Exploring pregnancy intention and pregnancy outcomes
Exploring determinants of pregnancy intention and relationships between pregnancy intention and outcome using routine data
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Despite everyone’s best efforts, unplanned pregnancies will always occur. Evidence suggests that they are linked with poor outcomes, such as low birthweight, being born too early (preterm birth) and depression after birth. However, there are some problems with the data, and there is none from England.
To find women with unplanned pregnancies we have introduced some questions into antenatal care at University College London Hospital, Homerton University Hospital and Guy's and St Thomas' Hospital. To know how best to support women with an unplanned pregnancy we need to know how unplanned pregnancies are linked to poor outcomes. We will use the data from these questions, as well as other information about the woman, to explore if and how unplanned pregnancies are associated with things like the baby being born too early or being low birthweight. We will use these findings to design services to meet the needs of these women. The aim of these services will be to prevent poor outcomes such as low birthweight.
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Please contact any of following people for more information:
Professor Jennifer Hall
jennifer.hall@ucl.ac.ukDuncan Cartner
duncan.cartner@nhs.net
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 Exploring pregnancy intention and pregnancy outcomes. This study looks at if and how unplanned pregnancies are associated with poor outcomes. The project is led by Jennifer Hall, a Professor of Reproductive Health at the UCL Institute for Women’s Health and Co-Director of the NIHR Policy Research Unit in Reproductive Health
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.
View Our Synthetic Data:
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).
