The challenge in disaster MHPSS is no longer simply recognizing inequity or advocating for contextualized care. The harder question is operational: how can limited specialist capacity be dynamically directed toward changing needs without producing fragmented services or unsustainable teams? Recent evidence has established substantial disparities in disaster-related health outcomesReference Stimpson, Rashed and Pandya1 and identified MHPSS, assessment, referral, and community engagement as core components of humanitarian response.Reference Sigua, Caymaz and Castro-Delgado2 What remains less clearly operationalized is how these components can be dynamically allocated as needs and local capacity evolve. A need-adaptive model could address this gap by standardizing the service backbone while adapting specialist involvement—operating as a continuous feedback cycle.
Assess for Action, Not Completeness
Early assessment should be deliberately brief and decision-oriented-identifying priority groups, access barriers, existing resources, referral gaps, and signals of escalating need. Following the Eastern Kentucky floods, CASPER illustrated that rapid community assessment can identify substantial mental health needs and preferences for locally delivered services, directly informing service configuration.Reference Johnson, Balasuriya and Riley3 The objective is sufficient information to guide the next response decision, not exhaustive data collection.
Match Tasks to Complexity
Frontline non‑specialist health care workers, following competency-based training, can provide basic psychosocial support, psychological first aid, psychoeducation, and referral. Primary care providers can manage conditions within their scope while specialists focus on complex cases, supervision, consultation, and referral coordination. Recent evidence supports non-specialist-delivered psychosocial interventions in disaster‑affected populations, with a meta-analysis demonstrating significant but small-to-moderate improvements in PTSD symptoms (SMD = 0.25, 95% CI: 0.11-0.39).Reference Peng, Xiao and Carter4 This reinforces task-sharing approaches, while indicating that further research is needed to optimize training, supervision, and implementation for large-scale deployment.
Deploy by Threshold, Not Geography Alone
Rather than establishing large parallel teams, a small multidisciplinary specialist unit could operate as a mobile hub across several sites, providing targeted outreach, remote consultation, supervision, and complex-case management. Specialist intensity would increase when predefined signals—service disruption, geographic isolation, unmet referral demand, concentration of high‑risk groups, or excessive specialist workload—cross agreed thresholds, and decrease as these indicators improve. This approach aligns with humanitarian response evidence emphasizing integrated MHPSS assessment, referral, and community engagement within coordinated local frameworks.Reference Sigua, Caymaz and Castro-Delgado2
Adapt Delivery, Preserve Standards
Local partners should shape language, communication channels, help-seeking pathways, and delivery formats, while safety, confidentiality, referral standards, and minimum competencies remain consistent. Coverage, waiting time, referral completion, specialist workload, and outcomes disaggregated by relevant characteristics can form a simple feedback loop for escalation, maintenance, or de-escalation. Culturally adapted psychosocial training programs have demonstrated the feasibility and acceptability of strengthening frontline capacity in under‑resourced settings.Reference Brown, River and Sheppard-Law5
From Uniform Prescription to Dynamic Allocation
The central shift is from asking “Which intervention should each subgroup receive?” to “What level of expertise is needed, where, and for how long?” Need‑adaptive MHPSS reconciles equity with operational feasibility: distributed frontline capacity provides reach, while specialist expertise moves temporarily toward unmet need rather than being permanently replicated at every site. This model is testable through implementation and cost-effectiveness research and offers a practical route from contextualized MHPSS principles to scalable field deployment.
Acknowledgments
During the writing process, we used ChatGPT/DeepSeek primarily as a language polishing tool. It helped us refine the phrasing and correct minor grammatical errors in a few sections, making the text read more smoothly. After using this service, we thoroughly reviewed and, where necessary, rewrote the content to ensure it accurately reflected our original meaning and scientific intent. We take full responsibility for the final version of the manuscript, and no AI tool was involved in generating data, analyzing results, or drawing scientific conclusions.
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this Letter to the Editor. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
