Determinantes sociales asociados con ideación suicida durante la pandemia por Covid-19 en México.

OBJETIVO: Estimar la prevalencia de la ideación suicida (IS) y su asociación con los determinantes sociales (DS) en la pobla-ción mexicana durante la pandemia de Covid-19. Material y métodos. Datos de la encuesta de Atención Psicológica a Distancia para la Salud Mental debido a la Contingencia por Covid-19 obtenidos durante 2020. La muestra fue de 79 665. Se realizaron modelos de regresión logística obteniendo razones de momios (RM) con intervalos de confianza del 95% (IC95%). RESULTADOS: La prevalencia de IS fue de 17.1% (mujeres:18.8% y hombres: 14.4%). Principales DS asociados fueron: ser mujer (RM=1.11; IC95% 1.06,1.13), mujeres jóvenes (RM=1.30; IC95% 1.09,1.54), escolaridad (RM=1.89; IC95% 1.14,3.12), soltera(o) (RM= 1.31; IC95% 1.24,1.38), desempleo (RM= 2.33; IC95% 2.21,2.45), distanciamiento social (RM 1.81; IC95%1.68,1.96), vivir solo (RM 1.18; IC95% 1.10,1.27), pérdida de familiar por Covid-19 (RM= 1.41; IC95%1.30,1.54), tener un diagnóstico de depresión (RM= 5.72; IC95% 5.41,6.05), ser víctima de violencia física (RM=2.71; IC95% 2.49,2.95), consumo excesivo de alcohol (RM=1.68; IC95%1.58,1.79) y drogas (RM= 3.13; IC95% 2.88,3.41), y sospecha o diagnóstico de Covid-19 (RM=1.79; IC95% 1.67,1.89). CONCLUSIONES: La prevalencia de IS durante la pandemia por Covid-19 fue elevada; se discute la relevancia de los DS estructurales e intermedios que influyen en la IS.

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.

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.

Violence against Women during the COVID-19 Pandemic in Mexico.

This study measured the prevalence of cases of domestic violence against women and some associated factors during the COVID-19 pandemic in Mexico. Data were collected through a remote survey during 2020. The sample included 47,819 women aged 15 years and older. Jointpoint regression and logistic regression models were used. The prevalence of violence was 11.5%, which decreased in July and subsequently increased. The associated factors were being unemployed (OR = 2.01; 95%CI 1.89-2.16); being partially and totally quarantined (OR = 1.58; 95%CI 1.43-1.75 and OR = 1.47; 95%CI 1.32-1.63); being a caregiver of children; being a caregiver of elderly and/or suffering from a chronic illness (OR = 1.27; 95%CI 1.19-1.36; OR = 1.42; 95%CI 1.33-1.53; OR = 1.59; 95%CI 1.47-1.73); losing a family member to COVID-19 (OR = 1.26; 95%CI 1.13-1.41); and binge drinking (OR = 1.94; 95%CI 1.78-2.12). The confinement measures increased gender inequalities, economic problems and workload which further evidenced violence against women.