Debbie Lawlor

ORCID iD
https://orcid.org/0000-0002-6793-2262
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University of Bristol profile

Sources:
Pure - University of Bristol Research Information System (2017-09-14)

Biography

I have a background in clinical medicine and public health practice which inform my more academic work. My academic career began in 2000, with completion of my PhD in 2003. Since then I have become an internationally recognised lead in aetiological epidemiology, established a multidisciplinary team of excellent researchers and held several institutional, national and international leadership roles. In 2012 I was elected to be a member of the Academy of Medical Sciences, and was also awarded National Institute of Health Research Senior Investigator status in the same year (this was renewed in 2017). For the two most recent years of enumeration (2015 and 2016) I have been identified as a Thomson Reuters Highly Cited Scientist (in the top 1% of my field). In 2017 I was awarded Commander of the British Empire (CBE) in the Queen’s Birthday Honours, in recognition of my research excellence and its impact. My research is underpinned by my interest in understanding how biological, social and environmental exposures from across the life course affect women's reproductive health and cardiometabolic health in women and men, in order to be able to develop methods to achieve optimal health in these areas. I have contributed to understanding the life course and genetic epidemiology of obesity, diabetes, cardiovascular disease and women’s reproductive health; with a particular interest in the relationship between women’s reproductive health and her, and that of her offspring’s and the next generation’s cardiometabolic risk. I am interested in developing methods for improving causal inference in epidemiology and have been at the forefront of developing and genetic variants as instrumental variables for making causal inference about modifiable non-genetic risk factors and appropriately comparing findings from such studies with those of other approaches, with differing assumptions and key sources of bias, to achieve best causal evidence.
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