People are living longer, with increasing numbers being treated with several different medicines taken together, known as polypharmacy. Half of people over 65 in the UK take at least five regular medicines each day and almost a quarter take more than seven. Medicines help people, but taking too many medicines can be a burden on patients and may cause problems. About 10% of medicines prescribed are considered to be inappropriate, costing the NHS an extra £1 billion every year. In some cases, patients are prescribed medicines that they no longer need. However, where medicines are recommended, medicines for one condition can affect other conditions and some combinations can worsen side effects and drug reactions. For some people, the balance between benefits and harms mean it would be better to stop certain medicines. We need to have better ways of knowing which people experiencing polypharmacy might benefit most from improvements in their medicines. To do this we will look at a large collection of routinely collected health records within the NHS to:
• Find out what factors might lead to many medicines being prescribed together
• Calculate the difference between the number of medicines prescribed and the number we expect for each individual patient
• Estimate the proportion of people taking potentially hazardous combinations of medicines, and the possible harms of this
This will help us to make a tool (Polypharmacy Assessment Score) which can be used by health professionals to find out which patients may be taking too many medicines.
With an ageing population, more people are living with multimorbidity, which often necessitates polypharmacy (using multiple medications). Medications carry obvious benefits, yet inappropriate polypharmacy is linked to adverse consequences including patient safety concerns, poor patient experience and wasted resources. National policies call for better approaches as current interventions fail to improve outcomes. Currently, the main approach to highlight polypharmacy uses a simple count of drugs, which carries limited value as it ignores individual patient characteristics and multimorbidity. There is an opportunity to improve the measurement of potentially inappropriate polypharmacy through developing a score that accounts for patient factors and clinical diagnoses.
Exploratory analyses will be performed in three parts using CPRD Aurum:
1. A multilevel regression model will explore predictors for polypharmacy and inform the development of a “Polypharmacy Assessment Score”. This will be constructed through calculating the discrepancies between the observed and expected count of prescribed medications, given individual patient characteristics and clinical diagnoses, thereby highlighting people that have unexpected levels of prescribing to highlight potentially inappropriate polypharmacy.
Parts 2 and 3 will examine different aspects of validity of the Polypharmacy Assessment Score:
2. To assess ‘construct validity’ of the score, cross-sectional analyses will use multilevel logistic regression to examine the prevalence of high-risk prescribing within populations with a range of different Polypharmacy Assessment Scores (e.g. higher vs lower scores). We will also compare this to standard cut offs of medication count as a baseline measure (counts of 5, 10 and 15 or more regular medications).
3. To assess ‘predictive validity’ of the score, a retrospective cohort study using linkages to HES hospitalisation and ONS mortality data will then explore differences in clinical outcomes (adverse drug reactions, unplanned hospitalisation and all-cause mortality) between differing scores of the Polypharmacy Assessment Score, again compared to standard cut offs of medication count.
• Potentially inappropriate prescribing using STOPP/START criteria (for aged >65 years) or PROMPT criteria (PRescribing Optimally in Middle-aged People’s Treatments – for <65 years)
• Adverse drug reactions
• Unplanned hospitalisation
• All-cause mortality
Darren Ashcroft - Chief Investigator - University of Manchester
Jung Yin Tsang - Corresponding Applicant - University of Manchester
Matthew Sperrin - Collaborator - University of Manchester
Rupert Payne - Collaborator - University of Exeter
Tom Blakeman - Collaborator - University of Manchester
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;CPRD Aurum Ethnicity Record