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Diarrheal diseases lead to an estimated 1.3 million deaths each year, with the majority of those deaths occurring in patients over five years of age. As the severity of diarrheal disease can vary widely, accurately assessing dehydration status remains the most critical step in acute diarrhea management. The objective of this study is to empirically derive clinical diagnostic models for assessing dehydration severity in patients over five years with acute diarrhea in low resource settings. We enrolled a random sample of patients over five years with acute diarrhea presenting to the icddr,b Dhaka Hospital. Two blinded nurses independently assessed patients for symptoms/signs of dehydration on arrival. Afterward, consecutive weights were obtained to determine the percent weight change with rehydration, our criterion standard for dehydration severity. Full and simplified ordinal logistic regression models were derived to predict the outcome of none (<3%), some (3–9%), or severe (>9%) dehydration. The reliability and accuracy of each model were assessed. Bootstrapping was used to correct for over-optimism and compare each model’s performance to the current World Health Organization (WHO) algorithm. 2,172 patients were enrolled, of which 2,139 (98.5%) had complete data for analysis. The Inter-Class Correlation Coefficient (reliability) was 0.90 (95% CI = 0.87, 0.91) for the full model and 0.82 (95% CI = 0.77, 0.86) for the simplified model. The area under the Receiver-Operator Characteristic curve (accuracy) for severe dehydration was 0.79 (95% CI: 0.76–0.82) for the full model and 0.73 (95% CI: 0.70, 0.76) for the simplified model. The accuracy for both the full and simplified models were significantly better than the WHO algorithm (p<0.001). This is the first study to empirically derive clinical diagnostic models for dehydration severity in patients over five years. Once prospectively validated, the models may improve management of patients with acute diarrhea in low resource settings.

Background North Kivu, in eastern Democratic Republic of the Congo (DRC), was the epicenter of the second largest Ebola Virus Disease (EVD) outbreak in history and has been an active conflict zone for decades. The COVID-19 pandemic further exacerbated an already complex situation. This qualitative study, conducted as part of a broader investigation into Ebola vaccine hesitancy and at the onset of COVID-19 vaccination rollout in the DRC, assessed perceptions, beliefs, and attitudes toward the COVID-19 virus and vaccines among community members and healthcare workers who had experienced the 2018–2020 EVD outbreak, during which a novel vaccine was also administered. Methods Between May and June 2021, thirty-three focus group discussions (FGDs) and fifteen key informant interviews were conducted across three health zones that had offered Ebola vaccination (rVSV-ZEBOV) during the 2018–2020 EVD outbreak. Participants included healthcare workers (HCWs) and community members, as well as leaders, administrative officials, and frontline workers who had supported outbreak response and vaccination services. Results Across all locations and groups, perceived risk of COVID-19 was low, with many participants doubting its existence and relevance, particularly when compared to other needs and endemic health conditions in their communities. COVID-19 was believed to primarily affect white, affluent individuals in urban areas, and not those residing in hot climates. Vaccine hesitancy was driven by perceived insufficient transparency and communication regarding COVID-19 vaccines, including questions about their composition, potential side effects, and rationale behind prioritizing certain groups. COVID-19 vaccine perceptions also reflected broader concerns, including mistrust of the government and rumors related to past Ebola outbreaks and vaccination efforts. To increase uptake, respondents recommended ensuring COVID-19 vaccination is voluntary and free of charge, working with non-traditional local demand generation and vaccination actors, strengthening local vaccine production capacity, and increasing the impact of interpersonal communication through social media messaging. Conclusions These findings add to growing evidence on how prior exposure to another disease of international concern (Ebola) and the associated vaccine, as well as historical context, influence perceptions of novel vaccines. Community engagement and tailored communication to address community concerns and misinformation are essential to building trust. Policymakers and implementers should consider more innovative approaches for developing and implementing vaccination policies in collaboration with local actors.

Objective: Framework Analysis (FA) and Applied Thematic Analysis (ATA) are qualitative methods that have not been as widely used/cited compared to content analysis or grounded theory. This paper compares methods of FA with ATA for mobile health (mHealth) research. The same qualitative data were analyzed separately using each methodology. The methods, utility, and results of each are compared, and recommendations made for their effective use. Methods: Formative qualitative data were collected in eight focus group discussions with physicians and nurses from three hospitals in Bangladesh. Focus groups were conducted via video conference in the local language, Bangla, and audio recorded. Audio recordings were used to complete a FA of participants’ opinions about key features of novel mHealth application (app) designed to support clinical management in patients with acute diarrhea, called FluidCalc: Rehydration Calculator for Acute Diarrhea. The resulting framework analysis was shared with the app design team and used to guide iterative development of the product for a validation study of the app. Subsequently, focus group audio recordings were transcribed in Bangla then translated into English for ATA; transcripts and codes were entered into NVivo qualitative analysis software. Code summaries and thematic memos explored the clinical utility of FluidCalc including clinicians’ attitudes about using this decision support tool. Results: Each of the two methods contributes differently to the research goal and have different implications for an mHealth research timeline. Recommendations for the effective use of each method in app development include: using FA for data reduction where specific outcomes are needed to make programming and design decisions and using ATA to capture the more nuanced issues that guide use, product implementation, training, and workflow. Conclusions: By describing how both analytical methods were used in this context, this paper provides guidance and an illustration for use of these two methods, specifically in mHealth design.

Background A myriad of factors including socio-economic hardships impact refugees, with females being additionally exposed to various forms of sexual and gender-based violence (SGBV). The aim of this qualitative analysis was to understand and to provide new insight into the experiences of SGBV among Syrian refugee women and girls in Lebanon. Methods The data are gained from a larger mixed-methods study, investigating the experiences of Syrian refugee girls in Lebanon, using an iPad and the data collection tool, SenseMaker®. The SenseMaker survey intentionally did not ask direct questions about experiences of SGBV but instead enabled stories about SGBV to become apparent from a wide range of experiences in the daily lives of Syrian girls. For this analysis, all first-person stories by female respondents about experiences of SGBV were included in a thematic analysis as well as a random selection of male respondents who provided stories about the experiences of Syrian girls in Lebanon. Results In total, 70 of the 327 first person stories from female respondents and 42 of the 159 stories shared by male respondents included dialogue on SGBV. While experiences of sexual harassment were mainly reported by women and girls, male respondents were much more likely to talk explicitly about sexual exploitation. Due to different forms of SGBV risks in public, unmarried girls were at high risk of child marriage, whereas married girls more often experienced some form of IPV and/or DV. In abusive relationships, some girls and women continued to face violence as they sought divorces and attempted to flee unhealthy situations. Conclusions This study contributes to existing literature by examining SGBV risks and experiences for refugees integrated into their host community, and also by incorporating the perceptions of men. Our findings shed light on the importance of recognizing the impact of SGBV on the family as a whole, in addition to each of the individual members and supports considering the cycle of SGBV not only across the woman’s lifespan but also across generations. Gendered differences in how SGBV was discussed may have implications for the design of future research focused on SGBV.

Introduction Disaster and humanitarian responders are at-risk of experiencing a wide range of physical and psychological health conditions, from minor injuries to chronic mental health problems and fatalities. This article reviews the current literature on the major health outcomes of responders to various disasters and conflicts in order to better inform individuals of the risks and to inform deploying agencies of the health care needs of responders. In March 2014, an EMBASE search was conducted using pre-defined search criteria. Two reviewers screened the resultant 2,849 abstracts and the 66 full-length manuscripts which are included in the review. The majority of research on health outcomes of responders focused on mental health (57 of 66 articles). Posttraumatic stress disorder (PTSD) and depression were the most studied diagnoses with prevalence of PTSD ranging from 0%-34% and depression from 21%-53%. Physical health outcomes were much less well-studied and included a wide range of environmental, infectious, and traumatic conditions such as heat stroke, insect bites, dermatologic, gastrointestinal, and respiratory diseases, as well as burns, fractures, falls, and other traumatic injuries. The prevalence of mental health disorders in responders may vary more and be higher than previously suggested. Overall health outcomes of responders are likely poorly monitored and under-reported. Improved surveillance systems and risk mitigation strategies should be employed in all disaster and conflict responses to better protect individual responders. Garbern SC , Ebbeling LG , Bartels SA . A systematic review of health outcomes among disaster and humanitarian responders. Prehosp Disaster Med. 2016;31(6):635-642.

The 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. We 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. Participants 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. Ebola 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.

[This corrects the article DOI: 10.1371/journal.pntd.0009266.].

Diarrhea is one of the most common illnesses among children worldwide. In low- and middle-income countries with limited health care resources, it can be deadly. Diarrhea can be caused by infections with viruses or bacteria. Antibiotics can treat bacterial infections, but they are not effective against viral infections. It can often be difficult to determine the cause of diarrhea. As a result, many clinicians just prescribe antibiotics. However, since diarrhea in young children is often due to viral infections, prescribing unnecessary antibiotics can cause children to have side effects without any benefit. Excessive use of antibiotics also contributes to the development of bacteria that are resistant to antibiotics, making infections harder to treat. Scientists are working to develop mobile health tools or ‘apps’ that may help clinicians identify the cause of diarrhea. Using computer algorithms to analyze information about the patient and seasonal infection patterns, the apps predict whether a bacterial or viral infection is the likely culprit. These tools may be particularly useful in low- or middle-income country settings, where clinicians have limited access to testing for bacteria or viruses. Garbern, Nelson et al. previously built an app to help distinguish cases of viral diarrhea in children in Mali and Bangladesh. Now, the researchers have put their app to the test in the real-world in a new group of patients to verify it works. In the experiments, nurses in Mali and Bangladesh used the app to predict whether a child with diarrhea had a viral or non-viral infection. The children’s stool was then tested for viral or bacterial DNA to confirm whether the prediction was correct. The experiments showed the app accurately identified viral cases of diarrhea. The experiments also showed that customizing the app to local conditions may further improve its accuracy. For example, a version of the app that factored in seasonal virus transmission performed the best in Bangladesh, while a version that factored in data from recent patients in the past few weeks performed the best in Mali. Garbern and Nelson et al. are now testing whether their app could help reduce unnecessary use of antibiotics in children with diarrhea. If it does, it may help minimize antibiotic resistance and ensure more children get appropriate diarrhea care.

Introduction: Acute diarrhea remains a leading cause of morbidity and mortality with over 6.3 billion cases and 1.3 million deaths annually. Despite the existence of standardized guidelines for diarrhea management, wide variability in clinical practice exists, particularly in resource-limited settings. The goal of this study was to qualitatively explore how diarrhea management in Bangladesh varies according to resource availability, clinical setting, and provider roles. Methodology: This was a secondary analysis of a cross-sectional qualitative study conducted in three diverse hospital settings (district hospital, subdistrict hospital, and specialty diarrhea research hospital) in Bangladesh. A total of eight focus group discussions with nurses and physicians were conducted. Applied thematic analysis was used to identify themes regarding variations in diarrhea management. Results: Of the 27 focus group participants, 14 were nurses and 13 doctors; 15 worked in a private diarrhea specialty hospital and 12 worked in government district or subdistrict hospitals. Several key themes emerged from the qualitative data analysis: 1) priorities in the clinical assessment of diarrhea 2) use of guidelines versus clinical judgment; 3) variability in clinician roles and between clinical settings influences care delivery; 4) impact of resource availability on diarrhea management; and 5) perceptions of community health workers’ role in diarrhea management. Conclusions: Findings from this study may aid in informing interventions to improve and standardize diarrhea management in resource-constrained settings. Resource availability, practices regarding diarrhea assessment and treatment, provider experience, and variability in provider roles are essential considerations when developing clinical tools in low- and middle- income countries.

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient’s admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson’s correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models’ detection of advanced sepsis and physicians’ documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson’s r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.