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Background Acute gastroenteritis (AGE) causes substantial morbidity and mortality in children < 5 years old accounting for 9 million hospitalizations. Prolonged hospitalization can cause dire consequences to the patient and healthcare system. However, data on factors associated with prolonged hospitalization for AGE in developing countries are limited. Objectives We aim to describe trends and assess factors associated with prolonged hospitalization among children < 5 years admitted with AGE in western Kenya. Methods Children with AGE ( ≥ 3 loose stools and/or ≥ 1 episode of unexplained vomiting with loose stool within 24 h) hospitalized at Siaya County Referral Hospital from January 2010 through December 2020 were included. Prolonged hospitalization was defined as admission for ≥ 5 days. Trends of prolonged AGE hospitalizations were assessed using Cochran–Armitage trend test, while factors associated with prolonged hospitalization for AGE were determined by unconditional logistic regression. Results Of the 12,546 all‐cause admissions among children < 5 years, 2271 (18.1%) children had AGE; 681 (32.8%) had prolonged hospitalization. There was a significant difference in the prevalence of prolonged hospitalization over time, with a peak in 2010 (42.8%) and a low in 2016 (10.8%). Older children (12–23 months: (adjusted odds ratio [aOR]: 0.69; 95% confidence interval [95% CI]: 0.49–0.97)) and those who vomited everything (aOR: 0.69; 95% CI: 0.52–0.90) were less likely to have prolonged hospitalization. Children who had a bulging fontanel (aOR: 3.21; 95% CI: 1.12–9.20) or chest in drawing (aOR: 1.49; 95% CI: 1.02–2.18) or were severely stunted (aOR: 2.67; 95% CI: 1.89–3.79) or severely wasted (aOR: 2.34; 95% CI: 1.65–3.30) were more likely to have prolonged hospitalization. Conclusion Children with severe diarrheal illness with malnutrition are at high risk of prolonged hospitalization. Targeted interventions such as increased clinical and diagnostics monitoring for at‐risk children with AGE may need to be prioritized to reduce possible prolonged hospitalization.

To evaluate factors associated with rotavirus diarrhea and to describe severity of illness among children <5 years old with non-dysenteric, moderate-to-severe diarrhea (MSD) in rural western Kenya. We analyzed data from children <5 years old with non-dysenteric MSD enrolled as cases in the Global Enteric Multicenter Study (GEMS) in Kenya. A non-dysenteric MSD case was defined as a child with ≥3 loose stools in 24 hrs. and one or more of the following: sunken eyes, skin tenting, intravenous rehydration, or hospitalization, who sought care at a sentinel health center within 7 days of illness onset. Rotavirus antigens in stool samples were detected by ELISA. Demographic and clinical information was collected at enrollment and during a single follow-up home visit at approximately 60 days. We analyzed diarrhea severity using a GEMS 17 point numerical scoring system adapted from the Vesikari score. We used logistic regression to evaluate factors associated with rotavirus infection. From January 31, 2008 to September 30, 2012, among 1,637 (92%) non-dysenteric MSD cases, rotavirus was detected in stools of 245 (15.0%). Rotavirus-positive compared with negative cases were: younger (median age, 8 vs. 13 months; p<0.0001), had more severe illness (median severity score, 9 vs 8; p<0.0001) and had to be hospitalized more frequently (37/245 [15.1%] vs. 134/1,392 [9.6%]), p <0.013). Independent factors associated with rotavirus infection included age 0-11 months old (aOR = 5.29, 95% CI 3.14-8.89) and presenting with vomiting ≥3 times/24hrs (aOR = 2.58, 95% CI [1.91-3.48]). Rotavirus was detected more commonly in warm and dry months than in the cool and rainy months (142/691 [20%] vs 70/673 [10%]) p<0.0001). Diarrhea caused by rotavirus is associated with severe symptoms leading to hospitalization. Consistent with other settings, infants had the greatest burden of disease.

Background Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities. Methods LDD was defined as a diarrhea episode lasting ≥ 7 days. We used 7 ML algorithms to build prognostic models for the prediction of LDD among children < 5 years using de-identified data from Vaccine Impact on Diarrhea in Africa study ( N  = 1,482) in model development and data from Enterics for Global Health Shigella study ( N  = 682) in temporal validation of the champion model. Features included demographic, medical history and clinical examination data collected at enrolment in both studies. We conducted split-sampling and employed K-fold cross-validation with over-sampling technique in the model development. Moreover, critical predictors of LDD and their impact on prediction were obtained using an explainable model agnostic approach. The champion model was determined based on the area under the curve (AUC) metric. Model calibrations were assessed using Brier, Spiegelhalter’s z -test and its accompanying p -value. Results There was a significant difference in prevalence of LDD between the development and temporal validation cohorts (478 [32.3%] vs 69 [10.1%]; p  < 0.001). The following variables were associated with LDD in decreasing order: pre-enrolment diarrhea days (55.1%), modified Vesikari score(18.2%), age group (10.7%), vomit days (8.8%), respiratory rate (6.5%), vomiting (6.4%), vomit frequency (6.2%), rotavirus vaccination (6.1%), skin pinch (2.4%) and stool frequency (2.4%). While all models showed good prediction capability, the random forest model achieved the best performance (AUC [95% Confidence Interval]: 83.0 [78.6–87.5] and 71.0 [62.5–79.4]) on the development and temporal validation datasets, respectively. While the random forest model showed slight deviations from perfect calibration, these deviations were not statistically significant (Brier score = 0.17, Spiegelhalter p -value = 0.219). Conclusions Our study suggests ML derived algorithms could be used to rapidly identify children at increased risk of LDD. Integrating ML derived models into clinical decision-making may allow clinicians to target these children with closer observation and enhanced management.

Shigellosis is an ongoing global public health crisis with >270 million annual episodes among all age groups; however, the greatest disease burden is among children in low- and middle-income countries (LMIC). The lack of a licensed Shigella vaccine and the observed rise in antimicrobial-resistant Shigella spp. highlights the urgency for effective preventative and interventional strategies. The inclusion of S. flexneri serotype 6 ( Sf 6) is a necessary component of a multivalent vaccine strategies based on its clinical and epidemiological importance. Given the genomic diversity of Sf 6 compared with other S. flexneri serotypes and Sf 6 unique O-antigen core structure, serotype-specific characterization of Sf 6 is a critical step to inform Shigella -directed vaccine and alternative therapeutic designs. Herein, we identified conserved genomic content among a large collection of temporally and geographically diverse Sf 6 clinical isolates and characterized genotypic and phenotypic properties that separate Sf 6 from non- Sf 6 S. flexneri serotypes.

Introduction Stunting affects one-fifth of children globally with diarrhea accounting for an estimated 13.5% of stunting. Identifying risk factors for its precursor, linear growth faltering (LGF), is critical to designing interventions. Moreover, developing new predictive models for LGF using more recent data offers opportunity to enhance model accuracy, interpretability and capture new insights. We employed machine learning (ML) to derive and validate a predictive model for LGF among children enrolled with diarrhea in the Vaccine Impact on Diarrhea in Africa (VIDA) study and the Enterics for Global Heath (EFGH) ― Shigella study in rural western Kenya. Methods We used 7 diverse ML algorithms to retrospectively build prognostic models for the prediction of LGF (≥ 0.5 decrease in height/length for age z-score [HAZ]) among children 6–35 months. We used de-identified data from the VIDA study ( n  = 1,106) combined with synthetic data ( n  = 8,894) in model development, which entailed split-sampling and K-fold cross-validation with over-sampling technique, and data from EFGH-Shigella study ( n  = 655) for temporal validation. Potential predictors ( n  = 65) included demographic, household-level characteristics, illness history, anthropometric and clinical data were identified using boruta feature selection with an explanatory model analysis used to enhance interpretability. Results The prevalence of LGF in the development and temporal validation cohorts was 187 (16.9%) and 147 (22.4%), respectively. Feature selection identified the following 6 variables used in model development, ranked by importance: age (16.6%), temperature (6.0%), respiratory rate (4.1%), SAM (3.4%), rotavirus vaccination (3.3%), and skin turgor (2.1%). While all models showed good prediction capability, the gradient boosting model achieved the best performance (area under the curve % [95% Confidence Interval]: 83.5 [81.6–85.4] and 65.6 [60.8–70.4]) on the development and temporal validation datasets, respectively. Conclusion Our findings accentuate the enduring relevance of established predictors of LGF whilst demonstrating the practical utility of ML algorithms for rapid identification of at-risk children.

AbstractBackgroundMortality attributed to respiratory illnesses is well characterized in children <5 years. However, there is paucity of data among older populations. Here, we leveraged data from the COVID-19 Vaccine Effectiveness Evaluation to establish the factors associated with mortality among patients with severe respiratory illness (SRI) in Kenya and Mali. MethodsWe enrolled patients (≥ 12 years) requiring hospitalization for SRI, defined as acute onset (≤ 14 days) of at least two of the following: cough, fever (reported/measured temperature of ≥38 °C), chills, rigors, myalgia, headache, sore throat, fatigue, congestion or runny nose, loss of taste or smell, or pneumonia diagnosis, from referral hospitals in Kenya and Mali. We collected demographic, clinical characteristics of the patients, and nasopharyngeal and oropharyngeal specimens for SARS-CoV-2 testing using RT-PCR. A mixed-effects logistic regression model was fitted to identify factors associated with 30-day mortality among patients with SRI. ResultsBetween July 2022 and October 2023 9947 SRI patients were enrolled, of whom 9743 were included in this analysis and 1620 (16.6 %) died (Kenya: 1533/7822 [20.0 %]; Mali: 87/1921 [4.5 %]). Compared to patients aged 12–24 years, those aged >64 years were more likely to die (adjusted Odds Ratio [aOR] = 2.36; 95 % Confidence Interval [95 % CI] 1.72–3.24). Patients who were in coma (aOR = 3.45; 95 %CI 2.27–5.24) or Intensive Care Unit (aOR = 2.98; 95 %CI 2.06–4.31), or had HIV infection (aOR = 2.47; 95 %CI 2.11–2.90), liver disease (aOR = 2.42; 95 %CI 1.57–3.74), cancer (aOR = 2.09; 95 %CI 1.46–2.99) or SARS-CoV-2 infected (aOR = 1.24; 95 %CI 1.02–1.52) were at increased risk of death. Additionally, diarrhea, malaise/fatigue, difficulty in breathing, confusion, mechanical ventilation, vasopressor support, malnutrition and admission to High Dependency Unit had significant associations. ConclusionMortality was heightened among SRI patients who were older, required critical care, had chronic conditions and infected with SARS-CoV-2 suggesting need for early identification of these conditions to improve possible treatment outcomes.

AbstractBackgroundUnderstanding the epidemiology of SARS-CoV-2 infection in settings with limited data, especially given the dynamic nature of the virus and the reported epidemiological heterogeneity across countries, is important. We used data from the COVID-19 Vaccine effectiveness evaluation to determine factors associated with SARS-COV-2 infection among patients (≥ 12 years) with severe respiratory illness (SRI) in Kenya and Mali. MethodsSRI was defined as acute onset (≤ 14 days) of at least two of the following: cough, fever, chills, rigors, myalgia, headache, sore throat, fatigue, congestion or runny nose, loss of taste or smell, or pneumonia diagnosis. We collected demographic and clinical characteristics of the patients, and nasopharyngeal and oropharyngeal specimens for SARS-CoV-2 testing using RT-PCR. We used a mixed effect logistic regression to determine factors associated with SARS-CoV-2 infection adjusting for age and sex while controlling for clustering by site and month of illness onset. ResultsBetween July 2022 and October 2023, a total of 9941 patients with SRI were enrolled, of whom, 588 (5.9 %) tested positive for SARS-CoV-2. Compared to patients aged 12–24 years, those who were aged >64 years were more likely to have SARS-CoV-2 infection (adjusted Odds Ratio [aOR] = 1.60; 95 % Confidence Interval [95 % CI] 1.07–2.40). Additionally, SRI patients presenting with cough (aOR = 1.37; 95 % Confidence Interval [95 % CI] 1.05–1.80), sore throat (aOR = 1.56; 95 % CI 1.23–1.99), runny nose (aOR = 1.51; 95 % CI 1.18–1.94), and ear pain discharge (aOR = 2.58; 95 % CI 1.43–4.66) were more likely to have SARS-CoV-2 infection compared to those who did not. SRI patients who had HIV were also more likely to have SAR-CoV-2 infection compared to those who did not (aOR =1.32; 95 % CI 1.04–1.67). ConclusionOlder adults and HIV patients were at increased-risk of SARS-CoV-2 infection consistent with WHO guidelines highlighting the need for targeted prevention and management strategies focused on them.

Objective To systematically map the research landscape of synthetic data in healthcare between 2000 and 2024, revealing prevalent topics and tracking their evolution over time and across geographic locations. Methods We applied structural topic modeling (STM) to map this landscape, identifying prevalent topics and their evolution over time and geography. PubMed articles from 2000 to 2024 with “synthetic data,” “artificial data,” or “simulated data” in the title/abstract were analyzed. Texts were preprocessed (lowercasing, stopword removal, stemming), and STM was run with year and continent as covariates. The optimal number of topics (K = 10) was selected based on held-out likelihood and interpretability. Topic trends and correlations were analyzed using stacked area charts and network analysis. Results Among 7533 articles, a 20-fold growth in publications was observed. North America (48.1%) and Europe (31.8%) dominated early research, while Asia's share rose from 4.7% to 24.1%. Topics grouped into four themes: Biomedical Imaging & Signal Processing (21.1%), Synthetic Data Applications (20.7%), Computational & Statistical Methods (34.3%), and Genomics & Molecular Biology (23.9%). Initially prominent topics such as “Bayesian Modeling” (23.1%–10.8%) and “Statistical Bias & Missing Data” (21.9%–7.1%) declined, while “Synthetic Data Generation” (2.7%–23.0%), and “Disease Modeling and Public Health” (3.5%–14.3%) grew significantly. Conclusion Synthetic data research in healthcare is expanding, with shifting regional contributions and evolving topic focus. Realizing its potential requires cross-disciplinary collaboration, bias mitigation, and equitable partnerships.

Abstract Background Although Shigella is an important cause of diarrhea in Kenyan children, robust research platforms capable of conducting incidence-based Shigella estimates and eventual Shigella-targeted clinical trials are needed to improve Shigella-related outcomes in children. Here, we describe characteristics of a disease surveillance platform whose goal is to support incidence and consequences of Shigella diarrhea as part of multicounty surveillance aimed at preparing sites and assembling expertise for future Shigella vaccine trials. Methods We mobilized our preexisting expertise in shigellosis, vaccinology, and diarrheal disease epidemiology, which we combined with our experience conducting population-based sampling, clinical trials with high (97%–98%) retention rates, and healthcare utilization surveys. We leveraged our established demographic surveillance system (DSS), our network of healthcare centers serving the DSS, and our laboratory facilities with staff experienced in performing microbiologic and molecular diagnostics to identify enteric infections. We joined these resources with an international network of sites with similar capabilities and infrastructure to form a cohesive scientific network, designated Enterics for Global Health (EFGH), with the aim of expanding and updating our knowledge of the epidemiology and adverse consequences of shigellosis and enriching local research and career development priorities. Conclusions Shigella surveillance data from this platform could help inform Shigella vaccine trials. A diarrhea-specific research platform able to perform population-based enumeration and hospital-based surveillance plus support mentorship of young scientists has been established in western Kenya to support Enterics for Global Health–Shigella surveillance and future vaccine trials.

Cryptosporidium is a leading cause of moderate-to-severe diarrhea (MSD) in young children in Africa. We examined factors associated with Cryptosporidium infection in MSD cases enrolled at the rural western Kenya Global Enteric Multicenter Study (GEMS) site from 2008-2012. At health facility enrollment, stool samples were tested for enteric pathogens and data on clinical, environmental, and behavioral characteristics collected. Each child's health status was recorded at 60-day follow-up. Data were analyzed using logistic regression. Of the 1,778 children with MSD enrolled as cases in the GEMS-Kenya case-control study, 11% had Cryptosporidium detected in stool by enzyme immunoassay; in a genotyped subset, 81% were C. hominis. Among MSD cases, being an infant, having mucus in stool, and having prolonged/persistent duration diarrhea were associated with being Cryptosporidium-positive. Both boiling drinking water and using rainwater as the main drinking water source were protective factors for being Cryptosporidium-positive. At follow-up, Cryptosporidium-positive cases had increased odds of being stunted (adjusted odds ratio [aOR] = 1.65, 95% CI: 1.06-2.57), underweight (aOR = 2.08, 95% CI: 1.34-3.22), or wasted (aOR = 2.04, 95% CI: 1.21-3.43), and had significantly larger negative changes in height- and weight-for-age z-scores from enrollment. Cryptosporidium contributes significantly to diarrheal illness in young children in western Kenya. Advances in point of care detection, prevention/control approaches, effective water treatment technologies, and clinical management options for children with cryptosporidiosis are needed.