Factors influencing childhood anaemia in Bangladesh: a two level logistic regression analysis

BackgroundAnemia is not only a major public health problem among children in developing countries, it is also an important predictor for their future growth and development. The objective of this study was to identify possible factors associated with anemia among pre-school children in Bangladesh after removing a cluster effect of the population, and to determine the prevalence of this condition.MethodsData for this study was extracted from the 2011 Bangladesh Demographic and Health Survey (BDHS-2011). In this survey, data was collected using a two-stage stratified cluster sampling approach. The chi-square test and a two-level logistic regression model were used for further analysis.ResultsData from 2231 children aged 6-59months were included for analysis. The prevalence of child anemia was noted to be 52.10%. Among these anemic children, 48.40% where from urban environment and 53.90% were from rural areas. The prevalence of mild, moderate and severe anemia among children was 57.10, 41.40 and 1.50% respectively. The two-level logistic regression model revealed that the following factors were associated with childhood anemia: children of anemic mothers (p<0.01), undernourished children (p<0.05), younger children (age<2years) (p<0.01) and children from poor family (p<0.05). Lastly, anemia was more common among children living in Barisal and Rangpur divisions compared to those from Dhaka division (p<0.01), and among non-Muslims than Muslim (p<0.05).ConclusionsOur study showed that prevalence of anemia among pre-school children in Bangladesh was very high (52.10%). We noted that young children of anemic mothers, from poor families, and being undernourished were at higher risk of developing anemia. Since most of these risk factors were related to socioeconomic conditions, they were potentially modifiable. Therefore, our findings may be useful for the health authorities to identify children at risk for remedial action and to plan for preventive measures.