Joint modeling of Anaemia and Malaria in children under five in Nigeria

Adebayo, Samson B.; Gayawan, Ezra; Heumann, Christian; Seiler, Christian

doi:10.1016/j.sste.2016.04.011

Cited by 31 publications

(70 citation statements)

References 20 publications

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“…Very few studies have jointly modelled anaemia and malaria. The studies that have done so have also utilised different techniques and thus answered different questions [ 3 , 49 ]. A bivariate probit model was used to jointly model anaemia and malaria in individuals between the ages of 15 and 60 in Alaba District, Southern Ethiopia, the result of which showed a positive correlation between malaria and anaemia; however, the magnitude of the correlation was not explored [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

“…The causes of anaemia in children are multifactorial and include malaria. In regions that are highly malaria endemic, malaria is one of the most common causes of childhood anaemia; however, severe anaemia can augment malaria morbidity and mortality in these regions [ 3 ]. Young children are yet to develop an immunity to malaria, therefore are more vulnerable.…”

Section: Introductionmentioning

confidence: 99%

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Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda

Roberts

Zewotir

2020

J Health Popul Nutr

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Background Anaemia and malaria are the leading causes of sub-Saharan African childhood morbidity and mortality. This study aimed to explore the complex relationship between anaemia and malaria in young children across the districts or counties of four contiguous sub-Saharan African countries, namely Kenya, Malawi, Tanzania and Uganda, while accounting for the effects of socio-economic, demographic and environmental factors. Geospatial maps were constructed to visualise the relationship between the two responses across the districts of the countries. Methods A joint bivariate copula regression model was used, which estimates the correlation between the two responses conditional on the linear, non-linear and spatial effects of the explanatory variables considered. The copula framework allows the dependency structure between the responses to be isolated from their marginal distributions. The association between the two responses was set to vary according to the district of residence across the four countries. Results The study revealed a positive association between anaemia and malaria throughout the districts, the strength of which varied across the districts of the four countries. Due to this heterogeneous association between anaemia and malaria, we further considered the joint probability of each combination of outcome of anaemia and malaria to further reveal more about the relationship between the responses. A considerable number of districts had a high joint probability of a child being anaemic but not having malaria. This might suggest the existence of other significant drivers of childhood anaemia in these districts. Conclusions This study presents an alternative technique to joint modelling of anaemia and malaria in young children which assists in understanding more about their relationship compared to techniques of multivariate modelling. The approach used in this study can aid in visualising the relationship through mapping of their correlation and joint probabilities. These maps produced can then help policy makers target the correct set of interventions, or prevent the use of incorrect interventions, particularly for childhood anaemia, the causes of which are multiple and complex.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda

Roberts

Zewotir

2020

J Health Popul Nutr

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“…Geoadditive Bayesian models have been used and described in details in several studies [32][33][34]. In brevity, suppose y i is the survival status of an infected individual i at location s i and υ is a vector of observed covariates, which could be categorical or continuous.…”

Section: Discussionmentioning

confidence: 99%

Spatial Modelling of Contribution of Individual Level Risk Factors for Mortality From Middle East Respiratory Syndrome Coronavirus in the Arabian Peninsula

Adegboye¹,

Gayawan²,

Hanna³

2017

Preprint

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“…Our approach to spatial analysis is based on the framework of structured additive regression model [ 21 ]. Geoadditive Bayesian models have been used and described in details in several studies [ 32 – 34 ]. In brevity, suppose y i is the survival status of an infected individual i at location s i and υ is a vector of observed covariates, which could be categorical or continuous.…”

Section: Methodsmentioning

confidence: 99%

Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula

2017

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BackgroundMiddle East respiratory syndrome coronavirus is a contagious respiratory pathogen that is contracted via close contact with an infected subject. Transmission of the pathogen has occurred through animal-to-human contact at first followed by human-to-human contact within families and health care facilities.Data and methodsThis study is based on a retrospective analysis of the Middle East respiratory syndrome coronavirus outbreak in the Kingdom of Saudi Arabia between June 2012 and July 2015. A Geoadditive variable model for binary outcomes was applied to account for both individual level risk factors as well spatial variation via a fully Bayesian approach.ResultsOut of 959 confirmed cases, 642 (67%) were males and 317 (33%) had died. Three hundred and sixty four (38%) cases occurred in Ar Riyad province, while 325 (34%) cases occurred in Makkah. Individuals with some comorbidity had a significantly higher likelihood of dying from MERS-CoV compared with those who did not suffer comorbidity [Odds ratio (OR) = 2.071; 95% confidence interval (CI): 1.307, 3.263]. Health-care workers were significantly less likely to die from the disease compared with non-health workers [OR = 0.372, 95% CI: 0.151, 0.827]. Patients who had fatal clinical experience and those with clinical and subclinical experiences were equally less likely to die from the disease compared with patients who did not have fatal clinical experience and those without clinical and subclinical experiences respectively. The odds of dying from the disease was found to increase as age increased beyond 25 years and was much higher for individuals with any underlying comorbidities.ConclusionInterventions to minimize mortality from the Middle East respiratory syndrome coronavirus should particularly focus individuals with comorbidity, non-health-care workers, patients with no clinical fatal experience, and patients without any clinical and subclinical experiences.

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Joint modeling of Anaemia and Malaria in children under five in Nigeria

Cited by 31 publications

References 20 publications

Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda

Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda

Spatial Modelling of Contribution of Individual Level Risk Factors for Mortality From Middle East Respiratory Syndrome Coronavirus in the Arabian Peninsula

Spatial modelling of contribution of individual level risk factors for mortality from Middle East respiratory syndrome coronavirus in the Arabian Peninsula

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