Association between low maternal serum aflatoxin B1 exposure and adverse pregnancy outcomes in Mombasa, Kenya, 2017-2019: A nested matched case-control study.

We examined the association between serum aflatoxin B1-lysine adduct (AFB1-lys) levels in pregnant women and adverse pregnancy outcomes (low birthweight, miscarriage and stillbirth) through a nested matched case-control study of pregnant women enroled at ≤28 weeks' gestation in Mombasa, Kenya, from 2017 to 2019. Cases comprised women with an adverse birth outcome, defined as either delivery of a singleton infant weighing <2500 g, or a miscarriage, or a stillbirth, while controls were women who delivered a singleton live infant with a birthweight of ≥2500 g. Cases were matched to controls at a ratio of 1:2 based on maternal age at enrolment, gestational age at enrolment and study site. The primary exposure was serum AFB1-lys. The study included 125 cases and 250 controls. The median gestation age when serum samples were collected was 23.0 weeks (interquartile range [IQR]: 18.1-26.0) and 23.5 (IQR: 18.1-26.5) among cases and controls, respectively. Of the 375 tested sera, 145 (38.7%) had detectable serum AFB1-lys: 36.0% in cases and 40.0% in controls. AFB1-lys adduct levels were not associated with adverse birth outcomes on multivariable analysis. Mid-upper arm circumference was associated with a 6% lower odds of adverse birth outcome for every unit increase (p = 0.023). Two-fifths of pregnant women had detectable levels of aflatoxin midway through pregnancy. However, we did not detect an association with adverse pregnancy outcomes, likely because of low serum AFB1-lys levels and low power, restricting meaningful comparison. More research is needed to understand the public health risk of aflatoxin in pregnant women to unborn children.

Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review.

Introduction: Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations. Methods: We conducted a systematic review via a PubMed search for the period 1990-2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications. Results: Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine-related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen-specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research. Conclusions: Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.

Genetic and potential antigenic evolution of influenza A(H1N1)pdm09 viruses circulating in Kenya during 2009-2018 influenza seasons.

Influenza viruses undergo rapid evolutionary changes, which requires continuous surveillance to monitor for genetic and potential antigenic changes in circulating viruses that can guide control and prevention decision making. We sequenced and phylogenetically analyzed A(H1N1)pdm09 virus genome sequences obtained from specimens collected from hospitalized patients of all ages with or without pneumonia between 2009 and 2018 from seven sentinel surveillance sites across Kenya. We compared these sequences with recommended vaccine strains during the study period to infer genetic and potential antigenic changes in circulating viruses and associations of clinical outcome. We generated and analyzed a total of 383 A(H1N1)pdm09 virus genome sequences. Phylogenetic analyses of HA protein revealed that multiple genetic groups (clades, subclades, and subgroups) of A(H1N1)pdm09 virus circulated in Kenya over the study period; these evolved away from their vaccine strain, forming clades 7 and 6, subclades 6C, 6B, and 6B.1, and subgroups 6B.1A and 6B.1A1 through acquisition of additional substitutions. Several amino acid substitutions among circulating viruses were associated with continued evolution of the viruses, especially in antigenic epitopes and receptor binding sites (RBS) of circulating viruses. Disease severity declined with an increase in age among children aged < 5 years. Our study highlights the necessity of timely genomic surveillance to monitor the evolutionary changes of influenza viruses. Routine influenza surveillance with broad geographic representation and whole genome sequencing capacity to inform on prioritization of antigenic analysis and the severity of circulating strains are critical to improved selection of influenza strains for inclusion in vaccines.

Rhinovirus dynamics across different social structures.

Rhinoviruses (RV), common human respiratory viruses, exhibit significant antigenic diversity, yet their dynamics across distinct social structures remain poorly understood. Our study delves into RV dynamics within Kenya by analysing VP4/2 sequences across four different social structures: households, a public primary school, outpatient clinics in the Kilifi Health and Demographics Surveillance System (HDSS), and countrywide hospital admissions and outpatients. The study revealed the greatest diversity of RV infections at the countrywide level (114 types), followed by the Kilifi HDSS (78 types), the school (47 types), and households (40 types), cumulatively representing >90% of all known RV types. Notably, RV diversity correlated directly with the size of the population under observation, and several RV type variants occasionally fuelled RV infection waves. Our findings highlight the critical role of social structures in shaping RV dynamics, information that can be leveraged to enhance public health strategies. Future research should incorporate whole-genome analysis to understand fine-scale evolution across various social structures.