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. 2021 Sep;24(13):4166-4176.
doi: 10.1017/S1368980020003109. Epub 2020 Sep 10.

Population attributable fraction estimates for factors associated with different types of anaemia among women in Ethiopia: multilevel multinomial analysis

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Population attributable fraction estimates for factors associated with different types of anaemia among women in Ethiopia: multilevel multinomial analysis

Kelemu Tilahun Kibret et al. Public Health Nutr. 2021 Sep.

Abstract

Objective: This study aimed to identify factors for different levels of anaemia among Ethiopian women and to estimate the population attributable fraction (PAF).

Design: This study was a detailed analysis of data of the 2016 Ethiopian Demographic and Health Survey data. Adjusted OR (AOR) with 95 % CI was computed using multilevel multinomial regression models, and the PAF were estimated using these AOR.

Setting: This study was conducted in Ethiopia.

Participants: Women of reproductive age.

Results: The PAF showed that the proportion of mild anaemia cases attributable to having no formal education was 14·6 % (95 % CI 3·4, 24·5), high gravidity (≥4) was 11·2 % (95 % CI 1·2, 19·9) and currently breast-feeding was 5·2 % (95 % CI 0·0, 10·7). Similarly, the proportion of moderate-severe anaemia cases attributable to being in a rural residence was 38·1 % (95 % CI 15·9, 54·8); poorest wealth quantile, 12·6 % (95 % CI 2·9, 24·6); giving birth in the last 5 years, 10·5 % (95 % CI 2·9, 18·2) and unimproved latrine facilities, 17 % (95 % CI 0, 32·5).

Conclusions: The PAF suggest that rural residency, low education, low wealth status, high parity, pregnancy and breast-feeding contribute substantially to the occurrence of anaemia among women in Ethiopia. Mild anaemia could be reduced by setting intervention strategies targeting women with low education, multigravida women and breast-feeding women, while preventing moderate-severe anaemia may require increasing income and improving living environments through the accessibility of hygienic latrines.

Keywords: Anaemia; Multinomial model; Population attributable fractions; Women.

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