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Institute of Epidemiology & Health Care

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Centre for Electronic Health Records research And Methodology (CEHRAM)

CEHRAM involves researchers working on a range of clinical and methodological topics in collaboration with colleagues in London, the UK and around the world. We have expertise in statistics, epidemiology, health economics and data science.

We work with electronic health records; for example, the Clinical Practice Research Datalink (CPRD) via the UCL Clinical Data Science and Technology Platform (STP).  

What we do

Using different data sources and methods we work on topics such as: 

Clinical: 

  • Long-term consequences of perinatal depression. 

  • How women use primary care services after childbirth? 

  • Who is prescribed psychotropic medication in the UK primary care? 

  • Treatment of anxiety and depression in people with dementia. 

  • Prediction of suicide using electronic health records. 

  • Are severe mental illnesses a risk factor for osteoporosis and fragility in primary care? 

  • How antipsychotic prescriptions are linked to long-term physical health conditions such as diabetes or cardiovascular diseases? 

  • The combined effect of antipsychotics and statins on diabetes risk in people with severe mental illnesses. 

  • Prescription of psychotropics in people with dementia and how this is linked to cardiovascular diseases and mortality risk . 

  • Cost-effectiveness of inpatient mental health rehabilitation services provided by the NHS and independent sector. 

  • A cluster-randomised clinical and health economic feasibility study of a theory-based manualised intervention to support people with tuberculosis disease during treatment. 

  • How long are menopausal women prescribed Hormone Replacement Therapy (HRT) for? 

  • How long are menopausal women prescribed Hormone Replacement Therapy (HRT) for? 

  • Are older statin users who start taking an antipsychotic medication at greater risk of developing diabetes? 

  • The prevalence of self-reported anxiety and depression in the Our Future Health cohort and how well these estimates are supported by evidence from linked hospital records and validated questionnaires. 

Methodological:    

  • New methods to deal with missing data in electronic health records. 

  • Innovative study designs and analytical methods to answer. clinical/epidemiological questions with routinely collected data.  

  • These methods include machine and deep learning tools, statistical methods, and sampling procedures (e.g., for causal inference). 

  • Most of these methods are developed and validated to support our ongoing clinical research. 

Who we are

Professor Irene Petersen photo
 

Irene Petersen (CEHRAM Director) is a Professor of Epidemiology and Health Informatics at UCL. For more than two decades, she has focused largely on the use of electronic health records for aetiological and epidemiological research. Since she moved to UCL in 2003, she has built a vibrant and productive research environment around analysis of primary care databases. Irene has led and supported several projects funded by MRC, NIHR and various charities and have co-authored around 250 papers based on electronic health records and population registries.

Juan Carlos Bazo Alvarez picture

JC Bazo-Alvarez (CEHRAM Co-Director) is a Senior Research Fellow specialising in methodological research, particularly in interrupted time series analysis and missing data handling for individual-level data, such as electronic health records. His applied research focuses on mental health and its long-term connection to physical health, with a strong emphasis on social determinants and the use of advanced statistical and machine learning methods. He has led, co-led and supported multiple projects funded by NIHR and international agencies and has provided data science and epidemiology consultancy to various organizations.

Broad research areas (PhD/Post-Doc)

If you are interested to do a PhD within CEHRAM, these are the research areas we may be able to support: 

  • Pharmacoepidemiology 

  • Methods to improve analyses of electronic health records 

  • Risks and benefits of prescribed medicine in pregnancy 

  • Methods for missing data handling in routinely collected data (e.g., electronic health records) 

  • Interrupted Time Series designs applied to individual-level data 

  • Machine Learning tools applied to advanced cluster analysis 

  • Machine and Deep Learning to improve health events predictions with electronic health records 

  • Validation of tools/procedures for defining health conditions using primary care data (e.g., algorithms and medical codes lists such as SNOMED)   

For informal questions about PhD/Post-Doc opportunities, please contact Prof Irene Petersen (irene.petersen@ucl.ac.uk) or Dr JC Bazo-Alvarez (juan.alvarez.16@ucl.ac.uk) 

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