We have worked to develop a Clinical Information Network (CIN) in Kenya as an early form of learning health systems (LHS) focused on paediatric and neonatal care that now spans 22 hospitals. CIN's aim was to examine important outcomes of hospitalisation at scale, identify and ultimately solve practical problems of service delivery, drive improvements in quality and test interventions. By including multiple routine settings in research, we aimed to promote generalisability of findings and demonstrate potential efficiencies derived from LHS. We illustrate the nature and range of research CIN has supported over the past 7 years as a form of LHS. Clinically, this has largely focused on common, serious paediatric illnesses such as pneumonia, malaria and diarrhoea with dehydration with recent extensions to neonatal illnesses. CIN also enables examination of the quality of care, for example that provided to children with severe malnutrition and the challenges encountered in routine settings in adopting simple technologies (pulse oximetry) and more advanced diagnostics (eg, Xpert MTB/RIF). Although regular feedback to hospitals has been associated with some improvements in quality data continue to highlight system challenges that undermine provision of basic, quality care (eg, poor access to blood glucose testing and routine microbiology). These challenges include those associated with increased mortality risk (eg, delays in blood transfusion). Using the same data the CIN platform has enabled conduct of randomised trials and supports malaria vaccine and most recently COVID-19 surveillance. Employing LHS principles has meant engaging front-line workers, clinical managers and national stakeholders throughout. Our experience suggests LHS can be developed in low and middle-income countries that efficiently enable contextually appropriate research and contribute to strengthening of health services and research systems.

Malaria and iron deficiency (ID) are common and interrelated public health problems in African children. Observational data suggest that interrupting malaria transmission reduces the prevalence of ID(1). To test the hypothesis that malaria might cause ID, we used sickle cell trait (HbAS, rs334 ), a genetic variant that confers specific protection against malaria(2), as an instrumental variable in Mendelian randomization analyses. HbAS was associated with a 30% reduction in ID among children living in malaria-endemic countries in Africa (n = 7,453), but not among individuals living in malaria-free areas (n = 3,818). Genetically predicted malaria risk was associated with an odds ratio of 2.65 for ID per unit increase in the log incidence rate of malaria. This suggests that an intervention that halves the risk of malaria episodes would reduce the prevalence of ID in African children by 49%.

OBJECTIVE: To describe the patient population, priority diseases and outcomes in newborns admitted <48 hours old to neonatal units in both Kenya and Nigeria. STUDY DESIGN: In a network of seven secondary and tertiary level neonatal units in Nigeria and Kenya, we captured anonymised data on all admissions <48 hours of age over a 6-month period. RESULTS: 2280 newborns were admitted. Mean birthweight was 2.3 kg (SD 0.9); 57.0% (1214/2128) infants were low birthweight (LBW; <2.5kg) and 22.6% (480/2128) were very LBW (VLBW; <1.5 kg). Median gestation was 36 weeks (interquartile range 32, 39) and 21.6% (483/2236) infants were very preterm (gestation <32 weeks). The most common morbidities were jaundice (987/2262, 43.6%), suspected sepsis (955/2280, 41.9%), respiratory conditions (817/2280, 35.8%) and birth asphyxia (547/2280, 24.0%). 18.7% (423/2262) newborns died; mortality was very high amongst VLBW (222/472, 47%) and very preterm infants (197/483, 40.8%). Factors independently associated with mortality were gestation <28 weeks (adjusted odds ratio 11.58; 95% confidence interval 4.73-28.39), VLBW (6.92; 4.06-11.79), congenital anomaly (4.93; 2.42-10.05), abdominal condition (2.86; 1.40-5.83), birth asphyxia (2.44; 1.52-3.92), respiratory condition (1.46; 1.08-2.28) and maternal antibiotics within 24 hours before or after birth (1.91; 1.28-2.85). Mortality was reduced if mothers received a partial (0.51; 0.28-0.93) or full treatment course (0.44; 0.21-0.92) of dexamethasone before preterm delivery. CONCLUSION: Greater efforts are needed to address the very high burden of illnesses and mortality in hospitalized newborns in sub-Saharan Africa. Interventions need to address priority issues during pregnancy and delivery as well as in the newborn.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti-SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April-June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti-SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April-June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa.