Abstract Background In two large studies conducted in Bangladesh, our recently developed artificial intelligence (AI)-based models for assessing dehydration severity in children under five years (DHAKA models) and patients over age five (NIRUDAK models) were significantly more accurate and reliable than the WHO IMCI and IMAI guidelines for diarrhea management. We incorporated these models into a novel mobile health (mHealth) clinical decision support tool (CDST), called “FluidCalc”, with the potential to improve acute diarrhea management by frontline health workers worldwide. Our objective was to assess the barriers and facilitators to uptake and use of our mHealth CDST in both a low-resource setting (Tanzania) and high-resource setting (United States (US)) among healthcare providers and stakeholders. Methods Qualitative data were collected through focus group discussions (FGDs) with healthcare providers and in-depth interviews (IDIs) with stakeholders and policymakers from February - July 2025 in Tanzania and February - March 2026 in the US. The Consolidated Framework for Implementation Research (CFIR) was used to guide discussions and elicit participant feedback. Audio recordings were transcribed and translated from Swahili to English where applicable, and data were analyzed using framework matrix analysis. Results 35 providers from different cadres participated in FGDs, and 13 stakeholders participated in IDIs. Facilitators to implementation included FluidCalc’s simplicity, ease of use, and offline functionality. Participants reported that the app could streamline clinical workflows, promote adherence to diarrhea management guidelines, facilitate task shifting, support antibiotic stewardship, and reduce errors in fluid rehydration calculations. FluidCalc was also viewed as a valuable teaching tool, and for supporting less experienced healthcare providers and trainees, and as useful during diarrheal disease outbreaks. Perceived barriers included the need for reliable digital infrastructure, including access to mobile devices, internet connectivity, and dependable electricity and lengthy institutional approval processes. Endorsement and approval from the Ministry of Health and health facility leadership were perceived as essential for successful implementation. Conclusion Healthcare providers and stakeholders believe FluidCalc has the potential to improve care for patients with acute diarrhea in both high- and low-resource settings. Addressing identified barriers and ensuring reliable digital health infrastructure are needed to support effective integration into patient care. Contributions to literature FluidCalc is a clinical decision support tool (CDST) for managing acute diarrhea. Few implementation studies have examined CDSTs for diarrhea management across both high-resource and low-resource settings. This study provides comparative insights into factors influencing implementation in two distinct health system contexts. Beyond technical performance and usefulness, CDST uptake depends on clinicians’ perceptions of the tool’s impact on workflow, workload, clinical autonomy, and compatibility with existing health systems. Additionally, organizational context shapes perceptions of digital innovations, helping explain why a tool may be perceived as an opportunity to improve care or as an added implementation burden.

BackgroundThe 2018-2020 Ebola Virus Disease (EVD) outbreak in Eastern Democratic Republic of the Congo (DRC) occurred amid armed conflict, institutional mistrust, and fragile health systems. The Ebola vaccine was deployed under emergency pre-licensure use, and concerns about it persisted. This study explored community and healthcare worker (HCW) perceptions of the Ebola vaccine to better understand the sociocultural and structural drivers of vaccine acceptance.MethodsWe conducted a qualitative study in three heavily affected health zones in North Kivu province (Beni, Butembo, and Mabalako) in 2021. Data were collected through thirty-three focus group discussions and 15 key informant interviews with EVD survivors, community members, HCWs, and local leaders, purposively sampled to capture diverse perspectives. Transcripts were analyzed using thematic and content analysis.ResultsParticipants reported concerns about the safety of the vaccine, mistrust in the institutions delivering it, and confusion due to rumors and inconsistent communication from the Ebola response. HCWs reported feeling coerced into vaccination rather than making a voluntary choice. Misinformation, logistical barriers, and perceptions of favoritism and stigmatization linked to ring vaccination were cited as preventing acceptance. Religion played a dual role, both fostering skepticism and encouraging acceptance depending on the stance of local faith leaders. Participants emphasized the need for transparent and balanced communication, equitable access, and greater involvement of trusted and competent community figures in vaccination efforts.ConclusionsEbola vaccine decision-making in Eastern DRC was shaped by complex interactions between institutional mistrust, perceived risk, religion, and access constraints within a broader context of sociopolitical instability. This study provides a critical baseline of perceptions during the vaccine's pre-licensure phase and highlights the importance of locally grounded engagement strategies. As vaccines become licensed, understanding local perceptions as well as leveraging the influence of trusted religious and community leaders will be essential for improving vaccine uptake.

Background/Objective:Pediatric sepsis remains a leading cause of child mortality worldwide. Early risk stratification using organ dysfunction scores may improve management. Bangladesh bears a high burden of pediatric infectious disease and sepsis, particularly in children under 5. This study compared the predictive performance of admission PELOD-2, pSOFA, and Phoenix Sepsis Scores for in-hospital mortality among pediatric sepsis patients. Methods:We conducted a secondary analysis of an observational study at a tertiary hospital in Dhaka, Bangladesh (February-December 2022). Of 100 enrolled children aged 2 months to 18 years with sepsis, 96 had sufficient data for score calculation. Results:Median age was 8 months (IQR 5-18), and in-hospital mortality was 23%. Admission PELOD-2 >3 and pSOFA >6 were associated with increased mortality. AUCs for mortality were 0.83 (pSOFA), 0.82 (PELOD-2), and 0.79 (Phoenix). Conclusions:All 3 scores showed good mortality discrimination. pSOFA and Phoenix may be more feasible in low-resource settings.

Polio is targeted for global eradication by 2029. Immunization campaigns face significant challenges in humanitarian emergencies due to instability, population displacement, and health infrastructure collapse. The two primary vaccines used, Inactivated Polio Vaccine (IPV) and Oral Polio Vaccine (OPV), have distinct delivery requirements. This scoping review maps key implementation barriers for each delivery mechanism in disaster settings and assesses reported coverage outcomes to inform vaccine selection in emergencies. Following the PRISMA extension for Scoping Reviews (PRISMA-ScR), we searched PubMed, EMBASE, Web of Science, and Cochrane Library (CENTRAL), supplemented by a targeted grey-literature search. Studies were included if they focused on OPV and/or IPV campaigns in humanitarian crises, provided data on coverage or logistics, and were published in English within the last 20 years. Included studies were appraised using JBI Critical Appraisal Tools. The search identified 1604 studies, of which 24 met inclusion criteria. Key IPV implementation barriers were higher operational costs, the requirement for skilled health workers, reliance on fixed-post delivery, and complex cold chain requirements. Prominent OPV barriers included bans on campaigns by armed groups, misinformation leading to vaccine refusal, and restricted access to populations due to insecurity. Three studies reported high IPV coverage (80

Background Laboratory biomarkers that predict progression from mild COVID-19 to hospitalization are not well defined. Objective To evaluate the performance of C-reactive protein (CRP) and plasma SARS-CoV-2 RNA for predicting hospitalization among mild COVID-19 outpatients enrolled in a randomized controlled trial of COVID-19 convalescent plasma (CCP). Methods CRP and plasma SARS-CoV-2 RNA were measured in 487 outpatient trial participants screened before March 2021. Weighted analyses accounting for sampling probabilities were used to estimate positive predictive values (PPVs) and sensitivities for progression to hospitalization, with performance compared across trial arms. Results Elevated CRP (>1 mg/dL) was present in 27% (131/487) of participants, and plasma SARS-CoV-2 RNA was detectable in 15% (74/487). In weighted analyses, PPVs for hospitalization were 15% (95% CI, 10–21) for elevated CRP alone, 25% (95% CI, 16–35) for detectable plasma RNA alone, and 37% (95% CI, 23–52) when both biomarkers were present. Among participants with elevated CRP and plasma RNA >500 copies/mL, PPV increased to 72% (95% CI, 37–100). Elevated CRP demonstrated 100% sensitivity for hospitalization in CCP-transfused participants and 54% in control plasma–transfused participants, while detectable plasma RNA demonstrated sensitivities of 60% and 50%, respectively. Among individuals meeting severe COVID-19 risk criteria based on sex, age, obesity, or comorbidities, the combination of normal CRP and undetectable plasma RNA have a high negative predictive value for hospital progression risk. Conclusions The combination of elevated CRP and detectable plasma SARS-CoV-2 RNA improves laboratory-based risk stratification for progression to hospitalization among outpatients with mild COVID-19.

Sepsis, defined as life-threatening acute organ dysfunction due to infection, is generally considered a hospital-based issue. However, sepsis usually begins in the community, where knowledge of sepsis is scarce, diagnosis is difficult, and resources vary. Community-based interventions might offer the best opportunity for prevention, prompt diagnosis, and improved outcomes. In this Viewpoint, we address current gaps and limitations in understanding of sepsis in the community and outline research priorities, clinical priorities, and existing initiatives across four domains: mitigation (ie, reduction of population-based sepsis risk), monitoring (ie, screening for sepsis in individuals at high risk of sepsis), measurement (ie, identification of sepsis in the community), and management (ie, treatment of sepsis in the community). We propose a pathway to improve the care of individuals with, or at risk of, sepsis in the community and delineate the next steps to advance the field.

Diagnostic models are typically evaluated by assessing their calibration and discrimination; however, neither criterion assesses the practical consequences of using a model. Decision Curve Analysis (DCA) is a method for measuring clinical utility for binary outcome models over a range of risk thresholds. While the utility of polytomous outcome models can be assessed by applying DCA to different dichotomizations of their categories, no method exists to synthesize the binary measures into a single value. This paper illustrates DCA for polytomous outcomes and extends its concepts to develop a summary utility measure for polytomous outcome models. We apply this method to three ordinal logistic regression models, including the NIRUDAK and DHAKA models for predicting dehydration severity in patients over and under five years of age, respectively. Combining the concepts of Standardized Net Benefit (sNB) and Weighted Area Under the Net Benefit Curve, we propose the Weighted Area Under the sNB Curve w A U C s N B , which can be determined for every dichotomization of a polytomous outcome. Next, we propose an average of w A U C s N B s weighted by the relative clinical importance of each dichotomized outcome. We term these weights importance weights and define this new measure as the Integrated Weighted Area Under the sNB Curve I w A U C s N B . We apply binary DCA to the dehydration models, discuss its limitations, and apply the Integrated w A U C s N B to evaluate the average utility of each model. Finally, we compare these models to criteria from the World Health Organization (WHO) and observe how the results vary for different distributional assumptions of the risk thresholds. Applied to the NIRUDAK, DHAKA, and WHO models, the Integrated w A U C s N B demonstrated that both the DHAKA and NIRUDAK models could classify individuals as benefiting from treatment better than the WHO algorithms and either of the reference strategies of treating everyone or no one.

IntroductionMilitaries and police forces have been increasingly deployed in response to humanitarian crises and public health emergencies. Existing studies have largely been concentrated around international interventions, overlooking US domestic contexts and the perceptions of those receiving aid.MethodsIn recognition of these gaps, this research involved a survey of 1,500 Americans to understand opinions toward the utilization of the US military and local law enforcement as COVID-19 domestic pandemic responders at an unprecedented scale.ResultsA majority were complimentary of and comfortable with these armed actors' role in the response and supportive of involvement in future crises, with the military regarded more favorably than police. Trust in civilians, the military, and police is found to be role-based; favorability was inherently tied to the nature of services provided, whether healthcare, logistics, or enforcement-related. Perceptions were also strongly linked to one's vaccination status, political party affiliation, ideology, age, and gender. Underlying trust in civilian providers was evident, but often did not preclude one from favorable views of the military and law enforcement.ConclusionUltimately, these results have implications on domestic policy in future national crisis scenarios and highlight the need for further research exploring if sentiment holds steady beyond the realm of public health and pandemics.

OBJECTIVES:Our primary objectives were to describe pathogen-specific symptom severity and duration in a prospective cohort study of children with acute gastroenteritis (AGE). Our secondary objective was to quantify health care resource utilization. METHODS:This secondary analysis of 2 trials included children aged 3 to 48 months with AGE. Children were eligible if they had ≥3 watery stools in the preceding 24 hours and were brought to the Emergency Department. Disease severity was quantified by frequency and duration of vomiting and diarrhea, and the Modified Vesikari Scale score. We used descriptive statistics to summarize severity and regression models to identify associations between pathogen type and outcomes. RESULTS:In total, 1565 trial participants had pathogen testing performed and completed follow-up. Viral pathogens were identified in 47.9% (749/1565) and bacterial pathogens in 5.9% (92/1565). Norovirus (322/1565; 20.6%) was the most frequently identified pathogen. Diarrhea duration was greatest (median 160h, IQR: 98, 216) for children with Salmonella . Vomiting (aOR: 11.02; 95% CI: 7.47, 16.26) occurred more commonly in children with viruses compared with bacteria. The mean duration of diarrhea was shorter for viruses compared with bacteria (aIRR: 0.81, 95% CI: 0.68, 0.96). Mean MVS scores were higher in children with viruses compared with those with bacteria (coefficient: 1.64, 95% CI: 0.46, 2.82). CONCLUSIONS:We describe the clinical course of viral and bacterial pathogens. Although statistically significant, differences in symptom severity across pathogens were not clinically meaningful for distinguishing between them based on symptoms alone.

Background The 2013-2016 West African Ebola Virus Disease (EVD) outbreak resulted in 28,600 cases and 11,300 deaths officially reported to the World Health Organization. Previous studies investigating factors associated with death had conflicting findings, interventions showing promising outcomes had small sample sizes, studies were often single- or dual-country based and most focused on laboratory-confirmed EVD and not on clinically-suspected EVD. We used the Ebola data platform of the Infectious Disease Data Observatory (IDDO) to review individual patient records to assess factors associated with death, and particularly whether there were differences between laboratory-confirmed and clinically-suspected cases. Methods This was a cohort study involving analysis of secondary data in the IDDO database. The study population included all patients classified as having either clinically-suspected or laboratory-confirmed EVD, admitted to 22 Ebola Treatment Units (ETU) in Guinea, Liberia and Sierra Leone between December 2013 and March 2016. Baseline characteristics and treatments were documented along with ETU exit outcomes. Factors associated with death were investigated by multivariable modified Poisson regression. Results There were 14,163 patients, of whom 6,208 (43.8%) were laboratory-confirmed and 7,955 (56.2%) were clinically-suspected. Outcomes were not recorded in 2,889 (20.4%) patients. Of the 11,274 patients with known outcomes, 4,090 (36.3%) died: 2,956 (43.6%) with laboratory-confirmed EVD and 1,134 (18.8%) with clinically-suspected EVD. The strongest risk factor for death was confirmed disease status. Patients with laboratory-confirmed disease had 2.9 times higher risk of death compared to clinically-suspected patients, after adjusting for other co-variables. Other factors significantly associated with death included a higher risk for patients aged ≥60 years and a lower risk for patients in Sierra Leone. Conclusions Although laboratory-confirmed patients admitted to ETUs fared worse than clinically-suspected patients, the latter still had a substantial risk of death and more attention needs to be paid to this group in future EVD outbreaks.

BACKGROUND:Sepsis, a life-threatening condition resulting from a dysregulated immune response to infection, disproportionately affects children in low- and middle-income countries (LMICs). Children with sepsis in LMICs face high mortality rates, with early detection and clinical monitoring posing significant challenges to effective management. There is great potential for digital technologies, such as wearable biosensor devices and mobile health (mHealth) clinical decision support (CDS) tools, together referred to as clinical decision support systems (CDSSs), to enable closer monitoring and more prompt recognition of children at risk of advanced sepsis and death. However, little is known about the perceptions of health care providers (HCPs) regarding the introduction of new digital health tools for pediatric sepsis care in LMICs. OBJECTIVE:The objective of this study was to assess HCPs' understanding, perceptions, and recommendations regarding the design and implementation of digital CDSSs for pediatric sepsis care in Bangladesh. METHODS:Between February and May 2024, 18 individual semistructured in-depth interviews were conducted with HCPs (nurses and physicians) at 3 urban hospitals in Bangladesh. The data were transcribed, translated from Bangla to English, and analyzed using a framework matrix analysis approach. Participants were asked about familiarity with digital health tools, feedback on CDSS design, perceptions of the system's utility, and barriers and facilitators to use of similar tools in clinical settings in Bangladesh. RESULTS:Participants reported overall positive perceptions toward the potential implementation of a CDSS for pediatric sepsis care in Bangladesh. Some key priorities for the design of a CDSS were durability, reusability, cost considerations, reliability, and accuracy. Clinicians desired the CDS tool to also have customizable alarm parameters and include additional functions such as glucose monitoring. Many favored audio (ringtone) or visual (light) alarms to alert about changes in captured vital signs. HCPs believed that a CDSS could enhance patient care by allowing greater staff capacity to monitor patients, reducing management time, and aiding in faster clinical decision-making, with some suggesting it could lower mortality rates. Concerns regarding implementation included internet availability, affordability of the wearable devices, and trust in the CDSS outputs compared to expert clinician judgement. CONCLUSIONS:The findings of this study highlight HCPs' perceptions toward the potential of wearable biosensor devices and CDS tools (CDSSs) for improving pediatric sepsis outcomes in LMICs and highlight the need to address implementation challenges to ensure the effective integration of CDSSs into health care systems.

Background The 2013-2016 West African Ebola Virus Disease (EVD) outbreak resulted in 28,600 cases and 11,300 deaths officially reported to the World Health Organization. Previous studies investigating factors associated with death had conflicting findings, interventions showing promising outcomes had small sample sizes, studies were often single- or dual-country based and most focused on laboratory-confirmed EVD and not on clinically-suspected EVD. We used the Ebola data platform of the Infectious Disease Data Observatory (IDDO) to review individual patient records to assess factors associated with death, and particularly whether there were differences between laboratory-confirmed and clinically-suspected cases. Methods This was a cohort study involving analysis of secondary data in the IDDO database. The study population included all patients classified as having either clinically-suspected or laboratory-confirmed EVD, admitted to 22 Ebola Treatment Units (ETU) in Guinea, Liberia and Sierra Leone between December 2013 and March 2016. Baseline characteristics and treatments were documented along with ETU exit outcomes. Factors associated with death were investigated by multivariable modified Poisson regression. Results There were 14,163 patients, of whom 6,208 (43.8%) were laboratory-confirmed and 7,955 (56.2%) were clinically-suspected. Outcomes were not recorded in 2,889 (20.4%) patients. Of the 11,274 patients with known outcomes, 4,090 (36.3%) died: 2,956 (43.6%) with laboratory-confirmed EVD and 1,134 (18.8%) with clinically-suspected EVD. The strongest risk factor for death was confirmed disease status. Patients with laboratory-confirmed disease had 2.9 times higher risk of death compared to clinically-suspected patients, after adjusting for other co-variables. Other factors significantly associated with death included a higher risk for patients aged ≥60 years and a lower risk for patients in Sierra Leone. Conclusions Although laboratory-confirmed patients admitted to ETUs fared worse than clinically-suspected patients, the latter still had a substantial risk of death and more attention needs to be paid to this group in future EVD outbreaks.

Many comparisons of statistical regression and machine learning algorithms to build clinical predictive models use inadequate methods to build regression models and do not have proper independent test sets on which to externally validate the models. Proper comparisons for models of ordinal categorical outcomes do not exist. We set out to compare model discrimination for four regression and machine learning methods in a case study predicting the ordinal outcome of severe, some, or no dehydration among patients with acute diarrhea presenting to a large medical center in Bangladesh using data from the NIRUDAK study derivation and validation cohorts. Proportional Odds Logistic Regression (POLR), penalized ordinal regression (RIDGE), classification trees (CART), and random forest (RF) models were built to predict dehydration severity and compared using three ordinal discrimination indices: ordinal c-index (ORC), generalized c-index (GC), and average dichotomous c-index (ADC). Performance was evaluated on models developed on the training data, on the same models applied to an external test set and through internal validation with three bootstrap algorithms to correct for overoptimism. RF had superior discrimination on the original training data set, but its performance was more similar to the other three methods after internal validation using the bootstrap. Performance for all models was lower on the prospective test dataset, with particularly large reduction for RF and RIDGE. POLR had the best performance in the test dataset and was also most efficient, with the smallest final model size. Clinical prediction models for ordinal outcomes, just like those for binary and continuous outcomes, need to be prospectively validated on external test sets if possible because internal validation may give a too optimistic picture of model performance. Regression methods can perform as well as more automated machine learning methods if constructed with attention to potential nonlinear associations. Because regression models are often more interpretable clinically, their use should be encouraged.

Introduction: In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety. Methods: A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites. Results: We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites. Conclusion: The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.

Background:Although multiple prognostic models exist for Ebola virus disease mortality, few incorporate biomarkers, and none has used longitudinal point-of-care serum testing throughout Ebola treatment center care. Methods:This retrospective study evaluated adult patients with Ebola virus disease during the 10th outbreak in the Democratic Republic of Congo. Ebola virus cycle threshold (Ct; based on reverse transcriptase polymerase chain reaction) and point-of-care serum biomarker values were collected throughout Ebola treatment center care. Four iterative machine learning models were created for prognosis of mortality. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days 1 and 2, days 3 and 4, and days 5 and 6 associated with mortality were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided period-specific areas under curve with 95% CIs. Results:Of 310 cases positive for Ebola virus disease, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium; low albumin during days 1 and 2; elevated C-reactive protein, BUN, and potassium during days 3 and 4; and elevated C-reactive protein and BUN during days 5 and 6. The area under curve substantially improved with each iteration: base model, 0.74 (95% CI, .69-.80); days 1 and 2, 0.84 (95% CI, .73-.94); days 3 and 4, 0.94 (95% CI, .88-1.0); and days 5 and 6, 0.96 (95% CI, .90-1.0). Conclusions:This is the first study to utilize iterative point-of-care biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to 6 days into patient care.

The tenth Ebola Virus Disease (EVD) outbreak (2018-2020, North Kivu, Ituri, South Kivu) in the Democratic Republic of the Congo (DRC) was the second-largest EVD outbreak in history. During this outbreak, Ebola vaccination was an integral part of the EVD response. We evaluated community perceptions toward Ebola vaccination and identified correlates of Ebola vaccine uptake among high-risk community members in North Kivu, DRC. In March 2021, a cross-sectional survey among adults was implemented in three health zones. We employed a sampling approach mimicking ring vaccination, targeting EVD survivors, their household members, and their neighbors. Outbreak experiences and perceptions toward the Ebola vaccine were assessed, and modified Poisson regression was used to identify correlates of Ebola vaccine uptake among those offered vaccination. Among the 631 individuals surveyed, most (90.2%) reported a high perceived risk of EVD and 71.6% believed that the vaccine could reduce EVD severity; however, 63.7% believed the vaccine had serious side effects. Among the 474 individuals who had been offered vaccination, 397 (83.8%) received the vaccine, 180 (45.3%) of those vaccinated received the vaccine after two or more offers. Correlates positively associated with vaccine uptake included having heard positive information about the vaccine (RR 1.30, 95% CI 1.06-1.60), the belief that the vaccine could prevent EVD (RR 1.23, 95% CI 1.09-1.39), and reporting that religion influenced all decisions (RR 1.13, 95% CI 1.02-1.25). Ebola vaccine uptake was high in this population, although mixed attitudes and vaccine delays were common. Communicating positive vaccine information, emphasizing the efficacy of the Ebola vaccine, and engaging religious leaders to promote vaccination may aid in increasing Ebola vaccine uptake during future outbreaks.

Background Sepsis is a leading cause of paediatric mortality worldwide, disproportionately affecting children in low- and middle-income countries. The impacts of climate change on the burden and outcomes of sepsis in low- and middle-income countries, particularly in paediatric populations, remain poorly understood. We aimed to assess the associations between climate variables (temperature and precipitation) and paediatric sepsis incidence and mortality in Bangladesh, one of the countries most affected by climate change. Methods We conducted retrospective analyses of patient-level data from the International Centre for Diarrhoeal Disease Research, Bangladesh, and environmental data from the National Oceanic and Atmospheric Administration. Using random forests, we assessed associations between sepsis incidence and sepsis mortality with temperature and precipitation between 2009-22. Results A nonlinear relationship between temperature and sepsis incidence and mortality was identified. The lowest incidence occurred at an optimum temperature of 26.6 degrees C with a gradual increase below and a sharp rise above this temperature. Higher precipitation levels showed a general trend of increased sepsis incidence. A similar distribution for sepsis mortality was identified with an optimum temperature of 28 degrees C. Conclusions Findings suggest that environmental temperature and precipitation play a role in paediatric sepsis incidence and sepsis mortality in Bangladesh. As children are particularly vulnerable to climate impacts, it is important to consider climate change in health care planning and resource allocation, especially in resource-limited settings, to allow for surge capacity planning during warmer and wetter seasons. Further prospective research from more globally representative data sets will provide more robust evidence on the nature of the relationships between climate variables and paediatric sepsis worldwide.

Background. Severe dehydration due to acute infectious diarrhea remains a leading cause of death among young children worldwide. Diarrhea with severe dehydration is a clinical syndrome with distinct management per the World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) and the WHO Global Task Force on Cholera Control (GTFCC) guidelines. We sought to characterize the pathogens causing severe dehydration using data from the Global Enteric Multicenter Study. Methods. We used the IMCI and GTFCC guidelines to define severe dehydration and quantitative polymerase chain reaction- based attribution models to assign the etiology of diarrhea associated with severe dehydration. Results. The IMCI or GTFCC guidelines classified 2284 of the 5304 (43%) cases with moderate-to-severe diarrhea as having severe dehydration. In one-third of the cases with severe dehydration, no pathogens were attributed. The top pathogens attributed to children with guidelines-classified severe dehydration varied by age and were similar among those requiring intravenous hydration and hospitalization. Rotavirus (30.9%), Cryptosporidium (12.0%), and heat-stable (ST) enterotoxigenic Escherichia coli (ETEC) (10.3%) were the most common pathogens for ages 0-11 months, while Shigella /enteroinvasive E coli (EIEC) (25.8%), rotavirus (19.3%), and ST-ETEC (10.9%) were the most common for ages 12-23 months. Shigella/EIEC (25.9%), Vibrio cholerae (10.4%), and rotavirus (9.2%) were the most common among ages 24-59 months. Conclusions. The findings inform prioritization of pathogens, in addition to V cholerae, that cause severe dehydration for future preventive and treatment efforts. The schema for prioritization is driven primarily by age stratifications.

BACKGROUNDCOVID-19 convalescent plasma (CCP) virus-specific antibody levels that translate into recipient posttransfusion antibody levels sufficient to prevent disease progression are not defined.METHODSThis secondary analysis correlated donor and recipient antibody levels to hospitalization risk among unvaccinated, seronegative CCP recipients within the outpatient, double-blind, randomized clinical trial that compared CCP to control plasma. The majority of COVID-19 CCP arm hospitalizations (15/17, 88%) occurred in this unvaccinated, seronegative subgroup. A functional cutoff to delineate recipient high versus low posttransfusion antibody levels was established by 2 methods: (i) analyzing virus neutralization-equivalent anti-Spike receptor-binding domain immunoglobulin G (anti-S-RBD IgG) responses in donors or (ii) receiver operating characteristic (ROC) curve analysis.RESULTSSARS-CoV-2 anti-S-RBD IgG antibody was volume diluted 21.3-fold into posttransfusion seronegative recipients from matched donor units. Virus-specific antibody delivered was approximately 1.2 mg. The high-antibody recipients transfused early (symptom onset within 5 days) had no hospitalizations. A CCP-recipient analysis for antibody thresholds correlated to reduced hospitalizations found a statistical significant association between early transfusion and high antibodies versus all other CCP recipients (or control plasma), with antibody cutoffs established by both methods-donor-based virus neutralization cutoffs in posttransfusion recipients (0/85 [0%] versus 15/276 [5.6%]; P = 0.03) or ROC-based cutoff (0/94 [0%] versus 15/267 [5.4%]; P = 0.01).CONCLUSIONIn unvaccinated, seronegative CCP recipients, early transfusion of plasma units in the upper 30% of study donors' antibody levels reduced outpatient hospitalizations. High antibody level plasma units, given early, should be reserved for therapeutic use.TRIAL REGISTRATIONClinicalTrials.gov NCT04373460.FUNDINGDepartment of Defense (W911QY2090012); Defense Health Agency; Bloomberg Philanthropies; the State of Maryland; NIH (3R01AI152078-01S1, U24TR001609-S3, 1K23HL151826NIH); the Mental Wellness Foundation; the Moriah Fund; Octapharma; the Healthnetwork Foundation; the Shear Family Foundation; the NorthShore Research Institute; and the Rice Foundation.