Trends in Addiction to Psychoactive Substances Among Homeless People in Colombia Using Artificial Intelligence.
DOI:
https://doi.org/10.9781/ijimai.2024.02.002Keywords:
Addiction, Artificial Intelligence, Homelessness, Psychoactive Substances, Social JusticeAbstract
Introduction: Currently, homelessness should not be seen as just another problem, but as a reality of inequality and the absence of social justice. In this sense, homeless people are subjected to social disengagement, lack of job opportunities or the instability of these, insecurity circumstances, these aspects being one of the causes associated with the consumption or addiction to psychoactive substances. Data: To define the proposed approach, data from the Census of Street Inhabitants - CHC- 2021 of the National Administrative Department of Statistics (DANE), which contains 19,375 records and 25 columns, were used. Methodology: This article presents an artificial intelligence approach that implements a model based on machine learning algorithms for identifying addiction trends to psychoactive substances in street dwellers in Colombia. Conclusions: Based on the results obtained, it is evident that the approach can serve as a support for decision making by municipal administrations in the definition of social public policies for the street-dwelling population in Colombia.
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P. C. Rosa, “Exclusiones del espacio público de los habitantes de la calle en la ciudad de Buenos Aires,” Territorios, no. 39, p. 157, 2018, doi: 10.12804/revistas.urosario.edu.co/territorios/a.5632.
P. Ruisoto and I. Contador, “The role of stress in drug addiction. An integrative review,” Physiology and Behavior, vol. 202, pp. 62-68, 2019, doi: 10.1016/j.physbeh.2019.01.022.
G. F. Koob and J. Schulkin, “Addiction and stress: An allostatic view,” Neuroscience and Biobehavioral Reviews, vol. 106, pp. 245-262, 2019, doi: 10.1016/j.neubiorev.2018.09.008.
E. Kpelly, S. Schauder, J. Masson, C. K. Kokou-Kpolou, and C. Moukouta, “Influence of attachment and psychotrauma in drug addiction,” Annales Médico-psychologiques, vol. 180, no. 6, pp. S81-S87, 2022, doi: 10.1016/j.amp.2020.11.019.
I. Sadło, E. Guz, A. Wójciuk, M. Brodowicz-Król, M. Kaczoruk, and P. Kaczor-Szkodny, “Addiction to psychoactive substances as a public health challenge,” Medycyna Ogólna i Nauki o Zdrowiu, vol. 27, no. 1, pp. 70-76, 2021, doi: 10.26444/monz/133713.
A. A. Moustafa et al., “The relationship between childhood trauma, early-life stress, and alcohol and drug use, abuse, and addiction: An integrative review,” Current Psychology, vol. 40, pp. 579-784, 2021, doi: 10.1007/s12144-018-9973-9.
C. Rudin and K. L. Wagstaff, “Machine learning for science and society,” Machine Learning, vol. 95, pp. 1-9, 2014. doi: 10.1007/s10994-013-5425-9.
C. Alexopoulos, V. Diamantopoulou, Z. Lachana, Y. Charalabidis, A. Androutsopoulou, and M. A. Loutsaris, “How machine learning is changing e-government,” in ACM International Conference Proceeding Series, 2019, pp. 354–363, 2019, doi: 10.1145/3326365.3326412.
P. Cadahia, A. Golpe, J. M. Martín-Álvarez, and E. Asensio, “Measuring anomalies in cigarette sales using official data from Spanish provinces: Are the anomalies detected by the Empty Pack Surveys (EPSs) used by Transnational Tobacco Companies (TTCs) the only anomalies?,” Tobacco Induced Diseases, vol. 19, e98, doi: 10.18332/tid/143321.
A. Andueza, M. Á. Del Arco-Osuna, B. Fornés, R. González-Crespo, and J. M. Martín-Álvarez, “Using the Statistical Machine Learning Models ARIMA and SARIMA to Measure the Impact of Covid-19 on Official Provincial Sales of Cigarettes in Spain,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 1, pp. 73-87, 2023, doi: 10.9781/ijimai.2023.02.010.
A. Suruliandi, T. Idhaya, and S. P. Raja, “Drug Target Interaction Prediction Using Machine Learning Techniques – A Review,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 6, pp. 86-100, 2024, doi: 10.9781/ijimai.2022.11.002.
C. F. Pereira, D. de Vargas, and L. S. Beeber, “An anxiety management intervention for people with substance use disorders (ITASUD): An intervention mapping approach based on Peplau’s theory,” Frontiers in Public Health, vol. 11, e1124295, 2023, doi: 10.3389/fpubh.2023.1124295.
J. Ochieng, “Prevalence of Psychoactive Substance Use and Associated Behavioral Risks among Secondary School Students in Tanzania,” East African Journal of Education and Social Sciences, vol. 3, no. 4, pp. 185–196, 2022, doi: 10.4314/eajess.v3i4.211.
J. J. Vázquez, S. Panadero, and I. Pascual, “The Particularly Vulnerable Situation of Women Living Homeless in Madrid (Spain),” The Spanish Journal of Psychology, pp. 1-9, 2019, doi: 10.1017/sjp.2019.58.
L. C. M. Campos, J. F. de Oliveira, C. Porcino, M. J. de O. U. Reale, M. V. S. Santos, and M. E. F. de Jesus, “Social Representations Held By Homeless Individuals Regarding Homeless Individuals Who Consume Drugs,” Revista Baiana de Enfermagem, vol. 33, pp. 1–9, 2019, doi: 10.18471/rbe. v33.26778.
T. Coombs, T. Ginige, P. Van Calster, A. Abdelkader, O. Corazza, and S. Assi, “New Psychoactive Substances in the Homeless Population: A Cross-Sectional Study in the United Kingdom,” International Journal of Mental Health and Addiction, e0123456789, 2023, doi: 10.1007/s11469-022-00988-7.
J. J. Vázquez, A. E. Berríos, and A. C. Suarez, “Health, disability, and consumption of psychoactive substances among people in a homeless situation in León (Nicaragua),” Social Work in Health Care, vol. 59, no. 9-10, pp. 694-708, 2020, doi: 10.1080/00981389.2020.1835785.
J. J. Vázquez, A. Suarez, A. Berríos, and S. Panadero, “Characteristics and needs of people living homeless in León (Nicaragua): Similarities and differences with other groups in severe social exclusion,” International Social Work, vol. 65, no. 2, pp. 328–342, 2022, doi: 10.1177/0020872819896820.
E. A. Salazar et al., “Inhabitants of the street in Colombia, some elements of your health,” Palarch’s Journal of Archaeology of Egypt/Egyptology, vol. 18, no. 8, pp. 3470–3476, 2021.
S. Farigua, J. Pedraza, and R. Ruiz, “Experiencias de habitantes de calle que asisten al Programa de Salud Camad Rafael Uribe Uribe en Bogotá,” Revista Ciencias de la Salud, vol. 16, no. 3, pp. 429–446, 2018.
R. C. Fiorati, R. Y. D. Carretta, L. M. Kebbe, B. L. Cardoso, and J. J. D. S. Xavier, “Social ruptures and the everyday life of homeless people: an ethnographic study,” Revista Gaucha de Enfermagem, vol. 37, e72861, 2017.
J. C. Cubillos Álzate, M. Cárdenas, and S. Perea, Boletines Poblacionales: Personas Adultas Mayores de 60 años Oficina de Promoción Social Ministerio de Salud y Protección Social, Ministerio de Salud, Bogotá D. C., 2020. Available: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/PS/boletines-poblacionales-envejecimiento.pdf [Accessed June 10, 2023]
H. D. Whitehead et al., “Validated method for the analysis of 22 illicit drugs and their metabolites via liquid chromatography tandem mass spectrometry (LC-MS/MS) in illicit drug samples collected in Chicago, IL,” Forensic Chemistry, vol. 33, e100475, 2023, doi: 10.1016/j.forc.2023.100475.
D. Yajaira, B. Fernández, Á. Segura-Cardona, L. Montoya-Velez, and M. Hernández-Rendón, “Consumo de basuco en usuarios de drogas inyectables en Colombia,” Revista Cubana de Salud Pública, vol. 42, no. 2, pp. 276-283, 2016.
A. H. Sadaka et al., “Effects of inhaled cannabis high in Δ9-THC or CBD on the aging brain: A translational MRI and behavioral study,” Frontiers in Aging Neuroscience, vol. 15, pp. 1-20, 2023, doi: 10.3389/fnagi.2023.1055433.
S. E. Koch, J. A. Marckel, J. Rubinstein, and A. B. Norman, “A humanized anti-cocaine mAb antagonizes the cardiovascular effects of cocaine in rats,” Pharmacology Research and Perspectives, vol. 11, no. 1, pp. 1–8, 2023, doi: 10.1002/prp2.1045.
F. Gosetti, et al, “From the Streets to the Judicial Evidence: Determination of Traditional Illicit Substances in Drug Seizures by a Rapid and Sensitive UHPLC-MS/MS-Based Platform,” Molecules, vol. 28, no. 1, e164, 2022, doi: 10.3390/molecules28010164.
P. J. Cooper et al., “Understanding and controlling asthma in Latin America: A review of recent research informed by the SCAALA programme,” Clinical and Translational Allergy, vol. 13, no. 3, e12232, 2023, doi: 10.1002/clt2.12232.
Ministerio de Salud y Protección Social, Política Integral para la Prevención y Atención del Consumo de Sustancias Psicoactivas, p. 44, 2019. Available: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/PP/politica-prevencion-atencion-spa.pdf [Accessed June 11, 2023]
G. Gesti, Política Pública Social para Habitantes de la Calle 2021-2031, p. 231, 2021. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/PS/abece-habitantes-calle-2022-2031.pdf [Accessed June 11, 2023]
S. Kwak et al., “Machine learning prediction of the mechanical properties of γ-TiAl alloys produced using random forest regression model,” Journal of Materials Research and Technology, vol. 18, pp. 520-530, 2022, doi: 10.1016/j.jmrt.2022.02.108.
P. Maranzano, P. Otto, and A. Fassò, “Adaptive LASSO estimation for functional hidden dynamic geostatistical model,” Stochastic Environmental Research and Risk Assessment, vol. 37, pp. 3615-3637, 2022, doi: 10.1007/s00477-023-02466-5.
S. Mohammadi, “A test of harmful multicollinearity: A generalized ridge regression approach,” Communications in Statistics - Theory and Methods, vol. 51, no. 3, pp. 724-743, 2022, doi: 10.1080/03610926.2020.1754855.
B. Das et al., “Comparison of bagging, boosting and stacking algorithms for surface soil moisture mapping using optical-thermal-microwave remote sensing synergies,” Catena, vol. 217, e106485, 2022, doi: 10.1016/j.catena.2022.106485.
M. Sipper and J. H. Moore, “AddGBoost: A gradient boosting-style algorithm based on strong learners,” Machine Learning with Applications, vol. 7, e100243, 2022, doi: 10.1016/j.mlwa.2021.100243.
P. W. Khan, S. J. Park, S. J. Lee, and Y. C. Byun, “Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor,” Electric Vehicles: Planning and Operations, vol. 2022, e8062932, 2022, doi: 10.1155/2022/8062932
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