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Economic evaluation of enhanced staff contact for the promotion of breastfeeding for low birth weight infants

Published online by Cambridge University Press:  15 April 2010

Stephen J. C. Rice
Affiliation:
University of York
Dawn Craig
Affiliation:
University of York
Felicia McCormick
Affiliation:
University of York
Mary J. Renfrew
Affiliation:
University of York
Anthony F. Williams
Affiliation:
University of London
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Abstract

Objectives: There is evidence that breastmilk feeding reduces mortality and short and long-term morbidity among infants born too soon or too small. The aim of this study was to evaluate the cost-effectiveness of enhanced staff contact for mothers with infants in a neonatal unit with a birth weight of 500–2,500 g from the perspective of the UK National Health Service.

Methods: A decision-tree model linked clinical outcomes with long-term health outcomes. The study population was divided into three weight bands: 500–999 g, 1000–1,749 g, and 1,750–2,500 g. Clinical and resource use data were obtained from literature reviews. The measure of benefit was quality-adjusted life-years. Uncertainty was evaluated using cost-effectiveness acceptability curves and sensitivity analyses.

Results: The intervention was less costly and more effective than the comparator in the base–case analysis for each birth weight group. The results were quite robust to the sensitivity analyses performed.

Conclusions: This is the first economic evaluation in this complex field and offers a model to be developed in future research. The results provide preliminary indications that enhanced staff contact may be cost-effective. However, the limited evidence available, and the limited UK data in particular, suggest that further research is required to provide results with confidence.

Information

Type
ASSESSMENTS
Copyright
Copyright © Cambridge University Press 2010
Figure 0

Figure 1. The decision tree for the enhanced staff contact intervention. Circles indicate that the following states are due to chance. A triangle indicates that the preceding state is the end state. The nodes with the same numbers share the same possible following events. The second part of the decision tree follows on from the first part where node 3 in the second part corresponds to node 3 in the first part.

Figure 1

Table 1. Odds ratios used in model

Figure 2

Table 2. Utility, life expectancy, and cost data used in the model

Figure 3

Table 3. Base–case results from the model

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