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Salud mental

versão impressa ISSN 0185-3325

Salud Ment vol.42 no.1 México Jan./Fev. 2019 

Original articles

Diagnosis of macrosocial risks of drug use in Mexican municipalities

Diagnóstico de riesgos macrosociales del consumo de drogas en municipios de la República Mexicana

Solveig E. Rodríguez-Kuri 1   * 

Raúl García-Aurrecoechea 2  

José Luis Benítez-Villa 2  

Carmen Fernández-Cáceres 3  

1Subdirección de Investigación, Centros de Integración Juvenil, A.C., Ciudad de México, México

2Departamento de Investigación Clínica y Epidemiológica, Centros de Integración Juvenil, A.C., Ciudad de México, México

3Dirección General, Centros de Integración Juvenil, A.C., Ciudad de México, México



Factors associated with drug use are defined in terms of their proximity to the phenomenon and can be classified as individual, microsocial, and macrosocial. Macrosocial factors include variables of a geographic, economic, demographic, and social nature, which can be compiled from population censuses and surveys.


To determine the levels of risk for drug use in municipalities in Mexico based on macro-social indicators.


Retrospective cross-sectional study, based on the analysis of population data, weighted by the Delphi method.


Sixty-four municipalities with a high or very high risk of drug use were identified. Factors such as the volume of drug seizures, prevalence of student use, alcohol supply, and inequality among the population were weighted as the factors with greatest risk for drug use.

Discussion and conclusion

These data serve as a benchmark for guiding the efficient, rational administration of resources assigned for dealing with the problem of addictions, since they make it possible to identify localities with a greater need for care services.

Keywords: Risk factors; drug users; social determinants of health; mental health; Delphi technique; substance abuse



Los factores asociados al consumo de drogas se definen en función de su proximidad con el fenómeno y pueden clasificarse en individuales, microsociales y macrosociales. Entre los factores macrosociales se incluyen variables de tipo geográfico, económico, demográfico y social, que es posible integrar a partir de censos y encuestas poblacionales.


Determinar niveles de riesgo del consumo de drogas en municipios de la República Mexicana con base en indicadores macrosociales.


Estudio transversal retrospectivo, basado en el análisis de datos poblacionales, ponderados mediante el método Delphi.


Se identificaron 64 municipios con alto o muy alto riesgo de consumo de drogas. Factores como el volumen de decomisos de drogas, prevalencia de consumo en estudiantes, oferta de alcohol y desigualdad entre la población fueron ponderadas como los factores de mayor riesgo para el consumo de drogas.

Discusión y conclusión

Estos datos representan un referente para orientar la administración eficiente y racional de los recursos destinados a atender el problema de las adicciones en tanto permiten identificar localidades que requieren servicios de atención con mayor prioridad.

Palabras clave: Factores de riesgo; usuarios de drogas; determinantes sociales de la salud; salud mental; técnica Delphi; abuso de sustancias


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.


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.


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).


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).


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


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.


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  


*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.


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Received: March 01, 2018; Accepted: November 28, 2018

Correspondence: Solveig E. Rodríguez Kuri Subdirección de Investigación, Centros de Integración Juvenil, A.C. Av. San Jerónimo 372, Col. del Pedregal, Álvaro Obregón, 01900 Ciudad de México, México. Phone: +52 (55) 5999 - 4949. Email:

Conflict of interest

The authors declare they have no conflict of interest.

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