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Spain where can I buy cocaine
In this paper, we examine the socioeconomic factors that determine cocaine consumption in adults aged over 18 years in Spain. For data from household alcohol and drug surveys for the period — EDADES , , , we used, first, a decision tree to identify the main predictors of cocaine use and high-risk profiles of cocaine consumers and, next, a multinomial logistic regression model to explain cocaine consumption in terms of sex, age, marital status, employment status, education level, income, household size and perceived health status. The results indicate that the main predictors of cocaine use are marital status, sex, employment status, age, education level and household size. The main cocaine user propensity profile is a young single man with a low level of education and unemployed, who should, therefore, be the main target of campaigns for preventing illicit drug use.. It is estimated that between 3. Illicit drug use annually results in about , deaths worldwide and causes distress and suffering in many more millions. Illicit drug use also undermines economic and social development, promotes crime, instability and insecurity and spreads diseases such as HIV UNODC, Cocaine consumption trends vary between countries. Spain, in fact, consistently has the highest prevalence of cocaine use worldwide. Cocaine use represents a major health and socioeconomic problem in Spain. Analysing causes can shed light on the population segments more prone to using drugs and help to design suitable targeted prevention strategies. What are the socioeconomic circumstances that place an adult in Spain at higher risk of becoming a cocaine user than adults in other similar countries? Studies on the socioeconomic determinants of cocaine use in Spain are relatively recent. To illustrate, Royo-Bordonada et al. On the other hand, and using data collected between and , Pulido et al. Cocaine consumption is very costly for society as noted for a number of studies. For the region of Galicia NW Spain by Oliva and Rivera estimated—for data for addicts attending drug dependency centres in the Galician healthcare network—a cost associated with illegal drug use of around million euros in In this paper, we tried to find an explanation for the high level of cocaine consumption in Spain with respect to other countries. In particular, bearing in mind that the average age of onset in Spain is 21 years for powder cocaine and The second objective was to determine the weight of different factors in the decision to consume. Achieving both goals we expected to be able to predict cocaine use in Spain by classifying and applying rules and by evaluating influential factors. The surveys collected data between November and March , between November and March , and between November and April To identify predictors of cocaine use consumption patterns and population groups at risk, we used a decision tree model that graphically represented relationships between variables. This enabled us to describe cocaine user profiles and to identify complex structural relationships and so, in turn, identify combinations of predictors. Combinations of socioeconomic variables, which, in isolation, might not represent determining factors, identified propensities for cocaine use. As for the second objective, we used a multinomial regression model to isolate the socioeconomic factors that had the greatest bearing on the prevalence of cocaine use in Spanish adults. Our results indicate that the most important predictors are marital status, sex, employment status, age, education level and household size. Specifically, profiles of Spanish adult population most likely to consume cocaine are single inactive men aged 25—50 years with an income in the third decile and single men under 36 years old, with only a basic education level, and employed or unemployed. The regression model calculated weights for the factors selected in the decision trees as the best predictors of cocaine use. Our results indicated that living with a partner increases the probability of being a non-user by 1. The model also points to the statistically significant influence of other variables such as household size, health perception, employment status, education level and income level. The results allow us to conclude that cocaine prevention and education policies in Spain should be reoriented to especially target single middle-aged inactive men with a low level of education, bearing in mind that this target is particularly difficult to reach because these individuals have no fixed points of contact or support in universities or companies. The rest of the paper is organized as follows. In Section 2 we describe the decision tree and multinomial regression models used to explain cocaine use in socioeconomic terms; we also describe how data was collected and descriptively analyzed. In Section 3 the main results are presented and, finally, in Section 4 we discuss those most relevant from the point of view of the adoption of targeted policies aimed at preventing or reducing cocaine use in individuals at risk. To measure consumption of cocaine we counted the number of days cocaine was used in the 12 months before the interview. This resulted in four cocaine user categories, namely, non-users, occasional, regular and frequent users. A non-user Y 0 has never consumed cocaine or has not done so in the last 12 months, whereas an occasional, regular and frequent user has consumed cocaine on 1—9 days Y 1 , 10—29 days Y 2 , and more than 30 days Y 3 in the past 12 months, respectively. We studied user profiles and the influence of socioeconomic variables using decision trees and logistic regression. To analyze the predictive value of variables that may influence cocaine use we used a recursive partitioning model of decision tree. This nonparametric technique, which treats all variables as categorical variables, takes into account interactions between the data. We identify the most important predictive variables from the set of explanatory variables and classify individuals into risk groups. Because cocaine use is a categorical variable we used decision trees to graphically represent a set of decision rules. Starting from a root node which includes all the individuals , the tree branches out into different child nodes contain subgroups of individuals. It uses a recursive algorithm that successively partitions the original data in other smaller and more homogeneous subgroups using binary partition sequences. CHAID Chi-squared Automatic Interaction Detection Kass, , a recursive non-binary classification algorithm, applies the chi-square test as a criterion for partitioning and uses the threshold above statistical significance as the stopping rule. The estimated parameters of the model are obtained using the maximum likelihood method, which determines the estimate that maximizes the probability of non-consumption and of occasional, regular, and frequent consumption of cocaine, given the vector of explanatory variables. Finally, statistical significance is tested using the likelihood ratio test that follows a chi-square distribution, whereas goodness-of-fit is tested using the likelihood ratio index. As a measure of an individual's economic circumstances we used the net monthly income of their household. In the EDADES surveys, household income is recorded as a categorical variable with eight possible responses referring to income intervals. However, since we needed a continuous measure of income, we applied an interval regression model of information provided by the respondent regarding overall household data. We thus could estimate the numerical income value from the boundaries of the intervals where the quantities are located. We took into account sex, age, educational level no education, primary, secondary, and university , employment status employed, unemployed, and inactive and region of residence. Using records for 44, individuals from the , and surveys, we generated predictions for the individual net monthly income variable. To examine the determinants of cocaine consumption and user profiles, we first used a decision tree model and then a multinomial logit regression model with four types of explanatory variables, namely, individual characteristics, household characteristics, employment characteristics, and socioeconomic characteristics. The variables used in the regression model were as follows: sex female omitted , age, marital status single, partnered , household size, health perception good, fair, and poor , educational level no education omitted to avoid collinearity , primary, secondary, and university , net monthly household income, and employment status. For the polytomous cocaine use variable, we jointly considered powder cocaine and freebase cocaine use, categorized in terms of four possible values, namely, non-consumption, occasional, regular and frequent consumption. An occasional, regular, and frequent user has consumed cocaine on 1—9 days Y 1 , 10—29 days Y 2 , and more than 30 days Y 3 in the past 12 months, respectively. Our data—for individuals aged 15—64 years and for the period ——were taken from the EDADES household surveys for , and Galicia et al. EDADES surveys are two-yearly surveys administered to a representative national sample stratified according to a multistage sampling procedure. The surveys collect data on alcohol and drug use and also compile information on socioeconomic, sociodemographic and geographic characteristics, and on overall health and occupation. Our final sample was obtained as follows. Table 1 below summarizes descriptive statistics referring to the prevalence of cocaine use in Spain. In addition, Fig. Note : Omitted categories: females, basic education, good health. Cocaine use frequency distribution. Of the population aged over 18 years living in Spain, 3. Prevalence rates are To analyze the socioeconomic circumstances, we first estimated the continuous variable of monthly net household income by applying an interval-regression model to income intervals as described in EDADES, , and the dependent variable and age, age squared, sex, education level, employment status, and region of residence explanatory variables. The limit values of the household income intervals were reflected in the survey. Table 2 below shows the regression estimates for household income intervals for 44, individuals. Interval-regression model coefficients for income estimation purposes. Notes : Inference based on robust standard errors, without weights. Reference categories: Female, no education, inactive, and Region 1 Andalusia. Statistical significance p 0. The model is statistically significant according to the Wald test and chi-square likelihood ratio LR Chi 2. Given the net monthly household income for each, we obtain the distribution of cocaine use by income categories summarized in Table 3. Cocaine use rates by income categories. The prevalence of cocaine use irrespective of consumption level among adults in Spain ranges between 2. For all the income categories except the first decile, cocaine use increases with income level to peak at 4. From this point, prevalence decreases as income rises. Prevalence of cocaine consumption by sex and by income categories is shown in Table 4. Cocaine use rates by sex and by income categories. Table 4 confirms a higher rate for men, irrespective of income level. Among individuals aged over 18 years in Spain, rates of cocaine use whether occasional, regular or frequent are strikingly different between the sexes, at 3. Here we analyzed which variables or groups of variables sex, age, marital status, educational level, health perception, employment status, household size and income could be good predictors of cocaine use for persons aged over 18 in Spain. We examined decision tree rule sets and flows so as to classify and determine risk profiles see Fig. CHAID decision tree: root, node 2 and two child nodes 7 and 8. The first branch of the decision tree indicated marital status to be the main predictor variable; this resulted in two nodes, referring to individuals living with a partner node 1 and single individuals node 2. Referring to node 2, of the single individuals Node 2 branches into two nodes representing the sex variable, with men node 8 indicated as having higher use rates—4. Node 8 branches into the nodes representing employment status nodes 18, 19 and 20 , where prevalence of use is higher for single men who are unemployed node 20 , at 5. The next strongest predictor for cocaine risk is the age variable nodes 32, 33 and 34 , which branches into nodes representing the age groups 18—36 years, 36—44 years and 44—65 years nodes 32, 33 and 34, respectively. The highest rates of cocaine use—5. For age groups over 44 years nodes 33 and 34 , use rates fall significantly maximum 2. Node 32 branches into nodes representing education level nodes 46, 47 and node 47 is no education, node 48 university education and node 46 other , where the highest use rate for single men, aged under 36 years and employed, is when they have basic education levels node Finally, node 48 university education branches into nodes representing the household size variable, where the smallest household size node 55 is associated with a high use rate. The best predictor for cocaine use is marital status. The highest rate of frequent cocaine use 7. Finally, with respect to accuracy, the model correctly classifies approximately The decision tree classifies all the individuals in cocaine risk profiles by combining and grouping the explanatory variables. For all levels of consumption occasional, regular and frequent , we obtain the following three high-risk profiles for cocaine use: 1 Single men, 25—50 years old, inactive, and with an income of around euros per month 0. The prevalence of cocaine use is Single men, employed, under 36 years old, and with a low education level 1. Single men, unemployed, and with a low education level 0. To measure the impact of socioeconomic and demographic determinants of cocaine use in individuals aged over 18 years for each level of use, we calculated multinomial logit regression model coefficients see Table 5. Logit regression model coefficients. Notes : Reference categories: Female, inactive, good health, no education. The marital status, sex, age, employment status, and household size variables exert a statistically significant influence on cocaine use in individuals aged over 18 years in Spain. For certain cocaine use categories, furthermore, education, income, and health are also significant. The sex variable exerts a statistically significant positive influence on occasional, regular, and frequent cocaine use and a statistically significant negative influence on non-use. Men are far more likely than women to be cocaine users; in particular, being female reduces prevalence of use by 1. This result agrees with those reported by Merline et al. Age exercises a statistically significant positive influence on non-use and a statistically significant negative influence on the use categories. The older the individual the less likely they are to use cocaine. One explanation for this pattern is that cocaine is typically consumed in social and leisure settings that tend to be frequented by younger people and younger people are more likely to be drawn to new experiences. Each additional year older increases the likelihood of being a non-consumer by 0. Individuals with a partner are less likely to use cocaine than those without a partner. Living with a partner has a statistically significant negative influence on cocaine use occasional, regular or frequent , increasing the probability of being a non-user by 1. The explanation may be the fact that the negative consequences of cocaine use also affect an individual's partner. Also, if the partner is not a consumer, consumption is likely to be lower, since the partner would have to be excluded. Being unemployed has a statistically significant positive influence on occasional, regular, and frequent cocaine use. The weight of this variable is highest for the occasional use category and corroborates the results of Royo-Bordonada et al. By contrast, the influence of unemployment is significant and negative in the case of non-use. This result corroborates those of Green et al. Being inactive or unemployed means an individual with more leisure hours and, with more opportunities to be alone, he or she has more opportunities to consume drugs. The probability of consuming cocaine—most especially on an occasional basis—is reduced in larger households. Normally, households are typically composed of members of different generations and probably include any or all of a partner, siblings, parents or children, so cocaine use may be difficult to share. Living with other people also reduces the risk for cocaine use because individuals may wish to avoid harming the people close to them. Education level is statistically significant for the frequent use category, with better educated individuals less likely to be frequent users. This result is consistent with results reported for U. The explanation is that educated individuals typically are more knowledgeable and aware of the repercussions of drug use. Education level has no significant influence on the other use categories, however. Whereas frequent consumption may be interpreted as causing physical and mental deterioration, occasional or regular use may be interpreted as diversion. Better educated individuals usually have more intellectual resources and are more likely to exercise the self-control necessary to avoid becoming addicted to cocaine. Net monthly household income has a statistically significant positive influence on non-use Fig. Cocaine non-use is more likely at higher incomes and occasional cocaine use falls as income levels grow. Indeed, a positive correlation between cocaine use and GNP per capita for 62 countries is reported Saiz, ; UNODC, , confirming greater cocaine use in wealthier countries. For a sample of men aged 18—45 years resident in five U. States, a statistically significant negative relationship between daily drug use and income level was confirmed Buchmueller and Zuvekas, We conclude that once cocaine use becomes regular or frequent, the income variable is no longer statistically significant, as the addicted individual will consume cocaine irrespective of their income. Impact of household income on non-use of cocaine. Impact of household income on occasional use of cocaine. For individuals who perceive their health to be fair, the probability of being a non-user is lower by 0. Individuals who perceive their health to be good tend not to be cocaine users, as they are probably fully aware of the implications for health. Similarly, if health is perceived to be poor, then frequent cocaine use is higher, probably for the same reasons. In this paper we analyzed the socioeconomic determinants of cocaine use by adults aged over 18 years in Spain in order to profile the population segments at greatest risk. We report a prevalence of cocaine use whether occasional, regular or frequent in Spain of 3. Decision tree analysis yielded information on factors with greater predictive power and enabled us to classify high-risk cocaine use profiles. A CHAID-type classification tree indicated the main predictors to be marital status, sex, employment status, age, education level, and household size. The individuals at greatest risk were indicated to be 1 single men, inactive, 25—50 years old, and with an income of around 1, euros per month; 2 single men, employed, under 36 years old, and with a basic education; and 3 single men, unemployed, under 36 years old, and owning a basic education. Prevalence rates for these profiles were These three groups accounted for 1. Multinomial logistic regression indicated the determinants for occasional, regular, and frequent cocaine use to be sex positive for men , marital status negative for having a partner , employment status positive for unemployment , household size negative for a larger household , and age negative for older age groups, with prevalence falling in line with increasing age. In addition, higher education levels are associated with lower levels of frequent use. Finally, individuals who perceive their health to be poor have a higher rate of frequent cocaine use. Monthly net household income has a statistically significant effect on non-use and occasional use. The higher the income level, the higher the probability of non-use and the lower the probability of occasional use. No significant differences were observed according to income levels, however, for regular and frequent use. When consumption acquires a certain frequency and the individual becomes addicted, it would appear that income is no longer a determining factor. Globally, our results would suggest that cocaine prevention and education campaigns in Spain should be reoriented to focus on single men aged under 36 years with a low education level living in small, low-income households. Understanding fully the consequences of cocaine use for an individual's own health and for their family may be decisive in the choice to use cocaine. Antelo and J. The subpopulation which consumes freebase cocaine has lower education levels, higher unemployment rates, more problems with justice, larger consumption levels of cocaine, and higher prevalence in the use of other illegal drugs.. The sample of 44, individuals refers all individuals with socio-demographic information that we can use to calculate their income level. Of these, 43, individuals have all the information about consumption of cocaine.. The difference is explained by the fact that we measure use of powder and freebase cocaine in the last 12 months for individuals aged 18—65 years, whereas EDADES refers only to powder cocaine consumed by individuals aged 15—65 years.. ISSN: Exportar referencia. DOI: On cocaine consumption: Some lessons from Spain. Descargar PDF. Manel Antelo a ,. Autor para correspondencia. Table 1. Table 2. Interval-regression model coefficients for income estimation purposes.. The main cocaine user propensity profile is a young single man with a low level of education and unemployed, who should, therefore, be the main target of campaigns for preventing illicit drug use. Palabras clave:. JEL classification:. Texto completo. Variable Definition Mean Std. Figure 1. Variables Coefficient Std. Table 3. Table 4. Figure 2. Table 5. Figure 3. Figure 4. Buchmueller, S. Drug use, drug abuse, and labour market outcomes. Health Econ. Galicia, S. Ten years of emergency attendances for cocaine-users in Spain. Med Clin. Gill, R. Green, E. Doherty, H. Reisinger, H. Chilcoat, M. Social integration in young adulthood and the subsequent onset of substance use and disorders among a Community population of urban African Americans. Addiction, , pp. Harder, H. Cocaine use and educational achievement: understanding a changing association over the past 2 decades. Public Health. An explanatory technique for investigating large quantities of categorical data. Merline, P. Schulenberg, J. Bachman, L. Substance use among adults 35 years of age: prevalence, adulthood predictors, and impact of adolescent substance use. Public Health, 94 , pp. Oliva, B. Los costes sociales del consume de drogas ilegales en la Comunidad de Galicia. Pulido, M. Brugal, L. Ballesta, G. Barrio, M. Bravo, et al. Gac Sanit. Royo-Bordonada, J. Cid-Ruzafa, J. Drug and alcohol use in Spain: consumption habits, attitudes and opinions. Public Health, , pp. Saffer, F. Front Health Policy Res. Adicciones, 19 , pp. Santonja, R. Secades, G. Psychosocial predictors of relapse in cocaine-dependent patients in treatment. Sonquist, E. Baker, J. Searching for Structure, Institute for Social Research. University of Michigan, ,. The subpopulation which consumes freebase cocaine has lower education levels, higher unemployment rates, more problems with justice, larger consumption levels of cocaine, and higher prevalence in the use of other illegal drugs. Of these, 43, individuals have all the information about consumption of cocaine. The difference is explained by the fact that we measure use of powder and freebase cocaine in the last 12 months for individuals aged 18—65 years, whereas EDADES refers only to powder cocaine consumed by individuals aged 15—65 years. Dependent variables DV. Occasional use. DV: 1 only occasional use; 0 otherwise. Regular use. Frequent use. Explanatory variables. Ln income. Logarithm of total monthly net equivalent income. No education. Primary education. DV: 1 only primary education; 0 otherwise. Secondary education. DV: 1 only secondary education; 0 otherwise. University education. DV: 1 only university education; 0 otherwise. Good health. Fair health. Poor health. Marital status. Household size. Wald Chi 2
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Spain where can I buy cocaine
Mixing the drug with animal salt did not allow the detection of the drug with the usual 'narcotests'. The container arrived at the port of Barcelona and was delivered 15 days later in Madrid, where six people were arrested. February 8, Seven people were arrested in the operation, including the manager of the importing company. The operation began when it became known that a container had been sent to the port of Barcelona that could be carrying cocaine mixed with the cargo, which consisted of mineral salt for animal feed, and which would not be detectable by the field reagents normally used. At that time, the cargo was inspected and samples were taken from different bags for analysis by the Barcelona Customs Laboratory, revealing that some of them contained cocaine. The bags containing the drugs had a mark that distinguished them from the rest. A thorough review of all the bags was therefore carried out, and a total of 34 bags with the same mark were found. All of them contained cocaine mixed with salt. For this reason, the telephone of the manager of the importing company was tapped by court order. Thanks to these wiretaps, it was learned that the merchandise was going to be transferred to a logistics warehouse in Madrid on January 11 with the intention of storing it for a few days while awaiting further unloading instructions. In order to identify the members of the criminal organization, a controlled delivery operation was set up for the merchandise, which left the port of Barcelona on January 11, and was unloaded at a logistics warehouse in the Zona Franca. Later, on January 16, the goods were loaded onto another truck which took them to a logistics warehouse in Valdemoro Madrid. After the unloading, a car with three people inside was detected near the warehouse and it was possible to see how one of the suspects who was inside the warehouse was leaving and holding meetings with the occupants of the car. At one point, one of the occupants of the car got out of the vehicle and also entered the warehouse, and the vehicle began to move off, at which point the Customs Surveillance team intervened and arrested the six people. The six detainees are of Colombian and Ecuadorian origin. One of them had flown from Colombia on January 17, arriving in Spain via Barcelona airport. According to the investigations, this person would be the 'notary', the person who supervises the delivery of the goods to the recipients in this type of operation. The seven detainees have been brought before the courts and three of them have been ordered to be imprisoned and the rest released on bail but without passports. In recent years, Colombian authorities have been warning of the use of increasingly sophisticated systems by drug traffickers to mix cocaine with other goods, so that the reagents that police forces normally use when they find a stash of cocaine do not give positive results, thus making it difficult to find shipments of this narcotic. Email: denunciasvigilanciaaduanera correo. Customs Supervision: Presentation of charges for smuggling and related offences Free telephone Container from Colombia with kilos of cocaine mixed with salt seized KB - pdf.
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