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Introduction
Scientific literature describes a wide array of risk and protective factors associated with substance use. Some authors define them according to their level of proximity to the phenomenon, because of which these factors can be classified as macrosocial, microsocial, and individual (Hawkins, Catalano, & Miller, 1992).
Macrosocial factors include economic, demographic and geographic variables, which affect the level of well-being of individuals. Another group of factors, the so-called microsocial factors, encompass aspects related to the subject’s network of close relations, including those in the familial, school and work sphere, and with their partners and peers. Lastly, individual factors incorporate variables related to the person, which include aspects that ranging from self-esteem to the presence of affective or behavioral disorders.
According to Hawkins et al. (1992), the best way to develop effective strategies to prevent alcohol and drug use is precisely one that focuses on risk factors. In this respect, and from a macrosocial point of view, the population is exposed to very different risk conditions, depending on their geographical, economic, and social status. Thus, drug trafficking routes or areas with a high influx of tourists, for example, pose a significant risk of use, since they encourage greater supply and accessibility. Likewise, living in a locality with a significant migratory flow or high crime rates increases the risk of drug use (United Nations and International Drug Control Program, 1998; Santos & Paiva, 2007; Zhang et al., 2015).
Nowadays it is possible to compile information on this type of indicators, based on the data provided by various government agencies drawn from the censuses and surveys undertaken periodically, whereby it is feasible to perform a diagnosis in macrosocial terms of the level of risk of drug use in the various communities in the country.
This can contribute to achieving a more efficient, rational administration of the resources assigned for addiction care, insofar as it makes it possible to identify localities with a greater need for these services.
Accordingly, since 1997, the Centros de Integración Juvenil (CIJ) have conducted a risk diagnosis of drug use in municipalities and delegations in Mexico on the basis of macrosocial indicators. This project constitutes a second update of the study, “Macrosocial risks of drug dependence at the municipal level and strategic care network in Mexico,” published in 1997 (Salinas et al., 1997), and first updated in 2011 (García, Rodríguez, Córdova, & Fernández, 2016). The results of these diagnoses have supported decision-making in the establishment of care units, at least at the CIJ, where three of the five units that have come into operation since the last study have been installed in municipalities classified as high-risk (García et al., 2016).
However, since drug use is a constantly changing phenomenon, it is necessary to periodically update its diagnosis. This study has compiled information from various sources, in order to obtain an approach to the problem of drug use in the country from a macrosocial perspective, in addition to offering an updated benchmark for care needs at the municipal level. The purpose of this study was to determine Macrosocial Levels of Risk for Drug Use in the 426 municipalities and urban delegations (communities with over 50 inhabitants) in Mexico to provide a useful parameter to plan the establishment of care units in the country.
Method
Type of study
Retrospective, cross-sectional study, based on the analysis of census data and population surveys, weighted by means of assessment based on the Delphi technique.
Procedure
Based on a set of population, geographic, economic, human development, violence and supply, and use data for substances detailed below, a risk index of drug use was obtained for each municipality or delegation included in the study.
Since each indicator has a different importance in the risk of substance use, a method of assessment, known as the Delphi technique was used, in order to assess the importance of the various risk indicators considered.
Delphi technique
The Delphi technique is a method based on a panel of experts, which allows for the exchange and contrast of opinions and individual arguments on a topic in order to make consensual decisions (García & Suárez, 2013). It is a method designed to obtain the opinion of a group of experts on a problem in a structured manner. The method incorporates a feedback exercise, which allows individual opinions to be brought closer to a consensus. This technique is especially useful when the available information is insufficient and requires the interpretation of specialists in the field (Boulkedid, Abdoul, Loustau, Sibony, & Alberti, 2011). Moreover, this technique guarantees three fundamental aspects for collecting information when using instruments of this nature, namely confidentiality, controlled iteration/feedback, and the response of the group in statistical form (Awad-Núñez, González-Cancelas, & Camarero-Orive, 2014).
Participants
The panel comprised 20 experts (11 women and nine men), whose professional career or work experience has provided them with extensive knowledge of the drug use problem and the associated risk and social conditioning factors associated with the latter, because of which they had sufficient elements to assess the importance of the macrosocial risk indicators considered in the study. The professional profile of the panel experts included mainly professionals in the field of health, such as psychologists, doctors, social workers, sociologists, and nurses with educational attainment corresponding to higher education or above. Most of them are affiliated to public or private organizations or institutions in the field of health, as well as teaching and research.
Given that the Delphi technique requires at least 15 judges to guarantee the validity of the consensus (Taylor-Powell, 2002) and due to the possibility of high attrition, 39 people were invited to participate to ensure that by the end of the survey and feedback process, there would be at least 15 judges. The panel of experts was eventually made up of 20 people, who encompassed the gamut of professional profiles mentioned above and completed the entire process. They were sent an email in which they were invited to participate in a survey on risk factors for drug use by answering questionnaire located in a virtual site, developed for this study, for which they were provided with an access link, as well as a username and password. They were asked to assign a score of zero to ten to each of the indicators listed, according to the importance they could have as risk factors for drug use among the inhabitants of a community.
Participants were told that two weeks after their first participation in the survey, they should return to the site where they would be informed of the result of the average weighting of all the judges. They were then asked to enter a discussion forum in which they could discuss the indicators with the lowest level of consensus, in other words, those with the most disparate ratings and the greatest deviation from the average. In addition, once they had expressed their opinions in the forum, they were asked to answer the questionnaire again, providing the grade they considered most appropriate, whether they decided to maintain the one they given in the first survey or chose to modify it, after finding out about the average grades and having participated in the discussion forum. The information gathering period ran from May 18 to 24, 2017, in its initial stage (first application of the questionnaire) and from May 30 to July 8 of the same year in the second stage (feedback, participation in the discussion forum, and second application of the questionnaire).
Measurements
Since the diagnostic studies that preceded the one presented here, the measurement indicators have shown variations since the conditions of the social context in which substance use takes place have also changed. Firstly, it should be noted that the number of urban municipalities in Mexico has altered with respect to the previous version of the study (García et al., 2016) because of demographic changes in Mexico, from 371 to 426. Moreover, certain information sources are no longer available or the level of specificity of their data has changed, from having municipal to state representativity or from state to regional representativity, such as the National Addictions Survey, the 2011 version of which (Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, Instituto Nacional de Salud Pública, & Secretaría de Salud, 2012) contains data at the regional level, while the previous survey (Secretaría de Salud, 2009), which included state data, is already too outdated to be considered as a parameter in this diagnosis. Accordingly, it was decided to incorporate information from the Survey on Drug Use in Students (Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz, Comisión Nacional Contra las Addicciones, & Secretaría de Salud, 2015) which presents state data and, athough it is not representative of the entire youth population of Mexico, it constitutes an important benchmark for substance use in the population enrolled in elementary (5th and 6th grades), middle and high school. Conversely, other sources have also emerged, more closely linked to the phenomenon that concerns us, which have been incorporated into this version.
Thus, whereas the last study included demographic, economic, geographical, educational, socio-familial, tourist influx and illegal drug production, and trafficking indicators, in this version of the study, indicators related to social violence have also been incorporated, as well as the number of nocturnal recreational spaces and those for alcohol sale and consumption.
In general, to undertake this diagnosis, efforts were made to incorporate indicators related to the drug use phenomenon in different ways. The following were therefore included: a) indicators directly related to use, such as survey data on the prevalence of use at the state level in the student population; b) factors that establish a more or less direct causal relationship, such as the presence of bars, canteens, and other establishments of this nature, which impact the supply of substances in localities; c) factors that imply an associative relationship, as in the case of indicators of violence and crimes which, although they do not maintain a causal relationship, may correlate with use; and d) structural factors, such as those related to demographic aspects, inequality, human development, etc., which, without having a linear relationship with substance use, may have a determinant effect on it.
Thus, eleven categories of indicators were considered: state prevalence of drug use, population, geographic, migration, education, employment, inequality and human development indicators, number of establishments for alcohol consumption, criminal activity and violence, seizures, and perception of sale and use of drugs. Table 1 provides a detailed list of Macrosocial Risk Indicators and their source.
Table 1. Risk indicators and weighted importance
| Category | Indicator | Importance weighted by indicator | Importance weighted by category | Justification of its inclusion | Sources |
|---|---|---|---|---|---|
| Demographic | Urban concentration (proportion of urban population of the state living in that municipality) | 6.2 | 6.3 | Prevalence of drug use is higher in urban areas and among young males. A high growth rate, as well as living in a large city, exposes people to a wide array of direct and indirect risks of using drugs. | Encuesta Nacional de la Dinámica Demográfica 2014 (INEGI, 2014) |
| Proportion of young people (average age) | 6.4 | Encuesta Intercensal, 2015. (INEGI, 2015) | |||
| Proportion of male population (%) | 5.8 | Censo de población y vivienda, 2010. (INEGI, 2011) | |||
| Average growth rate (percentage increase over 2010) | 6.2 | Anuarios estadísticos estatales 2009. (INEGI, 2010) | |||
| Forms part of a metropolitan area (yes - no) | 6.9 | Delimitación de las zonas metropolitanas, 2010. (CONAPO, SEDESOL, INEGI, 2010) | |||
| Geographical | Location on the northern border (border states yes - no) | 7.2 | 7.2 | Some of the municipalities with the highest prevalence of drug use in the country are located in states on the northern border of the country or constitute areas with a significant tourist influx. | Resultados de la actividad hotelera (Acumulados enero-diciembre 2016). (SECTUR, Subsecretaría de Planeación Turística, 2016). |
| Tourist center (According to SECTUR criteria yes - no) | 7.2 | Diagnósticos Turísticos Delegacionales 2014-2015. (SECTUR–Ciudad de México, 2015). | |||
| Educational | Proportion of persons aged 15 and over with no schooling (%) | 6.5 | 6.45 | Low educational attainment is associated with a higher risk of experimentation with drug use. | Encuesta Intercensal, 2015. (INEGI, 2015) |
| Educational Attainment (years) | 6.4 | ||||
| Night life | Presence of nightclubs and discos (No. of establishments) | 7.7 | 7.75 | Places with an impact on substance availability | Directorio Estadístico Nacional de Unidades Económicas, 2016 (INEGI, 2016). |
| Presence of bars, canteens and alcohol outlets (No. of establishments) | 7.8 | ||||
| Migration | Migration rate (Difference between number of emigrants and immigrants) | 6.1 | 6.1 | The mobility of the population to another country exposes them to a greater acculturation stress, which has been associated with an increased risk of drug use. | Encuesta Nacional de la Dinámica Demográfica 2014 (INEGI, 2014) |
| Inequality | Human Development Index (0 to 1) | 7.5 | 7.5 | Structural factors indirectly related to drug use | Indice de Desarrollo Humano en México. United Nations Development Program, 2016 |
| Gini coefficient (0 to 1) | 7.5 | Consejo Nacional de Evaluación de la Política de Desarrollo Social, 2010 | |||
| Unemployment | Unemployment level (%) | 6.8 | 6.8 | Like the inequality indexes, it involves a structural factor related to drug use | INEGI. Indicadores de ocupación y empleo al segundo trimestre de 2017. |
| Prevalence of drug use in students | High prevalence of drug use at least once in their lifetime among middle school students (%) | 8.1 | 8.05 | These are direct indicators of the risk of drug use in the rest of the population | Encuesta Nacional del Consumo de Drogas en Estudiantes, 2014. (INPRFM, CONADIC, SSA, 2015) |
| High prevalence of drug use at least once in their lifetime among high school students (%) | 8.0 | ||||
| Perception of sale and use of drugs and crimes in the community | Knowledge of alcohol consumption (% of population that reported having knowledge of this situation) | 7.3 | 7.27 | It suggests a perception of easy access to substances | Encuesta Nacional de Victimización y Percepción sobre Seguridad Pública, 2016. (INEGI, 2016) |
| Knowledge of drug use (% of population that reported having knowledge of this situation) | 7.6 | ||||
| Knowledge of sale of drugs (% of population that reported having knowledge of this situation) | 6.7 | ||||
| Knowledge of frequent robberies and assaults (perception) | 7.5 | ||||
| Criminal activity and violence | Robbery with violence (Freq.) | 7.5 | 6.94 | They correlate with drug trafficking and use | Incidencia delictiva del fuero común (SEGOB, 2017) |
| Theft without violence (Freq.) | 6.7 | ||||
| Homicides (Freq.) | 6.8 | ||||
| Kidnappings (Freq.) | 6.9 | ||||
| Sex offenses/rapes (Freq.) | 6.8 | ||||
| Volume of drug seizures | Marijuana seizures (Tons.) | 8.1 | 8.1 | They are evidence of greater exposure in the area | Incidencia delictiva por entidad federativa (SEGOB, 2017) |
| Cocaine seizures (Tons.) | 8.1 | ||||
| Heroin seizures (Tons.) | 8.1 | ||||
| Psychotropic seizures (units) | 8.1 |
Analysis
Once the risk rating for each indicator was obtained through the Delphi technique, and to prevent certain categories of indicators from being overrepresented by having a greater number of indicators than others, the risk rating of the indicators was averaged within each category. Based on these ratings, the values of the indicators were weighted and transformed, so that the parameters would be equivalent, even though the measurement units (persons, tons of drugs, etc.) varied. Each indicator was transformed on the basis of the weighted importance assigned, so that the maximum value obtained had the maximum value of the weighted importance assigned, based on a rule of 3, although in the case of categorical variables, such as belonging to a metropolitan area, a fixed weight was assigned for those cases and zero for those which did not belong to metropolitan areas. The sum of these scores was used to obtain a Macrosocial Risk Index for Drug Use (MRIDU) for each municipality or delegation.
Lastly, the risk level was estimated based on the number of standard deviations of the indices with respect to the average. Thus, municipalities with scores above two standard deviations were codified as Very High risk, those with between one and two deviations were coded as High risk, scores located between the average and one deviation corresponded to municipalities with Medium High risk and the same was done with scores below the average. In this case, the categories corresponded to the Medium Low, Low and, Very Low risk levels.
Results
Of the 426 municipalities or delegations with over 50 000 inhabitants, 16 were identified as having a Macrosocial Risk Index of Drug Use corresponding to a Very High level (MRIDU greater than 51.68), 48 as having a High level (MRIDU of 46.38 to 51.68), 119 as having a medium high risk level (MRIDU of 41.12 to 46.36), 174 as having a medium low risk level (MRIDU of 35.79 to 41.05), and 68 as having a low risk level (MRIDU of 30.56 to 35.74), while just one municipality was classified with a very low risk level (MRIDU of 29.59) ( Table 2 ).
Table 2. Index of macrosocial risk of drug use (IRMCD) in municipalities and delegations in Mexico*
| Municipality | State | MRIDU | Risk level | Municipality | State | MRIDU | Risk level | ||
|---|---|---|---|---|---|---|---|---|---|
| or borough | or borough | ||||||||
| 1 | Tijuana | Baja California | 63.95 | Very high | 46 | Nuevo Laredo | Tamaulipas | 47.78 | High |
| 2 | Cuauhtémoc | Cd. de México | 60.76 | Very high | 47 | Salinas Victoria | Nuevo León | 47.70 | High |
| 3 | Guadalajara | Jalisco | 58.77 | Very high | 48 | Valle de Chalco Solidaridad | México | 47.69 | High |
| 4 | Monterrey | Nuevo León | 58.54 | Very high | 49 | Tlalpan | Cd. de México | 47.69 | High |
| 5 | Playas de Rosarito | Baja California | 56.67 | Very high | 50 | Lázaro Cárdenas | Michoacán | 47.63 | High |
| 6 | Mexicali | Baja California | 56.39 | Very high | 51 | Navojoa | Sonora | 47.54 | High |
| 7 | Iztapalapa | Cd. de México | 56.36 | Very high | 52 | San Luis Potosí | San Luis Potosí | 47.52 | High |
| 8 | Puerto Vallarta | Jalisco | 55.03 | Very high | 53 | Chihuahua | Chihuahua | 47.48 | High |
| 9 | San Luis Río Colorado | Sonora | 54.47 | Very high | 54 | Huehuetoca | México | 47.38 | High |
| 10 | Ensenada | Baja California | 54.39 | Very high | 55 | Torreón | Coahuila | 47.37 | High |
| 11 | Gustavo A. Madero | Cd. de México | 54.12 | Very high | 56 | García | Nuevo León | 47.02 | High |
| 12 | Acapulco de Juárez | Guerrero | 53.02 | Very high | 57 | Tejupilco | México | 46.93 | High |
| 13 | Cd. Juárez | Chihuahua | 52.73 | Very high | 58 | Tlajomulco de Zúñiga | Jalisco | 46.78 | High |
| 14 | Puerto Peñasco | Sonora | 52.70 | Very high | 59 | Los Cabos | Baja California Sur | 46.73 | High |
| 15 | Agua Prieta | Sonora | 52.69 | Very high | 60 | San Miguel de Allende | Guanajuato | 46.69 | High |
| 16 | Miguel Hidalgo | Cd. de México | 52.52 | Very high | 61 | Venustiano Carranza | Cd. de México | 46.63 | High |
| 17 | Tecámac | México | 51.68 | High | 62 | Huatabampo | Sonora | 46.54 | High |
| 18 | Hermosillo | Sonora | 51.47 | High | 63 | Iztacalco | Cd. de México | 46.43 | High |
| 19 | Chalco | México | 51.44 | High | 64 | Tláhuac | Cd. de México | 46.38 | High |
| 20 | Benito Juárez | Cd. de México | 51.31 | High | 65 | Pesquería | Nuevo León | 46.36 | Medium high |
| 21 | Benito Juárez (Cancún) | Quintana Roo | 50.96 | High | 66 | Salamanca | Guanajuato | 46.30 | Medium high |
| 22 | Tecate | Baja California | 50.89 | High | 67 | Cuautitlán Izcalli | México | 46.29 | Medium high |
| 23 | Ecatepec de Morelos | México | 50.49 | High | 68 | Tultitlán | México | 46.27 | Medium high |
| 24 | Guaymas | Sonora | 50.33 | High | 69 | San Pedro Tlaquepaque | Jalisco | 46.22 | Medium high |
| 25 | Cajeme | Sonora | 50.29 | High | 70 | Empalme | Sonora | 46.21 | Medium high |
| 26 | Querétaro | Querétaro | 50.19 | High | 71 | Azcapotzalco | Cd. de México | 46.17 | Medium high |
| 27 | Nogales | Sonora | 50.03 | High | 72 | Chicoloapan | México | 46.07 | Medium high |
| 28 | Coyoacán | Cd. de México | 49.76 | High | 73 | Álvaro Obregón | Cd. de México | 46.04 | Medium high |
| 29 | Puebla | Puebla | 49.76 | High | 74 | Veracruz | Veracruz | 45.99 | Medium high |
| 30 | Reynosa | Tamaulipas | 49.67 | High | 75 | San José del Rincón | México | 45.89 | Medium high |
| 31 | Tlalnepantla de Baz | México | 49.41 | High | 76 | Irapuato | Guanajuato | 45.84 | Medium high |
| 32 | Manzanillo | Colima | 49.23 | High | 77 | Almoloya de Juárez | México | 45.81 | Medium high |
| 33 | Chimalhuacán | México | 48.84 | High | 78 | Tampico | Tamaulipas | 45.68 | Medium high |
| 34 | Nezahualcóyotl | México | 48.76 | High | 79 | Acambay de Ruíz Castañeda | México | 45.55 | Medium high |
| 35 | Guadalupe y Calvo | Chihuahua | 48.66 | High | 80 | Xochimilco | Cd. de México | 45.46 | Medium high |
| 36 | Morelia | Michoacán | 48.61 | High | 81 | Río Bravo | Tamaulipas | 45.29 | Medium high |
| 37 | Zumpango | México | 48.46 | High | 82 | Celaya | Guanajuato | 45.15 | Medium high |
| 38 | Solidaridad | Quintana Roo | 48.45 | High | 83 | Villa Victoria | México | 45.14 | Medium high |
| 39 | Naucalpan de Juárez | México | 48.22 | High | 84 | Ixtapaluca | México | 45.12 | Medium high |
| 40 | León | Guanajuato | 48.22 | High | 85 | Atizapán de Zaragoza | México | 45.11 | Medium high |
| 41 | Toluca | México | 48.12 | High | 86 | Cuernavaca | Morelos | 45.08 | Medium high |
| 42 | La Paz | México | 48.05 | High | 87 | Poncitlán | Jalisco | 44.94 | Medium high |
| 43 | Matamoros | Tamaulipas | 48.02 | High | 88 | Uruapan | Michoacán | 44.91 | Medium high |
| 44 | Zapopan | Jalisco | 47.92 | High | 89 | Acámbaro | Guanajuato | 44.86 | Medium high |
| 45 | Caborca | Sonora | 47.86 | High | 90 | San Felipe del Progreso | México | 44.83 | Medium high |
| 91 | Tecomán | Colima | 44.64 | Medium high | 138 | Tuxtla Gutiérrez | Chiapas | 42.81 | Medium high |
| 92 | Salvatierra | Guanajuato | 44.62 | Medium high | 139 | Ayala | Morelos | 42.81 | Medium high |
| 93 | Nicolás Romero | México | 44.58 | Medium high | 140 | Cadereyta Jiménez | Nuevo León | 42.78 | Medium high |
| 94 | El Marqués | Querétaro | 44.58 | Medium high | 141 | Apodaca | Nuevo León | 42.77 | Medium high |
| 95 | Zihuatanejo de Azueta | Guerrero | 44.40 | Medium high | 142 | Comonfort | Guanajuato | 42.73 | Medium high |
| 96 | Etchojoa | Sonora | 44.37 | Medium high | 143 | San Felipe | Guanajuato | 42.70 | Medium high |
| 97 | Chapala | Jalisco | 44.26 | Medium high | 144 | Gómez Palacio | Durango | 42.69 | Medium high |
| 98 | Moroleón | Guanajuato | 44.25 | Medium high | 145 | Villahermosa | Tabasco | 42.69 | Medium high |
| 99 | Cuajimalpa de Morelos | Cd. de México | 44.22 | Medium high | 146 | Lagos de Moreno | Jalisco | 42.66 | Medium high |
| 100 | Santa Cruz de Juventino Rosas | Guanajuato | 44.05 | Medium high | 147 | Villa de Allende | México | 42.65 | Medium high |
| 101 | Aguascalientes | Aguascalientes | 44.03 | Medium high | 148 | Coacalco de Berriozábal | México | 42.62 | Medium high |
| 102 | La Magdalena Contreras | Cd. de México | 44.02 | Medium high | 149 | Tonalá | Jalisco | 42.61 | Medium high |
| 103 | Uriangato | Guanajuato | 43.99 | Medium high | 150 | Huixquilucan | México | 42.58 | Medium high |
| 104 | Guadalupe | Nuevo León | 43.98 | Medium high | 151 | San José Iturbide | Guanajuato | 42.58 | Medium high |
| 105 | Pénjamo | Guanajuato | 43.89 | Medium high | 152 | Zinacantepec | México | 42.57 | Medium high |
| 106 | El Alto | Jalisco | 43.86 | Medium high | 153 | Jiutepec | Morelos | 42.50 | Medium high |
| 107 | Milpa Alta | Cd. de México | 43.84 | Medium high | 154 | Santiago Tuxtla | Veracruz | 42.49 | Medium high |
| 108 | Puruándiro | Michoacán | 43.80 | Medium high | 155 | Hidalgo del Parral | Chihuahua | 42.40 | Medium high |
| 109 | Cozumel | Quintana Roo | 43.78 | Medium high | 156 | Atotonilco el Alto | Jalisco | 42.37 | Medium high |
| 110 | Ixtlahuacán de los Membrillos | Jalisco | 43.75 | Medium high | 157 | San Juan del Río | Querétaro | 42.35 | Medium high |
| 111 | Temascalcingo | México | 43.74 | Medium high | 158 | Temixco | Morelos | 42.30 | Medium high |
| 112 | San Fernando | Tamaulipas | 43.71 | Medium high | 159 | Chilapa de Álvarez | Guerrero | 42.23 | Medium high |
| 113 | Acolman | México | 43.70 | Medium high | 160 | Texcoco | México | 42.22 | Medium high |
| 114 | La Barca | Jalisco | 43.67 | Medium high | 161 | Ameca | Jalisco | 42.21 | Medium high |
| 115 | Saltillo | Coahuila | 43.64 | Medium high | 162 | Valle de Bravo | México | 42.19 | Medium high |
| 116 | Cuautla | Morelos | 43.54 | Medium high | 163 | Ixtlahuaca | México | 42.16 | Medium high |
| 117 | Colima | Colima | 43.54 | Medium high | 164 | Tepatitlán de Morelos | Jalisco | 42.04 | Medium high |
| 118 | Poza Rica de Hidalgo | Veracruz | 43.53 | Medium high | 165 | Silao de la Victoria | Guanajuato | 42.04 | Medium high |
| 119 | Oaxaca de Juárez | Oaxaca | 43.50 | Medium high | 166 | Jiquipilco | México | 41.95 | Medium high |
| 120 | Las Choapas | Veracruz | 43.49 | Medium high | 167 | Zamora | Michoacán | 41.93 | Medium high |
| 121 | Altamira | Tamaulipas | 43.49 | Medium high | 168 | Victoria | Tamaulipas | 41.79 | Medium high |
| 122 | Camargo | Chihuahua | 43.40 | Medium high | 169 | Apaseo el Alto | Guanajuato | 41.75 | Medium high |
| 123 | Mazatlán | Sinaloa | 43.34 | Medium high | 170 | Metepec | México | 41.53 | Medium high |
| 124 | Tultepec | México | 43.33 | Medium high | 171 | Amealco de Bonfil | Querétaro | 41.50 | Medium high |
| 125 | Ixhuatlán de Madero | Veracruz | 43.25 | Medium high | 172 | Rioverde | San Luis Potosí | 41.49 | Medium high |
| 126 | Ocotlán | Jalisco | 43.20 | Medium high | 173 | Santa Catarina | Nuevo León | 41.48 | Medium high |
| 127 | Ramos Arizpe | Coahuila | 43.18 | Medium high | 174 | Ciudad Madero | Tamaulipas | 41.48 | Medium high |
| 128 | General Escobedo | Nuevo León | 43.16 | Medium high | 175 | Córdoba | Veracruz | 41.42 | Medium high |
| 129 | Juárez | Nuevo León | 43.15 | Medium high | 176 | San Juan de los Lagos | Jalisco | 41.42 | Medium high |
| 130 | San Luis de la Paz | Guanajuato | 43.11 | Medium high | 177 | General Zuazua | Nuevo León | 41.38 | Medium high |
| 131 | Coatzacoalcos | Veracruz | 43.04 | Medium high | 178 | Apatzingán | Michoacán | 41.37 | Medium high |
| 132 | Xochitepec | Morelos | 43.04 | Medium high | 179 | Othón PBlanco | Quintana Roo | 41.33 | Medium high |
| 133 | Tapachula | Chiapas | 42.93 | Medium high | 180 | Abasolo | Guanajuato | 41.22 | Medium high |
| 134 | Cuauhtémoc | Chihuahua | 42.88 | Medium high | 181 | Xilitla | San Luis Potosí | 41.19 | Medium high |
| 135 | Arandas | Jalisco | 42.87 | Medium high | 182 | Guanajuato | Guanajuato | 41.19 | Medium high |
| 136 | Encarnación de Díaz | Jalisco | 42.84 | Medium high | 183 | La Piedad | Michoacán | 41.12 | Medium high |
| 137 | Yuriria | Guanajuato | 42.84 | Medium high | |||||
Note:
*Only municipalities with a Very high, High and Medium high-risk level are included.
Four municipalities in Baja California (Tijuana, Playas de Rosarito, Mexicali, and Ensenada), four Mexico City boroughs (Cuauhtémoc, Iztapalapa, Gustavo A. Madero, and Miguel Hidalgo), three municipalities in Sonora (San Luis Río Colorado, Puerto Peñasco, and Agua Prieta), two in Jalisco (Guadalajara and Puerto Vallarta), one in Nuevo León (Monterrey), one in Guerrero (Acapulco) and one in Chihuahua (Ciudad Juárez) were identified as being at very high risk ( Table 2 ). Likewise, among the municipalities and boroughs with a high-risk level, 13 were identified in the State of Mexico, seven in Sonora, six in Mexico City, three in Tamaulipas, and two in each of the following states: Jalisco, Nuevo León, Chihuahua, Michoacán, Guanajuato, and Quintana Roo ( Table 2 ).
Discussion and conclusion
As can be seen, a significant number of municipalities in the country (64) have macrosocial conditions that presumably place their populations at a high or very high risk of substance use. They can therefore be considered key planning objectives for setting up care units for drug use. It is worth noting that factors such as the location of a municipality on a drug trafficking or production route (volume of drug seizures), state prevalences of use in the student population, presence of places where alcohol is sold, and inequality among the population were weighted by the participating experts as the macrosocial factors with the greatest risk of drug use, while population variables had the lowest weighting.
In this respect, one can say that at least one factor in each of the different types of indicators considered had a decisive influence on the macrosocial risk index obtained. In other words, those that have a direct relationship with use, in this case; state prevalence of use in the student population; those with a more or less direct causal relationship such as sites that have an impact on the supply of alcohol or other substances; those that involve an associative or correlational relationship with use, in this case, the perception of easy access to substances (knowledge of sale and use in the street) and structural factors, specifically those that denote conditions of inequality among the population, such as a low human development index and high Gini coefficient.
This study has limitations that deserve comment to facilitate the correct interpretation of results. First of all, it should be recalled that the data used are drawn from information sources with varying time scales and were collected for different purposes from those of this project. However, due to the lack of a single source of information, it was decided to use the most up to date available records. At the same time, although information was collected using an electronic card with the aim of incorporating new technologies into the research processes, using conventional instruments in a physical format or a face-to-face strategy for data collection could yield different results. Lastly, it is important to note that this diagnosis has a municipal scope and cannot provide data at the local level, and must therefore be complemented by other studies. Other diagnoses are therefore required to identify the areas of greatest risk for drug use within the municipality. An example of studies of this kind in Mexico is the Basic Target Community Study (BTCS), developed at Centros de Integración Juvenil, which identifies care needs at the local level based on area trips, as well as interviews with key informants, in order to obtain information on areas with the greatest care needs (Centros de Integración Juvenil, 2013). However, one limitation of this study is that, given its internal nature, it is only conducted in municipalities where this institution has care units. It would also be useful to conduct studies to determine the accessibility of services, both in the economic sense and about their geographical location, in order to facilitate treatment for those who so require.
