Preconception health care and congenital disorders: mathematical modelling of the impact of a preconception care programme on congenital disorders

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Abstract

Objective

This study aims to model the impact of preconception care on births with congenital disorders at a national level.

Design

Mathematical cross-sectional model based on life-table methodology.

Setting

Research conducted in Cambridge, United Kingdom.

Population

Women aged 15–45 years in England, 2001.

Methods

A mathematical model was constructed based on cross-sectional data from women aged 15–45 years in England undertaking one of three interventions, so as to reflect different strategies of preconception care: folic acid supplementation and fortification (representing national, universal interventions); alcohol intervention (reflecting primary care strategies); and diabetes management (targeting a population of high-risk women with a known chronic disease).

Main outcome measure

Reduction in the prevalence of congenital disorders at birth.

Results

Between 585 (lower estimate) and 1085 (upper estimate) congenital disorders could be prevented with a national preconception programme, based on a single-year national cohort in England. This represents an 8–15% reduction in annual notifications of congenital disorders in live births annually. According to modelled estimates, folic acid fortification or supplementation, alcohol intervention, and diabetic management may result in a 46, 32–62, 53, and 54% reduction in the live birth prevalence of specific congenital disorders, respectively. In an ideal scenario, the application of this model decreases the total annual number of congenital disorder notifications by approximately one-sixth.

Conclusion

A preconception care programme comprising three different strategies potentially can reduce the number of infants born with congenital disorders at a national level. This model provides strong support for preconception care to become a healthcare priority, and has major implications for healthcare planning.

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