Managing Pediatric Pain in Low-Resource Emergency Settings: Barriers,Advances,and Future Directions

Literature Search: A systematic search was executed in January 2025 across 3 electronic databases: PubMed, Scopus, and Google Scholar. The search timeframe encompassed publications from 2000 to 2025, with a deliberate emphasis on identifying literature from 2015 onward to capture recent developments and innovations.

Search Strategy: The search employed a combination of keywords and Medical Subject Headings (MeSH) terms including: “pediatric pain” OR “child pain,” “pain management” OR “analgesia,” “emergency department” OR “acute care,” “low-resource settings” OR “resource-limited,” “developing countries” OR “LMIC,” and “non-pharmacologic interventions.” Boolean operators (AND, OR) were used to combine these concepts.

Inclusion Criteria: Articles were included if they: (1) involved pediatric populations (0-18 years), (2) primarily addressed pain assessment or management in emergency or acute care settings, and (3) were conducted in, or were directly relevant to, low- and middle-income or resource-limited contexts as defined by the World Bank.

Exclusion Criteria: We excluded: (1) non-English language publications, (2) conference abstracts without accessible full text, (3) studies focused exclusively on adult populations or chronic pain in non-acute settings, and (4) articles not providing original data or relevant synthesis (eg, editorials without novel analysis).

Data Synthesis: The selected literature was narratively synthesized and organized into 7 major thematic areas: epidemiology and burden, barriers to assessment, pharmacologic strategies, non-pharmacologic strategies, task-shifting/training, digital/AI innovations, and policy recommendations. Data extraction focused on study design, population, key findings, and relevance to LMIC emergency contexts. Thematic sufficiency was considered achieved when iterative review of new articles yielded no substantially new concepts within the predefined themes.

Methodological Considerations: Consistent with the narrative review approach, no formal quality appraisal tool or quantitative meta-analysis was performed. This choice allows for the inclusion of a broad range of study designs necessary to map a complex, multidisciplinary field. To mitigate potential bias from including studies of variable quality, we explicitly discuss the limitations of the evidence base, including study design and generalizability, in a dedicated section of the Discussion.

Cultural and Professional Biases: Deep-seated cultural norms may view pain expression as a sign of weakness, leading children and families to minimize complaints. Concurrently, healthcare professionals may hold misconceptions about children’s pain tolerance or the necessity of treatment, resulting in inadequate assessment. ¹⁹ Studies consistently identify insufficient provider knowledge as a major impediment to effective pediatric pain relief. ²⁰

Challenges with Assessment Tools: The implementation of standardized tools is fraught with difficulty. Linguistic and cultural differences can render scales developed in Western contexts invalid or difficult to apply. Translations may not capture nuances, and facial expressions used in tools like the Wong-Baker FACES scale can be interpreted differently across cultures. ²¹ Inadequate training on tool use further compromises accuracy. Furthermore, resource shortages—including time, personnel, and dedicated assessment protocols—in busy EDs directly impede consistent pain evaluation. ²²

Cognitive and Contextual Limitations: Many tools rely on a child’s cognitive and linguistic abilities, posing challenges in low-literacy settings or with very young children. Adapting tools to local languages and contexts is essential. For instance, the Oucher Scale, which incorporates photographic facial expressions of children from specific ethnic groups, represents a successful model of cultural adaptation for improved self-report. ²³ Addressing these barriers requires a triad of interventions: cultural adaptation of tools, comprehensive provider training, and systemic support to integrate assessment into routine clinical workflow.

Guided Imagery: Involves directing a child’s attention to calming, pleasant mental scenarios, effectively reducing anxiety and perceived pain intensity with minimal resources. ³⁸

Distraction Techniques: Use engaging stimuli (toys, bubbles, stories, virtual reality) to divert attention from painful procedures. Simple methods are highly effective for young children. ³⁹

Music Therapy: Listening to or creating music can reduce distress, lower physiological arousal (heart rate, blood pressure), and provide comfort. ⁴⁰

Transcutaneous Electrical Nerve Stimulation (TENS): Low-cost TENS units disrupt pain signaling and are becoming a feasible option for musculoskeletal pain in some LMIC settings. ⁴¹

Acupressure & Massage: These techniques use pressure or touch to promote relaxation and circulation, proving particularly helpful for infants during minor procedures. ⁴²

The Need and Evidence: The specialist gap is severe. For example, Mozambique has only a handful of pain-trained physicians and nurses for its entire population, while Kenya’s few pediatric pain clinics are overwhelmed, leaving general practitioners and anesthesiologists to manage complex cases.^47,48 This urban concentration of care exacerbates geographic health inequities.

Challenges and Enablers: Successful task-shifting requires overcoming challenges including lack of ongoing supervision, interprofessional role conflicts, increased workload, and ambiguous regulatory/liability frameworks.^49,50 Mitigation requires clear scope-of-practice definitions, structured competency-based training, and strong mentorship systems. Interprofessional collaboration is key to building supportive teams. ⁵¹

Successful Models: Several African nations offer instructive models. Uganda’s policy change allowing trained nurses and clinical officers to prescribe morphine significantly improved access to strong analgesics. ⁵² Multi-country certificate courses in palliative care (eg, in Tanzania, South Africa) have built regional training capacity. ⁵³ Zimbabwe’s National Strategy for Palliative Care (2020) exemplifies a policy-driven approach to improve education, drug availability, and service integration. ⁵⁴ These examples demonstrate that with intentional training, supportive policy, and supervision, non-specialist healthcare workers can safely and effectively manage pediatric pain.

AI for Pain Assessment: Facial expression analysis using convolutional neural networks (CNNs) can detect pain with high accuracy (85%-95%), outperforming some traditional observer scales. ⁵⁵ Multimodal systems integrating facial cues with physiological data (eg, electrodermal activity) show even greater promise. ⁵⁶ However, these systems are often trained on homogeneous datasets and may not generalize across diverse ethnicities and cultures prevalent in LMICs, necessitating local validation.

Wearable Devices and Mobile Health (mHealth): Wearables that monitor physiological markers (heart rate variability, galvanic skin response) can provide continuous, objective pain data, though they remain underutilized in clinical practice. ⁵⁷ Mobile applications gamify pain assessment, improving engagement and adherence in children and adolescents, and can facilitate remote monitoring and analgesic scheduling.^58,59

Virtual Reality (VR) for Distraction: VR has strong evidence as a distraction tool during procedures like IV placement, significantly reducing pain and anxiety scores in children.^60,61 While headset costs are falling, the feasibility of VR in busy, under-resourced EDs requires careful consideration.

Feasibility and Ethical Considerations for LMICs: The translation of these innovations to LMIC settings is not straightforward. Critical barriers include:

• Infrastructure: Unreliable electricity and internet connectivity.

• Cost: Affordability of devices and maintenance.

• Digital Literacy: Among both providers and patients/caregivers.

• Data Privacy & Ethics: Robust frameworks for data security, consent, and ethical AI use are often underdeveloped.

• Context Validation: Tools must be validated in LMIC populations and clinical environments before scale-up.