Mexico City telehealth systems recorded abrupt mental health surges after disasters and public warnings
Disaster response is usually measured in damaged buildings, interrupted transit, and overloaded hospitals. This study adds a quieter but equally urgent metric: the capacity of mental health support systems to absorb fear, uncertainty, and acute distress in real time.
Across 138,471 interactions captured through national public institutions’ databases, the Screening Tool for Mental Health Problems and the Lifeline Emergency Phone Call Service (LEPCS) traced how people in Mexico City responded during the first year of the COVID-19 pandemic and beyond. Public announcements of quarantine measures, mental health press conferences, festivities during 2020, and earthquakes with public early warnings were all examined as triggers for changes in support demand. The design paired qualitative and quantitative analysis with principal component analysis, correlations, and one-way ANOVA, giving the investigators a way to map not only volume but also the psychological shape of the surge.
One day stood out with a 2,651% increase in demand.
Acute stress, anxiety, and depression clustered around high-threat events
The most important signal was not simply that calls rose after disruptive events, but that the content of those contacts converged around recognizable symptom patterns. The analysis identified responses associated with acute stress, depression, and anxiety, suggesting that telehealth services were not just fielding general distress but specific psychological states that shifted with the nature of the event. In practical terms, a quake warning does not produce the same mental health burden as a press conference about quarantine or a festive period layered on top of pandemic uncertainty. Each event appears to recruit a different emotional profile, and those profiles arrive fast.
That matters because telehealth triage is only useful if it can separate ordinary volume from event-driven psychological strain. Acute stress reactions can spike immediately after a threat signal, while depressive symptoms may broaden over time when uncertainty and isolation accumulate. Anxiety sits somewhere in between, often rising when people anticipate harm without a clear endpoint. A support system that cannot distinguish among those patterns risks sending the wrong intervention at the wrong moment.
Multiple threats produced the sharpest overloads
Complex scenarios with overlapping hazards generated the most severe short-term outbursts in support demand. The combination of pandemic conditions, official announcements, public gatherings, and seismic events created a setting in which one stressor could intensify another. In a city like Mexico City, where earthquake risk already shapes public behavior, adding a pandemic layer altered the meaning of every alert and every public message. A warning that might otherwise prompt vigilance instead became part of a broader psychological cascade.
This is where the study’s time-scale approach becomes especially useful. High-resolution analysis can identify the interval between a triggering event and the maximum demand on services, which is exactly the period when staffing and triage capacity have to be highest. The findings suggest that support needs do not rise in a smooth, predictable arc. They arrive in bursts, and those bursts can be severe enough to overwhelm ordinary service planning.
Telehealth overload needs an operational threshold, not just good intentions
The proposed value of this work lies in its attempt to define evaluation criteria for detecting potential overload in telehealth services. Public health systems often speak broadly about resilience, but resilience becomes measurable only when warning thresholds are tied to call volume, symptom mix, and event timing. This study moves in that direction by linking specific public events to short-term outbursts in demand and by identifying the moment at which those outbursts peak.
For mental health services, the bedside consequence is straightforward. If a mental health hotline receives a sudden surge after a quake alert or a major public announcement, the service needs more than goodwill; it needs escalation protocols, staffing buffers, and a triage model that can separate transient distress from higher-risk presentations. In telehealth, speed is not a luxury. Delay can turn a manageable wave of distress into a service failure.
The data also reinforce why mental health preparedness has to be integrated into disaster planning before an event occurs. Earthquakes are immediate, but emotional responses can be prolonged by the anticipation of aftershocks, repeated alerts, and the cumulative fatigue of living under threat. Pandemic-era public messaging can amplify or reduce distress depending on how people interpret the information and whether the system appears stable enough to help them cope. Support systems that are built only for routine care will struggle when the public enters a period of repeated alarms.
Secondary data analysis offered a broad view, but not a clinical one-to-one conversation
The study’s strength is its scale and event-wise structure. It examined real-world institutional data spanning 2020 through 2023 and used multiple analytic methods to search for patterns across many interactions. That approach makes it possible to detect population-level shifts that would be invisible in small clinical samples. It also gives emergency planners something concrete to work with: a way to anticipate when support demand may spike and what kinds of psychological responses are most likely to emerge.
At the same time, secondary anonymized data cannot capture every clinical nuance. A hotline interaction is not the same as a psychiatric evaluation, and a screening tool cannot fully substitute for in-person assessment. The analysis can identify patterns in distress, but it cannot by itself explain the full context of each caller’s risk, coping resources, or long-term outcome. Even so, the breadth of the dataset makes the findings hard to ignore.
The ethics framework around the work was also clear: primary data collection for the Screening Tool for Mental Health Problems received ethics approval from UNAM and the INPRFM, and participants provided informed consent at the time of data entry. The Lifeline dataset was analyzed as secondary, anonymized information, which fits the kind of population-level surveillance this project was designed to support.
What this study leaves on the table is not whether disaster events strain mental health systems. They do. The remaining question is how quickly health services can translate that knowledge into staffing rules, overload alerts, and event-specific psychological support before the next surge arrives.