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Motherhood meets epidemiology: measuring risk factors for breast–feeding cessation

Published online by Cambridge University Press:  02 January 2007

Jennifer K Peat ,
Jane Allen ,
Nguyen Nguyen ,
Andrew Hayen ,
Wendy H Oddy  and
Seema Mihrshahi
Jennifer K Peat*
Affiliation:
Clinical Epidemiology Unit, The Children's Hospital at Westmead, Locked Bag 4001, Westmead, New South Wales 2145, Australia
Jane Allen
Affiliation:
James Fairfax Institute of Paediatric Nutrition, The Children's Hospital at Westmead, Westmead, Australia
Nguyen Nguyen
Affiliation:
Garvan Institute of Medical Research, St Vincent Hospital, Sydney, Australia
Andrew Hayen
Affiliation:
NSW Department of Health, Sydney, Australia
Wendy H Oddy
Affiliation:
School of Public Health, Curtin University of Technology, Telethon Institute for Child Health Research, Perth, Australia
Seema Mihrshahi
Affiliation:
Clinical Epidemiology Unit, The Children's Hospital at Westmead, Locked Bag 4001, Westmead, New South Wales 2145, Australia
*
*Corresponding author: Email jennifp2@chw.edu.au
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Abstract

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Objective:

To examine statistical models that have been used to predict the cessation of breast–feeding.

Setting:

In nutritional epidemiology, a knowledge of risk factors that lead to breast-feeding cessation is essential to promote optimal infant health by increasing or sustaining breast–feeding rates. However, a number of methodological issues complicate the measurement of such risk factors. It is important when building multivariate models that variables entered into the model are not intervening variables, factors on the causal pathway or surrogate outcomes. Inclusion of these types of variable can lead to inaccurate models and biased results. A factor often cited to predict breast–feeding is ‘intention to breast–feed’ prior to the birth of the infant, although this factor is directly on the causal decision–making pathway. Another factor often cited is the age of introduction of formula feeding, which is actually part of the outcome variable because formula feeding defines the difference between full, complementary and no breast-feeding. Rather than include these as risk factors in multivariate models, factors removed from the causal pathway such as influences of educational practices, including advice to complementary feed, and beliefs and attitudes of families and health-care practitioners should be measured.

Conclusions:

The accurate quantification of modifiable risk factors is essential for designing public health education campaigns that are effective in sustaining or increasing breast–feeding duration.

Keywords

Breast–feeding epidemiologyBreast-feeding promotionInfant healthRisk factorsStatistical methods
Type
Research Article
Information
Public Health Nutrition , Volume 7 , Issue 8 , December 2004 , pp. 1033 - 1037
Copyright
Copyright © The Authors 2004

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