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

Semakula, Henry Musoke; Liang, Shuai; Mukwaya, Paul Isolo; Mugagga, Frank; Swahn, Monica H.; Nseka, Denis; Wasswa, Hannington; Kayima, Patrick

doi:10.1186/s40249-023-01090-3

Cited by 5 publications

(12 citation statements)

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“…5 , the first 10 ranked determinants caused the largest entropy reduction in malaria infections in refugee settlements. Although lack ITNs and IRS, age of household head, sex of household head, mother’s level of education, lack of knowledge on the causes, and prevention of malaria have been recently associated with malaria infections in refugee settlements in Uganda [ 14 ], in this study, they are not among the 10 ranked determinants because they indirectly influence malaria infections through other factors.…”

Section: Resultsmentioning

confidence: 99%

“…Households whose main sources of drinking water were open water sources, and piped water on premises, had the probabilities of their children testing positive for malaria increasing by 0.12% points (from 4.75 to 4.87%), and 0.04% points (from 1.34 to 1.38%) respectively. Open water sources near households and piped water systems, which are poorly managed have been associated with creating potential breeding sites that shortened the gonotrophic cycles while increasing malaria transmission [ 14 , 44 ]. However, as walk time distance to water sources increases, malaria infections tend to reduce due to prolonged gonotrophic cycles attributed to limited long-range flight abilities of mosquitoes [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…Another study conducted in Kiryandongo refugee camp in Uganda also utilized a logistic regression model, and concluded that Plasmodium falciparum and intestinal parasitic co-infection was associated with malaria and anaemia [ 13 ]. A recent study focusing on all the refugee settlements in Uganda also used a logistic regression model, and revealed that the use of pit latrines, open water sources, lack of ITNs, inadequate knowledge on malaria causes, and prevention, were the key drivers of malaria infections among children under-five [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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

Semakula,

Liang,

Mukwaya

et al. 2023

Malar J

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Semakula,

Liang,

Mukwaya

et al. 2023

Malar J

Self Cite

View full text Add to dashboard Cite

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“…Thus, integrated models [17] that consider all these factors are urgently required to enable decision makers, and stakeholders to draw appropriate conclusions in malaria control interventions in refugee settlements. The recent attempt to model household level determinants of malaria infections in refugee settlements in Uganda [14] was based on a logistic regression model which is not able to fully capture dependencies, uncertainties, complex interactions and ranking of the various malaria risk factors to inform and direct policy interventions on malaria control [11]. The same logistics regression models have been widely used to determine the signi cant malaria risk factors in many countries of SSA [6,11,47].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, these structurally disadvantaged populations characterized by racism, sexism, and economic deprivation [46] can lead to further geographical distribution of parasites (i.e. introducing new parasite strains in new locations), cause re-emergence or re-infections as well as lead to multiple, and co-infections with various strains malaria parasites [14].…”

Section: Discussionmentioning

confidence: 99%

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

Semakula

Liang

Mukwaya

et al. 2023

Preprint

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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 modeling, 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, we created a casefile containing malaria test results, demographic, social-economic and environmental information. 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, &pit latrines with slabs); (6)walk time distance to water sources, (between 0–10minutes); (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.

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Topical repellents for malaria prevention

Gabaldón Figueira,

Wagah,

Adipo

et al. 2023

Cochrane Database of Systematic Reviews

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Background Insecticide‐based interventions, such as long‐lasting insecticide‐treated nets (LLINs) and indoor residual spraying (IRS), remain the backbone of malaria vector control. These interventions target mosquitoes that prefer to feed and rest indoors, but have limited capacity to prevent transmission that occurs outdoors or outside regular sleeping hours. In low‐endemicity areas, malaria elimination will require that these control gaps are addressed, and complementary tools are found. The use of topical repellents may be particularly useful for populations who may not benefit from programmatic malaria control measures, such as refugees, the military, or forest goers. This Cochrane Review aims to measure the effectiveness of topical repellents to prevent malaria infection among high‐ and non‐high‐risk populations living in malaria‐endemic regions. Objectives To assess the effect of topical repellents alone or in combination with other background interventions (long‐lasting insecticide‐treated nets, or indoor residual spraying, or both) for reducing the incidence of malaria in high‐ and non‐high‐risk populations living in endemic areas. Search methods We searched the following databases up to 11 January 2023: the Cochrane Infectious Diseases Group Specialised Register; CENTRAL (in the Cochrane Library); MEDLINE; Embase; CAB Abstracts; and LILACS. We also searched trial registration platforms and conference proceedings; and contacted organizations and companies for ongoing and unpublished trials. Selection criteria We included randomized controlled trials (RCTs) and cluster‐randomized controlled trials (cRCTs) of topical repellents proven to repel mosquitoes. We also included non‐randomized studies that complied with pre‐specified inclusion criteria: controlled before‐after studies (CBA), controlled interrupted time series (ITS), and controlled cross‐over trials. Data collection and analysis Four review authors independently assessed trials for inclusion, and extracted the data. Two authors independently assessed the risk of bias (RoB) using the Cochrane RoB 2 tool. A fifth review author resolved any disagreements. We analysed data by conducting a meta‐analysis, stratified by whether studies included populations considered to be at high‐risk of developing malaria infection (for example, refugees, forest goers, or deployed military troops). We combined results from cRCTs with RCTs by adjusting for clustering and presented results using forest plots. We used the GRADE framework to assess the certainty of the evidence. We only included data on Plasmodium falciparum infections in the meta‐analysis. Main results Thirteen articles relating to eight trials met the inclusion criteria and were qualitatively described. We included six trials in the meta‐analysis (five cRCTs and one RCT). Effect on mala...

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

Cited by 5 publications

References 55 publications

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

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

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

Topical repellents for malaria prevention

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