Individuals often suffer from two or more health conditions at the same time. This is commonly known as ‘multimorbidity’. Sometimes the combination of medications that people with multimorbidity are prescribed by their doctor will result in harm to the patient, for example they may experience ill effects because of the interaction of the two medications. More research is needed to understand which drugs that are commonly prescribed together so researchers can detect which combinations of drugs doctors prefer to prescribe, and understand how often drug combinations that cause harm to patients are prescribed to patients compared with combinations that do not cause harm. Understanding the patterns of prescribing, for example by age, sex, ethnicity and socioeconomic status, will help inform guidance around prescribing in multimorbidity to help clinicians make better prescribing decisions for all patients. This will benefit patients because the risk of them receiving combinations of drugs that cause them ill effects will be reduced.
The aim of this study is to characterise the relationship between accrual of diagnoses and prescribed medications through the life course to inform medicines optimisation in people with multimorbidity. We will: calculate the frequency of medications and medication classes prescribed by age, sex, ethnicity and socioeconomic status (SES); identify and quantify the frequency of medications most often prescribed together; and investigate which medications or combinations of medications are most commonly prescribed with which health conditions, and are associated with increased or reduced emergency department presentations and mortality.
Our study is a retrospective cohort study among individuals >1 year whose records meet research standards set by CPRD. Prescription frequency will be calculated by mapping the CPRD Aurum Drug Issue Table to BNF and SNOMED-CT. CPRD AURUM and IMD data will be used to stratify our analyses by SES; HES APC and OP data to provide case ascertainment and study covariates; HES A&E data to assess emergency admissions; and ONS data to assess mortality. Age- and sex-standardised prescription rates will be used to quantify patterns of co-prescription and relationships to disease or disease combinations.
We will use partial correlation or risk-ratio to quantify the strength of association between prescribed drugs and diseases. Cox-proportional hazards regression will be used to quantify associations between prescribed drug and emergency department presentations and mortality, and time-varying exposures to investigate whether patients with specific prescribing patterns have higher rates of adverse outcomes. We will use network analysis to visualise non-random associations between drug-drug pairs and drug-disease pairs.
Understanding which prescriptions are commonly associated with an increase in emergency admissions and mortality will raise awareness about inappropriately prescribed drugs that lead to severe outcomes. In addition, we will uncover whether differences in prescribing patterns explain poorer outcomes in certain subgroups to inform prescribing changes to reduce health inequities.
1. Frequency of the most commonly prescribed drugs (and drug classes) by age, sex, ethnicity and socioeconomic status
2. Frequency of the most common combinations of drugs (and drug classes) by age, sex, ethnicity and socioeconomic status
3. Strength of association between prescribed drugs (and drug classes) and diagnosed health conditions by age, sex, ethnicity and socioeconomic status
4. Frequency and strength of association between emergency department presentations and prescribed drugs (and drug classes) by age, sex, ethnicity and socioeconomic status
5. Frequency and strength of association of mortality and prescribed drugs by age, sex, ethnicity and socioeconomic status
Aroon Hingorani - Chief Investigator - University College London ( UCL )
Natalie Fitzpatrick - Corresponding Applicant - University College London ( UCL )
Alasdair Warwick - Collaborator - University College London ( UCL )
Ana Torralbo - Collaborator - University College London ( UCL )
Andrej Ivanovic - Collaborator - University College London ( UCL )
Arturo Gonzalez-Izquierdo - Collaborator - University of Birmingham
Chris Finan - Collaborator - University College London ( UCL )
Harry Hemingway - Collaborator - University College London ( UCL )
Jorgen Engmann - Collaborator - University College London ( UCL )
Laura Pasea - Collaborator - University College London ( UCL )
Muhammad Qummer ul Arfeen - Collaborator - University College London ( UCL )
Rory Maclean - Collaborator - University College London ( UCL )
Spiros Denaxas - Collaborator - University College London ( UCL )
Tina Shah - Collaborator - University College London ( UCL )
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation