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In 2019, 80% of the 7.4 million global child deaths occurred in low- and middle-income countries (LMICs). Global and regional estimates of cause of hospital death and admission in LMIC children are needed to guide global and local priority setting and resource allocation but are currently lacking. The study objective was to estimate global and regional prevalence for common causes of pediatric hospital mortality and admission in LMICs. We performed a systematic review and meta-analysis to identify LMIC observational studies published January 1, 2005-February 26, 2021. Eligible studies included: a general pediatric admission population, a cause of admission or death, and total admissions. We excluded studies with data before 2,000 or without a full text. Two authors independently screened and extracted data. We performed methodological assessment using domains adapted from the Quality in Prognosis Studies tool. Data were pooled using random-effects models where possible. We reported prevalence as a proportion of cause of death or admission per 1,000 admissions with 95% confidence intervals (95% CI). Our search identified 29,637 texts. After duplicate removal and screening, we analyzed 253 studies representing 21.8 million pediatric hospitalizations in 59 LMICs. All-cause pediatric hospital mortality was 4.1% [95% CI 3.4%–4.7%]. The most common causes of mortality (deaths/1,000 admissions) were infectious [12 (95% CI 9–14)]; respiratory [9 (95% CI 5–13)]; and gastrointestinal [9 (95% CI 6–11)]. Common causes of admission (cases/1,000 admissions) were respiratory [255 (95% CI 231–280)]; infectious [214 (95% CI 193–234)]; and gastrointestinal [166 (95% CI 143–190)]. We observed regional variation in estimates. Pediatric hospital mortality remains high in LMICs. Global child health efforts must include measures to reduce hospital mortality including basic emergency and critical care services tailored to the local disease burden. Resources are urgently needed to promote equity in child health research, support researchers, and collect high-quality data in LMICs to further guide priority setting and resource allocation.

Summary: Background: Under-five mortality remains concentrated in resource-poor countries. Post-discharge mortality is becoming increasingly recognized as a significant contributor to overall child mortality. With a substantial recent expansion of research and novel data synthesis methods, this study aims to update the current evidence base by providing a more nuanced understanding of the burden and associated risk factors of pediatric post-discharge mortality after acute illness. Methods: Eligible studies published between January 1, 2017 and January 31, 2023, were retrieved using MEDLINE, Embase, and CINAHL databases. Studies published before 2017 were identified in a previous review and added to the total pool of studies. Only studies from countries with low or low-middle Socio-Demographic Index with a post-discharge observation period greater than seven days were included. Risk of bias was assessed using a modified version of the Joanna Briggs Institute critical appraisal tool for prevalence studies. Studies were grouped by patient population, and 6-month post-discharge mortality rates were quantified by random-effects meta-analysis. Secondary outcomes included post-discharge mortality relative to in-hospital mortality, pooled risk factor estimates, and pooled post-discharge Kaplan–Meier survival curves. PROSPERO study registration: #CRD42022350975. Findings: Of 1963 articles screened, 42 eligible articles were identified and combined with 22 articles identified in the previous review, resulting in 64 total articles. These articles represented 46 unique patient cohorts and included a total of 105,560 children. For children admitted with a general acute illness, the pooled risk of mortality six months post-discharge was 4.4% (95% CI: 3.5%–5.4%, I2 = 94.2%, n = 11 studies, 34,457 children), and the pooled in-hospital mortality rate was 5.9% (95% CI: 4.2%–7.7%, I2 = 98.7%, n = 12 studies, 63,307 children). Among disease subgroups, severe malnutrition (12.2%, 95% CI: 6.2%–19.7%, I2 = 98.2%, n = 10 studies, 7760 children) and severe anemia (6.4%, 95% CI: 4.2%–9.1%, I2 = 93.3%, n = 9 studies, 7806 children) demonstrated the highest 6-month post-discharge mortality estimates. Diarrhea demonstrated the shortest median time to death (3.3 weeks) and anemia the longest (8.9 weeks). Most significant risk factors for post-discharge mortality included unplanned discharges, severe malnutrition, and HIV seropositivity. Interpretation: Pediatric post-discharge mortality rates remain high in resource-poor settings, especially among children admitted with malnutrition or anemia. Global health strategies must prioritize this health issue by dedicating resources to research and policy innovation. Funding: No specific funding was received.

Abstract Background In low- and middle-income countries, approximately two thirds of maternal deaths occur in the postpartum period. Yet, care for women beyond 24 h after discharge is limited. The objective of this systematic review is to summarize current evidence on socio-demographic and clinical risk factors for (1) postpartum mortality and (2) postpartum hospital readmission. Methods A combination of keywords and subject headings (i.e. MeSH terms) for postpartum maternal mortality or readmission were searched. Articles published up to January 9, 2021 were identified in MEDLINE, EMBASE, and CINAHL databases, without language restrictions. Studies reporting socio-demographic or clinical risk factors for postpartum mortality or readmission within six weeks of delivery among women who delivered a livebirth in a low- or middle-income country were included. Data were extracted independently by two reviewers based on study characteristics, population, and outcomes. Included studies were assessed for quality and risk of bias using the Downs and Black checklist for ratings of randomized and non-randomized studies. Results Of 8783 abstracts screened, seven studies were included (total N = 387,786). Risk factors for postpartum mortality included Caesarean mode of delivery, nulliparity, low or very low birthweight, and shock upon admission. Risk factors for postpartum readmission included Caesarean mode of delivery, HIV positive serostatus, and abnormal body temperature. Conclusions Few studies reported individual socio-demographic or clinical risk factors for mortality or readmission after delivery in low- and middle-income countries; only Caesarean delivery was consistently reported. Further research is needed to identify factors that put women at greatest risk of post-discharge complications and mortality. Understanding post-discharge risk would facilitate targeted postpartum care and reduce adverse outcomes in women after delivery. Trial registration PROSPERO registration number: CRD42018103955.

IntroductionIn low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility.MethodsThis prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5–10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values.DiscussionThe current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. Clinical trial registrationhttps://clinicaltrials.gov/, identifier (NCT05730387).

Background More than 50 countries, mainly in Sub-Saharan Africa and South Asia, are not on course to meet the neonatal and under-five mortality target set by the Sustainable Development Goals (SDGs) for the year 2030. One important, yet neglected, aspect of child mortality rates is deaths occurring during the post-discharge period. For children living in resource-poor countries, the rate of post-discharge mortality within the first several months after discharge is often as high as the rates observed during the initial admission period. This has generally been observed within the context of acute illness and has been closely linked to underlying conditions such as malnutrition, HIV, and anemia. These post-discharge mortality rates tend to be underreported and present a major oversight in the efforts to reduce overall child mortality. This review will explore recurrent illness following discharge through determination of rates of, and risk factors for, pediatric post-discharge mortality in resource-poor settings. Methods Eligible studies will be retrieved using MEDLINE, EMBASE, and CINAHL databases. Only studies with a post-discharge observation period of more than 7 days following discharge will be eligible for inclusion. Secondary outcomes will include post-discharge mortality relative to in-hospital mortality, overall readmission rates, pooled estimates of risk factors (e.g. admission details vs discharge factors, clinical vs social factors), pooled post-discharge mortality Kaplan-Meier survival curves, and outcomes by disease subgroups (e.g. malnutrition, anemia, general admissions). A narrative description of the included studies will be synthesized to categorize commonly affected patient population categories and a random-effects meta-analysis will be conducted to quantify overall post-discharge mortality rates at the 6-month time point. Discussion Post-discharge mortality contributes to global child mortality rates with a greater burden of deaths occurring in resource-poor settings. Literature concentrated on child mortality published over the last decade has expanded to focus on the fatal outcomes of children post-discharge and associated risk factors. The results from this systematic review will inform current policy and interventions on the epidemiological burden of post-discharge mortality and morbidity following acute illness among children living in resource-poor settings. Systematic review registration PROSPERO Registration ID: CRD42022350975.

IntroductionEarly and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model for rapid identification of critically ill children at triage.MethodsThis was a prospective cohort study of acutely ill children presenting at Jinja Regional Referral Hospital in Eastern Uganda. Variables collected in the emergency department informed the development of a logistic model based on hospital admission using bootstrap stepwise regression. Low and high-risk thresholds for 90% minimum sensitivity and specificity, respectively generated three risk level categories. Performance was assessed using receiver operating characteristic curve analysis on a held-out test set generated by an 80:20 split with 10-fold cross validation. A risk stratification table informed clinical interpretation.ResultsThe model derivation cohort included 1,612 participants, with an admission rate of approximately 23%. The majority of admitted patients were under five years old and presenting with sepsis, malaria, or pneumonia. A 9-predictor triage model was derived: logit (p) = −32.888 + (0.252, square root of age) + (0.016, heart rate) + (0.819, temperature) + (−0.022, mid-upper arm circumference) + (0.048 transformed oxygen saturation) + (1.793, parent concern) + (1.012, difficulty breathing) + (1.814, oedema) + (1.506, pallor). The model afforded good discrimination, calibration, and risk stratification at the selected thresholds of 8% and 40%.ConclusionIn a low income, pediatric population, we developed a nine variable triage model with high sensitivity and specificity to predict who should be admitted. The triage model can be integrated into any digital platform and used with minimal training to guide rapid identification of critically ill children at first contact. External validation and clinical implementation are in progress.

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 data sets, 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. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. 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 data sets, 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 data sets 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 summary Open 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 data sets 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.

IntroductionTo determine the effect of parental participation in hospital care on neonatal and parental outcomes in low- and middle-income countries (LMICs) and to identify the range of parental duties in the care of hospitalized neonates in LMICs.MethodsWe searched CINAHL, CENTRAL, LILACs, MEDLINE, EMBASE and Web of Science from inception to February 2022. Randomized and non-randomized studies from LMICs were eligible if parents performed one or more roles traditionally undertaken by healthcare staff. The primary outcome was hospital length-of-stay. Secondary outcomes included mortality, readmission, breastfeeding, growth, development and parental well-being. Data was extracted in duplicate by two independent reviewers using a piloted extraction form.ResultsEighteen studies (eight randomized and ten non-randomized) were included from seven middle-income countries. The types of parental participation included hygiene and infection prevention, feeding, monitoring and documentation, respiratory care, developmental care, medication administration and decision making. Meta-analyses showed that parental participation was not associated with hospital length-of-stay (MD −2.35, 95% CI −6.78–2.07). However, parental involvement was associated with decreased mortality (OR 0.46, 95% CI 0.22–0.95), increased breastfeeding (OR 2.97 95% CI 1.65–5.35) and decreased hospital readmission (OR 0.36, 95% CI 0.16–0.81). Narrative synthesis demonstrated additional benefits for growth, short-term neurodevelopment and parental well-being. Ten of the eighteen studies had a high risk of bias.ConclusionParental participation in neonatal hospital care is associated with improvement in several key neonatal outcomes in middle-income countries. The lack of data from low-income countries suggests that there remains barriers to parental participation in resource-poor settings.Systematic review registration[https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=187562], identifier [CRD42020187562].

BackgroundThe majority of childhood deaths occur in low- and middle-income countries (LMICs). Many of these deaths are avoidable with basic critical care interventions. Quantifying the burden of pediatric critical illness in LMICs is essential for targeting interventions to reduce childhood mortality.ObjectiveTo determine the burden of hospitalization and mortality associated with acute pediatric critical illness in LMICs through a systematic review and meta-analysis of the literature.Data Sources and Search StrategyWe will identify eligible studies by searching MEDLINE, EMBASE, CINAHL, and LILACS using MeSH terms and keywords. Results will be limited to infants or children (ages >28 days to 12 years) hospitalized in LMICs and publications in English, Spanish, or French. Publications with non-original data (e.g., comments, editorials, letters, notes, conference materials) will be excluded.Study SelectionWe will include observational studies published since January 1, 2005, that meet all eligibility criteria and for which a full text can be located.Data ExtractionData extraction will include information related to study characteristics, hospital characteristics, underlying population characteristics, patient population characteristics, and outcomes.Data SynthesisWe will extract and report data on study, hospital, and patient characteristics; outcomes; and risk of bias. We will report the causes of admission and mortality by region, country income level, and age. We will report or calculate the case fatality rate (CFR) for each diagnosis when data allow.ConclusionsBy understanding the burden of pediatric critical illness in LMICs, we can advocate for resources and inform resource allocation and investment decisions to improve the management and outcomes of children with acute pediatric critical illness in LMICs.

BackgroundThe burden of pediatric critical illness and resource utilization by children with critical illness in resource limited settings (RLS) are largely unknown. Without specific data that captures key aspects of critical illness, disease presentation, and resource utilization for pediatric populations in RLS, development of a contextual framework for appropriate, evidence-based interventions to guide allocation of limited but available resources is challenging. We present this methods paper which describes our efforts to determine the prevalence, etiology, hospital outcomes, and resource utilization associated with pediatric acute, critical illness in RLS globally.MethodsWe will conduct a prospective, observational, multicenter, multinational point prevalence study in sixty-one participating RLS hospitals from North, Central and South America, Africa, Middle East and South Asia with four sampling time points over a 12-month period. Children aged 29 days to 14 years evaluated for acute illness or injury in an emergency department) or directly admitted to an inpatient unit will be enrolled and followed for hospital outcomes and resource utilization for the first seven days of hospitalization. The primary outcome will be prevalence of acute critical illness, which Global PARITY has defined as death within 48 hours of presentation to the hospital, including ED mortality; or admission/transfer to an HDU or ICU; or transfer to another institution for a higher level-of-care; or receiving critical care-level interventions (vasopressor infusion, invasive mechanical ventilation, non-invasive mechanical ventilation) regardless of location in the hospital, among children presenting to the hospital. Secondary outcomes include etiology of critical illness, in-hospital mortality, cause of death, resource utilization, length of hospital stay, and change in neurocognitive status. Data will be managed via REDCap, aggregated, and analyzed across sites.DiscussionThis study is expected to address the current gap in understanding of the burden, etiology, resource utilization and outcomes associated with pediatric acute and critical illness in RLS. These data are crucial to inform future research and clinical management decisions and to improve global pediatric hospital outcomes.

Abstract Background Sepsis is the leading cause of death and disability in children. Every hour of delay in treatment is associated with an escalating risk of morbidity and mortality. The burden of sepsis is greatest in low- and middle-income countries where timely treatment may not occur due to delays in diagnosis and prioritization of critically ill children. To circumvent these challenges, we propose the development and clinical evaluation of a digital triage tool that will identify high risk children and reduce time to treatment. We will also implement and clinically validate a Radio-Frequency Identification system to automate tracking of patients. The mobile platform (mobile device and dashboard) and automated patient tracking system will create a low cost, highly scalable solution for critically ill children, including those with sepsis. Methods This is pre-post intervention study consisting of three phases. Phase I will be a baseline period where data is collected on key predictors and outcomes before implementation of the digital triage tool. In Phase I, there will be no changes to healthcare delivery processes in place at the study hospitals. Phase II will involve model derivation, technology development, and usability testing. Phase III will be the intervention period where data is collected on key predictors and outcomes after implementation of the digital triage tool. The primary outcome, time to treatment initiation, will be compared to assess effectiveness of the digital health intervention. Discussion Smart technology has the potential to overcome the barrier of limited clinical expertise in the identification of the child at risk. This mobile health platform, with sensors and data-driven applications, will provide real-time individualized risk prediction to rapidly triage patients and facilitate timely access to life-saving treatments for children in low- and middle-income countries, where specialists are not regularly available and deaths from sepsis are common. Trial registration Clinical Trials.gov Identifier: NCT04304235 , Registered 11 March 2020.

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation. Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice.

Background Standardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings. Methods A preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings. Results We generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information. Conclusion Routine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.

Conducting collaborative and comprehensive epidemiological research on neonatal sepsis in low- and middle-income countries (LMICs) is challenging due to a lack of diagnostic tests. This prospective study protocol aims to obtain epidemiological data on bacterial sepsis in newborns and young infants at Kamuzu Central Hospital in Lilongwe, Malawi. The main goal is to determine if the use of whole blood transcriptome host immune response signatures can help in the identification of infants who have sepsis of bacterial causes. The protocol includes a detailed clinical assessment with vital sign measurements, strict aseptic blood culture protocol with state-of-the-art microbial analyses and RNA-sequencing and metagenomics evaluations of host responses and pathogens, respectively. We also discuss the directions of a brief analysis plan for RNA sequencing data. This study will provide robust epidemiological data for sepsis in neonates and young infants in a setting where sepsis confers an inordinate burden of disease.

Objectives:. Sepsis is responsible for a substantial proportion of global childhood morbidity and mortality. However, evidence demonstrates major inaccuracies in the use of the term “sepsis” in clinical practice, coding, and research. Current and previous definitions of sepsis have been developed using expert consensus but the specific criteria used to identify children with sepsis have not been rigorously evaluated. Therefore, as part of the Society of Critical Care Medicine’s Pediatric Sepsis Definition Taskforce, we will conduct a systematic review to synthesize evidence on individual factors, clinical criteria, or illness severity scores that may be used to identify children with infection who have or are at high risk of developing sepsis-associated organ dysfunction and separately those factors, criteria, and scores that may be used to identify children with sepsis who are at high risk of progressing to multiple organ dysfunction or death. Data Sources:. We will identify eligible studies by searching the following databases: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Study Selection:. We will include all randomized trials and cohort studies published between January 1, 2004, and March 16, 2020. Data Extraction:. Data extraction will include information related to study characteristics, population characteristics, clinical criteria, and outcomes. Data Synthesis:. We will calculate sensitivity and specificity of each criterion for predicting sepsis and conduct a meta-analysis if the data allow. We will also provide pooled estimates of overall hospital mortality. Conclusions:. The potential risk factors, clinical criteria, and illness severity scores from this review which identify patients with infection who are at high risk of developing sepsis-associated organ dysfunction and/or progressing to multiple organ dysfunction or death will be used to inform the next steps of the Pediatric Sepsis Definition Taskforce.

Background Sepsis is the leading cause of death in children under five in low- and middle-income countries. The rapid identification of the sickest children and timely antibiotic administration may improve outcomes. We developed and implemented a digital triage platform to rapidly identify critically ill children to facilitate timely intravenous antibiotic administration. Objective This quality improvement initiative sought to reduce the time to antibiotic administration at a dedicated children’s hospital outpatient department in Mbarara, Uganda. Intervention and study design The digital platform consisted of a mobile application that collects clinical signs, symptoms, and vital signs to prioritize children through a combination of emergency triggers and predictive risk algorithms. A computer-based dashboard enabled the prioritization of children by displaying an overview of all children and their triage categories. We evaluated the impact of the digital triage platform over an 11-week pre-implementation phase and an 11-week post-implementation phase. The time from the end of triage to antibiotic administration was compared to evaluate the quality improvement initiative. Results There was a difference of -11 minutes (95% CI, -16.0 to -6.0; p < 0.001; Mann-Whitney U test) in time to antibiotics, from 51 minutes (IQR, 27.0–94.0) pre-implementation to 44 minutes (IQR, 19.0–74.0) post-implementation. Children prioritized as emergency received the greatest time benefit (-34 minutes; 95% CI, -9.0 to -58.0; p < 0.001; Mann-Whitney U test). The proportion of children who waited more than an hour until antibiotics decreased by 21.4% (p = 0.007). Conclusion A data-driven patient prioritization and continuous feedback for healthcare workers enabled by a digital triage platform led to expedited antibiotic therapy for critically ill children with sepsis. This platform may have a more significant impact in facilities without existing triage processes and prioritization of treatments, as is commonly encountered in low resource settings.

Background: Neonates and infants comprise the majority of the 6 million annual deaths under 5 years of age around the world. Most of these deaths occur in low/middle income countries (LMICs) and are preventable. However, the clinical identification of neonates and infants at imminent risk of death is challenging in developing countries.Objective: To systematically review the literature on clinical risk factors for mortality in infants under 12 months of age hospitalized for sepsis or serious infections in LMICs.Methods: MEDLINE and EMBASE were systematically searched using MeSH terms through April 2017. Abstracts were independently screened by two reviewers. Subsequently, full-text articles were selected by two independent reviewers based on PICOS criteria for inclusion in the final analysis. Study data were qualitatively synthesized without quantitative pooling of data due to heterogeneity in study populations and methodology.Results: A total of 1,139 abstracts were screened, and 169 full-text articles were selected for text review. Of these, 45 articles were included in the analysis, with 21 articles featuring neonatal populations (under 28 days of age) exclusively. Most studies were from Sub-Saharan Africa and South Asia. Risk factors for mortality varied significantly according to study populations. For neonatal deaths, prematurity, low birth-weight and young age at presentation were most frequently associated with mortality. For infant deaths, malnutrition, lack of breastfeeding and low oxygen saturation were associated with mortality in the highest number of studies.Conclusions: Risk factors for mortality differ between the neonatal and young infant age groups and were also dependant on the study population. These data can serve as a starting point for the development of individualized predictive models for in-hospital and post-discharge mortality and for the development of interventions to improve outcomes among these high-risk groups.

Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). Due to reduced performance in neonates (< 1 month), we re-estimated the intercept and coefficients and selected new thresholds to maximize sensitivity and specificity. 11595 participants under the age of five (under-5) were included in the analysis. The proportion with an endpoint ranged from 8.9% in all children under-5 (including neonates) to 26% in the neonatal subset alone. The model achieved good discrimination for children under-5 with AUROC of 0.81 (95% CI: 0.79-0.82) but poor discrimination for neonates with AUROC of 0.62 (95% CI: 0.55-0.70). Sensitivity at the low-risk thresholds (CI) were 85% (83%-87%) and 68% (58%-76%) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (95% CI: 0.79-0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. The updated Smart Triage performs well in its predictive ability across different age groups and can be incorporated into current triage guidelines at local healthcare facilities. Additional validation of the model is indicated, especially for the neonatal model.

Sub-Saharan Africa accounts for two-thirds of the global burden of maternal and newborn deaths. Adverse outcomes among postpartum women and newborns occurring in the first six weeks of life are often related, though data co-examining patients are limited. This study is an exploratory analysis describing the epidemiology of postnatal complications among postpartum women and newborns following facility birth and discharge in Mbarara, Uganda. This single-site prospective cohort observational study enrolled postpartum women following facility-based delivery. To capture health information about both the postpartum women and newborns, data was collected and categorized according to domains within the continuum of care including (1) social and demographic, (2) pregnancy history and antenatal care, (3) delivery, (4) maternal discharge, and (5) newborn discharge. The primary outcomes were readmission and mortality within the six-week postnatal period as defined by the WHO. Multivariable logistic regression was used to identify risk factors. Among 2930 discharged dyads, 2.8% and 9.0% of women and newborns received three or more postnatal visits respectively. Readmission and deaths occurred among 108(3.6%) and 25(0.8%) newborns and in 80(2.7%) and 0(0%) women, respectively. Readmissions were related to sepsis/infection in 70(88%) women and 68(63%) newborns. Adjusted analysis found that caesarean delivery (OR:2.91; 95%CI:1.5-6.04), longer travel time to the facility (OR:1.54; 95%CI:1.24-1.91) and higher maternal heart rate at discharge (OR:1.02; 95%CI:1.00-1.01) were significantly associated with maternal readmission. Discharge taken on all patients including maternal haemoglobin (per g/dL) (OR:0.90; 95%CI:0.82-0.99), maternal symptoms (OR:1.76; 95%CI:1.02-2.91), newborn temperature (OR:1.66; 95%CI:1.28-2.13) and newborn heart rate at (OR:1.94; 95%CI:1.19-3.09) were risk factors among newborns. Readmission and death following delivery and discharge from healthcare facilities is still a problem in settings with low rates of postnatal care visits for both women and newborns. Strategies to identify vulnerable dyads and provide better access to follow-up care, are urgently required.

In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Four prospective cohort studies of children in two age groups (0-6 and 6-60 months) were conducted between 2012-2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74-0.80) for 0-6-month-olds and 0.75 (95%CI 0.72-0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be digitally integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community.

Objective New paediatric sepsis criteria are being developed by an international task force. However, it remains unknown what type of clinical decision support (CDS) tools will be most useful for dissemination of those criteria in resource-poor settings. We sought to design effective CDS tools by identifying the paediatric sepsis-related decisional needs of multidisciplinary clinicians and health system administrators in resource-poor settings.Design Semistructured qualitative focus groups and interviews with 35 clinicians (8 nurses, 27 physicians) and 5 administrators at health systems that regularly provide care for children with sepsis, April–May 2022.Setting Health systems in Africa, Asia and Latin America, where sepsis has a large impact on child health and healthcare resources may be limited.Participants Participants had a mean age of 45 years, a mean of 15 years of experience, and were 45% female.Results Emergent themes were related to the decisional needs of clinicians caring for children with sepsis and to the needs of health system administrators as they make decisions about which CDS tools to implement. Themes included variation across regions and institutions in infectious aetiologies of sepsis and available clinical resources, the need for CDS tools to be flexible and customisable in order for implementation to be successful, and proposed features and format of an ideal paediatric sepsis CDS tool.Conclusion Findings from this study will directly contribute to the design and implementation of CDS tools to increase the uptake and impact of the new paediatric sepsis criteria in resource-poor settings.

Bacterial sepsis is generally a major concern in ill infants. To help triaging decisions by front-line health workers in these situations, the World Health Organization (WHO) has developed danger signs (DS). The objective of this study was to evaluate the extent to which nine DS predict bacterial sepsis in young infants presenting with suspected sepsis in a low-income country setting. The study pragmatically evaluated nine DS in infants younger than 3 months with suspected sepsis in a regional hospital in Lilongwe, Malawi, between June 2018 and April 2020. Main outcomes were positive blood or cerebrospinal fluid (CSF) cultures for neonatal pathogens, and mortality. Among 401 infants (gestational age [mean ± SD]: 37.1±3.3 weeks, birth weight 2865±785 grams), 41 had positive blood or CSF cultures for a neonatal pathogen. In-hospital mortality occurred in 9.7% of infants overall (N = 39/401), of which 61.5% (24/39) occurred within 48 hours of admission. Mortality was higher in infants with bacterial sepsis compared to other infants (22.0% [9/41] versus 8.3% [30/360]; p = 0.005). All DS were associated with mortality except for temperature instability and tachypnea, whereas none of the DS were significantly associated with bacterial sepsis, except for "unable to feed" (OR 2.25; 95%CI: 1.17-4.44; p = 0.017). The number of DS predicted mortality (OR: 1.75; 95%CI: 1.43-2.17; p

The World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) guidelines recognize the importance of discharge planning to ensure continuation of care at home and appropriate follow-up. However, insufficient attention has been paid to post discharge planning in many hospitals contributing to poor implementation. To understand the reasons for suboptimal discharge, we evaluated the pediatric discharge process from hospital admission through the transition to care within the community in Ugandan hospitals. This mixed methods prospective study enrolled 92 study participants in three phases: patient journey mapping for 32 admitted children under-5 years of age with suspected or proven infection, discharge process mapping with 24 pediatric healthcare workers, and focus group discussions with 36 primary caregivers and fathers of discharged children. Data were descriptively and thematically analyzed. We found that the typical discharge process is often not centered around the needs of the child and family. Discharge planning often does not begin until immediately prior to discharge and generally does not include caregiver input. Discharge education and counselling are generally limited, rarely involves the father, and does not focus significantly on post-discharge care or follow-up. Delays in the discharge process itself occur at multiple points, including while awaiting a physical discharge order and then following a discharge order, mainly with billing or transportation issues. Poor peri-discharge care is a significant barrier to optimizing health outcomes among children in Uganda. Process improvements including initiation of early discharge planning, improved communication between healthcare workers and caregivers, as well as an increased focus on post-discharge care, are key to ensuring safe transitions from facility-based care to home-based care among children recovering from severe illness.

BackgroundMore than 50 countries, mainly in Sub-Saharan Africa and South Asia, are not on course to meet the neonatal and under-five mortality target set by the Sustainable Development Goals (SDGs) for the year 2030. One important, yet neglected, aspect of child mortality rates is deaths occurring during the post-discharge period. For children living in resource-poor countries, the rate of post-discharge mortality within the first several months after discharge is often as high as the rates observed during the initial admission period. This has generally been observed within the context of acute illness and has been closely linked to underlying conditions such as malnutrition, HIV, and anemia. These post-discharge mortality rates tend to be underreported and present a major oversight in the efforts to reduce overall child mortality. This review will explore recurrent illness following discharge through determination of rates of, and risk factors for, pediatric post-discharge mortality in resource-poor settings.MethodsEligible studies will be retrieved using MEDLINE, EMBASE, and CINAHL databases. Only studies with a post-discharge observation period of more than 7 days following discharge will be eligible for inclusion. Secondary outcomes will include post-discharge mortality relative to in-hospital mortality, overall readmission rates, pooled estimates of risk factors (e.g. admission details vs discharge factors, clinical vs social factors), pooled post-discharge mortality Kaplan-Meier survival curves, and outcomes by disease subgroups (e.g. malnutrition, anemia, general admissions). A narrative description of the included studies will be synthesized to categorize commonly affected patient population categories and a random-effects meta-analysis will be conducted to quantify overall post-discharge mortality rates at the 6-month time point.DiscussionPost-discharge mortality contributes to global child mortality rates with a greater burden of deaths occurring in resource-poor settings. Literature concentrated on child mortality published over the last decade has expanded to focus on the fatal outcomes of children post-discharge and associated risk factors. The results from this systematic review will inform current policy and interventions on the epidemiological burden of post-discharge mortality and morbidity following acute illness among children living in resource-poor settings.Systematic review registrationPROSPERO Registration ID: CRD42022350975.

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 data sets, 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. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. 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 data sets, 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 data sets 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.

BackgroundStandardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings.MethodsA preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings.ResultsWe generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information.ConclusionRoutine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.

BackgroundSepsis is the leading cause of death in children under five in low- and middle-income countries. The rapid identification of the sickest children and timely antibiotic administration may improve outcomes. We developed and implemented a digital triage platform to rapidly identify critically ill children to facilitate timely intravenous antibiotic administration.ObjectiveThis quality improvement initiative sought to reduce the time to antibiotic administration at a dedicated children's hospital outpatient department in Mbarara, Uganda.Intervention and study designThe digital platform consisted of a mobile application that collects clinical signs, symptoms, and vital signs to prioritize children through a combination of emergency triggers and predictive risk algorithms. A computer-based dashboard enabled the prioritization of children by displaying an overview of all children and their triage categories. We evaluated the impact of the digital triage platform over an 11-week pre-implementation phase and an 11-week post-implementation phase. The time from the end of triage to antibiotic administration was compared to evaluate the quality improvement initiative.ResultsThere was a difference of -11 minutes (95% CI, -16.0 to -6.0; p < 0.001; Mann-Whitney U test) in time to antibiotics, from 51 minutes (IQR, 27.0-94.0) pre-implementation to 44 minutes (IQR, 19.0-74.0) post-implementation. Children prioritized as emergency received the greatest time benefit (-34 minutes; 95% CI, -9.0 to -58.0; p < 0.001; Mann-Whitney U test). The proportion of children who waited more than an hour until antibiotics decreased by 21.4% (p = 0.007).ConclusionA data-driven patient prioritization and continuous feedback for healthcare workers enabled by a digital triage platform led to expedited antibiotic therapy for critically ill children with sepsis. This platform may have a more significant impact in facilities without existing triage processes and prioritization of treatments, as is commonly encountered in low resource settings.

Sepsis is a life-threatening dysfunction of the immune system leading to multiorgan failure that is precipitated by infectious diseases and is a leading cause of death in children under 5 years of age. It is necessary to be able to identify a sick child at risk of developing sepsis at the earliest point of presentation to a healthcare facility so that appropriate care can be provided as soon as possible. Our study objective was to generate a list of consensus-driven predictor variables for the derivation of a prediction model that will be incorporated into a mobile device and operated by low-skilled healthcare workers at triage. By conducting a systematic literature review and examination of global guideline documents, a list of 72 initial candidate predictor variables was generated. A two-round modified Delphi process involving 26 experts from both resource-rich and resource-limited settings, who were also encouraged to suggest new variables, yielded a final list of 45 predictor variables after evaluating each variable based on three domains: predictive potential, measurement reliability, and level of training and resources required. The final list of predictor variables will be used to collect data and contribute to the derivation of a prediction model.

BackgroundAfrica has the highest rates of child mortality. Little is known about outcomes after hospitalization for children with very severe anemia.ObjectiveTo determine one year mortality and predictors of mortality in Tanzanian children hospitalized with very severe anemia.MethodsWe conducted a prospective cohort study enrolling children 2-12 years hospitalized from August 2014 to November 2014 at two public hospitals in northwestern Tanzania. Children were screened for anemia and followed until 12 months after discharge. The primary outcome measured was mortality. Predictors of mortality were determined using Cox regression analysis.ResultsOf the 505 children, 90 (17.8%) had very severe anemia and 415 (82.1%) did not. Mortality was higher for children with very severe anemia compared to children without over a one year period from admission, 27/90 (30.0%) vs. 59/415 (14.2%) respectively (Hazard Ratio (HR) 2.42, 95% Cl 1.53-3.83). In-hospital mortality was 11/90 (12.2%) and post-hospital mortality was 16/79 (20.2%) for children with very severe anemia. The strongest predictors of mortality were age (HR 1.01, 95% Cl 1.00-1.03) and decreased urine output (HR 4.30, 95% Cl 1.04-17.7).ConclusionsChildren up to 12 years of age with very severe anemia have nearly a 30% chance of mortality following admission over a one year period, with over 50% of mortality occurring after discharge. Post-hospital interventions are urgently needed to reduce mortality in children with very severe anemia, and should include older children.

Energy poverty is prevalent in resource-limited settings, leading households to use inefficient fuels and appliances that contribute to household air pollution. Randomized controlled trials of household energy interventions in low and middle income countries have largely focused on cooking services. Less is known about the adoption and impact of clean lighting interventions. We conducted an explanatory sequential mixed methods study as part of a randomized controlled trial of home solar lighting systems in rural Uganda in order to identify contextual factors determining the use and impact of the solar lighting intervention. We used sensors to track usage, longitudinally assessed household lighting expenditures and health-related quality of life, and performed cost-effectiveness analyses. Qualitative interviews were conducted with all 80 trial participants and coded using reflexive thematic analysis. Uptake of the intervention solar lighting system was high with daily use averaging 8.23 ± 5.30 h d ^−1 . The intervention solar lighting system increased the EQ5D index by 0.025 (95% CI 0.002–0.048) and led to an average monthly change in household lighting costs by −1.28 (−2.52, −0.85) US dollars, with higher savings in users of fuel-based lighting. The incremental cost-effectiveness ratio for the solar lighting intervention was $2025.72 US dollars per quality adjusted life year gained making the intervention cost-effective when benchmarked against the gross domestic product per capita in Uganda. Thematic analysis of qualitative data from individual interviews showed that solar lighting was transformative and associated with numerous benefits that fit within a social determinants of health (SDOH) framework. The benefits included improved household finances, improved educational performance of children, increased household safety, improved family and community cohesion, and improved perceived household health. Our findings suggest that household solar lighting interventions may be a cost-effective approach to improve health-related quality of life by addressing SDOH.

BACKGROUND:Pediatric hospital mortality from infectious diseases in resource constrained countries remains unacceptably high. Improved methods of risk-stratification can assist in referral decision making and resource allocation. The purpose of this study was to create prediction models for in-hospital mortality among children admitted with suspected infectious diseases. METHODS:This two-site prospective observational study enrolled children between 6 months and 5 years admitted with a proven or suspected infection. Baseline clinical and laboratory variables were collected on enrolled children. The primary outcome was death during admission. Stepwise logistic regression minimizing Akaike's information criterion was used to identify the most promising multivariate models. The final model was chosen based on parsimony. RESULTS:1307 children were enrolled consecutively, and 65 (5%) of whom died during their admission. Malaria, pneumonia and gastroenteritis were diagnosed in 50%, 31% and 8% of children, respectively. The primary model included an abnormal Blantyre coma scale, HIV and weight-for-age z-score. This model had an area under the curve (AUC) of 0.85 (95% CI, 0.80-0.89) with a sensitivity and specificity of 83% and 76%, respectively. The positive and negative predictive values were 15% and 99%, respectively. Two alternate models with similar performance characteristics were developed withholding HIV and weight-for-age z-score, for use when these variables are not available. CONCLUSIONS:Risk stratification of children admitted with infectious diseases can be calculated based on several easily measured variables. Risk stratification at admission can be used for allocation of scarce human and physical resources and to guide referral among children admitted to lower level health facilities.

BACKGROUND:Children discharged from hospitals in developing countries are at high risk of morbidity and mortality. However, few data describe these outcomes among children seen and discharged from rural outpatient centers. OBJECTIVE:The objective of this exploratory study was to identify predictors of immediate and follow-up morbidity and mortality among children visiting a rural health center in Uganda. METHODS:Subjects 0-12 years of age seeking care with a caregiver were consecutively enrolled from a single rural health center in Southwestern Uganda. Baseline variables were collected by research nurses and outcomes of referral, admission or death were recorded (immediate events). Death, hospital admission and health seeking occurring during the 30 days following the clinic visit were also determined (follow-up events). Univariate logistic regression was performed to identify baseline variables associated with immediate outcome and follow-up outcomes. RESULTS:Over the four-month recruitment period 717 subjects were enrolled. There were 85 (11.9%) immediate events (10.1% were admitted, 2.2% were referred, none died). Forty-seven (7.8%) events occurred within 30 days after the visit (7.3% sought care from a health provider, 1.5% were admitted and 0.5% died). Variables associated with immediate events included living more than 30 minutes from the health center, age older than 5 years, having received an antimalarial prior to the visit, having seen a community health worker prior to the visit, elevated respiratory rate or temperature, and depressed weight-for-age z score or decreased oxygen saturation. These variables were not associated with follow-up events. CONCLUSIONS:Sick-child visits at a rural health center in South Western Uganda were associated with rates of mortality and subsequent admission of less than 2% in the period following the sick child visits. Other types of health seeking behavior occurred in approximately 7% of subjects during this same period. Several variables were associated with immediate events but there were no reliable predictors of follow-up events, possibly due to low statistical power.

ObjectivesMortality following hospital discharge is an important and under-recognized contributor to overall child mortality in developing countries. The primary objective of this systematic review was to identify all studies reporting post-discharge mortality in children, estimate likelihood of death, and determine the most important risk factors for death.Search strategyMEDLINE and EMBASE were systematically searched using MeSH terms and keywords from the inception date to October, 2012. Key word searches using Google Scholar™ and hand searching of references of retrieved articles was also performed. Studies from developing countries reporting mortality following hospital discharge among a pediatric population were considered for inclusion.ResultsThirteen studies that reported mortality rates following discharge were identified. Studies varied significantly according to design, underlying characteristics of study population and duration of follow-up. Mortality rates following discharge varied significantly between studies (1%-18%). When reported, post-discharge mortality rates often exceeded in-hospital mortality rates. The most important baseline variables associated with post-discharge mortality were young age, malnutrition, multiple previous hospitalizations, HIV infection and pneumonia. Most post-discharge deaths occurred early during the post-discharge period. Follow-up care was examined in only one study examining malaria prophylaxis in children discharged following an admission secondary to malaria, which showed no significant benefit on post-discharge mortality.ConclusionsThe months following hospital discharge carry significant risk for morbidity and mortality. While several characteristics are strongly associated with post-discharge mortality, no validated tools are available to aid health workers or policy makers in the systematic identification of children at high risk of post-discharge mortality. Future research must focus on both the creation of tools to aid in defining groups of children most likely to benefit from post-discharge interventions, and formal assessment of the effectiveness of such interventions in reducing morbidity and mortality in the first few months following hospital discharge.