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BackgroundThe World Health Organization (WHO) has developed danger signs (DS) to help front-line health workers triage interventions in children with severe illnesses. Our objective was to evaluate the extent to which DS predict bacterial sepsis in young infants presenting with acute illness.Methodology/Principal FindingsThis prospective study evaluated nine DS in infants younger than 3 months with suspected sepsis in a large regional hospital in Lilongwe, Malawi, between June 2018 and April 2020. The main outcomes were positive blood or cerebrospinal fluid (CSF) cultures and mortality. Blood (n=85/401) and CSF (n=2/204) cultures were positive in 21.2% and 1% of infants, respectively (N=401; gestational age mean ± SD: 37.1±3.3 weeks, birth weight 2865±785 grams). In-hospital deaths occurred in 9.7% (N=39/401) of infants (61.5% within 48h of admission). In univariate analyses, all DS were associated with mortality except for temperature instability and tachypnea, whereas “infant was unable to feed” was the only DS significantly associated with bacterial sepsis.After co-variable adjustments, number of DS predicted mortality (OR: 1.75; 95%CI: 1.43–2.16; pConclusion/SignificanceDS predicted fatal outcomes but not positive cultures in a large regional hospital setting. These data imply that the incidence of bacterial sepsis and attributable mortality are unlikely to be accurate based on clinical signs alone, in infants in LMIC settings.

BackgroundSubstantial mortality occurs after hospital discharge in children under 5 years old with suspected sepsis. A better understanding of its epidemiology is needed for effective interventions aimed at reducing child mortality in resource limited settings.MethodsIn this prospective observational cohort study, we recruited 0-60-month-old children admitted with suspected sepsis from the community to the paediatric wards of six Ugandan hospitals. The primary outcome was six-month post-discharge mortality among those discharged alive. We evaluated the interactive impact of age, time of death, and location of death on risk factors for mortality.Findings6,545 children were enrolled, with 6,191 discharged alive. The median (interquartile range) time from discharge to death was 32 (10–92) days, with a six-month post-discharge mortality rate of 5·5%, constituting 51% of total mortality. Deaths occurred at home (45%), intransit to care (18%), or in hospital (37%) during a subsequent readmission. Post-discharge death was strongly associated with weight-for-age z-scores < -3 (adjusted hazard ratio [aHR] 5·04; 95%CI: 3·97–6·37), referral for further care (aHR 9·08; 95%CI 6·68–12·34), and unplanned discharge (aHR 3·36; 95%CI 2·64–4·28). The hazard ratio of those with severe anaemia increased with time since discharge, while the hazard ratios of discharge vulnerabilities (unplanned, poor feeding) decreased with time. Children with severe anaemia (InterpretationPaediatric post-discharge mortality following suspected sepsis is common, with diminishing, though persistent, risk over the 6 months after discharge. Efforts to improve post-discharge outcomes are critical to achieving Sustainable Development Goal 3.2 (ending preventable childhood deaths under 5 years of age).FundingGrand Challenges Canada (#TTS-1809-1939), Thrasher Research Fund (#13878), BC Children’s Hospital Foundation, and Mining4Life.

BackgroundIn low- and middle-income countries, health workers use pulse oximeters for intermittent spot measurements of SpO2. However, the accuracy and reliability when used for spot measurements has not been determined. We evaluated the repeatability of spot measurements, and the ideal observation time of measurement to guide recommendations during spot check measurements.MethodsTwo one-minute measurements were done for the 3,903 subjects enrolled in the study, collecting 1Hz SpO2and signal quality index (SQI) data. The repeatability between the two measurements was assessed using an intraclass correlation coefficient (ICC), calculated using a median of all seconds of non-zero SpO2values for each recording (any quality, Q1), and again with a quality filter only using seconds with SQI ≥ 90% (good quality, Q2). The ICC was also calculated for both these conditions using subsets of the minute, in increasing increments of 5 seconds, up to the whole minute. Lastly, the whole minute ICC was calculated with good quality (Q2), including only records where both measurements had a mean SQI > 70% (Q3).FindingsThe repeatability ICC with condition Q1 was 0.591 (95% confidence interval (CI) = 0.570, 0.611). Using only the first 5 seconds of each measurement reduced the repeatability to 0.200 (95% CI = 0.169, 0.230). Filtering with Q2, the whole minute ICC was 0.855 (95% CI = 0.847, 0.864). The ICC did not improve beyond the first 35 seconds. For Q3, the repeatability rose to 0.908 (95% CI = 0.901, 0.914).ConclusionsTraining guidelines must emphasize the importance of signal quality and duration of measurement, targeting a minimum of 35 seconds of adequate-quality, stable data. In addition, the design of new devices should incorporate user prompts and force quality checks to encourage more accurate pulse oximetry measurement.Trial RegistrationClinical Trials.govIdentifier:NCT04304235, Registered 11 March 2020.

Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw datasets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low- and-middle income country.Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the dataset, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified dataset was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access.Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community.AUTHOR SUMMARYOpen Data is data that anyone can access, use, and share. Open Data has the potential to facilitate collaboration, enrich research, and advance the analytic capacity to inform decisions. Importantly, Open Data plays a role in fulfilling obligations to research participants and honoring the nature of medical research as a public good. Leaders in industry, academia, and regulatory agencies recognize the value in increased transparency and are focusing on how to openly share data while minimizing the safety risks to research participants. For example, making data open can pose a privacy risk to research participants who have shared personal health information. This risk can be mitigated using data de-identification, a process of removing personal information from a dataset so that an individual’s identity is no longer apparent or cannot be reasonably ascertained from the data. We introduce a simple, statistical risk-based framework for de-identification of clinical data that can be followed by any researcher. This framework will guide open data sharing while improving the protection of research participants.