San Miguel where can I buy cocaine

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San Miguel where can I buy cocaine

Official websites use. Share sensitive information only on official, secure websites. Contact: jin. Cocaine use disorder CUD is a significant public health issue. Behavioral interventions such as contingency management CM have been demonstrated to be highly effective in promoting cocaine abstinence. However, identifying individual characteristics associated with cocaine relapse may help improve treatment outcomes. Cocaine demand is a behavioral economic measure that shares a scientific foundation with CM. In the current study, we assessed baseline cocaine demand using a hypothetical cocaine purchasing task. All participants received one month of CM treatment for cocaine abstinence, and treatment responders were defined as presenting six consecutive cocaine negative urine samples from thrice weekly clinic visits. Demand data were well described by the exponentiated demand model. Subsequent quantile regression analyses examining the relationship between CM treatment response and Q 0 revealed statistically reliable effects of being a non-responder across three of the lower percentiles i. Overall, these findings provide further support for the utility of exponentiated demand model. To our knowledge, this is the first study to demonstrate an association between baseline demand and contingency management response and systematically extend the findings of prior demand research to a novel drug class, cocaine. The number of cocaine-related overdose deaths in the U. Cocaine increases the risk of morbidity and mortality and is associated with a host of problems negatively affecting not only users, but society at large. One such intervention is contingency management CM , an evidence-based treatment that promotes clinically-relevant targets e. The roots of CM are based on an extensive behavioral literature showing that drug use, like other behavior, is sensitive to environmental consequences. CM delivered in the context of a community reinforcement approach a behavioral-based skills training intervention was the only treatment to increase cocaine abstinence at the end of treatment, 12 weeks, and at longest follow-up range 16 to 96 weeks. CM in conjunction with a community reinforcement approach was also found to be superior to other psychosocial interventions with regard to patient dropout. Despite strengths noted above, not all participants respond to CM treatment and identifying individual factors influencing CM response remains an important research goal. One individual factor that may be informative to CM treatment outcomes is drug demand. Drug demand is a behavioral economic measure that characterizes the relationship between the price e. Assessing these choices under conditions of constraint e. To our knowledge, no studies have assessed whether cocaine demand is related to CM outcomes. However, a relationship is plausible given the shared focus on the reinforcing effects of drug and non-drug rewards. Drug demand is often characterized by a demand curve, which graphically depicts consumption of a drug, which presumably represents the putative value of a drug, across a range of drug prices. In both Equation 1 and the Hursh and Silberberg exponential equation, Q represents consumption of a given drug at price C. Elasticity describes the degree to which consumption changes with changes in price, with greater elasticity indicative of relatively quicker decreased consumption with increased price. The parameter k represents the range of consumption values log 10 units. Several other values can be derived from the equation that help fully characterize the demand function. Omax is the maximal response output e. This also reflects where responding for drug changes from relatively inelastic to elastic. Pmax is the price point at which Omax is observed. The breakpoint represents the price at which no more drug is consumed. Benefits of the exponentiated equation include being able to include 0 consumption values, overall better fits of the data, as well as better fits of consumption values including Q 0 Koffarnus et al. The purpose of the current study was to examine the relationship between baseline cocaine demand and response to a four-week abstinence-induction CUD treatment phase involving CM with high-magnitude incentives. Cocaine demand was assessed using a hypothetical cocaine purchasing task. CM response was defined as achieving abstinence, verified by six consecutive cocaine negative urine drug screens from thrice weekly clinic visits. To our knowledge, this is the first study to assess the relationship between cocaine demand and CM treatment outcomes. Given the reinforcement-based conceptual framework shared by both drug demand assessment and CM treatment, we expected to observe meaningful relationships between these measures. The current study was conducted in the context of an ongoing parent study investigating sequential, multiple assignment, randomized trial SMART design involving two distinct treatment phases NCT Phase 1 involved 4 weeks of high-magnitude reinforcement CM targeting abstinence, defined as cocaine-negative urine drug screens. In addition to CM, participants were randomized to receive one of two evidence-based therapies i. No significant differences were noted between the two therapy groups in the current study sample, and results from the two groups were therefore combined. Phase 2 involved 8 weeks of continued treatment that was augmented with pharmacotherapy modafinil or placebo for CM non-responders Schmitz et al. The current study focuses on Phase 1 only. Participants meeting moderate or severe diagnostic criteria for SUDs other than cocaine, marijuana, or nicotine were excluded. Severe alcohol use disorder was exclusionary. All participants provided written informed consent. Participants were asked how many rocks of cocaine they would purchase for the day at various prices i. Participants were instructed to assume the following: that their income and savings were what they usually are; the quality of cocaine is the type they usually purchased; there are no other sources of cocaine; any cocaine purchase had to be used within that day; and their craving and desire for cocaine was how they currently felt. The ASI-Lite can be used as a descriptive measure or to determine composite severity scores for drug and alcohol severity. The ASI-Lite also assesses several domains commonly affected by substance use e. In the current study, the ASI-Lite was used to obtain descriptive data about substance use, including number of days past-month and years lifetime of cocaine use. Current and past use is assessed as well as mode of use and substance of choice Kellogg et al. While administration time of the KMSK is approximately 5 minutes, it is effective in providing rapid dimensional analyses for various substance use exposure Butelman et al. Participants that submitted 6 consecutive 2 weeks cocaine negative urine samples by week 4 were classified as responders. The two week criteria was chosen as the parent trial required a binary outcome i. Similar definitions have been used in other cocaine trials involving achievement of abstinence early in treatment Bisaga et al. Those not meeting response criteria were classified as non-responders. All demand curves were fit in GraphPad Prism 6. All statistical analyses were performed using the R statistical computing environment R Core Team, While it is customary to exclude these data in studies assessing goodness of fit e. Demand data were fit to Equation 1 including zero values as replacing zeros in the exponentiated model is not necessary. The value of k was set to 4 based on the observed range of consumption values. Any potential confounding variable i. Fully exploring the relationship between CM treatment response and demand curve parameters presented two noteworthy analytic challenges. In the present analysis, these values were considered real values, as they were properly systematic part of a monotonically increasing response , measured and recorded without error, and represented theoretically relevant levels of responding i. Second, both parameters exhibited strong positive skew. To address these challenges, demand curve parameters were each modeled as a function of CM treatment outcomes responder vs. The WMW is agnostic of distribution assumptions but does not allow the inclusion of co-variables. Demand curve parameter differences were then examined via parametric tests to improve precision in estimation via inclusion of information regarding outcome distributions and to statistically control for potential confounding variables described in the Results. Multiple linear regression was used to model demand curve parameters as a function of CM response with covariates. Each transformed demand curve parameter was then modeled as a function of CM group while allowing for covariate adjustment. Notably, this approach is similar to previous efforts in the field, including Strickland et al. Another example of a transformation approach was used by Bruner and Johnson , who applied the square root transformation. While this transformation did not yield normality in the present data, it is another excellent choice in that it can handle positive data and zeroes. Quantile regression was then used as an alternate option for modeling a range of percentiles along the distribution of Q 0 as a function of CM treatment response without the volatility of the choice of constant in log transformation while also allowing covariates and requiring no distributional assumptions. This analysis provided an additional level of inference regarding the relationship between the variables of interest: it allows the inspection of parameter effects across specific locations of an outcome while also resolving the problems of the linear regression with only a minimal increase to interpretation complexity i. Previous work has demonstrated the utility of this technique for exploration particularly in skewed data : examples include investigations of the relationship between mindfulness and depression Radford et al. Quantile regression was implemented via the R package quantreg version 5. The three statistical techniques described above provide slightly different yet complementary levels of information regarding the relationship between demand curve parameters and CM treatment response. Although these parametric versions of these models establish baseline demand as a criterion and CM response as a predictor, it is essential to note that the mathematics of these models are agnostic to the causal direction, and flipping the direction of effects in this manner can lead to difficulty in interpretation. Removing this extreme value from analyses resulted in an identical pattern of inferences, and as such the value was included in all analyses given its inherent theoretical value as described above. Table 1 provides an overview of descriptive statistics by CM treatment response groups responder vs. Compared to non-responders, responders had more days abstinent during CM treatment, were more likely to have a cocaine-negative sample at intake, and reported less severe scores on lifetime metrics of cocaine use. Summary of participant socio-demographic and drug-use characteristics by CM treatment response group. A Pearson product-moment correlation coefficient was computed to assess the relationship between the observed directly calculated from observed data points and derived Equation 1 values for Q 0 and Omax. Computed correlations used Spearman-method with pairwise-deletion. Linear regression on log-transformed values, however, supported the effect of CM treatment response group such that non-responders demonstrated In other words, higher demand for cocaine was related to worse response to CM treatment as measure by cocaine positive urine samples. These results suggest that the difference between the responder and non-responder groups are driven by lower Q 0 values. Parameter estimates across the range of quantile regression models, provided in Table 2 , are measured in units of the raw metric of Q 0 and may be interpreted in the same manner as simple regression coefficients e. Figure 2 illustrates demand curves for responders open symbols and non-responders closed symbols at the 15 th percentile top left , 25 th percentile top right , 30 th percentile bottom right , and 50 th percentiles. The 50 th percentile is provided as an illustration of how demand data would be presented traditionally and potentially miss significant differences revealed at lower percentiles. Asterisks highlight percentile values where statistically reliable effects of being a CM treatment non-responder relative to responder were found. Demand curves fit to various percentiles i. Note that the x-axis is on a log scale with a break for Q 0 , which represents consumption at near 0 price. None of our three statistical analyses i. The present analysis explored the relationship between behavioral economic measures of demand and CM treatment response. We discuss these findings in greater depth below. A closer inspection of the effect of CM response group membership along different levels of the demand intensity distribution via quantile regression concurred with the linear model that overall, higher Q 0 was found in non-responders. The relationship between Q 0 and CM treatment non-response was most reliable at lower levels of Q 0 and stronger but unreliable at higher levels of Q 0. Overall, these findings suggest that the relationship between Q 0 and CM treatment response was driven by lower levels of baseline Q 0 , whereby ostensibly treatment-seeking participants reporting zero values for Q 0 were able to achieve abstinence responder while those with Q 0 above a certain threshold invariably did not non-responder. Interestingly, in the tobacco study, baseline indices of demand predicted treatment outcomes only for individuals for whom smoking abstinence was not a target of CM. Note that in the current study, all participants received CM for cocaine abstinence. To our knowledge, this is the first study to demonstrate that baseline demand is associated with cocaine-treatment response and systematically extend the findings of prior demand research to a novel drug class, cocaine. The current results also complement findings from studies examining the role of another behavioral economic measure, delay discounting, on treatment outcomes. In a seminal study, baseline delay discounting predicted smoking relapse among pregnant women that had quit smoking once they discovered they were pregnant Yoon et al. One interpretation of these findings is that when treatment is relatively weak e. In the current study, Q 0 was significantly associated with a relatively high magnitude CM treatment J. Schmitz et al. Taken together, these findings highlight the potential utility of behavioral economic measures such as drug demand and delay discounting, particularly in the context of CM treatment. Behavioral economic measures have the potential to inform CM treatment in potentially three ways: 1 identify individuals that are at greater risk of relapse and in need of greater support as in the current study, 2 provide guidance on how to adjust CM parameters e. These findings also highlight the utility and importance of multiple statistical analytic techniques for exploring demand data. Here, where a routine non-parametric test failed to demonstrate a relationship between predictor and outcome, a linear model that systematically tested incorporated information about the distribution of the response Q 0 was able to find evidence for a relationship. Quantile regression was able to take the level of inference even further by examining the relationship across the entire range of the response without the need to complicate the interpretation via data transformation. Our preliminary findings suggest that these types of analyses may be particularly useful in contexts where parameter distributions may be highly influenced by relatively extreme responding, as in the case of treatment-seeking participants reporting zero consumption across all prices of drug. While such responding is often excluded when testing fits of different models of demand, these data are clearly relevant and expected in the context of treatment studies. On the other hand, we also observed participants reporting relatively high levels of cocaine consumption, particularly among CM treatment non-responders. Although these data were orderly and clearly reflect the high valence of cocaine, they likely did not mirror true patterns of cocaine consumptions. The current study also supports the utility of the relatively new exponentiated hyperbolic model illustrated in Equation 1 Koffarnus et al. As this is an ongoing study, a potential limitation is the relatively small sample size. For example, a larger sample size may have revealed significant differences at higher percentile values Figure 1. The current parent study was based on a previous trial by our group that also utilized a 4-week abstinence-induction phase with high magnitude CM Schmitz et al. We expect the differences are due largely to the smallness of the current sample size. Nevertheless, the low response rate reflects the challenge of achieving initial cessation of cocaine use in severe users, as has been noted in the literature c. Therefore, although the number of treatment responders may be low in the current study, it is comparable to what we have observed previously and may reflect the severity of cocaine use in our population. However, a growing body of research has support the utility of hypothetical purchasing tasks with purchasing tasks predicting drug use, cue reactivity, convergence with established clinical assessments, and reliability over time for a review see Roma et al. Additionally, the imbalance in CM response group sizes may have affected the precision in the estimation of effects for the smaller group. Finally, we did not assess longitudinal changes in demand measures. However, assessment of changes in cocaine demand over time in relation to treatment status is a future target of interest once the parent study is completed. Results from the current study revealed an association between Q 0 and CM treatment response, which may help inform interventions for CUD in the future. Model estimates of CM treatment response group effect non-responder relative to responder on Q o by percentile. None of the authors have any conflicts of interest to declare. This data has not been presented elsewhere, but has been accepted as a poster presentation for the annual conference for the College of Problems on Drug Dependence San Antonio, TX, June Jin H. Sarah A. Guadalupe G. Angela L. Scott D. Jessica N. Michael F. Joy M. As a library, NLM provides access to scientific literature. Psychol Addict Behav. Published in final edited form as: Psychol Addict Behav. Find articles by Jin H Yoon. Find articles by Robert Suchting. Find articles by Sarah A McKay. Find articles by Guadalupe G San Miguel. Find articles by Anka A Vujanovic. Find articles by Angela L Stotts. Find articles by Scott D Lane. Find articles by Jessica N Vincent. Find articles by Michael F Weaver. Find articles by Austin Lin. Find articles by Joy M Schmitz. Issue date Feb. PMC Copyright notice. The publisher's version of this article is available at Psychol Addict Behav. Open in a new tab. Significant correlations are in bold. Note that asterisks indicate the following:. 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. Socio-Demographic Characteristics. Demand Curve Parameters. Cocaine Use Characteristics. Other Drug Use.

San Miguel where can I buy cocaine

San Miguel was an important operational center for the once-powerful Perrones drug cartel, which owned various properties in the city used to launder money. The department is also a hotbed for gang activity, with the Mara Salvatrucha MS13 street gang enjoying widespread territorial control in departmental capital, San Miguel, among other areas. The scope of gang extortion is purportedly increasing and appears unaffected by fluctuations in state security presence. In addition, the gang has forcefully recruited underage girls for prostitution. Several powerful factions of the MS13 have a strong presence in the department; these groups are significant buyers of illicit firearms and also launder money through small businesses. In the San Miguel municipality, the MS13 enjoys widespread territorial control and municipal authorities may need to negotiate with the gang to launch infrastructure projects or hold political campaign events. In some areas, the MS13 imposes its own social rules and controls who enters and exits a particular territory. Nonetheless, the gang still controls some smaller territories in the department, where it extorts locals and sell drugs. The gang also uses violence to reinforce its territorial control and make sure that extortion victims pay up. Though the group was hard hit by arrests in the late s and s, there are signs that remnants of the organization — including family members — are still smuggling cocaine, arms and contraband through the department. The department is also connected to San Salvador and Guatemala via the Pan-American highway, a major smuggling route. The city of San Miguel has long served as an operational center for the Perrones, and remnants of this group are purportedly still active in the cocaine trade. In all, this appears to be a substantial criminal economy. Cannabis: The MS13 is reportedly present in 11 out of 20 municipalities, including departmental capital, San Miguel. There is also a smaller Barrio 18 presence. These gangs control the sale of cannabis in San Miguel, one of their main sources of income. Considering this gang activity, the cannabis trafficking market appears to generate at least modest revenues in San Miguel. Environmental Crime: Illegal logging and wildlife trafficking appear minimal in San Miguel. In one case, a Taiwanese individual allegedly worked with locals to extract timber and export it to Asia, but whether the network remains active is unclear. Human Trafficking: Human trafficking rings in San Miguel have forced national and foreign victims into labor and sexual exploitation. The city of San Miguel houses numerous brothels, where women and girls from El Salvador and Honduras have been forced into prostitution. Guatemalan nationals have been coerced into selling fruit for no pay. There are also signs that human organs are trafficked in the department, along with newborn babies that get sold for adoption. In all, human trafficking appears to be a moderate criminal economy in San Miguel. San Miguel has historically represented a large proportion of the migrants who move to the United States. Many are driven by poverty and violence, as well as the chance to reunite with US-based family members. Street gangs are the driving force behind extortion, targeting local businesses, merchants, market stalls, transport operators, taxi drivers, restaurants, and the manufacturing sector. Gangs usually base their extortion fees on how much a company earns. Failure to pay extortion fees can result in death or disappearance. Gangs tend to control extortion rackets remotely, obliging victims to drop off payments at a certain location, or forcing taxi drivers to pick up extortion fees for them, meaning they can continue extorting even when security forces increase their presence. The scope of extortion appears to be increasing, with some gangs now asking for one-off payments from families and businesses that they would not ordinarily target, including companies employing former gang members. Money Laundering: Criminal and private sector actors in San Miguel have participated in national corruption networks committing financial crimes. Subscribe to our newsletter to receive a weekly digest of the latest organized crime news and stay up-to-date on major events, trends, and criminal dynamics from across the region. Donate today to empower research and analysis about organized crime in Latin America and the Caribbean, from the ground up. Skip to content. Stay Informed With InSight Crime Subscribe to our newsletter to receive a weekly digest of the latest organized crime news and stay up-to-date on major events, trends, and criminal dynamics from across the region.

San Miguel where can I buy cocaine