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.

Climatic burden of eating at home against away-from-home: A novel Bayesian Belief Network model for the mechanism of eating-out in urban China.

Dietary patterns of eating away-from-home (AFH) considerably differ from those of eating at home in urban China, thus generating varied carbon footprints. However, few studies have investigated the effect of eating places on diet-related climatic burden, and few have modelled the mechanism under the condition of eating-out because the decision of consumers on whether to eat AFH or at home is determined by multiple non-linear socioeconomic factors. Here, we compared the carbon footprints of eating at home and AFH using household survey data from 12 Chinese provinces, and developed a Bayesian Belief Network (BBN) model to identify key factors of eating AFH. Our findings show that eating AFH leads to higher climatic burdens though respondents consume less food on average than when eating at home. However, in urban areas, the carbon footprint generated increases more rapidly from eating at-home than when eating AFH. The BBN model was found to have strong capability to predict the possibility of eating out with an accuracy of 89%. Although diet patterns and embedded carbon footprint vary considerably across provinces from northeastern to southwestern China, sufficient evidence could not be found to support the influence of geographic factors on the decision of respondents to eat AFH at large scale. Instead, individual occupation and income were found to be the two key contributors. Thus, merely estimating the carbon footprint of food consumption is currently not sufficient, but social and economic elements need to be quantitatively considered to differentiate the eating-place effect on diet-related climatic burden.

Food consumption and waste and the embedded carbon, water and ecological footprints of households in China.

Strategies for reducing food waste and developing sustainable diets require information about the impacts of consumption behavior and waste generation on climatic, water, and land resources. We quantified the carbon, water, and ecological footprints of 17,110 family members of Chinese households, covering 1935 types of foods, by combining survey data with available life-cycle assessment data sets. We also summarized the patterns of both food consumption and waste generation and analyzed the factors influencing the observed trends. The average person wasted (consumed) 16 (415) kg of food at home annually, equivalent to 40 (1080) kg CO2e, 18 (673) m(3), and 173 (4956) gm(2) for the carbon, water and ecological footprints, respectively. The generation of food waste was highly correlated with consumption for various food groups. For example, vegetables, rice, and wheat were consumed the most and accounted for the most waste. In addition to the three plant-derived food groups, pork and aquatic products also contributed greatly to embedded footprints. The data obtained in this study could be used for assessing national food security or the carrying capacity of resources.

Assessing Major Accident Risks to Support Land-Use Planning Using a Severity-Vulnerability Combination Method: A Case Study in Dagushan Peninsula, China.

Major accident risks posed by chemical hazards have raised major social concerns in today's China. Land-use planning has been adopted by many countries as one of the essential elements for accident prevention. This article aims at proposing a method to assess major accident risks to support land-use planning in the vicinity of chemical installations. This method is based on the definition of risk by the Accidental Risk Assessment Methodology for IndustrieS (ARAMIS) project and it is an expansion application of severity and vulnerability assessment tools. The severity and vulnerability indexes from the ARAMIS methodology are employed to assess both the severity and vulnerability levels, respectively. A risk matrix is devised to support risk ranking and compatibility checking. The method consists of four main steps and is presented in geographical information-system-based maps. As an illustration, the proposed method is applied in Dagushan Peninsula, China. The case study indicated that the method could not only aid risk regulations on existing land-use planning, but also support future land-use planning by offering alternatives or influencing the plans at the development stage, and thus further enhance the roles and influence of land-use planning in the accident prevention activities in China.