As a company dedicated to harnessing cutting-edge technology in the field of healthcare, we are proud to say that many members of our team hail from a rigorous academic background. One of our data scientists, Dr Rob Eyre, has published a paper in the peer-reviewed journal, Emerging Themes in Epidemiology.
Rob’s paper looks at modelling fertility in a poor rural region of South Africa, using an innovative non-linear approach. The full paper can be found here.
A common issue throughout much quantitative Public Health research is the application of a range of standardised statistical methods – even when such methods are not appropriate. Such standard methods frequently assume that the relationships being modelled are linear, in spite of the fact that this assumption is often unjustified. One such area where this is the case is in the modelling of how fertility changes over different socio-economic characteristics (such as age, education and social status).
A core aspect of the work we do here at Spectra Analytics (now Patchs Health) involves using more modern, sophisticated and well-thought-out methods in order to provide better results to our clients. In line with this, Rob’s research used an innovative combination of a non-linear parametric model of fertility over age, with the use of the highly flexible semi-parametric machine learning method of Gaussian process regression. This brought in further variables such as socio-economic status, for which no established fertility pattern model exists.
Rob and his research colleagues – Thomas House of the University of Manchester, F. Xavier Gómez-Olivé of the Agincourt research unit in South Africa, and Frances Griffiths of the University of Warwick – successfully applied this method to data from the Agincourt Health and Socio-Demographic Surveillance System (HDSS), run by the Medical Research Council/University of the Witwatersrand Rural Public Health and Health Transitions (Agincourt) Research Unit. This is an annual census performed in a poor rural region of South Africa, collecting information on births, deaths, migration, and many different health aspects. The results of their analysis provided more robust and reliable estimates of the fertility patterns within the Agincourt study area – free from unjustified assumptions of linearity.
The researchers hope that their work will encourage others working in fertility modelling to look beyond standard methodology and be more thoughtful about which methods they use. They also hope that everyone working in the field will remain mindful about the assumptions they make when using these methods.
(Image by A Khosa, courtesy of agincourt.co.za)