Browsing by Author "Liang, Song"

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    Determinants of malaria infections among children in refugee settlements in Uganda during 2018–2019
    (Infectious Diseases of Poverty, 2023) Semakula, Henry M.; Liang, Song; Mukwaya, Paul I.; Mugagga, Frank; Swahn, Monica; Nseka, Denis; Wasswa, Hannington; Kayima, Patrick
    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.
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    Determinants of malaria infections among children in refugee settlements in Uganda. Analysis of data from Uganda malaria indicator survey, 2018-19
    (Analysis of data from Uganda malaria indicator survey, 2023) Musoke, Henry S.; Liang, Song; Mukwaya, Paul Isolo; Mugagga, Frank; Swahn, Monica; Nseka, Denis; Wasswa, Hannington; Kayima, Patrick
    Globally, 5% of 247 million global malaria cases are reported Uganda which is also one of the top refugee hosting countries in Africa, with 1.36 million refugees currently. Although malaria is an emerging challenge for humanitarian response in refugee settlements, little is known about its risk factors. The study analysed data from the 2018-19 Uganda Malaria Indicator Survey (UMIS) to determine the factors associated with malaria infections among refugee children under ve years of age.
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    Integrated modelling of the determinants of household food insecurity during the 2020– 2021 COVID-19 lockdown in Uganda
    (BioMed Central, 2024-11) Semakula, Henry Musoke; Liang, Song; McKune, Sarah Lindley; Mukwaya, Paul Isolo; Mugagga, Frank; Nseka, Denis; Wasswa, Hannington; Kayima, Patrick; Achuu, Simon Peter; Mwendwa, Patrick; Nakato, Jovia
    Background-The determinants of household food insecurity (HFI) do not act in isolation, and are known to be complex, stochastic, nonlinear, and multidimensional. Despite this being especially true in periods of shocks, studies that focus on integrated modelling of the HFI determinants during the COVID-19 lockdown are scarce, with no available evidence on Uganda. The main objective of this study was to develop Bayesian belief network (BBN) models to analyse, rank, and illustrate the conceptual reasoning, and complex causal relationships among the determinants of HFI during the COVID-19 lockdown. This study was based on seven rounds of Uganda’s High-Frequency Phone Surveys data sets collected during the lockdown. A total of 15,032 households, 17 independent determinants of HFI, and 8 food security indicators were used in this study. Metrics of sensitivity, and prediction performance were used to evaluate models’ accuracy.ResultsEight BBN models were developed for each food insecurity indicator. The accuracy rates of the models ranged between 70.5% and 93.5%, with an average accuracy rate of 78.5%, indicating excellent predictive performance in identifying the determinants of HFI correctly. Our results revealed that approximately 42.2% of the sampled households (n = 15,032) in Uganda were worried about not having enough food. An estimated 25.2% of the respondents reported skipping a meal, while 32.1% reported consuming less food. Less than 20% of the households experienced food shortage, hunger, or having nothing to eat. Overall, 30.6% of the households were food insecure during the lockdown. The top five ranked determinants of HFI were identified as follows: (1) households’ inability to produce enough food; (2) households’ inability to buy food; (3) reduced household income; (4) limited cash assistance, and (5) households’ inability to stock adequate food supplies.ConclusionsRanking, rather than the statistical significance of the determinants of HFI, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted interventions within the constraints of limited funding. These findings emphasize the importance of intervening on the most highly ranked determinants of HFI to enhance the resilience of local food systems, and households’ capacity to cope with recurring and unforeseen shocks. Agricultural Science Database