Joint Regression and Association Modelling of Child Comorbidities in Uganda
Keywords:
Comorbidity, copula regression, joint modellingAbstract
Though morbidity in children living in low-income countries is commonly characterized by more than one health condition, studies to determine the determinants of child morbidity usually study one of the illnesses or a combination of the illnesses, but independently, thus ignoring possible dependencies. In many such cases a multivariate regression approach would be appropriate. Thus, in this paper, we specifically aimed at comorbidity among under-five children using joint response model that accommodates the interdependence between three illnesses (Diarrhoea, Acute Respiratory Infection (ARI) and fever) in assessing their risk factors. We considered child illness as a trivariate binary outcome (Y1, Y2, Y3) and carried out a trivariate copula regression model to jointly model Diarrhoea, ARI and fever. Older ages(3&4 years) mother having primary or higher education and not being in the poorest quintile are associated with reduced prevalence of ARI; while cooking with wood/straw and other fuels, rather than charcoal, is associated with increased prevalence of ARI. Being aged 1,2 or 4 years, higher level of education for the mother, urban residence, and not being in the poorest quintile are associated with reduced prevalence of fever; while, surprisingly having an improved source of drinking water is associated with increased prevalence of fever. Being 2 years or older , being in the three upper quintiles of household wealth and using charcoal for cooking are associated with reduced prevalence of diarrhea. Male children have higher prevalence of diarrhea. The three illnesses are strongly correlated with each other even after accounting for covariates at marginal level.This study has illustrated an easily applicable technique to joint modelling of common childhood illnesses which allows exploration of the correlations between them. We strongly recommend the use of this method for correlated outcomes, whatever the type of variable.
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