The research team, led by Carlos Rodrigo Garibay Rubio of Universidad Iberoamericana and the Mexico City Secretariat of Integrated Risk Management and Civil Protection, together with Genta Nakano and Katsuya Yamori of Kyoto University, set out to answer a question that has long frustrated emergency planners: when exactly does psychological need spike, and what kinds of events trigger it? Traditional disaster mental health research has typically worked at coarse time scales, comparing survey responses months or years apart. Such approaches can identify lasting conditions like post-traumatic stress disorder, but they miss the acute, short-lived surges in demand that can overwhelm a support system within hours. Garibay Rubio and colleagues reasoned that if high-resolution time-scale data from information and communication technology-based systems and telehealth services could be analyzed systematically, researchers could identify the events associated with short-term outbursts in support demands, estimate when those demands would peak, and characterize the calls for service that arrive during them.

To do this, the team assembled an unusually rich dataset drawn from two national public institutions’ databases: the Screening Tool for Mental Health Problems, a validated instrument administered through telehealth channels, and the Lifeline Emergency Phone Call Service, a crisis hotline operated by the Mexico City government. Together, these sources comprised 138,471 interactions recorded during the first year of the COVID-19 pandemic in Mexico City, with additional earthquake-related data extending through 2023. The interactions captured responses associated with acute stress, depression, and anxiety, along with the operational characteristics of each call for service. Because the data were collected continuously and timestamped, the researchers could track demand on a daily basis, something that survey-based studies simply cannot achieve.

The analytical approach combined qualitative and quantitative methods. The team applied principal component analysis to reduce the multidimensional response data to interpretable components, computed correlations between demand patterns and event types, and used one-way analysis of variance to test whether demand differed significantly across event categories. The events they examined fell into four broad classes: public announcements of quarantine measures, mental health press conferences held by government officials, festivities during 2020, and earthquakes with public early warnings reported between 2020 and 2023. Each class represented a different kind of psychological trigger, from anticipatory anxiety about confinement to the acute alarm generated by an earthquake alert sounding on millions of phones.

The results confirmed that different event types produce distinct psychological signatures. Quarantine announcements were associated with patterns of acute stress and anxiety, as populations confronted the reality of confinement and its implications for work, family, and social life. Mental health press conferences, intended to inform and reassure, also shaped demand, suggesting that official communication itself functions as a psychological event with measurable consequences. Festivities during 2020, celebrated under pandemic restrictions, generated their own patterns, reflecting the tension between cultural traditions and public health measures. Earthquakes with public early warnings produced the most acute responses, with demand surging in the immediate aftermath of alerts, consistent with the well-documented psychological impact of seismic alarm systems on populations living in high-risk zones.

The most striking finding, however, concerned compound scenarios. When multiple threats converged, the effect on support demand was not merely additive but explosive. The documented case with a 2,651 percent increase on a single day illustrates the challenge: a support system calibrated for baseline demand can be overwhelmed almost instantaneously when a pandemic, a quarantine announcement, and an earthquake alert coincide. The researchers found that these complex scenarios with multiple threats were associated with significant short-term outbursts, posing a formidable challenge to support systems in terms of resources and timely response. The finding has direct implications for emergency planning, because it suggests that resource allocation strategies based on average demand or single-event scenarios will systematically underestimate the peaks that matter most.

Beyond documenting these surges, the study makes a novel methodological contribution: it proposes evaluation criteria for detecting potential system overloads in telehealth services before they occur. By monitoring demand patterns in real time and comparing them against thresholds derived from the historical data, operators could identify when a system is approaching overload and deploy early countermeasures, such as additional staff, automated screening tools, or triage protocols that prioritize the most urgent cases. The researchers argue that this event-wise approach, combined with the high-resolution time-scope analysis, provides valuable insights for emergency planning and resource allocation that could be applied to other cities and other types of disasters.

The study’s implications extend beyond Mexico City. Early warning systems for earthquakes, tsunamis, and other hazards are proliferating worldwide, and each alert that reaches a population generates a psychological response with measurable consequences for support services. The Mexican case is particularly instructive because the country operates one of the most extensive seismic early warning systems in the world, and its population has lived with the reality of major earthquakes for decades. The researchers suggest that the patterns they identified, including the timing of maximum demand associated with different event types, could inform the design of support systems in other high-risk regions, where the same dynamics of anticipation, alarm, and recovery are likely to play out.

The research also highlights the value of ICT-based and telehealth systems as scientific instruments in their own right. Because these platforms record interactions continuously and anonymously, they generate data that can reveal psychological dynamics invisible to traditional survey methods. The Screening Tool for Mental Health Problems, administered through telehealth channels with informed consent and ethical approval from the UNAM and INPRFM ethics committees, captured validated measures of mental health problems at population scale. The Lifeline dataset, comprising operational aggregates with no personally identifiable information, documented the real-time behavior of a crisis hotline under stress. Together, they demonstrate that the digital infrastructure built for service delivery can double as a monitoring system for population mental health, providing the evidence base for preparedness planning.

As climate change intensifies extreme weather, urbanization concentrates populations in hazard zones, and pandemics continue to emerge, the demand on psychological support systems will only grow. The Mexican experience during 2020 offers a warning and a roadmap: support systems must be designed not for average conditions but for the compound scenarios that produce the most extreme surges, and the digital tools that deliver care can also provide the early warning that keeps those systems from collapsing when they are needed most. The 2,651 percent surge documented by the research team is a reminder that in disaster preparedness, the psychological dimension is not a secondary concern but a central variable, one that can be measured, anticipated, and planned for with the right data and the right analytical approach.

Subject of Research: Psychological responses to compound disasters and the resulting short-term surges in mental health support-system demand

Article Title: Disaster preparedness and mental health: understanding psychological responses and support-system demands

Article References: Garibay Rubio, C. R., Nakano, G., Yamori, K., Peralta Gutiérrez, A. R., Morales Chainé, S., Robles García, R., Landa-Ramírez, E., Bojorge Estrada, A., & Tejadilla Orozco, D. I. (2026). Disaster preparedness and mental health: understanding psychological responses and support-system demands. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09201-9

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09201-9

Keywords: disaster preparedness, mental health, telehealth, COVID-19, earthquake early warning, acute stress, anxiety, depression, crisis hotline, principal component analysis, emergency planning, Mexico City