A Bayesian multinomial modeling of spatial pattern of co-morbidity of malaria and non-malarial febrile illness among young children in Nigeria.
BACKGROUND
Children in developing countries continue to suffer mortality and morbidity from a number of illnesses, among which are malaria and non-malarial febrile illnesses, which epidemiologically overlap. We examined the spatial pattern and risk factors of co-morbidity of malaria and non-malarial febrile illness among children aged 6-59 months in Nigeria.


METHODS
Using data from the 2010 Nigeria Malaria Indicator Survey, we considered the co-morbidity of malaria and non-malarial febrile illness among the children as multicategorical and selected a mixed multinomial logit model capable of incorporating covariates of different types. Inference was Bayesian, based on multicategorical linear mixed-model representation.


RESULTS
We found that the risk of co-morbidity of malaria and non-malarial febrile illness increases as a child advances in age while the risk of non-malarial fever reduces after about 32 months of age. Area of residence (urban or rural), wealth index and type of roofing material used in the dwelling are other important risk factors for the co-morbidity found in this study. Further, children from four of Nigeria's 37 states are at high risk of malaria.


CONCLUSIONS
Disease preventive measures need to be intensified, with more focus on rural areas and the poor. Campaigns for use of insecticide-treated bed nets need be more aggressive in all Nigerian states.
Department of Mathematical Sciences, Redeemer's University, Redemption City, Nigeria ezrascribe@yahoo.com.
Ezra
E
Gayawan
PLAN-Health Project, Management Sciences for Health, Abuja, Nigeria.
Ekundayo D
ED
Arogundade
Planning, Research and Statistics Directorate, National Agency for Food and Drug Administration and Control, Abuja, Nigeria.
Samson B
SB
Adebayo
D000367:Age Factors; D001499:Bayes Theorem; D001512:Bedding and Linens / Q000706:statistics & numerical data; D002675:Child, Preschool; D015897:Comorbidity / Q000639:trends; D003252:Construction Materials / Q000009:adverse effects; D005260:Female; D005334:Fever / Q000453:epidemiology* / Q000517:prevention & control*; D062305:Geographic Mapping; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007306:Insecticides / Q000008:administration & dosage; D016014:Linear Models; D008288:Malaria / Q000453:epidemiology* / Q000517:prevention & control*; D008297:Male; D015233:Models, Statistical; D009032:Mosquito Control / Q000379:methods; D009549:Nigeria / Q000453:epidemiology; D012307:Risk Factors; D012424:Rural Population; D014505:Urban Population
D007306:Insecticides
Effective disease management; Insecticide-treated net; Malaria endemic regions; Nigeria; Plasmodium falciparum; Spatial analysis