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Weight, height, and midupper arm circumference are associated with haemoglobin levels in adolescent girls living in rural India: A cross-sectional study.

Anand S AhankariLaila J TataAndrew W Fogarty
Published in: Maternal & child nutrition (2019)
We aimed to explore the association of physical parameters with haemoglobin (Hb) levels to test the hypothesis that impaired physical development is associated with anaemia. A cross-sectional survey study recruited adolescent girls (13 to 17 years) living in rural areas of Maharashtra state of India. Data were collected on physical parameters include height, weight, and midupper arm circumference (MUAC). Hb levels were measured using Sahli's haemometer. Linear regression was conducted to test the hypothesis. Data were collected from 1,010 girls on physical parameter and Hb levels. The majority of the adolescent girls were diagnosed with anaemia (87%). The regression analysis adjusted for age gave a significant association of Hb levels with all three variables (MUAC, weight, and height). Hb increased by 0.11 g/dl with an each centimetre of increase in MUAC (95% confidence interval, CI, [0.08, 0.15], P < .001). Each kilogram of increase in the body weight showed an increase in Hb levels (0.02 g dl, 95% CI [0.01, 0.03], P = .001). With an each centimetre of increase in height, Hb increased by 0.01 g dl (95% CI [0.00, 0.02], P = .022). There was a consistent association between three measures of somatic growth and anaemia in the study population. It is likely that life-course exposures from conception onwards contribute to this, and the public health implications are that preventing anaemia is a challenge that requires a multifaceted interventional approach. Understanding the importance of the timing of these life exposures will help design interventions that can achieve optimal results.
Keyphrases
  • body mass index
  • physical activity
  • body weight
  • public health
  • mental health
  • weight gain
  • weight loss
  • air pollution
  • iron deficiency
  • risk factors
  • machine learning
  • data analysis
  • genome wide