Stephanie Chow Garbern

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.

Background: Sepsis is a leading cause of pediatric mortality worldwide, disproportionately affecting children in low- and middle-income countries (LMICs). However, timely recognition of potential sepsis and access to healthcare resources needed to diagnose pediatric sepsis according to international guidelines are challenging in LMICs. This exploratory study aimed to develop machine learning (ML) models to detect pediatric sepsis and septic shock using a simplified set of clinical data contextualized for practical use in resource-limited settings. Methods: This was a secondary analysis of an observational study of 100 children with potential sepsis admitted to a non-profit referral hospital in Dhaka, Bangladesh. The outcomes were sepsis as defined by a Phoenix Sepsis Score (PSS) ≥ 2 and septic shock (sepsis plus PSS cardiovascular sub-score ≥ 1). Models were trained using either clinical + laboratory variables or clinical-only variables. A single 24 h worst-value assessment window was derived per patient; stratified 5-fold cross-validation was used to maintain class proportions across the training and test folds. Model performance was assessed using area under the precision–recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals (CIs) derived from a 2000-resample patient-level bootstrap of out-of-fold classifications. Logistic regression coefficients were used to assess feature contributions. Results: For sepsis classification, the non-laboratory model achieved an AUPRC of 0.942 (95% CI: 0.884–0.979) and an AUROC of 0.945 (95% CI: 0.890–0.983), with comparable performance from the clinical + laboratory model (AUPRC 0.941, 95% CI: 0.880–0.981; AUROC 0.945, 95% CI: 0.881–0.986). For septic shock, AUROCs of 0.870 (95% CI: 0.761–0.952) and 0.878 (95% CI: 0.758–0.967) were observed. However, these estimates should be interpreted cautiously, given the low prevalence (23%) and absence of external validation. SpO₂:FiO₂ ratio, GCS, and systolic blood pressure were consistently strong predictors across models. Conclusions: ML models using pragmatic clinical variables demonstrate preliminary diagnostic performance, with the non-laboratory model showing discrimination comparable to models incorporating laboratory data. Logistic regression demonstrated the most stable performance and may represent an early proof of concept for assistive diagnostic support. However, these models are not clinically usable without external validation. These findings are hypothesis-generating; external validation in larger, independent cohorts is essential before any clinical use, particularly for septic shock.

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.

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.

COVID-19 vaccine hesitancy in Pakistan is a barrier to optimal vaccine uptake and has been situated within a context of hesitancy towards other vaccines. A mixed-methods study was conducted during the initial COVID-19 vaccine roll-out in 2021 in four union councils in Peshawar, consisting of a cross-sectional survey, eight focus group discussions (FGDs) with community members and eight in-depth interviews with healthcare workers (HCWs) to assess perceptions toward vaccines. Multivariable logistic regression was used to assess factors associated with COVID-19 vaccine hesitancy. Of 400 survey participants, 57.3% were vaccine acceptant and 42.8% vaccine hesitant. Just over half (56.8%) perceived COVID-19 vaccines to be safe. Most (88%) reported trust in HCWs to provide accurate vaccine information. FGDs revealed that women received less information about the vaccine compared to men and cultural restrictions were barriers even for those willing to be vaccinated. Correlates of vaccine acceptance included male sex (aOR 2.25; 95% CI 1.29–3.91), age 50 years or greater (aOR 1.74; 95% CI 1.19–6.31), social network support (e.g., vaccine acceptance among an individual’s social network) in receiving COVID-19 vaccines (aOR 2.38; 95% CI 1.45–3.89), community concern about COVID-19 spread (aOR 2.84; 95% CI 1.73–4.66), and trust in HCWs to provide vaccine information (aOR 3.47; 95% CI 1.62–7.42). Future vaccine promotion should prioritize engaging community leaders, sharing transparent information, combatting misinformation and rumors, and implementing household-based interventions especially targeting the importance of vaccination among women and young people to increase uptake.

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.

Refusals and delay in receiving COVID-19 vaccines due to vaccine hesitancy present major barriers to optimal vaccine uptake and thus epidemic control, with crisis-affected populations facing additional challenges. International Medical Corps conducted a cross-sectional survey to evaluate knowledge, attitudes, and perceptions regarding COVID-19 and identify determinants of COVID-19 vaccine hesitancy among 413 healthcare workers (HCW) working at 90 national primary healthcare centers in Lebanon, just prior to widespread vaccine rollout. Logistic regression was used to assess predictors of vaccine acceptance using the health belief Model. More than half (56.9%) of HCWs were categorized as being vaccine-hesitant, while 43.1% reported they would receive a vaccine when available. Perceived benefits, cues to action, and social norms were positively associated with vaccine acceptance. More than half (56.4%) believed the national health system was not capable of ensuring safe administration of the COVID-19 vaccine. Targeted interventions that emphasize the benefits of COVID-19 vaccination, highlight cues to vaccinate, and promote vaccination as a social norm are critical in motivating HCWs to get vaccinated. Increasing HCW trust in Lebanon’s national health system is critical to building confidence toward COVID-19 vaccines and has implications for the success of future responses to infectious disease outbreaks.

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.

Sepsis is the leading cause of child mortality worldwide and is responsible for over 60% of all paediatric deaths [1]. It is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. It encompasses a continuum that ranges from sepsis to severe sepsis, septic shock, multiple organ dysfunction syndrome and death [1–7]. […]

In high-income nations, gender has been associated with injury characteristics. This study evaluated injury epidemiology and care based on gender at the Centre Hospitalier Universitaire de Kigali in Rwanda. Patients presenting to the emergency department with acute injuries were prospectively enrolled from 27 January–28 June 2020, and descriptive statistics were performed with comparisons between males and females. Of 601 patients, 25.6% were female and 74.4% were male. There were gender differences in the mechanism of injury, with females more likely to be injured in falls (43.5% versus 23.0%, p = 0.001); meanwhile, males were more likely to suffer road traffic accidents (52.6% versus 39.6%, p = 0.006). The severity of injury was similar between genders based on the mean Kampala Trauma Score (14.4 versus 14.7, p = 0.09). Females were more likely to have been transported by prehospital services (87.7% versus 72.9%, p = 0.001), and less likely to receive acute treatment during the first six hours of care (67.5% versus 78.1%, p = 0.009). There was no significant difference in mortality between females and males (2.0% versus 1.3%, p = 0.568). This study highlights differences in the epidemiology and care between males and females presenting for emergency injury care in Rwanda. These findings can inform future research and developments in gender-centered healthcare delivery.