Reproductive risk factors assessment for anaemia among pregnant women in India using a multinomial logistic regression model

Abstract

Objective: To assess reproductive risk factors for anaemia among pregnant women in urban and rural areas of India.

Method: The International Institute of Population Sciences, India, carried out third National Family Health Survey in 2005-2006 to estimate a key indicator from a sample of ever-married women in the reproductive age group 15-49 years. Data on various dimensions were collected using a structured questionnaire, and anaemia was measured using a portable HemoCue instrument. Anaemia prevalence among pregnant women was compared between rural and urban areas using chi-square test and odds ratio. Multinomial logistic regression analysis was used to determine risk factors.

Results: Anaemia prevalence was assessed among 3355 pregnant women from rural areas and 1962 pregnant women from urban areas. Moderate-to-severe anaemia in rural areas (32.4%) is significantly more common than in urban areas (27.3%) with an excess risk of 30%. Gestational age specific prevalence of anaemia significantly increases in rural areas after 6 months. Pregnancy duration is a significant risk factor in both urban and rural areas. In rural areas, increasing age at marriage and mass media exposure are significant protective factors of anaemia. However, more births in the last five years, alcohol consumption and smoking habits are significant risk factors.

Conclusion: In rural areas, various reproductive factors and lifestyle characteristics constitute significant risk factors for moderate-to-severe anaemia. Therefore, intensive health education on reproductive practices and the impact of lifestyle characteristics are warranted to reduce anaemia prevalence.

Keywords: Anemia; Inde; India; Mujeres embarazadas; Regresión logística multinomial; anaemia; anémie; facteurs de risque de la reproduction; factores de riesgo reproductivos; femmes enceintes; multinomial logistic regression; pregnant women; reproductive risk factors; régression logistique multinomiale.