public health impact. The systematic reviews highlighted those gaps in scientific evidence, pointing to the most critical research needs for strengthening the evidence foundation for potential regulation of menthol. For e-cigarettes, the research priorities identified in this article relate to key evidence gaps that need to be addressed to achieve a greater and more certain understanding of the population impact of ecigarettes.People with HIV are twice as likely to engage in heavy alcohol use and two to three times more likely to meet criteria for an alcohol use disorder in their lifetime than the general population . Heavy alcohol use not only promotes the transmission of HIV through sexual risk-taking behavior and non-adherence to antiretroviral therapy , but also directly exacerbates HIV disease burden by compromising the efficacy of ART and increasing systemic inflammation . In addition to increased risk for physical illness , there is substantial evidence indicating that comorbid HIV and heavy alcohol use is more detrimental to brain structure and results in higher rates of neurocognitive impairment than either condition alone . The impact of comorbid HIV and heavy alcohol use on the central nervous system is especially important to consider in the context of aging. The population of older adults with HIV is rapidly growing; approximately 48% of PWH in the U.S. are aged 50 and older and the prevalence of PWH over the age of 65 increased by 56% from 2012 to 2016 . Trajectories of neurocognitive and brain aging appear to be steeper in PWH , possibly due to chronic inflammation and immune dysfunction, long-term use of ART, frailty, and cardiometabolic comorbidities . In addition to HIV, rates of alcohol use and misuse are also rising in older adults . The neurocognitive and physical consequences of heavy alcohol use are more severe among older than younger adults, and several studies also report accelerated neurocognitive and brain aging in adults with AUD . While mechanisms underlying these effects are poorly understood, older adults may be more vulnerable to alcohol-related neurotoxicity due to a reduced capacity to metabolize alcohol, lower total-fluid volume, and diminished physiologic reserve to withstand biological stressors . Altogether, these studies support a hypothesis that PWH may be particularly susceptible to the combined deleterious effects of aging and heavy alcohol use. For example, in a recent longitudinal report, Pfefferbaum et al. reported that PWH with comorbid alcohol dependence exhibited faster declines in brain volumes in the midposterior cingulate and pallidum above and beyond either condition alone. There is considerable heterogeneity, however, in profiles of neurocognitive functioning across individuals with HIV and AUD . Patterns of alcohol consumption rarely remain static throughout the course of an AUD, but rather are often characterized by discrete periods of heavy use. This episodic pattern of heavy consumption may similarly impact the stability of HIV disease , which may in part explain why some PWH with AUD exhibit substantial neurocognitive deficits while others remain neurocognitively intact. Self-report estimates of alcohol use, however, often fail to predict neurocognitive performance .For example, some studies classify individuals based on DSM criteria for AUD whereas others define heavy drinking based on “high-risk” patterns of weekly consumption . These methods characterize the chronicity of drinking and psychosocial aspects of alcohol misuse, but they are suboptimal for quantifying discrete periods of heavy exposure and high level intoxication that may confer higher risk for neurocognitive dysfunction. Binge drinking, defined by the National Institute on Alcohol Abuse and Alcoholism as 4 or more drinks for women and 5 or more drinks for men within approximately 2 hours, may more precisely capture discrete episodes of heavy exposure. The relationship between binge drinking and neurocognitive functioning remains poorly understood across the lifespan and particularly in the context of HIV. Thus, the current study examined two primary aims to better understand the impacts of HIV, binge drinking, and age on neurocognitive functioning. The first study aim examined the independent and interactive effects of HIV and binge drinking on global and domain-specific neurocognitive functioning. We hypothesized that: 1) neurocognitive performance would be poorer with each additional risk factor such that the HIV-/Binge- group would exhibit the best neurocognition, followed by the single-risk groups , and finally the dual-risk group; and 2) these group differences would be explained by a detrimental synergistic effect of HIV and binge drinking on neurocognition. The second study aim examined whether the strength of the association between age and neurocognition differed by HIV/Binge group. We hypothesized that: 1) older age would relate to poorer neurocognition; and 2) that this negative relationship would be strongest in the HIV+/Binge+ group. A modified timeline follow-back interview was used to assess drinking behavior in the last 30 days .