Related Experiment Video
Updated: Feb 15, 2026
Isolation and Culture of Hippocampal Neurons from Prenatal Mice
Published on: July 26, 2012
Prenatal diagnosis and socioeconomic status in the non-invasive prenatal testing era: A population-based study
Lisa Hui1,2,3,4, Jenna Barclay1, Alice Poulton1
1Public Health Genetics, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia.
Background:
Advances in technology can bring great benefits to human health, but their implementation may be influenced by socioeconomic factors, particularly in the field of prenatal screening for Down syndrome.
Aim:
To analyse screening test indications for, and diagnostic yield of, invasive prenatal diagnostic testing (PNDx) according to socioeconomic status.
Methods:
Retrospective analysis of population-based data on PNDx and karyotype results for 2014-2015 in the Australian state of Victoria. Women having PNDx < 25 weeks due to combined first trimester screening (CFTS), second trimester serum screening (STSS), or noninvasive prenatal testing (NIPT) results were included. PNDx data were analysed by indication and maternal Index of Relative Socio-economic Advantage and Disadvantage (IRSAD), the latter determined by postcode.
Results:
There were 145 206 births in 2014-2015; 1906 women underwent PNDx for the indication of CFTS (70.1%), NIPT (17.8%) or STSS (12.0%). Covariates positively associated with NIPT-indicated PNDx, compared with CFTS-indicated testing, were residence in a region of socioeconomic advantage, metropolitan status and maternal age. Women from the most advantaged regions had higher adjusted odds ratios (aOR) of NIPT-indicated testing compared with women from disadvantaged regions (aOR 5.72, 95% CI: 2.95-11.09). The diagnostic yield of PNDx increased with socioeconomic region, from 14% in the lowest IRSAD quintile to 31.2% in the highest (P < 0.0001).
Conclusion:
Population-based data reveal significant disparities in screening indications for PNDx and hence, in diagnostic yield, according to socioeconomic region. This finding may have ethical and policy implications for prenatal screening in Australia.
Related Concept Videos
01:11Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
01:24Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:21Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
01:25What is Population Genetics?
00:30What are Populations and Communities?
02:04Conservation of Small Populations