Pediatric upper-airway microbiota and respiratory infections in tropical low- and middle-income settings: A narrative review and low-biomass research framework

Pediatric upper-airway microbiota has been increasingly linked to respiratory-health phenotypes, but the transferability of these signals to low- and middle-income countries (LMICs) remains uncertain. Most phenotype-specific evidence comes from high-income settings, where diagnostic capacity, antibiotic exposure, vaccination coverage, environmental risks, and sample-handling conditions may differ substantially from LMIC contexts. This narrative review synthesizes current evidence on pediatric nasal and nasopharyngeal microbiota, respiratory-health phenotypes, LMIC contextual modifiers, and low-biomass microbiome methodology. Original studies linking upper-airway microbiota patterns with clinically defined respiratory outcomes were mapped and synthesized thematically. Across studies, respiratory-health signals were better characterized as phenotype-dependent community shifts than as disease-specific organisms. Corynebacterium- and Dolosigranulum- enriched profiles were commonly associated with more stable or lower-risk respiratory states, whereas Haemophilus-, Streptococcus- , and Moraxella-dominant profiles recurred across infection-associated and inflammatory phenotypes. In LMIC settings, these signals may be modified by pre-sampling antibiotics, repeated infectious exposure, household crowding, environmental pollution, nutritional vulnerability, vaccination heterogeneity, diagnostic uncertainty, and transport or storage constraints. Interpretable low-biomass research requires standardized sampling, contamination controls, transparent 16S rRNA workflows, and context-rich metadata. Pediatric upper-airway microbiota should therefore be interpreted as a context-dependent ecological signal rather than an immediate diagnostic biomarker. Future LMIC studies should prioritize phenotype-specific hypotheses, rigorous but feasible low-biomass workflows, and locally relevant metadata to support respiratory phenotyping, antibiotic-stewardship research, vaccine-effect evaluation, and child-health interventions.