Background: Several large multisite studies have been conducted to describe etiology-specific burden of diarrhea among children in low-resource settings. Here, we combined data across studies to describe geographic and temporal trends in incidence and attributable fractions (AFs) of etiology-specific moderate-to-severe diarrhea (MSD), and to evaluate etiology-specific case fatality ratios (CFRs). Methods: We harmonized case definitions and analytic methods across the Global Enteric Multicenter Study (GEMS), Malnutrition and Enteric Disease (MAL-ED), Vaccine Impact on Diarrhea in Africa (VIDA), AntiBiotics for Children with severe Diarrhea (ABCD), and Enterics for Global Health (EFGH) studies. Cases were 6-35-month-olds with acute MSD. Incidence estimates for GEMS, VIDA, and EFGH were adjusted for enrollment, healthcare seeking, and diagnostic testing. AFs were calculated as the proportion of MSD cases attributed to each etiology, and CFRs were estimated within 14 and 90 days of an MSD episode. Findings: Pre-rotavirus vaccine introduction, rotavirus had the highest incidence and was the leading etiology among 6-11-month-olds, accounting for approximately 22-28% of MSD; the proportion of diarrhea due to rotavirus declined following vaccine introduction, with average AF 10-11% in Africa and Asia. Shigella incidence was highest among 12-23-month-olds and was the dominant etiology among 12-23 and 24-35-month-olds, causing approximately one-third to one-half of MSD. Overall, 90-day mortality declined substantially over time, from 2.21% in GEMS to 0.30% in EFGH. Bacterial (2.52%) and protozoal pathogens (3.55%) had higher average CFRs than viral pathogens (1.42%). Conclusion: Harmonized analysis of five multisite studies reveals consistent evidence that rotavirus and Shigella are the dominant causes of MSD in children under three years in low-resource settings, with burden shifting toward Shigella following rotavirus vaccine introduction.
Assessing nurses’ attitudes toward artificial intelligence in Kazakhstan: psychometric validation of a nine-item scale
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare, yet the attitudes and knowledge of nurses, who are the key mediators of AI implementation, remain underexplored.



