Bayesian spatio-temporal modelling of child anemia in Ethiopia using conditional autoregressive model

Household environment associated with anaemia among children aged 6-59 months in Ethiopia: a multilevel analysis of Ethiopia demographic and health survey (2005-2016)

BACKGROUND

Anaemia continues to be a major public health challenge globally, including in Ethiopia. Previous studies have suggested that improved household environmental conditions may reduce anaemia prevalence; however, population-level evidence of this link is lacking in low-income countries. Therefore, this study aimed to examine the association between environmental factors and childhood anaemia in Ethiopia.

METHODS

In this study, we conducted an analysis of the data from the Ethiopian Demographic and Health Survey (EDHS), a nationally representative population-based survey conducted in Ethiopia between 2005 and 2016. The study included a total of 21,918 children aged 6-59 months. Children were considered anemic if their hemoglobin (Hb) concentration was less than 11.0 g/dl. To examine the association between environmental factors and anemia, we used multilevel mixed-effect models. These models allowed us to control for various confounding factors including: child, maternal, household and community-level variables. The study findings have been reported as adjusted odds ratios (AORs) along with 95% confidence intervals (CIs) at a significance level of p < 0.05.

RESULTS

The study found the overall prevalence of childhood anaemia to be 49.3% (95%CI: 48.7-49.9) between 2005 and 2016 in Ethiopia. The prevalence was 47.6% (95%CI: 46.1-49.1) in 2005, 42.8% (95%CI: 41.8-43.8) in 2011, and increased to 57.4% (95%CI: 56.3-58.4) in 2016. The pooled data showed that children from households practising open defecation were more likely to be anaemic (AOR: 1.19, 95% CI: 1.05-1.36). In our survey specify analysis, the odds of anaemia were higher among children from households practising open defecation (AOR: 1.33, 95% CI: 1.12-1.58) in the EDHS-2011 and EDHS-2016 (AOR: 1.49, 95% CI: 1.13-1.90). In contrast, neither household water sources nor the time to obtain water was associated with anaemia after controlling for potential confounders. The other variables significantly associated with childhood anaemia include: the child's age (6-35 months), not fully vaccinated (AOR: 1.14, 95%CI: 1.05-1.24), children not dewormed in the last 6 months (AOR: 1.11, 95%CI: 1.01-1.24), children born to mothers not working (AOR: 1.10, 95%CI: 1.02-1.19), children from poor households (AOR: 1.18: 95%CI: 1.06-1.31), and rural residence (AOR: 1.23, 95%CI: 1.06-1.42).

CONCLUSION

In Ethiopia, about fifty percent of children suffer from childhood anemia, making it a serious public health issue. Open defecation is a major contributing factor to this scourge. To address this issue effectively, it is recommended to strengthen initiatives aimed at eliminating open defecation that involve various approaches, including sanitation infrastructure development, behavior change campaigns, and policy interventions. In addition, to reduce the burden of anemia in children, a multi-faceted approach is necessary, involving both prevention and treatment strategies.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.