Title: Suicide risk assessment in primary care attenders using big data from general practice
Funder: Department of Health and Social Care

Abstract: Research question What are the risk factors for suicide in primary care attenders and can we use these risk factors to produce an accurate risk assessment tool for primary care? Background There are 700,000 suicides globally each year (3). In the UK, suicide is the leading cause of death in adults under the age of 35 (4) and has a significant healthcare and societal burden. Despite a decade of policy to reduce the suicide rate in the UK by 10% (5), the rate has increased (6). Most research into suicide risk occurs in a secondary mental healthcare setting. This is a sub-optimal source of data as only 27% of people who die from suicide are under the care of a mental health team at the time of their death in the UK (7), however 91% have seen their General Practitioner (GP) in the year before their death, on average seven times in that year (8). Healthcare professionals often misclassify suicide risk, with over 80% those who die from suicide considered to have an immediate suicide risk of 'low to none' at their last contact (7). Published models for suicide risk prediction show that machine learning can outperform other models (9-28) but many of these models do not use data available in a clinical setting. None of the models are trained solely primary care data, the first point of access for healthcare in the UK. Aims and Objectives This work aims to establish risk factors for suicide in primary care attenders and to explore the use of these risk factors to create a risk assessment tool for suicide. The objectives for this research are to: Identify risk factors for suicide in primary care attenders based on existing understanding of risk factors for suicide in emergency and psychiatric settings as well as identifying primary care specific risk factors. To establish if there is a difference in these risk factors between those coded as male and female, and if there is a difference by age group. To explore the feasibility of using these risk factors to develop and validate a risk assessment tool for suicide.Methods Variables established from the literature or clinical plausibility will be tested for correlation with increased risk of suicide. Classification trees will also be used to suggest potentially novel risk factors or novel interactions between risk factors. Those variables that show an association on univariate analysis will be used to generate the models. Cox regression with backwards elimination will be used to derive a model which will then be validated. This will be compared to machine learning derived models.Timelines A Gantt chart can be found in the supplemental table. Impact and dissemination There is clear unmet need in this area. A recent economic evaluation calculates (29) a primary care model with a positive predictive value of greater than 0.3% would result in a reduction in the suicide rate of over 10%, saving around 600 lives per year in the UK (30).

Lay abstract: Aim(s) of the researchThe aim of this research is to improve our understanding of what makes a person most at risk of dying from suicide. Through a better understanding, we hope to improve the early recognition of those most at risk when they visit their General Practitioner (GP).Background to the researchSuicide is the leading cause of death in adults under the age of 35 and is the second biggest cause of death in new mothers in the UK. There are around 6,000 deaths from suicide each year, and despite efforts to reduce this, numbers were increasing as of 2019. One in five people have suicidal thoughts at some point in their lives and predicting which of these people will go on to die is difficult. It's been shown that healthcare staff cannot accurately tell who will die from suicide. Eight out of ten of people who die from suicide were thought to have an immediate risk of suicide of 'low to none' at their last meeting with a mental health professional. Most research on risk factors for suicide is based on information from people under the care of mental health teams but only a quarter of people who die from suicide are under the care of a mental health team at the time of their death. However, nine out of ten have seen their GP in the year before their death, on average seven times in that year. A recent publication that reviewed the last 50 years of suicide research suggested that we should move away from examining individual risk factors 'to machine learning-based risk algorithms'. Though this may improve accuracy, it will still not address the lack of research on suicide in people who are not under the care of a mental health team. This work aims to address that need by looking at risk factors and risk algorithms for suicide in primary care attenders. Design and methods usedWe will be using the Clinical Practice Research Datalink (CPRD), a large database of 60 million primary care records. We will look at 'known' risk factors and see if these are still true in a primary care setting. We will also look for new risk factors that may only be risks for the people who attend primary care. Finally, we will try to combine these risks into a risk assessment tool that might help us understand who is most at risk, there are several different statistical and machine learning methods we will try. CPRD is based on GP records and an accurate risk assessment tool made using CPRD may also be accurate in an actual GP clinic. Patient/service user, carer, and public involvementService users, and those who have been bereaved by suicide have been involved in the planning of this project and the writing of this summary. Regular meetings will occur with a patient and public involvement and engagement (PPIE) group to exchange ideas and discuss developments in the project. PPIE participants will be offered a funded place on a training course for PPIE participants discussing machine learning methods. DisseminationThe work will be disseminated via academic publications, academic conferences, and presented to the press for publication in the news. UCL's media department will produce a press release and circulate the findings on social media. We will also disseminate these results to NHS England and to mental health trusts nationally.

Research activity codes: 2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS;3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING
Health categories: MENTAL HEALTH