A self-funded PhD studentship is available under the supervision of Dr Prasad Nishtala at the University of Bath. This is a collaborative project which will involve working with Dr Hamish Jamieson from the Big Data and Ageing Research Group at Christchurch, NZ. This research project provides an opportunity to combine big data analytics and advance translational research in ageing.
The study will be the first Pharmacoepidemiological study to systematically investigate drug-related adverse events in older people. Previous international research on the prevalence of drug-related hospital admissions in older people has specifically looked at recognisable adverse drug events, for example, bleeding from anticoagulants, hypoglycaemia from antidiabetic medicines, and sedation from opioids. However, various high-risk medicines have been linked to functional impairment and geriatric syndromes (e.g., cognitive impairment and falls), which may not be routinely recognised as adverse drug events Geriatric syndromes are common and have substantial negative implications for functioning, quality of life, and mortality in older adults in the community and in the hospital. This study is designed to address this deficit. This research will conduct additional studies to analyse older people admitted to hospitals for falls, delirium, and the medicines that may have contributed to these presentations. This research whilst unique to New Zealand will provide a worldwide perspective to research on adverse drug events in high-risk populations.
In this study, we propose to analyse New Zealand’s pharmaceutical collections (pharms) and national minimum dataset (NMDS) and the world-leading interRAI data to identify drug-related adverse events leading to hospital admissions. The key advantage of using the pharms and national minimum datasets is that prescribing information from pharms including patient demographics can be linked to hospital admissions derived from NMDS and clinical data from interRAI. Lack of data on high risk medicines contributing to hospital admissions has been a bane of previous research world-wide, and it creates a novel opportunity to study this work. If high risk medicines that are frequently implicated in hospital admissions can be identified, then this representative information can be used by policy makers to mitigate medication errors by targeting inappropriate prescribing of high-risk medicines. It is anticipated that there is extensive data available in pharms, NMDS and interRAI to unearth real-world population data about drugs contributing to hospital admissions, patient harm, and death.
In summary, linking population level health data with dispensing data presents a rich opportunity to identify high risk drugs contributing to hospital admissions. A health system intervention at the national level by embedding a ‘ready reckoner’ tool of high risk medicines to electronic prescribing may mitigate drug-related adverse events.
This project will involve a collaboration between the Department of Pharmacy and Pharmacology at the University of Bath and the world leading Big Data and Ageing Research Group at Christchurch, University of Otago (https://www.otago.ac.nz/big-data-better-ageing/index.html)
Pharmacoepidemiology, ageing, adverse drug reactions, data mining, statistics
Applicants are requested to submit their CV and a one-page expression of interest statement on how their research interests aligns with big data analytics and geriatric Pharmacoepidemiology. The documents should be emailed to Dr Prasad Nishtala ([Email Address Removed]).
A full University application will be required for shortlisted applicants.
Applicants should possess a Master’s degree (1st Class) qualification from disciplines of Statistics, Pharmacy, Medicine or closely related discipline, preferably from a UK University.
Nishtala PS, Chyou T. Sequential pattern mining to predict prescribed medications following hospitalisation for heart failure. Pharmacoepidemiology and Drug Safety 2018, 27: 456.
Nishtala PS, Chyou T, Held F, Le Couteur DG, Gnjidic D. Association Rules Method and Big Data: Evaluating frequent medication combinations associated with fractures in older adults. Pharmacoepidemiology and Drug Safety, Accepted March 2018.
Venӓlӓinen O, Bell JS, Kirkpatrick CM, Nishtala PS, Liew D. Adverse drug reactions associated with cholinesterase inhibitors-sequence symmetry analyses using prescription claims data. Journal of the American Medical Directors Association, 2017; 18:186-189
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FTE Category A staff submitted: 54.20
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