Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda.

BACKGROUND: Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria's transmission complexity, control, and integrated modelling, with no available evidence on Uganda's refugee settlements. Using the 2018-2019 Uganda's Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda. METHODS: In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance. RESULTS: Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model's spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises). CONCLUSION: Ranking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

Determinants of malaria infections among children in refugee settlements in Uganda during 2018-2019.

BACKGROUND: While 5% of 247 million global malaria cases are reported in Uganda, it is also a top refugee hosting country in Africa, with over 1.36 million refugees. Despite malaria being an emerging challenge for humanitarian response in refugee settlements, little is known about its risk factors. This study aimed to investigate the risk factors for malaria infections among children under 5 years of age in refugee settlements in Uganda. METHODS: We utilized data from Uganda's Malaria Indicator Survey which was conducted between December 2018 and February 2019 at the peak of malaria season. In this national survey, household level information was obtained using standardized questionnaires and a total of 7787 children under 5 years of age were tested for malaria using mainly the rapid diagnostic test. We focused on 675 malaria tested children under five in refugee settlements located in Yumbe, Arua, Adjumani, Moyo, Lamwo, Kiryadongo, Kyegegwa, Kamwenge and Isingiro districts. The extracted variables included prevalence of malaria, demographic, social-economic and environmental information. Multivariable logistic regression was used to identify and define the malaria associated risk factors. RESULTS: Overall, malaria prevalence in all refugee settlements across the nine hosting districts was 36.6%. Malaria infections were higher in refugee settlements located in Isingiro (98.7%), Kyegegwa (58.6%) and Arua (57.4%) districts. Several risk factors were significantly associated with acquisition of malaria including fetching water from open water sources [adjusted odds ratio (aOR) = 1.22, 95% CI: 0.08-0.59, P = 0.002], boreholes (aOR = 2.11, 95% CI: 0.91-4.89, P = 0.018) and water tanks (aOR = 4.47, 95% CI: 1.67-11.9, P = 0.002). Other factors included pit-latrines (aOR = 1.48, 95% CI: 1.03-2.13, P = 0.033), open defecation (aOR = 3.29, 95% CI: 1.54-7.05, P = 0.002), lack of insecticide treated bed nets (aOR = 1.15, 95% CI: 0.43-3.13, P = 0.003) and knowledge on the causes of malaria (aOR = 1.09, 95% CI: 0.79-1.51, P = 0.005). CONCLUSIONS: The persistence of the malaria infections were mainly due to open water sources, poor hygiene, and lack of preventive measures that enhanced mosquito survival and infection. Malaria elimination in refugee settlements requires an integrated control approach that combines environmental management with other complementary measures like insecticide treated bed nets, indoor residual spraying and awareness.

Mapping the susceptibility of persons with disabilities to landslides in a highland landscape of Bushika Sub County, Mount Elgon, Eastern Uganda.

Terrain parameters such as slope aspect, angle, curvature, stream power and altitude have been noted to spur landslide occurrence as well as, acting as a hindrance to evacuation efforts. Yet, persons with disabilities (PWDs) are seldom given priority during rescue and recovery programmes during pre- and post-disaster evacuation. The study was guided by two objectives, namely, (1) to map the landslide risk for households of PWDs and (2) to investigate the disability type that is perceived to be most affected by landslides. A cross-sectional household survey was adopted employing snowball sampling, Key Informant Interviews (KII), and Focus Group Discussions (FGDs) for primary data collection. A 30-m Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) was used for terrain spatial landslide risk analysis in ArcGis 10.8 and System for Automated Geoscientific Analyses (SAGA) tools. A one-sample t-test in Statistical Package for Social Sciences (SPSS) version 23 was used to analyse the score values on a five-point Likert scale to ascertain the perceived landslide effect on the different disability categories. Qualitative data was subjected to content analysis. We found out that majority of PWDs live in high-risk landslide zones with 1400 m - 1700 m, S-E, 10-80, > 10, and -0.8-0.13 of altitude, aspect, slope angle, Stream Power Index (SPI), and slope curvature, respectively. T-test results revealed that blind and deaf-blind were perceived as most affected by landslides with t(31) = 58.42, mean = 4.7, p < 0.0001, and t(31) = 34.8, mean 4.6, p < 0.0001. The deaf people were perceived to also be highly affected by landslides with t(31) = 34.4, mean = 3.9, p < 0.0001. In conclusion, PWDs in Bushika were highly susceptible to landslide hazards and yet considered as a minority for rescue and recovery during landslide occurrences. We recommend for prioritisation of inclusive disaster programmes such as disaster training, relocation, and resettlement to reduce vulnerability and enhance landslides disaster resilience of PWDs especially in high-risk areas.

Similar, yet different! Comparing Ugandan secondary cities' food system and nutritional transformations to findings from African primary cities.

This research focuses on the food, farming and health experiences of two secondary cities of Uganda (Mbale and Mbarara), comparing findings with studies of primary African cities. We draw from survey data, focus groups with healthcare professionals, and in-depth interviews with varied residents. A feminist geographic perspective explored intersections of food, farming and health with varied aspects of identity, and with place (city itself, but also with rural areas). By comparing our secondary city findings to findings from primary African cities this paper sheds light on whether and how food systems in secondary African cities are transforming, and how urban life at this scale is being experienced. Our analysis suggests a good deal of similarity of food insecurity, dietary diversity, and of non-communicable disease experiences and understandings. The main difference was around the food access strategies, the access to land, and the engagement with agriculture and interaction with the rural. How this might change as these secondary cities grow further is not clear but there should not be an assumption that primary city experiences will inevitably be followed. Our findings offer important insights for future research and for those planning for Ugandan and potentially other African secondary city futures. In comparison to primary SSA cities our findings suggest less advance along theorised nutritional transitions (greater hybridity), a higher relevance of the rural for viable urban lives, yet comparable experience of non-communicable disease. This is intriguing, has implications for theory, and warrants further research.

Vulnerability of smallholder sorghum farmers to climate variability in a heterogeneous landscape of south-western Uganda.

Smallholder farmers in sub-Saharan Africa are at a greater risk to the impacts of climate variability. We therefore sought to assess vulnerability of smallholder sorghum farmers to climate variability in Kigezi highlands of south-western Uganda. A vulnerability index that integrates selected socio-economic and biophysical variables was obtained through key informant interviews and household surveys, from 230 conveniently sampled sorghum farming households within three sub-counties differentiated by altitude. Rainfall data were obtained from Uganda National Meteorological Authority. Quantitative data were analysed using Statistical Package for Social Sciences (version 23) and STATA software to generate inferential and descriptive statistics, notably frequencies, percentages and chi-square tests, to establish relationship between variables. Content analysis was used to generate themes emerging from the qualitative data. The overall vulnerability index results indicate Kashambya as the most vulnerable (6.9), followed by Bubare (1.8), while Kamwezi was the least vulnerable (-0.2). This study recommends targeted extension services such as access to customised weather information and better agronomic practices to reduce smallholder sorghum farmers' vulnerability.