Abstract Background and Aims Chronic Kidney Disease (CKD) affects approximately one in ten people around the world and is associated with an increased risk of adverse cardiorenal outcomes and ...mortality. In low- and middle-income countries (LMICs), the situation is aggravated by the paucity of kidney replacement therapies (KRTs). The objective of this study was to review the literature around CKD prevalence to understand the future epidemiology and economic burden of CKD in four LMICs from different continents. Method Inside CKD is a project designed to perform a microsimulation on a representative patient cohort to simulate renal disease progression into the future, and to model the effects of interventions based on population and disease characteristics. A pragmatic literature review was performed to obtain the inputs required to adapt the Inside CKD model to project the epidemiological and economic burden of CKD in four World Bank (2022) classified LMICs (Bolivia, Kenya, Sri Lanka and Uzbekistan). Using a data quality scoring system designed for the Inside CKD programme, a model input template was created to guide the country-specific literature search, to identify country demographics, epidemiological data including CKD staging data, renal registries, comorbidities (type 2 diabetes and hypertension), complications data (stroke, myocardial infarction, heart failure) and economic data including health costs and quality-of-life measures. Additional needs, challenges or country specific risk factors have also been identified. Results Literature reviews identified the best sources of demographic, epidemiological data as well as the main drivers of CKD in the four countries (e.g. hypertension, type 2 diabetes, heart failure). In addition, the reviews identified the best proxy data to use when country-specific data was not available. The UN World Prospects database was selected to derive the current and future population dynamics in the four countries. For Kenya, data from local sources, sub-Saharan African and Uganda were utilised to estimate the primary drivers of CKD: a 2018 meta-analysis reported a CKD prevalence of 18% in sub-Saharan Africa and was used to derive the eGFR and UACR data required to project CKD prevalence. CKD prevalence by stage was also identified for Bolivia. A large-scale study across 12 countries with consistent methodology estimated a CKD prevalence of around 5.5% in Bolivia. CKD data from Kazakhstan was selected as a proxy for Uzbekistan, which had a low prevalence of 1.3%. Country-specific challenges were also identified: in Kenya, results showed the importance of malnutrition, infection and climate as key drivers of CKD. In Sri Lanka and Uzbekistan, environmental factors (air and water pollution) and agricultural activity may contribute to high CKD prevalence in rural areas alongside more common risk factors such as hypertension. In Sri Lanka, CKD has been estimated at around 8.2% in the general population versus 15.0% in areas where groundwater is consumed. For Bolivia, additional parameters including occupation, altitude, seasonal weather variation and extreme temperatures may exacerbate CKD risk. Conclusion CKD is a major public health problem in LMICs but with diverse drivers. Local capacities and healthcare financing priorities vary between regions and depend upon competing demands of acute conditions, infections and non-communicable diseases. Forecasting future CKD burden is helpful for policy and planning purposes. There is a clear need to tailor policies beyond early screening and proactive management to tackle country-specific challenges such as occupation, infection, seasonal weather variation and genetics.
AbstractCage fish farming is essential to increasing fish output, alleviating the declining capture fishery resources, and advancing aquaculture development in Uganda. There are limited studies ...assessing farmers’ knowledge, attitude, and perceptions towards cage fish farming technology. This study assessed the knowledge, attitude, and perceptions (KAP) of fishery-dependent communities around Lake Victoria towards cage fish farming technology. Using a simple random sample approach, 384 respondents from fourteen districts provided information on demographic traits, knowledge, attitudes, and perceptions towards cage fish farming. The analysis utilized descriptive statistics and a multinomial logit model. Results revealed that cage fish farmers’ knowledge, attitude, and perceptions were significantly associated with age, level of education, extension visits, social capital, experience, and television access. In conclusion, this study recommends that extension visits be enhanced to develop farmers’ knowledge, attitudes, and perceptions towards cage fish farming. The study’s implications underscore the importance of developing and implementing farmer-centered policies in the aquaculture sector.
Landsat series multispectral remote sensing imagery has gained increasing attention in providing solutions to environmental problems such as land degradation which exacerbate soil erosion and ...landslide disasters in the case of rainfall events. Multispectral data has facilitated the mapping of soils, land-cover and structural geology, all of which are factors affecting landslide occurrence. The main aim of this research was to develop a methodology to visualize and map past landslides as well as identify land degradation effects through soil erosion and land-use using remote sensing techniques in the central region of Kenya. The study area has rugged terrain and rainfall has been the main source of landslide trigger. The methodology comprised visualizing landslide scars using a False Colour Composite (FCC) and mapping soil erodibility using FCC components applying expert based classification. The components of the FCC were: the first independent component (IC1), Principal Component (PC) with most geological information, and a Normalised Difference Index (NDI) involving Landsat TM/ETM+ band 7 and 3.
The FCC components formed the inputs for knowledge-based classification with the following 13 classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, exposed volcanic rocks, agriculture, green forest, new forest regrowth areas, clear, turbid and salty water. Validation of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% of the points coincided well with landslide areas mapped in the year 2000. The classification maps showed landslide areas on the steep ridge faces, other erosions in agricultural areas, highly erodible zones being already weathered rocks, while runoff were mainly fluvial deposits. Thus, landuse and rainfall processes play a major role in inducing landslides in the study area.
•Landslide scar and soil erodibility mapping using Landsat TM & ETM+ was performed.•Landslide scar visualization was done using FCC comprising: IC1, PC & modified NDMIDR.•The FCC was classified in knowledge based classification.•13 classes were classified ranging from: degraded land, vegetated, and water covers.•Mapped landslide areas were validated with GPS data with 66% coincidence.
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