Original Research

Maternal anthropometry and pregnancy outcomes: a proposal for the monitoring of pregnancy weight gain in outpatient clinics in South Africa

HS Kruger
Curationis | Vol 28, No 4 | a1012 | DOI: https://doi.org/10.4102/curationis.v28i4.1012 | © 2005 HS Kruger | This work is licensed under CC Attribution 4.0
Submitted: 28 September 2005 | Published: 28 September 2005

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HS Kruger, School of physiology, nutrition and consumer sciences, North-West University, South Africa

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The aim of this review was to develop a framework for the monitoring of pregnancy weight gain in South African outpatient clinics. Studies showed that intrauterine malnutrition have more serious consequences for children than postnatal malnutrition. Undernutrition, as well as overnutrition during pregnancy, was associated with adverse pregnancy outcomes. The IOM published recommended weight gains by prepregnancy body mass index (BMI). Wasting in pregnant women can be defined as a mid-upperarm circumference (MUAC) < 22cm. Low prepregnancy BMI is considered a risk factor for preterm birth and intra-uterine growth retardation. Pregnant women in developing countries start to attend antenatal clinics late in pregnancy, so that prepregnancy BMI may be unknown and antenatal care can be based on pregnancy weight gain only. A framework is proposed that identifies the critical points for action during pregnancy to improve birth outcomes. Health care providers should measure height, weight and MUAC and try to classify pregnant women according to weight status, set weight gain goals and monitor gestational weight gain between follow-up visits. Women with short stature (<145cm), low body weight (<45kg), and/or MUAC<22cm are considered to be at risk of adverse pregnancy outcomes. Weekly weight gains should range from 0.3kg for overweight women to 0.5kg or more for underweight women from the second trimester. Genetic background, age, general health, HIV and educational status, cigarette smoking, past nutritional status of the mother, parity, multiple pregnancies, climate, socioeconomic conditions and the availability of health services should be adjusted for in statistical analyses.


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