Spatial modeling of the association between distance to hospital emergency care and severe anemia among children aged 1−59 months in Busia County

Abstract

Background: Access to Emergency Care (EC) services is a core component towards ending preventable paediatric deaths under the SDG 3.2. Physical access to EC is one of the facets of access that has been shown to be associated with health outcomes. Standard regression models often used to assess the association have key limitations including failure to adjust for either spatial heterogeneity in the risk of outcomes or spatial autocorrelation in outcome incidence. This study aimed to develop a Bayesian Model-Based geostatistical model to assess the association between physical access to EC services and severe anaemia among anaemic paediatric admissions in Busia County Hospital using the INLA-SPDE framework and compare changes in the observed association with results from the standard logistic regression. Methods: Data from a hospital surveillance for paediatric admissions aged 1−59 months who reside in a malaria endemic setting and were anaemic were assembled. Four models were fitted, two under the INLA-SPDE framework and two under the standard logistic regression framework. Physical access was defined as village travel times to the county hospital and adjustments for known confounders were done including spatial variations in Plasmodium falciparum Prevalence Rate (Pf PR) as the underlying driver of anaemia. Differences in the travel time coefficients were assessed across the models. Results: In the developed model, INLA-SPDE model with spatially varying coefficient for Pf PR, the association between physical access to EC services and severe anaemia was significant only among admissions within 30−59 minutes of travel time (AOR: 1.94, 95% CI:1.18−3.08) when compared to admissions within < 30 minutes of travel time to Busia County Hospital. In the standard logistic regression models and standard INLA-SPDE model, the risk of severe anaemia was associated with poor physical access across all other admissions in comparison to admissions within < 30 minutes of travel time. However, coefficient confidence intervals under the standard INLA-SPDE model were wider compared to those in the standard logistic regression models. Conclusions: In assessing the association between physical access to EC services and health outcomes, it is vital to not only adjust for spatial heterogeneity in the underlying drivers of health outcomes, but also to appropriately model the association. Further, in the presence of spatial dependence, models should account for spatial autocorrelation so as not to underestimate standard errors.

KEY WORDS: Spatial heterogeneity, Spatial autocorrelation, INLA-SPDE, Anaemia, Malaria, Travel time