Multilevel modelling of sociodemographic predictors of various levels of anaemia among women in Mali

Abstract

Objective: Anaemia currently affects 40-80 % of women in Africa. While risk factors for anaemia have been intensively studied, research has rarely compared risk factors between mild anaemia and moderate/severe anaemia. Also, the contribution of neighbourhood to the prevalence of anaemia has been rarely studied. The aim of the present study was to identify and compare individual and contextual factors associated with various levels of anaemia among women. Design: A multilevel analysis of data from the 2001 Mali Demographic and Health Survey (n 3763) was carried out. Outcomes variables were mild anaemia, moderate-to-severe anaemia and any anaemia. Multilevel regression analyses were performed for each outcome. Setting: Mali, West Africa. Subjects: Women (n 3763) aged 15 to 49 years, including 512 pregnant women. Results: Among the eleven potential risk factors included in the models, two factors were associated with mild anaemia (BMI and education), three with any anaemia (pregnancy, BMI and education) and six with moderate-to-severe anaemia (pregnancy, BMI, education, wealth, childhood residency and region of residence). Clustering of anaemia within communities was 20 % for moderateto-severe anaemia and 13 % for mild anaemia. Despite significant differences in the prevalence of anaemia across regions in Mali, no difference between regions in the risk of mild anaemia was found and only the region of Gao showed a significantly higher risk of moderate-to-severe anaemia. Conclusions: The findings indicate that sociodemographic risk factors as well as clustering of anaemia varies with the severity of anaemia. Specific studies are needed to identify risk factors of mild anaemia as well as its consequences, as mild anaemia accounts for 20-40 % of total prevalence of anaemia in Africa.

Prevalence and risk factors for anemia severity and type in Malawian men and women: urban and rural differences

“…Though studies have shown associations between low socioeconomic status and all anemia levels, our findings indicate that women with higher educational levels had higher odds of mild anemia [32, 34, 39]. We observed gender-specific variations in prevalence of anemia among different occupation groups.…”

“…Many studies have shown inverse relationships between BMI and anemia [31, 32, 34, 42]. A multi-country, multi-level analysis of hemoglobin levels of African women showed stronger associations of anemia with socioeconomic and contextual factors than with BMI [11].…”

“…Though misclassification of exposure variables is likely to be minimal, exposure measured at time of survey may not reflect past exposure that may be important for a chronic condition such as anemia. Previous research has shown that place of long-term childhood residence may influence anemia risk more than place of current residence and the complete effect of SES on health may not be captured by measuring SES at a single point in time [32, 52, 53]. Finally, the large number of missing HIV and anemia observations may have introduced bias in to the sampling.…”

Spatial and social factors drive anemia in Congolese women

“…Since 1985, global prevalence estimates for anemia have risen drastically (Stoltzfus, 2001). Estimates are particularly high in sub-Saharan Africa, where 40-80% of women are estimated to be anemic (Ngnie-Teta et al, 2007b, Ngnie-Teta, 2009). In the Democratic Republic of the Congo (DRC), 52.8% of non-pregnant women and 67.3% of pregnant women were estimated by the WHO to be anemic (less than 11 g/dl hemoglobin in the blood), making anemia a severe public health problem in the country (Who, 2008).…”

“…In women, anemia is associated with higher risk for maternal morbidity and mortality and lower productivity, and pregnant women are furthermore at higher risk for anemia. Maternal anemia may also lead to higher risks for premature births, perinatal and neonatal death, and low birth weight (Ngnie-Teta, 2009). Common symptoms include fatigue, weakness, fainting, chest pain, and even heart attacks in severe cases.…”