Publications by authors named "Jonathan I Kennedy"
Article Synopsis
- * Researchers will analyze data from UK health records spanning from 2000 to 2019, examining various health outcomes during different stages: antenatal, peripartum, postnatal, and long-term mental health.
- * Ethical approval has been secured, and findings are set to be published in peer-reviewed journals and presented at major conferences for wider dissemination.
Article Synopsis
- The study looks at how many pregnant women are taking multiple medications, which has become more common over the last 20 years.
- It gathered data from a big medical records database to see how often women used 2 or more medicines during pregnancy.
- The findings show that about 25% of women used multiple medications in their first trimester, and some risk factors for this include being overweight, from certain ethnic groups, or being a smoker.
Background: Although maternal death is rare in the United Kingdom, 90% of these women had multiple health/social problems. This study aims to estimate the prevalence of pre-existing multimorbidity (two or more long-term physical or mental health conditions) in pregnant women in the United Kingdom (England, Northern Ireland, Wales and Scotland).
Study Design: Pregnant women aged 15-49 years with a conception date 1/1/2018 to 31/12/2018 were included in this population-based cross-sectional study, using routine healthcare datasets from primary care: Clinical Practice Research Datalink (CPRD, United Kingdom, n = 37,641) and Secure Anonymized Information Linkage databank (SAIL, Wales, n = 27,782), and secondary care: Scottish Morbidity Records with linked community prescribing data (SMR, Tayside and Fife, n = 6099).
Article Synopsis
- The study focuses on developing a core outcome set (COS) for maternal and offspring health in pregnant women who have pre-existing multimorbidity, which may negatively impact health outcomes.
- It employs a four-stage design: a systematic literature search, focus groups for personal insights, a Delphi survey for prioritization, and a consensus meeting to finalize the outcomes.
- Ethical approval has been granted by the University of Birmingham, ensuring the study follows proper ethical guidelines and will effectively gather and disseminate relevant findings.
(1) Background: We aimed to develop a transparent machine-learning (ML) framework to automatically identify patients with a condition from electronic health records (EHRs) via a parsimonious set of features. (2) Methods: We linked multiple sources of EHRs, including 917,496,869 primary care records and 40,656,805 secondary care records and 694,954 records from specialist surgeries between 2002 and 2012, to generate a unique dataset. Then, we treated patient identification as a problem of text classification and proposed a transparent disease-phenotyping framework.
View Article and Find Full Text PDFBackground: Patients' smoking status is routinely collected by General Practitioners (GP) in UK primary health care. There is an abundance of Read codes pertaining to smoking, including those relating to smoking cessation therapy, prescription, and administration codes, in addition to the more regularly employed smoking status codes. Large databases of primary care data are increasingly used for epidemiological analysis; smoking status is an important covariate in many such analyses.
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