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The basis of the experiment is the stories of the Telegram chats where you can buy everything. They can be found by finding a QR code in the city. Marijuana is mostly marketed in these chats. Stickers can be found on road signs, trash cans, and even house walls. The aim of LTV was to find heavy drugs and find out how much they cost and when they can get them. Within approximately 20 minutes of studying Telegram chats and their users, it is possible to access new correspondence with a much wider offer — job advertisements for illegal transport of people, so-called vape rip-offs and also graphically beautifully designed offers for the purchase of prescription medicines. Of course, drugs too. Just like the supermarket, here are the top items of the week, with promotions and special offers every day. LTV started a chat with one of the sellers. He said he could get both mushrooms and synthetic drugs. The choice fell in favor of amphetamine. After agreeing on a location, all you have to do is transfer the money to a Lithuanian bank account. However, LTV did not make the transfer, nor did it go collect the drugs. The seller got angry at what had happened. It turns out he had already put amphetamine somewhere in Ziepniekkalns and was ready to send the coordinates of the package. A discount is even offered to buy the drugs after all. The drug market has grown sharply this year. Those people are working with us and testifying, it's not for another market, it's for our market. It is possible to fight some Telegram chats, but not eradicate all. Investigation takes time, but new chats are created daily. The investigation takes time, but new chats are founded daily. Medical workers have repeatedly reported an increase in overdoses. Their blood very often contains a mixture of all sorts of substances. And the frequency of cocaine use has increased recently. And cocaine is already used by students. There is demolition of Emergency Medical Service cars, demolition of our premises, also hotels. So huge aggression. And as if they are substances that shouldn't cause aggression. So we realize there's something else in there. The rest is of unknown origin. They mix it up at home and all that. There are no medical facilities. Select text and press Report a mistake to send a suggested correction to the editor. Take note — story published 1 year ago. October 19, , Authors: Eng. Seen a mistake? Tell us about a mistake. Related articles. Education and Science. Follow Eng. Large companies think most about environmental impact in Latvia. Study: Latvians look for cheapest deal in car insurance. Gender gap in unemployment widens in Latvia. Another large solar park comes online in northern Latvia. Intriguing discoveries in the Baltic Sea off the Latvian coast. Leave some leaves in your backyard. Hodges: 'We have an excessive fear of Russian escalation'. Large shipment of donated fertilizer leaves Latvia for Sri Lanka. Eight Saeima deputies face up to 'administrative responsibility' in last six months. Latvian Saeima issues statement reaffirming support for Ukraine. Saeima starts viewing draft budget. Baltic Assembly meets in Vilnius. Latvians do well for foreign language skills. Security Service investigates anti-Latvian remarks by shop assistant. Latvian Radio: Verdict looms in notorious Bunkus murder case. More Russian entertainers added to Latvia's entry blacklist. Divided views over idea to cut writer's monument in half. Three for the Weekend: Lights, Festival, Exhibition. Basic hygiene training for food deliverers underway in Latvia. Latvian witch tales told in bilingual book. Survey: Youth still use Latvian language. Latvia's investment agency might dismiss 30 employees. Don't forget about winter tires, Latvian road authority says. Insights from inside Ukrainian public media. LTV buys new portable TV stations. Sunny autumn weekend in store for Latvia. Weather will remain sunny until end of week in Latvia. October snow unlikely this year in Latvia. Sunny weekend in store for Latvia. Warm weather forecast for Latvia on Thursday. Weekend will get sunnier in Latvia. Most important. Most read.

Demand Curves for Hypothetical Cocaine in Cocaine-Dependent Individuals

Vars buy cocaine

Official websites use. Share sensitive information only on official, secure websites. Correspondence should be addressed to: Matthew W. Drug purchasing tasks have been successfully used to examine demand for hypothetical consumption of abused drugs including heroin, nicotine, and alcohol. In these tasks drug users make hypothetical choices whether to buy drugs, and if so, at what quantity, at various potential prices. These tasks allow for behavioral economic assessment of that drug's intensity of demand preferred level of consumption at extremely low prices and demand elasticity sensitivity of consumption to price , among other metrics. However, a purchasing task for cocaine in cocaine-dependent individuals has not been investigated. This study examined a novel Cocaine Purchasing Task and the relation between resulting demand metrics and self-reported cocaine use data. Demand curves were generated from responses on the Cocaine Purchasing Task. Correlations compared metrics from the demand curve to measures of real-world cocaine use. Group and individual data were well modeled by a demand curve function. The validity of the Cocaine Purchasing Task was supported by a significant correlation between the demand curve metrics of demand intensity and O max determined from Cocaine Purchasing Task data and self-reported measures of cocaine use. Partial correlations revealed that after controlling for demand intensity, demand elasticity and the related measure, P max , were significantly correlated with real-world cocaine use. Results indicate that the Cocaine Purchasing Task produces orderly demand curve data, and that these data relate to real-world measures of cocaine use. Keywords: cocaine, behavioral economics, demand curve, response output curve, purchasing task, addiction, drug dependence, drug abuse, unit price, human. Behavioral economics offers a framework to understand how the behavior of an organism is maintained by different commodities, or reinforcers Lea ; Hursh ; Hursh Behavioral economics has been especially useful in analyzing the self-administration of drugs by both humans and non-human animals e. The main dependent variable in behavioral economics is the amount of reinforcer consumed, which is considered the demand for that reinforcer. To illustrate the metrics obtained from such behavioral economic analyses, Figure 1 shows a hypothetical demand curve reinforcers earned or consumption corresponding to the left y-axis, and the associated response output curve responses emitted corresponding to the right y-axis. Demand intensity is the organism's preferred level of consumption of that reinforcer when it is available at extremely low prices, and is represented in Figure 1 by the value on the left y-axis where the demand curve intersects the left y-axis. Demand elasticity is the slope of the demand curve at any given price; however the deceleration of the demand curve across prices the general curvature of the function serves as a global index of elasticity. This figure depicts two additional important variables that may be calculated from demand curves, P max and O max. The price at which response output for a reinforcer reaches its maximal value is P max , which is closely related to elasticity Johnson and Bickel The corresponding maximal response output e. The corresponding expected peak response output O max is 6, responses. This figure shows a hypothetical demand curve with unit price on the x-axis, reinforcer consumption on the left y-axis, and responses emitted on the right y-axis. The number of reinforcers consumed is represented by the closed circles, and the corresponding number of responses emitted is represented with the open squares. By examining multiple metrics, demand curves provide a multidimensional assessment of drug reinforcement. For example, one hypothetical individual might show high demand intensity for cocaine, preferring to consume large quantities when the drug is cheaply available, but show high elasticity, with relatively small increases in price resulting in large decreases in consumption. Another hypothetical individual might show low intensity of demand, yet show relative inelasticity by continuing to defend this relatively low level of consumption even at very large prices. Thus, either individual could be said to exhibit greater cocaine reinforcement depending on the demand curve aspect under consideration. It is possible, therefore, that different dimensions of reinforcement may be associated with distinct clinical patterns or treatment responses. In the experimental laboratory, demand curves are often generated by modeling price with fixed-ratio FR schedules. This can be costly and time-intensive, because a separate session is required to assess a single price, and because the resources and regulatory requirements to conduct human drug self-administration studies with humans are considerable. Also, there may be ethical concerns with allowing some drugs of abuse to be self-administered in treatment-seeking populations. Hypothetical drug purchasing tasks offer a more time- and cost-efficient alternative. Purchasing tasks are questionnaires that ask participants to self-report on behavior in hypothetical situations, with price expressed as monetary prices rather than FR work requirements. In such a task, participants are asked how much drug they would purchase and consume at a wide range of prices. Drug purchasing tasks have been successfully used to assess simulated consumption of a particular drug across several drugs of abuse, including heroin Jacobs and Bickel , nicotine Jacobs and Bickel ; MacKillop et al. Similar to demand curve analyses of human drug self-administration studies, these tasks have produced orderly data. Purchasing tasks may also have clinical utility in predicting and assessing treatment outcomes. MacKillop and Murphy found that participants who had greater O max values and lower sensitivity to price i. Another study by McClure et al. Furthermore, such tasks appear to be reliable. A hypothetical purchasing task for alcohol demonstrated good test-retest reliability when the task was implemented twice at a two-week interval Murphy et al. To date, there has been little work exploring a purchasing task for cocaine. Petry and colleagues have conducted several studies investigating the purchasing of cocaine in the context of polydrug abuse. Petry and Bickel included cocaine as one of several drugs for hypothetical purchasing among participants who were currently or formerly dependent on heroin. The results showed that cocaine served as a substitute for heroin when the price of heroin rose, and that purchasing of cocaine was income elastic i. This work focused on cross-price relationships of different commodities, and not on own-price relationships for cocaine. Petry provided polydrug using participants with imitation paper money, and asked them to choose several drugs of abuse, including cocaine, and other commodities such as rent, food, and entertainment, to purchase. As income increased, cocaine-dependent participants increased their purchasing of both alcohol and cocaine, while demand for other drug and non-drug commodities was not affected. Petry investigated the effects of varying prices of alcohol, cocaine, and valium on purchasing of several drugs of abuse in alcohol-dependent individuals. Cocaine purchases followed the law of demand, meaning that, cocaine purchases decreased with increases in price of cocaine. Moreover, these studies found that drug purchasing during these tasks was significantly correlated with urinalysis results and self-reported years of lifetime drug use. The aim of the present study was to independently assess a Cocaine Purchasing Task in cocaine-dependent individuals. The validity of the task was evaluated by assessing the orderliness of the data, and by examining correlations between variables resulting from the Cocaine Purchasing Task and aspects of self-reported cocaine use. In addition, participants were between years of age, were not dependent on drugs other than cocaine except for caffeine or nicotine , and did not have a history of psychiatric treatment in the past six months. Participants provided a urine sample that was positive for cocaine. During the session, participants worked in a quiet experimental room. Participants completed tasks other than the Cocaine Purchasing Task that are not immediately relevant to the current analyses and are therefore not described here. The Cocaine Purchasing Task was administered as a paper-and-pencil questionnaire. Participants were read the following instructions by a research assistant before responding, with either crack rock or powdered cocaine stated in the brackets, depending on the form of cocaine most often used by the participant:. This is a series of questionnaires designed to assess choices for cocaine or crack across changes in price. This information is entirely for research purposes. All questions about purchasing cocaine or crack are completely hypothetical pretend. Please answer the questions honestly and thoughtfully. You may buy as much or as little as you'd like. Pretend that this is the only crack or cocaine available to you. You cannot purchase crack or cocaine except what you choose below. Therefore, assume you have no other crack or cocaine stashed away, and you cannot get them from other sources. In other words, this is the only place you can buy any crack or cocaine today. Imagine it is of average quality. Please complete the entire table. If you have any questions, please ask us for help. Participants wrote how many units of cocaine they would purchase at each price. Participants also filled out questionnaires assessing relevant demographics, including other drug use, age, race, sex, monthly income, and cigarettes smoked per day. To ensure quality of the data, individual participant data were assessed to eliminate data that showed evidence of disorder or that could not be modeled with the demand curve equation. To assess the orderliness of the purchasing data, an algorithm designed for assessing delay discounting data was adapted Johnson and Bickel, We found that 8 of 86 participants exhibited nonsystematic data according to criterion 1. Of these 8 participants, 6 participants had a single nonsystematic data point out of a possible One participant had 2 nonsystematic data points, and one participant had 3 nonsystematic data points. Individual functions were visually assessed, and the 2 participants who had more than one nonsystematic data point were omitted from data analysis. In addition, we omitted 5 participants from analysis because their curve-calculated P max values were negative, making it impossible to calculate curve-calculated O max values. Observed O max values from independent participants were rank-ordered. Finally, data from 1 participant was omitted due to lack of variability in non-zero consumption values. For the Cocaine Purchasing Task, the units of cocaine purchased were plotted as a function of price. A demand curve was fit to these data with nonlinear regression using the equation by Hursh et al. The independent variable p represents price, and the dependent variable c represents consumption cocaine units purchased and consumed at a particular price. Free parameters are l, b , and a. Parameter l represents demand intensity , or number purchased at prices close to zero preferred level of consumption with no price constraint. Parameter b represents the initial slope of the demand curve in log coordinates, i. Parameter a represents the rate of change in slope in log coordinates , and serves as a global i. O max is the predicted maximum response output, and is calculated for each individual participant by substituting P max for p in the above equation. In addition to curve-calculated values, observed values i. Observed O max was determined by calculating the maximum across prices money spent on cocaine at any single price for each participant. Money spent at each price point was determined by multiplying the number of cocaine units purchased by the price per unit. Observed P max was defined as the price associated with observed O max. For those individual participants who had maximum expenditure observed O max at more than one price, P max was calculated as the antilog of the mean of all prices at which observed O max was observed i. Demand curve free parameters were transformed prior to conducting correlations to obtain normal distributions. Using methods described by Tabachnick and Fidell Table 4. Initial slope b was transformed by log 10 1. Curve-calculated P max , curve-calculated O max , observed demand intensity, observed P max , observed O max , money spent on cocaine daily, and units of cocaine purchased daily were normalized using log 10 transformations. Pearson correlations were conducted among demand curve variables demand intensity l , initial slope b , demand elasticity a , curve-calculated P max , curve-calculated O max , observed demand intensity, observed P max , observed O max , and measures of self-reported cocaine use money spent on cocaine per day and units of cocaine purchased per day to examine associations between demand curve variables and self-reported cocaine use. To assess effects of income on cocaine consumption, Pearson correlations were conducted between self-reported income and demand curve variables. Partial correlations were conducted to examine correlations between demand elasticity and measures of cocaine use while controlling for demand intensity. In order to examine if correlations between demand curve measures and self-report measures money spent daily and units used daily were significantly different in magnitude when using curve-calculated vs. To test for effects of participant sex, t-tests compared the variables resulting from the Cocaine Purchasing Task, and measures of self-reported cocaine use between males and females. Sixty-four Other relevant participant demographics are presented in Table 1. The left y-axis filled circles of Figure 2 shows the group median cocaine consumption data along with the demand curve equation fit to these data. The demand curve equation was a good fit to the median consumption data, with an R 2 value of 0. The right y-axis open circles of Figure 2 shows the group median response output data total money spent at each price along with the response output curve fit to these data. Response output increased systematically as a function of price. The left y-axis filled circles shows the group median demand data for hypothetical cocaine units of cocaine purchased at each price , along with the demand curve fit to these data. The right y-axis open circles shows the group median response output data money spent at each price , along with the response output curve fit to these data. Price per unit of cocaine is shown on the x-axis. For the majority of the individual participants, the demand curve equation also provided a good fit to the data, with a median R 2 of 0. Figure 3 shows demand curves for representative individual participants. The top panel shows data from two participants with relatively good fits. The bottom panel shows data from two participants with relatively poor fits. Of the 74 participants, only 8 had R 2 values that were lower than 0. The median observed value of demand intensity was This figure shows demand curves for representative individual participants. The top panel shows data from two participants for which the demand curves were good fits to the data. The bottom panel shows data from two participants with demand curves that were poorer fits to the data. Correlations among demand curve variables including curve-calculated l, a, b, P max , and O max , observed demand intensity, observed P max , observed O max , and metrics of self-reported cocaine use daily money spent on cocaine and units used daily are presented in Table 2. Scores of curve-calculated demand intensity l were significantly positively correlated with observed demand intensity, curve-calculated demand elasticity a , curve-calculated O max , observed O max , money spent on cocaine daily, and units of cocaine used daily. Curve-calculated demand intensity was negatively correlated with curve-calculated P max and observed P max. Observed demand intensity was positively correlated with curve-calculated slope b , curve-calculated O max , observed O max , money spent on cocaine daily, and units of cocaine used daily. Observed demand intensity was negatively correlated with curve-calculated P max and observed P max. Curve-calculated P max was positively correlated with observed P max and curve-calculated O max. Curve-calculated O max was positively correlated with observed O max , money spent on cocaine daily, and units of cocaine used daily. Observed O max was positively correlated with money spent on cocaine daily and units of cocaine used daily. Money spent on cocaine daily was positively correlated with units of cocaine used daily. No statistically significant differences were found. To examine the relationship between Cocaine Purchasing Task measures and self-reported measures of cocaine use beyond the influence of demand intensity, Table 3 shows results of partial correlations between demand measures and measures of cocaine use while controlling for curve-calculated demand intensity. Money spent on cocaine daily was significantly positively correlated with curve-calculated P max , observed P max , curve-calculated O max , observed O max , and units of cocaine purchased daily. Units of cocaine used daily was significantly positively correlated with curve-calculated P max , curve-calculated O max , observed O max , and money spent on cocaine daily. Partial correlations between demand curve measures and self-reported cocaine use money spent on cocaine per day and units of cocaine purchased per day while controlling for curve-calculated demand intensity l. Interestingly, self-reported income was not significantly correlated with any of the demand curve outcome measures. There were no significant effects of sex on any variables derived from the Cocaine Purchasing Task or self-reported measures of cocaine use. Two main findings were shown in the current study. First, the Cocaine Purchasing Task produced orderly data, consistent with previous hypothetical assessments of demand curves for other drugs, and previous assessments of actual drug self-administration. Second, demand measures assessing demand intensity and O max but not measures related to demand elasticity from the Cocaine Purchasing Task were significantly correlated with self-reported measures of real-world cocaine use. After controlling for the effects of demand intensity, measures related to demand elasticity were also found to correlate with real-world measures of cocaine use. Each finding will be discussed in turn, followed by a brief discussion regarding potential limitations of the current study, as well as possible future directions. The demand curve equation provided a good fit to the group median data as well as to the large majority of participants. These data are consistent with orderly demand curves generated from a wide variety of drugs, including hypothetical alcohol Murphy and MacKillop ; MacKillop and Murphy , hypothetical cigarettes MacKillop et al. Consistent with previous studies, in the present study consumption of the drug cocaine was high at low prices and decreased monotonically as price increased. Of note, we found that the median curve-calculated demand intensity l underestimated the observed value of demand intensity i. Interestingly, this is contrary to prior studies that used the same equation and reported both observed and curve-calculated demand intensity. MacKillop et al. In two different studies, Murphy and MacKillop and Murphy et al. The external validity of the Cocaine Purchasing Task was supported by significant correlations with self-reported cocaine use. That is, greater demand intensity both curve-calculated and observed and O max both curve-calculated and observed from the Cocaine Purchasing Task were associated with greater amounts of cocaine used and money spent on cocaine per day. Interestingly, demand elasticity a and closely related measures both curve-calculated P max and observed P max were not significantly related to self-reported cocaine use measures. However, demand elasticity as well as measures of P max were found to significantly correlate with real-world measures of cocaine use after controlling for effects of intensity with the exception that observed P max and units consumed daily were not significantly correlated. These data suggest that the most valuable information regarding real-world behavior stemmed from demand intensity consumption at low prices and O max maximal response output or money spent. However, the observation that elasticity-related measures were related to real-world situations after controlling for demand intensity nevertheless highlights the relevance of elasticity. It is possible that other measures of real-world cocaine use may differ, as the measures used in the current study may have been somewhat related to demand intensity i. A strength of using behavioral economic demand curves to assess the reinforcing efficacy of drugs is that it results in multiple parameters related to reinforcement. Although the present study found demand intensity and demand elasticity to differ in their relation to other drug use variables, previous literature has shown examples in which demand intensity and demand elasticity show correspondence. For example, Wade-Galuska et al. Murphy et al. Similarly, Murphy et al. However, previous research also shows that demand intensity and demand elasticity can show differing relationships to measures related to drug consumption. Galuska et al. MacKillop and Murphy found in college students that intensity, but not elasticity, O max , or P max , was correlated with self-reported drinks per week at baseline. Furthermore, MacKillop and Murphy found that O max , P max , and elasticity predicted post-intervention weekly alcohol use, but intensity did not. In college students who smoke, MacKillop et al. In college student drinkers, Murphy and MacKillop found that intensity and O max , but not elasticity and P max , were significantly correlated with drinks per week, episodes of heavy drinking, and severity of problem drinking. McClure et al. More research is needed to investigate the extent to which different purchasing task parameters inform various aspects of drug consumption in the real world. Regardless, these findings extend the generality of the utility of purchasing tasks to characterizing demand for hypothetical cocaine. These findings also suggest that the Cocaine Purchasing Task may be a time- and cost-efficient proxy measure of the reinforcing effects of cocaine. One limitation of the current study that should be noted is that the Cocaine Purchasing Task is a simulated self-report measure for purchasing cocaine, and it is possible that the data may have differed if consequences were real. However, a number of studies in the area of delay discounting another behavioral economic framework extensively applied to drug abuse have shown that similar hypothetical decisions concerning money choices provide a close approximation to data collected when consequences are real Johnson and Bickel ; Baker et al. Furthermore, the significant correlations between the Cocaine Purchasing Task variables and self-reported variables related to real-world cocaine use provide further evidence suggesting that the hypothetical Cocaine Purchasing Task may provide an approximation of behavior had the consequences been real. Another potential limitation is that some prices on the Cocaine Purchasing Task may be unrealistic and never encountered in the illicit market e. This wide range of multiplicatively increasing prices was used because it is similar to the sequences of response requirements within progressive ratio schedules upon which purchasing tasks are based. However, the orderliness of the data across prices, combined with realistic consumption values at prices within the more realistic price range i. Nonetheless, future studies should consider using more plausible market prices, as very low or very high prices may cause readers to call into question the credibility of results. Future research on the Cocaine Purchasing Task may assess its clinical utility in predicting treatment outcomes, as has been demonstrated with other drug purchasing tasks. For example, MacKillop and Murphy found that heavy-drinking college students showing greater curve-calculated O max values and greater breakpoints indicating lower elasticity for alcohol on an alcohol purchasing task had poorer treatment outcomes at 6 months following a brief intervention. A study in adolescent smokers found that greater demand intensity on a cigarette purchasing task in nicotine-dependent individuals was significantly associated with lower motivational levels to quit or reduce smoking as measured by a reliable and valid scale Murphy et al. Another study showed that non-treatment seeking smokers randomly assigned to receive the smoking cessation medication varenicline showed significant increases in demand elasticity compared to those receiving placebo McClure et al. However, Madden and Kalman found that another smoking cessation medication, bupropion, had no significant effect on demand for cigarettes using a cigarette purchasing task. As a library, NLM provides access to scientific literature. Psychopharmacology Berl. Published in final edited form as: Psychopharmacology Berl. Find articles by Natalie R Bruner. Find articles by Matthew W Johnson. Issue date Mar. PMC Copyright notice. The publisher's version of this article is available at Psychopharmacology Berl. Open in a new tab. Demographic Mean SD Age years Correlations among demand curve variables and metrics of self-reported cocaine use. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Quick Test intelligence score a.

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Vars buy cocaine