Air pollution and preterm birth
The effects of air pollution on pregnancy outcomes and preterm birth
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On 14 Nov 2024, the House of Lords Preterm Birth Committee published evidence on the challenges of preventing preterm birth and the importance of lowering the rate in the UK from 8% to 6% by 2025. The global rate of preterm birth remains staggeringly high and has been estimated by the World Health Organisation to be around 9-18%, making it the leading cause of death in children under 5 years old. The House of Lords recommendations emphasise the crucial need of research to address the disparities in preterm birth rates and outcomes across different socioeconomic and ethnic groups. Our vision is to identify the women at greatest risk of preterm birth, which is necessary to understand the complex pathological mechanisms more effectively targeting preventative treatments. To date, most pre-clinical research has focused on using animal models to model the pathophysiology. However, studies in mice cannot be used for early prediction to identify risk, and subsequent prevention of preterm birth.
We are an inter-disciplinary team of UK scientists with expertise in obstetrics, women’s and child health, epidemiology, climate science, inflammation, computational modelling, machine learning and artificial intelligence. Together we have a long history with existing strengths underlying preterm birth research that crosses multiple disciplines in preterm birth.
Our project aims to predict the effects of air pollution exposure on pregnancy outcomes and risk of preterm birth. Air pollution is known to be associated with an increased risk of preterm birth, poor placental function, pre-eclampsia, and poor fetal growth. Whilst deep learning models can consider large numbers of variables and high dimensional data to form predictions, we need consistency in the data from electronic patient health records (EPIC) in order to link with a spatiotemporal model for ambient pollution levels in London (Arain et al., 2023).
We aim to develop and validate a deep learning model to predict the risk of preterm birth and other adverse pregnancy outcomes using data from EPIC electronic health records at UCLH for a cohort of 18000 patients. We will obtain corresponding data on exposure to ambient pollution using non-identifiers for postcode (area) and date of delivery (month). The model will review the temporal sequence of events within a patient’s medical history and current pregnancy, identifying significant interactions and will predict the risk of preterm birth. It will also determine the threshold and gestation at which pollution exposure has the greatest impact.
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Please contact any of following people for more information:
Tina Chowdhury
Senior Lecturer in Regenerative Medicine
Tina Chowdhury is a Reader in Regenerative Medicine at the Centre for Bioengineering, QMUL and is Chief Investigator of the clinical trial said: “Preterm birth is the leading cause of neonatal death world-wide. Children born prematurely have higher rates of diseases and life-long disabilities. We are developing algorithms that link patient data on air pollution exposure to identify the women who are at greater risk of delivering preterm, according to their genetics, lifestyle and environmental situation. At the end of the clinical trial, we will know the timepoints at which pollution exposure has the greatest impact on the mother and baby.”
Anna David
Professor and Consultant in obstetrics and maternal/fetal medicine at UCLH
Director for the EGA Institute for Women’s Health at UCL
Anna David has said “research shows that there are significant inequalities in health between men and women, who are rich or poor and who live in urban or rural communities and are from different ethnicity. We need to educate young people on how their likelihood of starting a family and having healthy children is affected by air pollution, smoking, obesity, alcohol, diet and other life-style factors.”
View Our Synthetic Data:
The information you see here is synthetic data. It’s not real data, does not contain real patient details, and cannot be traced back to any real patients. It is only designed to look like real health records.
We created these data to show the kind of information used in a research project called Pollution in Preterm Birth. This study looks at how air pollution may affect babies who are born too early. The project is led by Tina Chowdhury, a researcher in regenerative medicine.
Because this data is randomly generated using a tool called datafaker, some parts may not make sense — for example, a birth date might appear after a death date. That’s because the columns are made separately and don’t always link together in a realistic way.
Please note that this data is part of our preliminary version of synthetic datasets. We’re actively improving our process so that over time, more datasets will be available, and the data will look more and more like real-world data, without ever containing any real patient details.
This dataset is only for demonstration and learning purposes. Any similarity to real people is purely coincidental.
How to browse our synthetic data:
1) In the embedded table above, click the ‘view’ button next to the file you’d like to look at.
2) A new window will open up to Figshare, where the file is stored. You will see a collection of tiles containing the file folder on the top half of the page, and a project description on the bottom half of the page.
3) To view the data in your web browser, click the ‘eye’ icon on your desired file tile.
4) The tabular data will display in your browser. You can expand the screen as needed using the double headed arrow ‘full screen’ icon in the bottom right corner of the table.
5) To download the data, click the ‘download file’ icon on your desired file tile.
6) The files are in CSV format, which is like a simple version of an Excel spreadsheet.
Tip: Each row in the file is a ‘record’ (like a line in a spreadsheet), and each column is a type of information (like date, condition, or measurement).
