Title: Harnessing digital data to improve the benefit:harm balance of opioids for non-cancer pain
Funder: Department of Health and Social Care

Abstract: Background: Opioid use for non-cancer pain has been increasing over the last 15-20 years in North America and Europe and has emerged as a major public health concern in the last decade. Opioids have led to a major societal burden in North America; however, less is known about their utilisation in vulnerable subgroups and safety in Europe and the UK. Some opioid users may be particularly vulnerable to related harms due to individual factors such as older age, comorbidities and polypharmacy. Maximising benefits and limiting risks of treatment for such patients is therefore a national priority. This fellowship application is timely due to the availability of new linked data sources, methodological advances, novel technology such as mhealth, and implementation of findings through health informatics. Aim: To improve the benefit/harm balance of opioids in the treatment of non-cancer pain Objectives To evaluate the comparative safety of opioids in relation to short, long-term safety outcomes and mortality To evaluate treatment patterns associated with persistent opioid use, dependence and harms in sub-populations susceptible to opioid overprescribing (post-surgery and musculoskeletal conditions) To develop and validate prediction models for key opioid-associated safety outcomes To evaluate the benefits and harms of opioids using social media and mobile (m)health data To develop safety indicators to be implemented into practice using actionable analyticsDesign and methodology: The fellowship will harness strengths of electronic health records (EHRs) regionally, nationally and internationally. Using novel techniques such as weighted cumulative dosing, it will model the dynamic pattern of opioid use to accurately estimate how cumulative effects of time varying exposure can affect adverse events. Machine learning will be used alongside traditional predictive modelling for individual risk prediction. Benefits and harms will be measured directly from patients using new data sources such as social media and mhealth. Using computerised decision support tools, resulting safety indicators will be implemented in Greater Manchester initially. Timelines for delivery: The research will be delivered over a 5-year period. Anticipated impact and dissemination: It is not clear which opioid regimens place patients at risk of adverse events, what degree of risk is conferred or what predisposes patients to such events. This work will unpick some of the complexities of opioid-associated adverse events. It will move risk estimation towards personalised risk, whereby patients can be informed of their individual risk of side effects before commencing the drug. Although focussed on opioids, the skills and methods developed will have wider application for investigating the safety of other medications. The results will be directly relevant to clinicians, patients, public health, and policy makers to drive improvements in outcomes and future prescribing. As a member of Medicines and Healthcare products Regulatory Agency (MHRA) Opioids Expert Working Group, the results will have the potential to influence policy/guidelines where appropriate. With direct links with NICE, Public Health England and the Science Media Centre, it will also allow wider dissemination of future outputs.

Lay abstract: Background: Opioid use including prescription opioids (such as codeine, tramadol, oxycodone) for non-cancer pain and related harmful effects have reached epidemic proportions in the US and Canada, with rising trends in the UK. Whilst the 'opioid epidemic' is well-reported in North America, less information is available nationally. Hospital admissions in the UK due to opioid-related toxicity, have doubled over a decade. Half of all deaths related to drug-toxicity in England/Wales are due to an opioid. Opioid users may be particularly vulnerable to related harms due to factors including older age, existing health-conditions and drug-interactions. However, there are limited alternative options for pain-relief medicines. Therefore, depriving everyone of opioids is not a solution. Identifying risk-factors and patient subgroups where harms outweigh the benefits is imperative for informed decision-making and safe prescribing. Currently it is unclear which patients are at the highest risk of opioid-related harms and who could be safely prescribed them. This fellowship application is timely because new data sources, advances in methods and novel technology are now available to allow important questions to be addressed that was not possible previously.Aims Within the class of opioids, are there certain drugs with a higher risk of death and serious side effects? How do these change with dose, strength and when taken with other medications? In patient groups at risk of opioid overprescribing: what is observed and why? Specifically, in people undergoing surgery and those with joint problems, are there factors associated with persistent use, dependence and opioid-related harms? Individual prediction: Given everything known about a patient, if they start taking an opioid at a particular dose what their risk of developing a serious side-effect? Can we learn more about the benefits and harms of opioids by capturing directly from patients using digital data from social media and smartphones? How can we best share research findings with clinical teams in real-time before a patient receives their first opioid prescription and promote safe prescribing?Design and methods: The fellowship will utilise regional, national and international electronic datasets from GP practices and hospitals. Traditional and novel statistics, such as using artificial intelligence and methods that incorporate the changing nature of taking opioids, will be used to deliver the objectives outlined in the proposal. In addition, only patients themselves know whether they take their medicines, how they benefit and whether they develop side effects. In order to measure this better, we can capture this information directly from patients through mobile phones/wearables and social media following their consent.Patient involvement and roles: This project has been informed following recommendations from the Centre's Research User Group, a group of lay individuals with musculoskeletal/pain conditions. Two patient partners have been recruited to improve the relevance, quality and validity (including a patient who is a National Rheumatoid Arthritis Society Ambassador on opioids and has campaigned nationally, and another patient with first-hand experience of opioid-related harms). Both are well-informed and passionate about the outlined work. They will attend project meetings virtually or in-person, help with communication of risk/benefit and disseminate findings through their networks by tailoring key messages to patients and stakeholders including patient pain/arthritis organisations.Dissemination: The results will be directly relevant to clinicians, patients, public health, policy makers to drive improvements in outcomes and future prescribing. Results will be also be disseminated through the Centre's and University's communication teams. I am a member of Medicines and Healthcare products Regulatory Agency (MHRA) Opioids Expert Working Group, therefore the results will have potential to directly influence policy/guidelines. The MHRA group also has representative links with NICE, Public Health England and the Science Media Centre, to allow implementation and dissemination.

Research activity codes: 6.1 PHARMACEUTICALS;7.3 MANAGEMENT AND DECISION MAKING
Health categories: GENERIC HEALTH RELEVANCE