The impact of neglected tropical diseases on women and girl refugees: A call for increased awareness and strategic intervention

Duffey, Megan M.; Patel, Tulsi; Koukaz, Yasmine; Sepulveda, Theresa; Barbour, Kelli; Fredricks, Karla; Weatherhead, Jill E.

doi:10.3389/fitd.2022.1095174

Cited by 2 publications

(6 citation statements)

References 43 publications

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“…The household level risk factors of malaria infections are known to be complex, stochastic, nonlinear, multidimensional, and do not act in isolation [21]. In refugee settlements, these determinants are also linked to a range of closely related factors including poverty, low levels of education, low access to basic social services, inadequacy of some public policies, racism, sexism, and economic deprivation [9,46]. 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.…”

Section: Discussionmentioning

confidence: 99%

“…5) are crucial in enhancing the mosquito survival, biting and feeding, parasite development, and breeding [1,44]. The vulnerability of refugee children to malaria infections is dependent on parents' personal behaviors, gender roles, physical and environmental factors, social-cultural aspects, and access rights [9]. Ranking and prioritising risk factors of malaria infections in refugee settlements rather than providing their statistical signi cance is an important component because, it helps to allocate resources to malaria control interventions within the constraint of limited humanitarian funding [48].…”

Section: Discussionmentioning

confidence: 99%

“…Malaria infections can even be more devastating among the structurally disadvantaged populations (i.e. refugees, internally displaced, and asylum-seekers) who live in con ned settlements characterized by poor sanitation, poor housing infrastructure, limited access to health care services, inadequate malaria vector control, and economic deprivation [9,10]. Considering the complexity of malaria transmission dynamics, modeling the determinants of malaria presents numerous challenges in regards to inclusion of uncertainties, nonlinearity, and dynamism [11].…”

Section: Introductionmentioning

confidence: 99%

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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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…The household level risk factors of malaria infections are known to be complex, stochastic, nonlinear, multidimensional, and do not act in isolation [ 21 ]. In refugee settlements, these determinants are also linked to a range of closely related factors including poverty, low levels of education, low access to basic social services, inadequacy of some public policies, racism, sexism, and economic deprivation [ 9 , 46 ]. 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.…”

Section: Discussionmentioning

confidence: 99%

“…Malaria infections can even be more devastating among the structurally disadvantaged populations (i.e. refugees, internally displaced, and asylum-seekers) who live in confined settlements characterized by poor sanitation, poor housing infrastructure, limited access to health care services, inadequate malaria vector control, and economic deprivation [ 9 , 10 ]. Considering the complexity of malaria transmission dynamics, modelling the determinants of malaria presents numerous challenges in regards to inclusion of uncertainties, non-linearity, and dynamism [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

The impact of neglected tropical diseases on women and girl refugees: A call for increased awareness and strategic intervention

Cited by 2 publications

References 43 publications

Bayesian belief network modeling approach for predicting and ranking risk factors for malaria infections among children under five 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

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

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