Clinical decision support algorithm to optimize management of respiratory tract infection in children attending primary health facilities in Kilimanjaro Region, Tanzania

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Abstract In low- and middle-income countries (LMICs), respiratory tract infections (RTIs) are a leading cause of preventable death among young children (< 5 years of age). Severe RTIs, usually involving the lower respiratory tract, constitute a potentially life-threatening medical problem that requires effective diagnosis and management, including evaluation for antibacterials. At the same time, the vast majority of RTIs in young children are non-severe and often caused by viruses. For these exceedingly common, non-severe viral RTI cases, antibacterials are not appropriate and could cause harm. Yet in LMICs of Africa and Asia, research studies have shown that antibacterials are prescribed for over 75% of outpatient pediatric RTI visits. RTI management is thus highly problematic: on the one hand, a common syndrome that is grossly over-treated with inappropriate antibacterials; on the other hand, clinicians in low-resourced LMIC settings can understandably be concerned that withholding antibacterials could run the risk of a pediatric RTI progressing to a severe, life-threatening condition. This K43 application presents a career development program to 1) develop a clinical prediction rule that uses a parsimonious composite of clinical covariates and novel biomarkers to accurately differentiate viral from bacterial RTI and to provide prognostic risk stratification of disease severity in young children presenting to health facilities in Kilimanjaro Region, Tanzania; 2) conduct formative social science research to understand caregiver and healthcare provider expectations, attitudes and acceptability thresholds for withholding antibacterials in uncomplicated viral RTI; 3) use human-centered design methodology to package the prediction rule and the attitudes, expectations and needs of caregivers and healthcare providers into a user-friendly, effective clinical decision support algorithm that could be tested in future studies for feasibility, safety, and efficacy. The candidate for this career development award is a Tanzanian medical doctor with advanced training in clinical research, public health, and epidemiology. He has conducted clinical research on RTI in Tanzania since 2016. For this mentored research award, the candidate has assembled an exceptional team of mentors with expertise in clinical-epidemiologic research of infectious diseases in Tanzania, clinical prediction analysis, human-centered intervention design in Tanzania and other LMICs, as well as a collaborator with expertise in algorithm development for innovative approaches to RTI management in LMICs. At the conclusion of this award, the candidate will have developed unique expertise 1) in clinical prediction for infectious disease management in sub-Saharan Africa and 2) in human-centered design of clinical decision support algorithms. He will emerge as a global leader in intervention design for management of infectious diseases—a highly-skilled independent investigator focused on implementation of strategies that will confront early childhood mortality and the growing threat of antimicrobial resistance.