How far are we from adhering to national asthma guidelines: The awareness factor

AbstractBackgroundThe Saudi national asthma protocol (SNAP) for asthma management was released in 1995 in an attempt to streamline asthma management practices in Saudi Arabia and improve the quality of care for asthma. Up to our knowledge, few studies assessed the adherence of Saudi physicians to the national asthma guidelines.ObjectivesThe objective of this present study was to assess the level of adherence of pediatricians and primary care physicians (PCPs) to the current SNAP recommendations and identify barriers to physician adherence.MethodologyThis is a cross-sectional study involving pediatricians and PCPs selected randomly from five major governmental hospitals in Riyadh, Saudi Arabia. Subjects were administered a self-administered questionnaire comprising 24 questions assessing their awareness of SNAP and their level of adherence to the recommendations.ResultsThe response rate was 38% (80/206). Out of most of the physicians who responded, 70% (56) were aware of SNAP, and only 78.2% (n=43) of them had modified their management of asthmatic patients according to the SNAP recommendations. The level of knowledge of the pharmacotherapy and diagnostic parts of the guidelines ranged between 41.5% and 90.7% in the pharmacotherapy part, and 53.7–59.6% in the diagnostic part. The most common barriers to adherence to SNAP were lack of awareness (25.2%), patient non- compliance (18.9%) and lack of resources (13.5%). There was no significant difference in awareness between pediatricians and PCPs (69.2%, 70.7% respectively).ConclusionThis study reveals a substantial gap between the actual care provided by pediatricians and PCPs to asthmatic patients and the recommendations formulated in the Saudi National Asthma Protocol (SNAP). Lack of awareness remains the most common barrier for adherence to the guidelines followed by patient non-compliance. To improve SNAP guideline adherence, tailored interventions that address barriers to adherence need to be implemented

Integrating the soybean‑maize‑chicken value chains to attain nutritious diets in Tanzania

Open Access Article; Published online: 09 Sep 2021In Tanzania, diets are dominated by starchy staple crops such as maize, levels of malnutrition are high and largely attributed to lack of dietary diversity. We employed fuzzy cognitive mapping to understand the current soybean, maize and chicken value chains, to highlight stakeholder relationships and to identify entry points for value chain integration to support nutritious diets in Tanzania. The fuzzy cognitive maps were constructed based on information gathered during household interviews with 569 farming households, followed by a participatory workshop with 54 stakeholders involved in the three value chains. We found that the soybean, maize and chicken value chains were interconnected, particularly at the level of the smallholder farming systems and at processing facilities. Smallholder farming households were part of one or more value chains. Chicken feed is an important entry point for integrating the three value chains, as maize and soybean meal are the main sources of energy and protein for chicken. Unlike maize, the utilization of soybean in chicken feed is limited, mainly due to inadequate quality of processing of soybean grain into meal. As a result, the soybean grain produced by smallholders is mainly exported to neighbouring countries for further processing, and soybean meal is imported at relatively high prices. Enhancing local sourcing and adequate processing of soybean, coupled with strengthening the integration of smallholder farmers with other soybean, maize and chicken value chain actors offers an important opportunity to improve access to nutritious diets for local people. Our method revealed the importance of interlinkages that integrate the value chains into a network within domestic markets

Consistency, variability, and predictability of on-farm nutrient responses in four grain legumes across east and west Africa

Open Access Article; Published online: 26 May 2023Grain legumes are key components of sustainable production systems in sub-Saharan Africa, but wide-spread nutrient deficiencies severely restrict yields. Whereas legumes can meet a large part of their nitrogen (N) requirement through symbiosis with N2-fixing bacteria, elements such as phosphorus (P), potassium (K) and secondary and micronutrients may still be limiting and require supplementation. Responses to P are generally strong but variable, while evidence for other nutrients tends to show weak or highly localised effects. Here we present the results of a joint statistical analysis of a series of on-farm nutrient addition trials, implemented across four legumes in four countries over two years. Linear mixed models were used to quantify both mean nutrient responses and their variability, followed by a random forest analysis to determine the extent to which such variability can be explained or predicted by geographic, environmental or farm survey data. Legume response to P was indeed variable, but consistently positive and we predicted application to be profitable for 67% of farms in any given year, based on prevailing input costs and grain prices. Other nutrients did not show significant mean effects, but considerable response variation was found. This response heterogeneity was mostly associated with local or temporary factors and could not be explained or predicted by spatial, biophysical or management factors. An exception was K response, which displayed appreciable spatial variation that could be partly accounted for by spatial and environmental covariables. While of apparent relevance for targeted recommendations, the minor amplitude of expected response, the large proportion of unexplained variation and the unreliability of the predicted spatial patterns suggests that such data-driven targeting is unlikely to be effective with current data

Data set of smallholder farm households in banana-coffee-based farming systems containing data on farm households, agricultural production and use of organic farm waste

Open Access Journal; Published online: 16 Feb 2021The data was collected in the Karagwe and Kyerwa districts of the Kagera region in north-west Tanzania. It encompasses 150 smallholder farming households, which were interviewed on the composition of their household, agricultural production and use of organic farm waste. The data covers the two previous rainy seasons and the associated vegetation periods between September 2016 and August 2017. The knowledge of experts from the following institutions was included in the discussion on the selection criteria: two local non-profit organisations, i.e., WOMEDA and the MAVUNO Project; the International Institute of Tropical Agriculture (IITA); and the National Land Use Planning Commission (NLUPC). Households were selected for inclusion if all of the following applied to them: 1) less than 10 acres of land (4.7 ha) registered in the village offices, 2) no agricultural training, and 3) decline in the fertility of their land since they started farming (self-reported). We selected 150 smallholder households out of a pool of 5,000 households known to WOMEDA in six divisions of the Kyerwa and Karagwe districts. The questionnaire contained 54 questions. The original language of the survey was Kiswahili. All interviews were audio recorded. The answers were digitalised and translated into English. The data set contains the raw data with 130 quantitative and qualitative variables. For quantitative variables, the only analysis that was made was the conversion of units, e.g., land area was converted from acres to hectares, harvest from buckets to kilograms and then to tons, and heads of livestock to Tropical Livestock Units (TLU). Qualitative variables were summarised into categories. All data has been anonymised. The data set includes geographical variables, household information, agricultural information, gender-specific responsibilities, economic data, farm waste management, and water, energy and food availability (Water-Energy-Food (WEF) Nexus). Variables are written in italics. The following geographical variables are part of the data set: district, division, ward, village, hamlet, longitude, latitude, and altitude. Household information includes start of farming, household size, gender and age of household members. Agricultural information includes land size, size of homegarden, crops, livestock and livestock keeping, trees, and access to forest. Gender-specific responsibilities includes producing and exchanging seeds, weed control, terracing, distributing organic material to the fields, care of annual and perennial crops, harvesting of crops, decisions about the harvest and animal products, selling and buying products, working on their own farm and off-farm, cooking, storing food, collecting and caring for drinking water, washing, and toilet cleaning. Economic data includes distance to the market, journey time to market, transport methods, labourers employed by the household, working off-farm, and assets such as type of house. Variables relevant to the WEF Nexus are drinking water source and treatment, meals per day, months without food, cooking fuel, and type of toilet. Variables on farm waste management are the use of crop residues, food and kitchen waste, livestock manure, cooking ash, animal bones, and human urine and faeces. The data can be potentially reused and further developed for the purpose of agricultural production analysis, socio-economic analysis, comparison to other regions, conceptualisation of waste and nutrient management, establishment of land use concepts, and further analysis on food security and healthy diets

Nutrient deficiencies are key constraints to grain legume productivity on "non-responsive" soils in sub-Saharan Africa

Open Access Journal; Published online: 10 Nov 2021Leguminous plants are known to require phosphorus fertilizers and inoculation with nitrogen fixing rhizobia for optimum yield but other nutrients may also be lacking. In this study, the most limiting nutrients for legume growth were determined in soils where the crops had not responded to P and rhizobial inoculation in field trials, using the double pot technique. Soils were collected from 17 farmers' fields in West Kenya, Northern Nigeria, Eastern and Southern Rwanda, South-west and North-west Sierra Leone. Plant growth and mean biomass were measured on soils to which a full nutrient solution, containing phosphorus (P), potassium (K), magnesium (Mg), sulfur (S) and micronutrients (MN) were added, and which were compared to a control (no nutrient added), and individual omissions of each nutrient. The relationship between soil properties and nutrient deficiencies was explored. Nutrient limitations were found to differ between soils, both within and across countries. Generally, each soil was potentially deficient in at least one nutrient, with K, P, Mg, MN and S emerging as most limiting in 88, 65, 59, 18, and 12% of tested soils, respectively. While K was the most limiting nutrient in soils from Kenya and Rwanda, P was most limiting in soils from Nigeria. P and K were equally limiting in soils from Sierra Leone. Mg was found limiting in two soils from Kenya and three soils from Rwanda and one soil each in Nigeria and Sierra Leone. Micronutrients were found to be limiting in one soil from Nigeria and one soil from Rwanda. Estimates of nutrient deficiency using growth and mean biomass were found to be correlated with each other although the latter proved to be a more sensitive measure of deficiency. With few exceptions, the relation between soil parameters and nutrient deficiencies was weak and there were no significant relations between deficiency of specific nutrients and the soil content of these elements. Although our results cannot be translated directly to the field, they confirm that individual and multiple nutrient deficiencies were common in these “non-responsive” soils and may have contributed to reported low yields. This highlights the need for balanced nutrition in legume production in SSA