Carlos Rodrigo Garibay Rubio1, Katsuya Yamori2, Genta Nakano2, Astrid Renneé Peralta Gutiérrez3, Silvia Morales Chainé4, Rebeca Robles García5, Edgar Landa-Ramírez6, Alexis Bojorge Estrada7, Alejandro Bosch Maldonado8, Diana Iris Tejadilla Orozco9
1Graduate School of Informatics, Kyoto University, Yoshidahonmachi, Sakyo Ward, Kyoto 606-8317, Japan; 2Disaster Prevention Research Institute, Gokasho, Uji, Kyoto 611-0011, Japan; 3Independent researcher, Gokasho Sanbanwari 37, Obaku shiei jutaku, Uji, Kyoto 611-0011, Japan; 4Faculty of Psychology, National University of Mexico, Circuito Ciudad Universitaria Avenida, C.U., 04510 Ciudad de México, Mexico; 5Research Center for Global Mental Health, National Institute of Psychiatry “Ramón de la Fuente Muñiz”, Calz. México-Xochimilco 101, Colonia, Huipulco, Tlalpan, 14370 Ciudad de México, CDMX, Mexico; 6Ministry of Health, “Hospital General Dr. Manuel Gea González”, Calz. de Tlalpan 4800, Belisario Domínguez Secc. 16, Tlalpan, 14050 Ciudad de México, CDMX, Mexico; 7Ministry of Health, Psychiatric Services, Av. Marina Nacional 60, Tacuba, Miguel Hidalgo, 11410 Ciudad de México, CDMX, Mexico; 8General Directorate of Community Attention, National Autonomous University of México, 04510 Mexico City,
CDMX, Mexico; 9Ministry of Health, Child Psychiatric Hospital “Juan N Navarro” Av. San Fernando 86, Belisario Domínguez Secc 16, Tlalpan, 14080 Ciudad de México, CDMX, Mexico.
Abstract
The prevalence of mental health problems constitutes an open challenge for modern societies, particularly for low and middle-income countries with wide gaps in mental health support. With this in mind, five datasets were analyzed to track mental health trends in Mexico City during the pandemic’s first year. This included 33,234 responses to an online mental health risk questionnaire, 349,202 emergency calls, and city epidemiological, mobility, and online trend data.
The COVID-19 mental health risk questionnaire collects information on socioeconomic status, health conditions, bereavement, lockdown status, and symptoms of acute stress, sadness, avoidance, distancing, anger, and anxiety, along with binge drinking and abuse experiences. The lifeline service dataset includes daily call statistics, such as total, connected, and abandoned calls, average quit time, wait time, and call duration. Epidemiological, mobility, and trend data provide a daily overview of the city’s situation.
The integration of the datasets, as well as the preprocessing, optimization, and machine learning algorithms applied to them, evidence the usefulness of a combined analytic approach and the high reuse potential of the data set, particularly as a machine learning training set for evaluating and predicting anxiety, depression, and post-traumatic stress disorder, as well as general psychological support needs and possible system loads.
This is an open-access article provided by Elsevier, ScienceDirect.
Cite this article
Carlos Rodrigo Garibay Rubio, Katsuya Yamori, Genta Nakano, Astrid Renneé Peralta Gutiérrez, Silvia Morales Chainé, Rebeca Robles García, Edgar Landa-Ramírez, Alexis Bojorge Estrada, Alejandro Bosch Maldonado, Diana Iris Tejadilla Orozco, Machine learning-ready mental health datasets for evaluating psychological effects and system needs in Mexico city during the first year of the COVID-19 pandemic, Data in Brief, Volume 57, 2024, 110877, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110877.