The COVID-19 pandemic is having a profound effect on people’s lives. Those with a diagnosis of severe mental health problems may be being disproportionately affected. There are many reports of a lack of access to mental health services and resources for people with a diagnosis of severe mental illness at this time Many of the mental health services that continue to operate during the pandemic have had to make considerable changes to the way they are able to provide mental health care, through limiting contact or reducing the amount of activity and treatment they provide. There are also greater social and economic risks for people with a diagnosis of severe mental illness, many of whom are already at high risk of social isolation and exclusion due to their mental illness. Despite this, most of the attention on the impact of COVID-19 on mental health has been on anxiety and depression in the general population.
This study aims to better understand how the COVID pandemic has changed the treatment that people with a diagnosis of severe mental illness receive, and how this might influence their health outcomes including incidents of self-harm, relapse of their illness, and death.
We aim to do this by comparing access to care and health outcomes of people with a diagnosis of severe mental illness during the pandemic in comparison to previous years. We will also look at whether factors such as age, ethnicity, socioeconomic status, the type of illness and any mental or physical comorbidity, or the type of treatment people are receiving influence these differences in access to care and health outcomes during the pandemic.
Our study aims to provide evidence that could support better application of treatments during pandemics in order to protect the health of those with a diagnosis of severe mental illness in order to reduce further health inequalities and associated economic crisis that pandemics may cause in this group of people.
Preventing people from developing a psychotic illness is a priority in healthcare. The current way of doing so is to identify people who are displaying initial symptoms of psychosis. These people are considered as having “at risk mental state” (ARMS). However, there are a number of problems with only using ARMS to identify those at risk of psychosis. First, it relies on these symptoms being detected before they develop into a full psychotic episode, which only happens in around 5% of all cases. Second, it assumes that someone with ARMS will transition to a full psychosis, which does not always happen.
Many people develop psychosis after presenting to mental health services with a different mental illness, such as depression or anxiety. One way to reduce the number of those who transition to a psychotic illness is to better understand this group.
A group of researchers have developed a tool to identify and predict who in this group of people who are already under mental health care are at risk of developing psychosis. In their studies, the risk tool displayed good predictive accuracy. However, a prediction tool can often only work on the population that it is tested on. Performance of these tools often gets worse when it is tested on a different group of people or people in a different geographic location.
When someone starts experiencing a psychotic illness for the first time they will be treated by an Early Intervention in Psychosis (EIP) team. EIP teams try to intervene as soon as people develop signs or symptoms of psychosis. They treat individuals with a combination of medication (if appropriate) and talking therapy, provide advice and education about the illness to both them and their families, and help them manage their physical health and social needs through a case manager. Randomised trials have shown that EIP treatment is the most effective treatment for first episode psychosis and they are now offered throughout England.
The NHS has set out eight key interventions that EIP teams need to deliver to the people they treat to deliver best-evidenced treatment. These are based on the National Institute for Health and Care Excellence (NICE) guidelines for psychosis. However, many EIP teams are unable to deliver these key interventions to their patients, resulting in poorer care. A further problem is that it is difficult to know who is missing out on these treatments, and where in the country it is happening, because many EIP struggle to accurately report this data to the NHS. This is due to the time-consuming nature of collecting this information for each patient from clinical records.
Electronic health records are now the most common method of recording patient clinical data. Most of this information is recorded in what is called ‘free-text’ unstructured notes. This is information entered in a similar way to traditional paper clinical notes, in full sentences, much like a letter, or diary entry. To collect summary data on the NICE interventions given by an EIP service, or all EIP services, someone would have to manually read each individuals’ notes, which is time consuming and not feasible.
In this project we aim to use computer data science to do this data collection automatically. We will do this by using a technique called natural language processing (NLP). NLP is a way to program a computer to process and analyse free-text notes. We will use NLP to identify the eight recommended NICE interventions. We will then test whether the NLP can identify these interventions accurately enough to be confident in using them by comparing the NLP results to results that we have manually collected.
These NLP algorithms can then be used to better understand local and national provision of EIP treatments.
When someone starts experiencing a psychotic illness for the first time they will be treated by an Early Intervention in Psychosis (EIP) team. EIP teams try to intervene as soon as people develop signs or symptoms. They treat individuals with a combination of medication (if appropriate) and talking therapy, provide advice and education about the illness to both them and their families, and help them manage their physical health and social needs through a case manager. Randomised trials have shown that EIP treatment is the most effective treatment for first episode psychosis. Much like many other specialty care services in the NHS, EIP is only provided for a limited time, usually for between two and three years.
When someone finishes EIP treatment, their next care provider is usually a GP or a standard community mental health team. However, because EIP teams are a relatively new service in the NHS, there is very little known about what happens to people once they have been discharged.
This study aims to use the routine clinical data to understand the care pathways of people once they leave EIP services. Specifically, we aim to: determine the number of people being discharged back to their GP or transferred to other mental health teams, and whether they stay with that service over time; understand the clinical (e.g. diagnosis) and demographic (e.g. age, gender) characteristics that make someone more likely to be cared for by a specific service; measure how many people relapse back to mental health services; and, develop and prediction tool to be able to predict the outcomes of people leaving EIP treatment.
This study aims to find out how many people in Oxford Health NHS Foundation Trust with a diagnosis of psychosis have children. A search on the database will be carried out to find out who has children and who doesn’t, we will then compare these two groups to see how they differ. We might find, for example, that women with psychosis are more likely to have children than men or people who are older. Identifying how many people with psychosis have children and learning about what makes it more likely for this population to have a child will help us understand this population better. This information will help us think of interventions that are specifically designed for these people with psychosis and their children.
This is a prescribed app. It should only be used alongside a face to face intervention provided by a mental health worker. Check with your local service to see if they subscribe to the app.