Spanish Town where can I buy cocaine

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Spanish Town where can I buy cocaine

These datasets underpin the analysis presented in the agency's work. Most data may be viewed interactively on screen and downloaded in Excel format. All countries. Topics A-Z. The content in this section is aimed at anyone involved in planning, implementing or making decisions about health and social responses. Best practice. We have developed a systemic approach that brings together the human networks, processes and scientific tools necessary for collecting, analysing and reporting on the many aspects of the European drugs phenomenon. Explore our wide range of publications, videos and infographics on the drugs problem and how Europe is responding to it. All publications. More events. More news. We are your source of drug-related expertise in Europe. We prepare and share independent, scientifically validated knowledge, alerts and recommendations. About the EUDA. Much of our knowledge about cocaine trafficking routes results from law enforcement activity and intelligence. Information on types and quantities of drugs seized and information on the origin and destination of shipments give indications of the main routes and modes of transport. However, such information is affected by factors such as law enforcement strategies, resources and priorities, as well as temporary changes to routes and practices in response to interdiction efforts or new opportunities. Hence, caution is needed in interpreting these data. The 1 tonnes of cocaine seized worldwide in was the highest ever to be reported. As in previous years, the vast majority of the global total was seized in the Americas, followed by western and central Europe. Although small in comparison with the Americas and Europe, quantities seized in emerging cocaine markets in Africa and Asia also reached record highs in UNODC, a see Figure Global quantity of cocaine seized. The source data for this graphic is available in the source table on this page. Record levels of cocaine production have been matched by record quantities seized, especially from containers handled in the numerous ports along the transatlantic cocaine routes. Most of the cocaine seized in the EU is transported by sea, primarily in maritime shipping containers. Cocaine is shipped to the EU directly from the countries of production but also from neighbouring countries of departure in South and Central America as well as the Caribbean. Based on quantities of cocaine seized in European ports and in ports elsewhere destined for Europe see Box Detailed analysis of cocaine seized in or destined for EU , Brazil about 71 tonnes , Ecuador The latter is a relative newcomer in the top countries of origin for shipments destined for Europe, confirming that Central America now has a more significant role EMCDDA and Europol, This is likely to reflect a diversification of trafficking routes within the Americas. In total, 25 countries in the Americas reported seizures of cocaine with Europe as the intended destination in In addition to the countries previously listed, also mentioned are Paraguay There have traditionally been two main areas through which maritime and air shipments of cocaine transit en route to Europe: the Caribbean, and the West African mainland and neighbouring islands of Cape Verde, Madeira and the Canaries. While these are likely to remain significant transit areas, there are indications that North Africa continues to grow in importance and that transhipment through the Western Balkans, while remaining more limited in scope, may also have increased. From the Caribbean, cocaine is typically shipped on pleasure craft via the Azores, or by air, either on direct flights or via a variety of transit points. The quantities of cocaine seized in the West African mainland and neighbouring islands, together with those seized in Europe coming from the region appear to be small in comparison to the amounts seized at departure points in the Americas. Data reported at the international level indicate that cocaine seizures in West Africa totalled This includes a finding that more than 16 tonnes of cocaine was seized in the region that year, reversing the dearth of seizures recorded since Bird, Large seizures were also reported in This includes a shipment of 2. According to media reporting, Gambian drug authorities were searching for the owner of the consignment, a French national, in connection with the incident News24, ; Reuters, Although considerably less cocaine was seized in North Africa 1. However, developments in the last four or five years suggest that the region, particularly its coasts on the Atlantic and the Mediterranean, is a growing transit and storage hub for cocaine both arriving by sea directly from South America and coming via West Africa by land for onward transport to Europe or elsewhere, for instance the Middle East EMCDDA and Europol, ; GI TOC, The main cocaine hub of North Africa is probably Morocco. The country has traditionally seized the largest quantities of cocaine in the region, which continued in with seizures totalling 1. There are indications that the Moroccan total could be even higher. For instance, in October , more than 1. The drugs were concealed in a container on a ship that had departed from Brazil and was bound for Antwerp, Belgium, and Portbury, a middle-sized port in Bristol, United Kingdom Kundu, On a much smaller scale than Morocco, international cocaine trafficking activities in Algeria and Libya reported in the last edition of this report EMCDDA and Europol, ; Micaleff, appear to have continued in the recent period. In , Algeria reported seizing about kilograms UNODC, a , while there are indications that Libyan seizures amounted to over 44 kilograms. In December , there were two significant seizures from containers bound for Libya: one in Ecuador on a container bound for Libya and Syria kilograms , and another in Malta from a container originating in Ecuador kilograms Dixon, This area is a known hub for the transhipment of drugs at sea, particularly cannabis resin from Morocco, on ships bound for Europe or Libya, while significant quantities of cocaine have been seized in the port of Oran recently Ben Yahia and Farrah, For instance, non-routine data reported by the European Border and Coast Guard Agency Frontex on drug seizures at a number of border posts between the EU and the Western Balkans in and indicate that comparatively small amounts of cocaine enter the EU from that region. Meanwhile, Frontex data confirm the large quantities of cocaine seized in Spain, a traditional entry point for cocaine into the EU Frontex, Nevertheless, individual seizures in and could suggest that cocaine trafficking through ports in the Western Balkan region, particularly in Albania and Montenegro, may have increased. For example, kilograms was seized in the port of Durres, Albania in April Exit. Montenegrin authorities also suspect that more than 3 tonnes were smuggled through the port of Bar in the second half of Kajosevic, Furthermore, Kosovo seized a shipment of kilograms of cocaine in May after it had been smuggled through the Albanian port of Durres Halili, For the fourth consecutive year, record amounts of cocaine were seized in Europe in At Meanwhile, new records were also established elsewhere in , including Greece 1. This indicates that entry points of cocaine shipments are diversifying and that cocaine consumer markets are developing in eastern Europe including Turkey see Cocaine retail markets: multiple indicators suggest continued growth and diversification. Source: The source data for this graphic is available in the source table on this page. Data available for a few key countries indicate that even larger quantities of cocaine were seized in the EU in For instance, data on cocaine seizures in Belgium indicate that almost 92 tonnes of cocaine was seized in , almost all in the port of Antwerp. If seizures made elsewhere of shipments that were destined for Antwerp are taken into account, then close to tonnes of cocaine was seized in connection with Antwerp in Belgian Federal Police, personal communication. The preliminary data available from a few countries indicates that more than tonnes of cocaine were seized in the EU in , exceeding the previous record European total Cocaine traffickers flexibly use a wide range of innovative trafficking methods, which evolve over time in response to enforcement efforts and other factors. Although cocaine also enters the EU by air, the main route used to smuggle the drug into Europe is still the maritime route from South America to western Europe, especially taking advantage of the licit containerised trade. Maritime transport allows the smuggling of large quantities, and the nature of international commercial maritime traffic means that a vast number of routes can be and are used. In addition, smaller, private sailing boats or even semi-submersible vessels are capable of bringing in large quantities of cocaine in single shipments, entering Europe at many points see Box First two semi-submersible drug-smuggling vessels captured in Europe. Criminal networks involved in cocaine trafficking now also more frequently employ methods of transport such as mother ships, pleasure craft, fishing vessels, cruise ships and the drop-off method. Major European container ports have recorded many seizures of large cocaine shipments in recent years. In addition to targeting major ports, organised criminal networks are now increasingly shipping larger amounts of cocaine from South America to smaller ports in the EU or neighbouring countries, where security measures may be easier to circumvent. Corruption of maritime and aviation port employees and security officials throughout Europe is, in most cases, a key condition for the successful use of these facilities for cocaine importation by criminal networks. Most of the cocaine available in Europe continues to be smuggled into the largest container ports of the EU located in Belgium Antwerp , the Netherlands Rotterdam and Spain Valencia and Algeciras. The German authorities have attributed the recent increase in seizures in the port of Hamburg to the activities of Balkan and Albanian-speaking organised crime networks BKA, The use of these ports shows how criminal networks continue to exploit established commercial maritime routes between Latin America and Europe to smuggle cocaine into the EU. Taking advantage of the large volume of containerised trade in goods between the two regions, criminal networks are able to conceal large quantities of cocaine in individual shipments. As a result, the main European container ports have recorded increasing numbers of large-volume cocaine seizures in recent years. While these main container ports continue to be targeted, it is likely that law enforcement activities at these facilities have pushed some criminal networks to find alternative smuggling routes, resulting in a recent intensification of cocaine shipments to smaller ports in the EU or in neighbouring countries where security measures may be viewed as easier to evade. The available data suggest that cocaine trafficking towards the EU continued during the COVID pandemic without any apparent disruption. At least tonnes of cocaine destined for Europe was seized in Some tonnes was seized at departure ports located in Latin America, and approximately tonnes was seized at EU ports. As in previous years, the largest quantity of cocaine in the EU was seized in Belgian ports 69 tonnes , followed by Dutch 45 tonnes and Spanish ports 26 tonnes. In Latin America, the largest amount was seized at ports in Brazil 45 tonnes , Ecuador 24 tonnes and Colombia 18 tonnes see Figure Top 10 countries for quantity of cocaine destined for Europe seized at ports in A total of 60 tonnes of cocaine was seized in such secondary EU ports in see Figure Top 10 EU seizing ports for quantity of cocaine. For a description of different concealment methods, see Figure Maritime trafficking: diversification of modus operandi. The available data indicates that in Latin America, the largest quantities of cocaine destined for Europe were seized in Guayaquil, Ecuador 23 tonnes , and Santos, Brazil 21 tonnes. If all seizures departing from these ports are taken into consideration, totalling about 61 tonnes shipped from Guayaquil and 31 tonnes from Santos, it becomes clear that these two ports are used intensively by cocaine trafficking criminal networks. Analysis of concealment methods emphasises some key features that enable large quantities of cocaine to be smuggled to the EU. Criminal networks are creative and appear to be able to rapidly adapt to avoid detection, which explains why a range of modi operandi are implemented. The data indicate that the port of Antwerp is probably the main entry point for cocaine smuggled into the EU. Europol intelligence suggests that most of the cocaine entering Antwerp is destined initially for the Netherlands, where further distribution is arranged. Focusing exclusively on cocaine seized outside Europe and destined for Belgian ports in the period, a variety of modi operandi can be observed in the 10 main shipping ports see Figure Top 10 source ports for quantity of cocaine destined for Belgian ports. The reasons for this are unclear and require additional research and closer monitoring. That said, Ecuador, which shares land borders with both Colombia and Peru, two of the main cocaine-producing countries, seems to have transformed in the last decade or so from a transit country into a major trafficking hub. Furthermore, it is now also reportedly emerging as a cocaine-producing country Pichel, Such transformation is thought to have fuelled recent violence between local gangs rumoured to be used by important cocaine trafficking networks from Colombia and Mexico. A recent surge in assassinations, which nearly doubled between and , including violent prison gang riots in which hundreds of inmates died in , are reportedly linked with cocaine trafficking in the country, particularly in the port of Guayaquil. Only the top 5 ports with data available for all years are shown. Operations supported by Europol have exposed the role played by corruption in the functioning of drug markets in Europe, a factor that may have been underestimated in the past. Corruption is a crime enabler for all types of criminal activities and is a significant facilitator of drug trafficking activities. In this regard, corruption is used by traffickers to gain entry to ports, to access drugs hidden in containers, to set up or ensure control over businesses used as covers for smuggling activities, such as renting transport vehicles or storage premises, and also to facilitate money laundering, among others. Indeed, various modi operandi used to smuggle cocaine through EU ports require corruption, and recent investigations in some EU ports have provided valuable insights on the methods used by criminal networks to retrieve cocaine shipments from containers in the EU. Of course, the threat of corruption is not restricted to EU ports, since it is also used in most other ports targeted by criminal groups globally. Subsequently, criminal actors will need to introduce the drugs into the container and replace the seal that has been broken during this process with a counterfeited one. Usually, this operation takes place after customs have cleared the container. In the destination port, the criminal network will, again, need inside help and information as they usually require the targeted container to be placed in a specific location in order to facilitate access to it, for instance on the ground and with unimpeded access to the doors. Additionally, they must receive confirmation that the container has been placed in the desired location and, depending on the type of rip-off method used, extract the cocaine from the container and transport it outside the port area. Other modi operandi may also require corruption as a facilitator. Concealing cocaine within shipments of legitimate goods can also use corruption, for instance in order to be informed and take necessary action if the container has been selected for physical inspection, or to receive customs clearance without such inspection. According to a law enforcement official from Seaport Police, a unit of the Dutch Police, interviewed in the press in , acts of corruption involving staff from shipping companies were identified in the port of Rotterdam in the previous two and a half years Driessen and Meeus, An investigation that led to the seizure of An ex-governor, a harbour master, three police officers and a lawyer were among the 22 suspects arrested during this operation Eeckhaut, Workers in other large EU ports such as Le Havre, France, or Alicante, Spain, have been targeted by criminal networks and engaged to facilitate cocaine importation. Recruitment into corrupt activities is usually carried out by offering large sums of cash or other valuable assets and services, but can also be achieved using violence and intimidation Gil, Kidnappings and murders of port workers have been reported in various EU ports in recent years Auvray, ; Europol, e. Cocaine smuggling by air primarily involves the use of commercial passenger flights, cargo aircraft and general aviation private aircraft. Fairly large shipments of cocaine are smuggled directly from South America and the Caribbean to western Europe by private business aircraft, and use of this method is expected to increase in the future. Stricter border controls and more effective security checks may encourage criminal networks to use secondary international airports and small airfields see Box Private jets: the Achilles heel in the fight against cocaine trafficking by air. With some travel restrictions remaining in place and significantly reduced air passenger traffic, it is likely that the use of air couriers will remain limited compared to the pre-COVID period. In the pre-pandemic period, smaller quantities of cocaine were smuggled using commercial flights. Couriers flew from airports in South and Central America and the Caribbean to major European airports, either directly or after stopovers in countries such as Morocco, Nigeria and the United Arab Emirates. After arrival at the main EU distribution hubs, cocaine shipments are primarily trafficked by road in passenger vehicles and lorries to local markets. Intra-EU trafficking of cocaine also involves commercial flights, light aircraft and helicopters, railway, sea transport, and post and parcel services. Cocaine loads are often hidden in sophisticated concealed compartments in cars, trucks and other vehicles, sometimes with shipments of other drugs. These compartments are also used to transport cash back to the distribution hubs. In addition, Europol intelligence indicates that some European criminal networks orchestrate cocaine shipments directly from South America to Asia without the drugs ever entering the EU. In , cocaine was the most frequently seized substance from air couriers intercepted at European airports and the third most frequently detected drug at European mail centres, after cannabis and other psychotropic substances Council of Europe, The COVID pandemic accelerated an expansion in the use of post and parcel services to fulfil orders placed online. Postal and parcel services are exploited for the distribution of drugs such as cannabis, cocaine, synthetic drugs Council of Europe, , new psychoactive substances, counterfeit currency, stolen and fraudulent documents, and many other illicit commodities. The distribution of illicit goods using post and parcel services is set to increase further in line with the expected growth of online retail activity. Consult the list of references used in this resource. Homepage Quick links Quick links. GO Results hosted on duckduckgo. Main navigation Data Open related submenu Data. Latest data Prevalence of drug use Drug-induced deaths Infectious diseases Problem drug use Treatment demand Seizures of drugs Price, purity and potency. Drug use and prison Drug law offences Health and social responses Drug checking Hospital emergencies data Syringe residues data Wastewater analysis Data catalogue. Selected topics Alternatives to coercive sanctions Cannabis Cannabis policy Cocaine Darknet markets Drug checking Drug consumption facilities Drug markets Drug-related deaths Drug-related infectious diseases. Recently published Findings from a scoping literature…. Penalties at a glance. Frequently asked questions FAQ : drug…. 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Southern Spain: The European drugs gateway

Spanish Town 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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