Household Food Security Status and Child Health Outcomes in Kenya

Interminable access to sufficient, nutritious, and safely prepared food is a human right. Attributed to insufficient food and nutrient intake, malnutrition is a major health burden in developing economies that has maimed socioeconomic development. In children, undernourishment impairs the functioning of the immune system, increases susceptibility to diseases, and undermines physical and cognitive development. In Kenya, there exists a paucity of empirical corroboration of the effect of household food security status (HFSS) on child health outcomes. Using data drawn from the 2014 Kenya Demographic and Health Survey, this paper focuses on analyzing the causal link between HFSS and child health outcomes and to provide evidencebased policy recommendations to promote child health outcomes. We employed three measures of HFSS: households that lacked food/enough money to purchase food, the Reduced Coping Strategy Index (CSI), and the Food Consumption Score (FCS). The child health production function was estimated using the two-stage residual inclusion (2SRI) technique to control for potential endogeneity. The results indicate that households that lacked food/enough money to purchase food were significantly associated with stunted, wasted, and underweight growth in children. Similarly, the Reduced CSI was a significant determinant of stunted and underweight growth in children. However, the effect was insignificant relative to wasted growth. The findings also indicate that FCS contributes significantly to improvements in child health outcomes. Our evidence has the potential to inform policies on the promotion of child health outcomes. We recommend implementation of programs such as social assistance, integration of nutrition and WASH, and capacity-building to promote women’s knowledge of health, nutrition, and better child-care practices

Non-Invasive Sampling of Schistosomes from Humans Requires Correcting for Family Structure

For ethical and logistical reasons, population-genetic studies of parasites often rely on the non-invasive sampling of offspring shed from their definitive hosts. However, if the sampled offspring are naturally derived from a small number of parents, then the strong family structure can result in biased population-level estimates of genetic parameters, particularly if reproductive output is skewed. Here, we document and correct for the strong family structure present within schistosome offspring (miracidia) that were collected non-invasively from humans in western Kenya. By genotyping 2,424 miracidia from 12 patients at 12 microsatellite loci and using a sibship clustering program, we found that the samples contained large numbers of siblings. Furthermore, reproductive success of the breeding schistosomes was skewed, creating differential representation of each family in the offspring pool. After removing the family structure with an iterative jacknifing procedure, we demonstrated that the presence of relatives led to inflated estimates of genetic differentiation and linkage disequilibrium, and downwardly-biased estimates of inbreeding coefficients (F[subscript IS]). For example, correcting for family structure yielded estimates of F[[subscript ST] among patients that were 27 times lower than estimates from the uncorrected samples. These biased estimates would cause one to draw false conclusions regarding these parameters in the adult population. We also found from our analyses that estimates of the number of full sibling families and other genetic parameters of samples of miracidia were highly intercorrelated but are not correlated with estimates of worm burden obtained via egg counting (Kato-Katz). Whether genetic methods or the traditional Kato-Katz estimator provide a better estimate of actual number of adult worms remains to be seen. This study illustrates that family structure must be explicitly accounted for when using offspring samples to estimate the genetic parameters of adult parasite populations

Acute seizures attributable to falciparum malaria in an endemic area on the Kenyan coast

Falciparum malaria is an important cause of acute symptomatic seizures in children admitted to hospitals in sub-Saharan Africa, and these seizures are associated with neurological disabilities and epilepsy. However, it is difficult to determine the proportion of seizures attributable to malaria in endemic areas since a significant proportion of asymptomatic children have malaria parasitaemia. We studied children aged 0–13 years who had been admitted with a history of seizures to a rural Kenyan hospital between 2002 and 2008. We examined the changes in the incidence of seizures with the reduction of malaria. Logistic regression was used to model malaria-attributable fractions for seizures (the proportion of seizures caused by malaria) to determine if the observed decrease in acute symptomatic seizures was a measure of seizures that are attributable to malaria. The overall incidence of acute symptomatic seizures over the period was 651/100 000/year (95% confidence interval 632–670) and it was 400/100 000/year (95% confidence interval 385–415) for acute complex symptomatic seizures (convulsive status epilepticus, repetitive or focal) and 163/100 000/year (95% confidence interval 154–173) for febrile seizures. From 2002 to 2008, the incidence of all acute symptomatic seizures decreased by 809/100 000/year (69.2%) with 93.1% of this decrease in malaria-associated seizures. The decrease in the incidence of acute complex symptomatic seizures during the period was 111/100 000/year (57.2%) for convulsive status epilepticus, 440/100 000/year (73.7%) for repetitive seizures and 153/100 000/year (80.5%) for focal seizures. The adjusted malaria-attributable fractions for seizures with parasitaemia were 92.9% (95% confidence interval 90.4–95.1%) for all acute symptomatic seizures, 92.9% (95% confidence interval 89.4–95.5%) for convulsive status epilepticus, 93.6% (95% confidence interval 90.9–95.9%) for repetitive seizures and 91.8% (95% confidence interval 85.6–95.5%) for focal seizures. The adjusted malaria-attributable fractions for seizures in children above 6 months of age decreased with age. The observed decrease in all acute symptomatic seizures (809/100 000/year) was similar to the predicted decline (794/100 000/year) estimated by malaria-attributable fractions at the beginning of the study. In endemic areas, falciparum malaria is the most common cause of seizures and the risk for seizures in malaria decreases with age. The reduction in malaria has decreased the burden of seizures that are attributable to malaria and this could lead to reduced neurological disabilities and epilepsy in the area

Interactions between Natural Populations of Human and Rodent Schistosomes in the Lake Victoria Region of Kenya: A Molecular Epidemiological Approach

One of the world's most prevalent neglected diseases is schistosomiasis, which infects approximately 200 million people worldwide. Schistosoma mansoni is transmitted to humans by skin penetration by free-living larvae that develop in freshwater snails. The origin of this species is East Africa, where it coexists with its sister species, S. rodhaini. Interactions between these species potentially influence their epidemiology, ecology, and evolutionary biology, because they infect the same species of hosts and can hybridize. Over two years, we examined their distribution in Kenya to determine their degree of overlap geographically, within snail hosts, and in the water column as infective stages. Both species were spatially and temporally patchy, although S. mansoni was eight times more common than S. rodhaini. Both species overlap in the time of day they were present in the water column, which increases the potential for the species to coinfect the same host and interbreed. Peak infective time for S. mansoni was midday and dawn and dusk for S. rodhaini. Three snails were coinfected, which was more common than expected by chance. These findings indicate a lack of obvious isolating mechanisms to prevent hybridization, raising the intriguing question of how the two species retain separate identities

Multi-Site Benchmark Classification of Major Depressive Disorder Using Machine Learning on Cortical and Subcortical Measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders

Children’s and adolescents’ rising animal-source food intakes in 1990–2018 were impacted by age, region, parental education and urbanicity

Animal-source foods (ASF) provide nutrition for children and adolescents’ physical and cognitive development. Here, we use data from the Global Dietary Database and Bayesian hierarchical models to quantify global, regional and national ASF intakes between 1990 and 2018 by age group across 185 countries, representing 93% of the world’s child population. Mean ASF intake was 1.9 servings per day, representing 16% of children consuming at least three daily servings. Intake was similar between boys and girls, but higher among urban children with educated parents. Consumption varied by age from 0.6 at <1 year to 2.5 servings per day at 15–19 years. Between 1990 and 2018, mean ASF intake increased by 0.5 servings per week, with increases in all regions except sub-Saharan Africa. In 2018, total ASF consumption was highest in Russia, Brazil, Mexico and Turkey, and lowest in Uganda, India, Kenya and Bangladesh. These findings can inform policy to address malnutrition through targeted ASF consumption programmes.publishedVersio

Incident type 2 diabetes attributable to suboptimal diet in 184 countries

The global burden of diet-attributable type 2 diabetes (T2D) is not well established. This risk assessment model estimated T2D incidence among adults attributable to direct and body weight-mediated effects of 11 dietary factors in 184 countries in 1990 and 2018. In 2018, suboptimal intake of these dietary factors was estimated to be attributable to 14.1 million (95% uncertainty interval (UI), 13.8–14.4 million) incident T2D cases, representing 70.3% (68.8–71.8%) of new cases globally. Largest T2D burdens were attributable to insufficient whole-grain intake (26.1% (25.0–27.1%)), excess refined rice and wheat intake (24.6% (22.3–27.2%)) and excess processed meat intake (20.3% (18.3–23.5%)). Across regions, highest proportional burdens were in central and eastern Europe and central Asia (85.6% (83.4–87.7%)) and Latin America and the Caribbean (81.8% (80.1–83.4%)); and lowest proportional burdens were in South Asia (55.4% (52.1–60.7%)). Proportions of diet-attributable T2D were generally larger in men than in women and were inversely correlated with age. Diet-attributable T2D was generally larger among urban versus rural residents and higher versus lower educated individuals, except in high-income countries, central and eastern Europe and central Asia, where burdens were larger in rural residents and in lower educated individuals. Compared with 1990, global diet-attributable T2D increased by 2.6 absolute percentage points (8.6 million more cases) in 2018, with variation in these trends by world region and dietary factor. These findings inform nutritional priorities and clinical and public health planning to improve dietary quality and reduce T2D globally.publishedVersio

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects