BackgroundMost of the deaths among neonates in low-income and middle-income countries (LMICs) can be prevented through universal access to basic high-quality health services including essential facility-based inpatient care. However, poor routine data undermines data-informed efforts to monitor and promote improvements in the quality of newborn care across hospitals.MethodsContinuously collected routine patients’ data from structured paper record forms for all admissions to newborn units (NBUs) from 16 purposively selected Kenyan public hospitals that are part of a clinical information network were analysed together with data from all paediatric admissions ages 0–13 years from 14 of these hospitals. Data are used to show the proportion of all admissions and deaths in the neonatal age group and examine morbidity and mortality patterns, stratified by birth weight, and their variation across hospitals.FindingsDuring the 354 hospital months study period, 90 222 patients were admitted to the 14 hospitals contributing NBU and general paediatric ward data. 46% of all the admissions were neonates (aged 0–28 days), but they accounted for 66% of the deaths in the age group 0–13 years. 41 657 inborn neonates were admitted in the NBUs across the 16 hospitals during the study period. 4266/41 657 died giving a crude mortality rate of 10.2% (95% CI 9.97% to 10.55%), with 60% of these deaths occurring on the first-day of admission. Intrapartum-related complications was the single most common diagnosis among the neonates with birth weight of 2000 g or more who died. A threefold variation in mortality across hospitals was observed for birth weight categories 1000–1499 g and 1500–1999 g.InterpretationThe high proportion of neonatal deaths in hospitals may reflect changing patterns of childhood mortality. Majority of newborns died of preventable causes (>95%). Despite availability of high-impact low-cost interventions, hospitals have high and very variable mortality proportions after stratification by birth weight.

This study shows that OFP is frequently reported by young adults aged 30-31 and supports a multifactorial etiology with factors from many domains, including local mechanical factors, psychological and co-morbidities. However, none of the childhood factors considered in this study were associated with OFP in adulthood.

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Objectives To investigate biotin interference on three cardiac troponin (cTn) assays and demonstrate a method to overcome biotin interference. Methods cTn levels were measured in (1) plasma from healthy volunteers on 10-mg daily biotin supplementation mixed with a plasma with known elevated troponin, (2) plasmas with known elevated cTn after mixing in reagent biotin to simulate supplementation, and (3) biotin-spiked plasma specimens pretreated with streptavidin-agarose beads. Results Daily biotin ingestion (10 mg) and studies simulating daily biotin use resulted in significant interference in the Gen5 cardiac troponin T (cTnT) assay; the contemporary Gen 4 cTnT and high-sensitivity cardiac troponin I (hs-cTnI) assays were unaffected. The biotin interference threshold was 31, 315, and more than 2,000 ng/mL for Gen5 cTnT, cTnT, and hs-cTnI assays, respectively. Streptavidin pretreatment blocked biotin interference in cTn assays. Conclusions Biotin interference is possible at plasma concentrations achievable by ingestion of over-the-counter supplements that may lead to delayed or missed diagnosis of myocardial injury with the Gen5 cTnT assay.

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Background Two neonatal mortality prediction models, the Neonatal Essential Treatment Score (NETS) which uses treatments prescribed at admission and the Score for Essential Neonatal Symptoms and Signs (SENSS) which uses basic clinical signs, were derived in high-mortality, low-resource settings to utilise data more likely to be available in these settings. In this study, we evaluate the predictive accuracy of two neonatal prediction models for all-cause in-hospital mortality. Methods We used retrospectively collected routine clinical data recorded by duty clinicians at admission from 16 Kenyan hospitals used to externally validate and update the SENSS and NETS models that were initially developed from the data from the largest Kenyan maternity hospital to predict in-hospital mortality. Model performance was evaluated by assessing discrimination and calibration. Discrimination, the ability of the model to differentiate between those with and without the outcome, was measured using the c-statistic. Calibration, the agreement between predictions from the model and what was observed, was measured using the calibration intercept and slope (with values of 0 and 1 denoting perfect calibration). Results At initial external validation, the estimated mortality risks from the original SENSS and NETS models were markedly overestimated with calibration intercepts of − 0.703 (95% CI − 0.738 to − 0.669) and − 1.109 (95% CI − 1.148 to − 1.069) and too extreme with calibration slopes of 0.565 (95% CI 0.552 to 0.577) and 0.466 (95% CI 0.451 to 0.480), respectively. After model updating, the calibration of the model improved. The updated SENSS and NETS models had calibration intercepts of 0.311 (95% CI 0.282 to 0.350) and 0.032 (95% CI − 0.002 to 0.066) and calibration slopes of 1.029 (95% CI 1.006 to 1.051) and 0.799 (95% CI 0.774 to 0.823), respectively, while showing good discrimination with c-statistics of 0.834 (95% CI 0.829 to 0.839) and 0.775 (95% CI 0.768 to 0.782), respectively. The overall calibration performance of the updated SENSS and NETS models was better than any existing neonatal in-hospital mortality prediction models externally validated for settings comparable to Kenya. Conclusion Few prediction models undergo rigorous external validation. We show how external validation using data from multiple locations enables model updating and improving their performance and potential value. The improved models indicate it is possible to predict in-hospital mortality using either treatments or signs and symptoms derived from routine neonatal data from low-resource hospital settings also making possible their use for case-mix adjustment when contrasting similar hospital settings.

We developed a provider behavior change intervention to improve communication between providers and parents of hospitalized newborns and young children aged up to 24 months that focused on orientation and emotional support for providers, coaching and emotional support for parents, and monitoring for structural change.nThe intervention influenced outcomes including having a positive association in improving providers' knowledge of respectful, responsive nurturing care, improving provider-parent communication and partnership, improving the ability of parents to provide responsive care to their newborns, and increasing parent empowerment.n The providers' understanding of parents' needs and parents' understanding of providers' working environment resulted in a pragmatic behavior change intervention promoting responsive care of hospitalized newborns and young children.