The impact of prenatal diagnosis on the occurrence of chromosome abnormalities
- 1 January 1986
- journal article
- research article
- Published by Wiley in Prenatal Diagnosis
- Vol. 6 (1) , 1-11
- https://doi.org/10.1002/pd.1970060102
Abstract
From the public health point of view, several formal attempts have been made to measure the impact of prenatal diagnosis (PND) on the incidence of Down's Syndrome (DS), but the results have varied widely. The impact of PND (reduction in the birth rate of chromosomally abnormal neonates) is related to utilization rates but quantitative estimates of this have not been established. In a three‐year (1981–1983) total population study from Queensland, Australia, we present results to measure the impact of a voluntary PND programme on the birth incidence of DS, and also other chromosomally abnormal births. Utilization rates for the PND service were 15·5 per cent in that population of mothers 35 years and over. Numbers and rates of all cases of chromosomal abnormalities are presented, subclassified by type of diagnosis–‐either by PND or by clinical diagnosis after birth. For the total population, 7·3 per cent of cases of DS were detected prenatally, and 15·4 per cent of all chromosome abnormalities. (A method for measuring the impact of PND is described.) Using this in conjunction with our demographic data, we estimate that with a 15 per cent utilization rate of PND by older mothers, 14 per cent of DS births can be prevented in this age group, or a 5 per cent overall reduction can be achieved if mothers of all ages are considered. One index–‐the ratio of the percentage of DS births which are preventable compared with the population utilization rates of PND–‐has potential for widespread use. Queensland data for this ratio is 0·34, a figure consistent with that from other studies. Thus a 3·5 per cent drop in the overall DS birth rate may be expected for each 10 per cent increase in the utilization rates of PND for mothers of 35 years and over. A diagram is presented which may serve as a model for improved data collection and better impact estimates in the future.Keywords
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