Prevalence of anaemia in women of reproductive age in Meghalaya: A logistic regression analysis
To examine the prevalence of anaemia in Meghalaya, India, by exploring the 2005-2006 National Family Health Survey (NFHS -3) data. Materials and methods: The study population consisted of 3934 ever-married women of reproductive age (15-49 years) from the state Meghalaya, India, which were taken from the NFHS-3 survey, to explore the predictors responsible for the prevalence of anaemia by using different background characteristics such as age, place of residence, nutritional status, number of children ever born, pregnancy status, educational achievement, and economic status. As a response variable, anaemia levels were categorised as a dichotomous variable, and the predicted probabilities were worked out through a binary logistic regression model, to assess the contribution of the predictors on anaemia. Results: A logistic regression analysis was performed for some selected predictors related to anaemia levels. All the predictors, except total children ever born, were found to be statistically significant. The mean haemoglobin concentration was evaluated as 117.43 g/L, and 49.6% of the women were found to be anaemic. Women of the age group 20-24 years, are at high risk of anaemia [P = 0.320, O.R. (95% C.I.) = 1.509 (0.671, 3.390)]. Conclusion: Pregnant, under nutritious, and poorest women are at high risk of anaemia. Urban women are also at high risk; however, higher educated women are at low risk of anaemia. The habit of cigarette smoking/pan/bidi/gutka etc. also increases the risk of anaemia.
Prevalence of anaemia in women of reproductive age in Meghalaya: A logistic regression analysis
To examine the prevalence of anaemia in Meghalaya, India, by exploring the 2005-2006 National Family Health Survey (NFHS -3) data. Materials and methods: The study population consisted of 3934 ever-married women of reproductive age (15-49 years) from the state Meghalaya, India, which were taken from the NFHS-3 survey, to explore the predictors responsible for the prevalence of anaemia by using different background characteristics such as age, place of residence, nutritional status, number of children ever born, pregnancy status, educational achievement, and economic status. As a response variable, anaemia levels were categorised as a dichotomous variable, and the predicted probabilities were worked out through a binary logistic regression model, to assess the contribution of the predictors on anaemia. Results: A logistic regression analysis was performed for some selected predictors related to anaemia levels. All the predictors, except total children ever born, were found to be statistically significant. The mean haemoglobin concentration was evaluated as 117.43 g/L, and 49.6% of the women were found to be anaemic. Women of the age group 20-24 years, are at high risk of anaemia [P = 0.320, O.R. (95% C.I.) = 1.509 (0.671, 3.390)]. Conclusion: Pregnant, under nutritious, and poorest women are at high risk of anaemia. Urban women are also at high risk; however, higher educated women are at low risk of anaemia. The habit of cigarette smoking/pan/bidi/gutka etc. also increases the risk of anaemia.
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