I am currently a DPhil student investigating multimorbidity using the Clinical Practice Research Datalink. I have a Masters in Global Health Sciences (Oxon) and has trained as a clinician in Melbourne, Australia.
My research interests include:
Multimorbidity
Electronic health records
Chronic conditions
Non-communicable diseases
Preventative medicine
Publication
Tran J, Rahimi K et al., Patterns and temporal trends of comorbidity among patients with incident cardiovascular disease in UK adults between 2000 and 2014: a population-based cohort study, 2017, (under review)
Rahimi K, Mohseni H, Otto CM, Conrad N, Tran J, et al., Elevated blood pressure and risk of mitral regurgitation: A longitudinal cohort study of 5.5 million United Kingdom adults, PLOS Medicine 14(10): e1002404, 2017
Conrad N, MSc, Judge A, Tran J et al., Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals, Lancet, 2017
Emdin C, Odutayo A, Wong C, Tran J et al., Meta-Analysis of Anxiety as a Risk for Cardiovascular Disease, The American Journal of Cardiology, 2016
Ballard M, Tran J, Hersch F, Supporting Better Evidence Generation and Use Within Social Innovation in Health in Low- and Middle-Income Countries: a Qualitative Study, PLOS one, 2016
Seruya M, Tran J, Kumar S, Forrest C, Chong D, CT-Based Morphometric Analysis of Extended Strip Craniectomy for Sagittal Synostosis. Journal of Craniofacial Surgery, 2013
Waddell LB, Tran J, Zheng XF et al., A study of FHL1, BAG3, MATR3, PTRF and TCAP in Australian muscular dystrophy patients, Neuromuscul Disord 21: 776-781, 2011
Waddell LB, Lemckert FA, Zheng XF, Tran J, Evesson FJ et al., Dysferlin, annexin A1, and mitsugumin 53 are upregulated in muscular dystrophy and localize to longitudinal tubules of the T-system with stretch, J Neuropathol Exp Neurol 70: 302-313, 2011
Projects
Chronic conditions in the UK
Aim: To examine the incidence and prevalence of chronic conditions in a large representative cohort of adults in the UK, and determine temporal trends in annual incidence and prevalence using crude and age-standardised rates.
Chronic conditions dominate the causes for mortality and morbidity in the UK. The Global burden of disease study 2013 shows that chronic conditions, including ischaemic heart disease, chronic obstructive pulmonary disease, lung cancer, dementia and depression, make up 80% of the top 25 leading causes of disability-adjusted life-years in the UK, and this trend is expected to continue to rise (1). Despite this, the recent literature describing the prevalence and incidence of chronic conditions is wide and varied and it is difficult to understand how common chronic conditions are in the UK. Electronic health records, like the Clinical Practice Research datalink (CPRD), a linked database of general practice records in the UK, are increasingly used in epidemiology to describe how common different conditions are.
We are using the CPRD to investigate the prevalence and incidence of 60 common chronic conditions in the UK over time, and stratified by age, sex and socioeconomic status. This work is being used to inform future work on chronic conditions using the CPRD.
Multimorbidity in patients with incident cardiovascular disease
Aims: To describe prevalence of multimorbidity and comorbidity of chronic conditions in people with incident non-fatal CVD (ischaemic heart disease, stroke/TIA). To examine crude and age-standardised prevalence and frequencies, stratified by calendar year, age, sex and socioeconomic status.
Multimorbidity, which is defined as the presence of two or more conditions in the same individual, is increasingly becoming a burden for patients, practitioners and the healthcare system (2-4). Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally, and is associated with a high burden of multimorbidities that play a role in disease prognosis and management (5-8).
The objective of this study is to examine the burden of comorbidity and multimorbidity in patients with non-fatal incident cardiovascular disease. We will investigate how prevalence of multimorbidity and 58 comorbid chronic conditions in these patients changes over time, and how patterns vary by age group, sex and socioeconomic status. Age and sex-standardised rates will be computed by applying direct age-standardisation to the 2013 European Standard Population (9) using 10-year age bands up to 90+ years old, and averaging the sex-specific age-standardised rates where appropriate.