Clinical cascades as a novel way to assess physical readiness of facilities for the care of small and sick neonates in Kenya and Uganda

• Authors: Melissa C. Morgan, University of California San Francisco; Hilary Spindler, University of California San Francisco; Harriet Nambuya, Jinja Regional Referral Hospital; Grace M. Nalwa, Maseno University; Gertrude Namazzi, Makerere University; Peter Waiswa, Makerere University; Phelgona Otieno, Kenya Medical Research Institute; John Cranmer, Emory University; Dilys M. Walker, University of California San Francisco
• Journal Title: PLoS ONE
• Volume: Volume 13, Number 11
• Publisher: Public Library of Science | 2018-11-21, Pages e0207156-e0207156
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1371/journal.pone.0207156
• Subjects: Health Sciences, Obstetrics and Gynecology; Health Sciences, Public Health
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; RESPECTFUL MATERNITY CARE; SYSTEMATIC ANALYSIS; DEVELOPMENTAL CARE; HIV CARE; NEWBORN; EXPERIENCES; ENGAGEMENT; INDICATORS; STRATEGIES; PROGRESS

Abstract

Background Globally, there were 2.7 million neonatal deaths in 2015. Significant mortality reduction could be achieved by improving care in low- and middle-income countries (LMIC), where the majority of deaths occur. Determining the physical readiness of facilities to identify and manage complications is an essential component of strategies to reduce neonatal mortality. Methods We developed clinical cascades for 6 common neonatal conditions then utilized these to assess 23 health facilities in Kenya and Uganda at 2 time-points in 2016 and 2017. We calculated changes in resource availability over time by facility using McNemar’s test. We estimated mean readiness and loss of readiness for the 6 conditions and 3 stages of care (identification, treatment, monitoring-modifying treatment). We estimated overall mean readiness and readiness loss across all conditions and stages. Finally, we compared readiness of facilities with a newborn special care unit (NSCU) to those without using the two-sample test of proportions. Results The cascade model estimated mean readiness of 26.3–26.6% across the 3 stages for all conditions. Mean readiness ranged from 11.6% (respiratory distress-apnea) to 47.8% (essential newborn care) across both time-points. The model estimated overall mean readiness loss of 30.4–31.9%. There was mild to moderate variability in the timing of readiness loss, with the majority occurring in the identification stage. Overall mean readiness was higher among facilities with a NSCU (36.8%) compared to those without (20.0%). Conclusion The cascade model provides a novel approach to quantitatively assess physical readiness for neonatal care. Among 23 facilities in Kenya and Uganda, we identified a consistent pattern of 30–32% readiness loss across cascades and stages. This aggregate measure could be used to monitor and compare readiness at the facility-, health system-, or national-level. Estimates of readiness and loss of readiness may help guide strategies to improve care, prioritize resources, and promote neonatal survival in LMICs.