Abstract
Childhood illnesses such as anaemia and malaria continue to pose significant health risks for children under five in sub-Saharan Africa. These conditions often co-occur, reflecting complex interdependencies driven by shared biological and environmental factors. Traditional analytical approaches typically assume independence between health outcomes, potentially overlooking meaningful associations. This study aims to identify the dependence structure among anaemia and malaria in children under five using a joint modeling approach. Data were drawn from the most recent Malaria Indicator Survey (MIS), comprising 5,755 children. A bivariate probit model was estimated using a Gaussian copula framework with probit margins for each outcome. This approach allowed for the simultaneous modeling of the marginal probability of each illness and the latent correlations among them. The model incorporated a range of child-, household-, and maternal-level covariates. Average Marginal Effects (AMEs) were calculated to facilitate interpretation. The copula correlation (ρ) between anaemia and malaria from the copula structure was estimated to quantify the dependence between the two illnesses. Descriptive analyses revealed substantial co-occurrence: 13.8% of children experienced both illnesses simultaneously. The joint copula model detected significant positive copula correlations (ρ ≈ 0.19) between anaemia and malaria. Several covariates displayed differential effects in the joint versus separate models. For instance, the association between maternal anaemia and child malaria risk reversed direction once interdependence was accounted for. Joint modeling yielded more stable and plausible estimates across outcomes. This study highlights the critical importance of accounting for outcome dependence in the analysis of child morbidity. The significant correlations among malaria and anaemia suggest that these illnesses are influenced by common or interacting risk factors. Joint modeling using a Gaussian copula framework enhances both the precision of marginal effect estimates and the understanding of comorbidity patterns. These findings support the design of integrated health interventions that address the interconnected nature of childhood illnesses in high-burden settings.