Interactions Between Household Air Pollution, Dietary Diversity and Nutrition Status in Machinga District, Malawi

Background: Household air pollution (HAP) and poor nutrition have both been identified as leading causes of morbidity and mortality in Sub-Saharan Africa. Although air pollution from cooking is an important source of HAP, the relationship between HAP and nutrition has not been fully explored. Objectives: The primary objective was to determine whether there is a relationship between HAP and indicators of nutritional status and dietary intake. The study also explored associations of HAP exposure and dietary diversity. Methods: Data were collected during a household survey (N=193) in Machinga District, Malawi. The survey included socioeconomic and dietary intake questions, and anthropometric measurements of primary cooks and children under 5. Focus groups were held with household cooks to provide qualitative information on the relationship between aspects of cooking, including fuel use and food choice. Results: We found positive associations of household fuel consumption with maize consumption and with child weight-for-age and weight-for-length z-scores. Household fuel consumption was not associated with dietary diversity or body mass index of household cooks. However, share of high quality firewood used was positively associated with household dietary diversity and child height-for-age z-score. Conclusions: The findings indicate a connection between dependence on starchy staples and HAP, which suggests that increasing dietary diversity may have a positive effect on reducing HAP. The findings also show that a higher share of high-quality fuelwood is positively associated with child growth, possibly also due to lower HAP.Master of Public Healt

Rainfall Forecasting in Burkina Faso Using Bayesian-Wavelet Neural Networks

This work aims to forecast rain locally in Tambarga, Burkina Faso, to be able to fight against a worm inducing the disease called schistosomiasis. The chosen approach relies on a machine-leaning technique called Artificial Neural Networks, which simulates the synapses of a brain, with climatic parameters as inputs, activation functions and outputs in the form of rain prediction. A special case of Neural Networks using Bayesian Computations is used, along with as a transform allowing to capture the changes in climatic conditions, called Wavelet Transform. The precipitation is forecasted in different manners: binary forecast on the presence or absence of rain, linear forecast on the daily and weekly intensity, as well as a rain-class forecast. The most successful predictions have been found to be the binary forecast, as well as the weekly windowed cumulative rain forecast. The daily cumulative rain, as well has the classes forecast have not produced satisfying results, mainly because of the high temporal variability of the observations, as well as the very unequal distribution of observations in the different rain classes. In the end, it has been shown that it is possible to use Bayesian Networks to forecast precipitation in some extent, and that the wavelet transform of the inputs has a positive impact on the accuracy of the prediction

Shiga toxin-producing Escherichia coli O157 associated with human infections in Switzerland, 2000-2009

Shiga toxin-producing Escherichia coli (STEC), an important foodborne pathogen, can cause mild to severe bloody diarrhoea (BD), sometimes followed by life-threatening complications such as haemolytic uraemic syndrome (HUS). A total of 44 O157 strains isolated from different patients from 2000 through 2009 in Switzerland were further characterized and linked to medical history data. Non-bloody diarrhoea was experienced by 15·9%, BD by 61·4% of the patients, and 29·5% developed HUS. All strains belonged to MLST type 11, were positive for stx2 variants (stx2 and/or stx2c), eae and ehxA, and only two strains showed antibiotic resistance. Of the 44 strains, nine phage types (PTs) were detected the most frequent being PT32 (43·2%) and PT8 (18·2%). By PFGE, 39 different patterns were found. This high genetic diversity within the strains leads to the conclusion that STEC O157 infections in Switzerland most often occur as sporadic case

Communication and optimal hierarchical networks

We study a general and simple model for communication processes. In the model, agents in a network (in particular, an organization) interchange information packets following simple rules that take into account the limited capability of the agents to deal with packets and the cost associated to the existence of open communication channels. Due to the limitation in the capability, the network collapses under certain conditions. We focus on when the collapse occurs for hierarchical networks and also on the influence of the flatness or steepness of the structure. We find that the need for hierarchy is related to the existence of costly connections.Comment: 7 pages, 2 figures. NATO ARW on Econophysic

General Practitioners' Attitudes towards Essential Competencies in End-of-Life Care: A Cross-Sectional Survey.

Identifying essential competencies in end-of-life care, as well as general practitioners' (GPs) confidence in these competencies, is essential to guide training and quality improvement efforts in this domain. To determine which competencies in end-of-life care are considered important by GPs, to assess GPs' confidence in these competencies in a European context and their reasons to refer terminally ill patients to a specialist. Cross-sectional postal survey involving a stratified random sample of 2000 GPs in Switzerland in 2014. Survey development was informed by a previous qualitative exploration of relevant end-of-life GP competencies. Main outcome measures were GPs' assessment of the importance of and confidence in 18 attributes of end-of-life care competencies, and reasons for transferring care of terminally-ill patients to a specialist. GP characteristics associated with main outcome measures were tested using multivariate regression models. The response rate was 31%. Ninety-nine percent of GPs considered the recognition and treatment of pain as important, 86% felt confident about it. Few GPs felt confident in cultural (16%), spiritual (38%) and legal end-of-life competencies such as responding to patients seeking assisted suicide (35%) although more than half of the respondents regarded these competencies as important. Most frequent reasons to refer terminally ill patients to a specialist were lack of time (30%), better training of specialists (23%) and end-of-life care being incompatible with other duties (19%). In multiple regression analyses, confidence in end-of-life care was positively associated with GPs' age, practice size, home visits and palliative training. GPs considered non-somatic competencies (such as spiritual, cultural, ethical and legal aspects) nearly as important as pain and symptom control. Yet, few GPs felt confident in these non-somatic competencies. These findings should inform training and quality improvement efforts in this domain, in particular for younger, less experienced GPs

Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping

Mapping floods using satellite data is crucial for managing and mitigating flood risks. Satellite imagery enables rapid and accurate analysis of large areas, providing critical information for emergency response and disaster management. Historical flood data derived from satellite imagery can inform long-term planning, risk management strategies, and insurance-related decisions. The Sentinel-1 satellite is effective for flood detection, but for longer time series, other satellites such as MODIS can be used in combination with deep learning models to accurately identify and map past flood events. We here develop a combined CNN--LSTM deep learning framework to fuse Sentinel-1 derived fractional flooded area with MODIS data in order to infer historical floods over Bangladesh. The results show how our framework outperforms a CNN-only approach and takes advantage of not only space, but also time in order to predict the fractional inundated area. The model is applied to historical MODIS data to infer the past 20 years of inundation extents over Bangladesh and compared to a thresholding algorithm and a physical model. Our fusion model outperforms both models in consistency and capacity to predict peak inundation extents.Comment: CVPR 2023: Earthvision Worksho