Work Package 3: Clinical Interpretation of Results

Work package 3 will look at how the results from work packages 1 and 2 can be applied to real-world situations, to improve healthcare outcomes for patients with multiple long-term conditions. The research in this work package will be divided into three parts: mental health, inflammation, and how ethnicity and various social factors may affect the best treatment options for patients.

Mental health

Patients who have mental illnesses often suffer from physical illnesses too. This may be due to unhealthy illness-related behaviours, shared underlying causes, or side effects of medication. For example, antidepressants may lead to weight gain, heart abnormalities and increased bleeding, all of which could make physical health problems worse.

Some physical health medications, such as medicine to control blood pressure, can be associated with increased risks of depression, whereas other medications (such as anti-inflammatory medicines) may improve depression. On the other hand, treatment of mental illnesses may improve physical health.

Mental illnesses are recorded in various ways in electronical health records. Using AI technology to analyse these large sets of information, we will explore the relationship between mental health illnesses and other conditions such as obesity, diabetes, heart disease etc, as well as the impact of combining medications used to treat these conditions on overall health outcomes.

We will also explore the interaction between personal/social factors and depression/anxiety on health outcomes, and how this might be modified by antidepressant usage.


Inflammation is linked to many diseases such as heart disease, stroke, cancer, and depression, and is known to contribute to the development of MLTC. Anti-inflammatory medicines are also linked to the development of health conditions such as diabetes and infections.

We will test whether having an inflammatory disorder and taking specific anti-inflammatory drugs is linked to developing multiple conditions. We will look for evidence to support the use of anti-inflammatory drugs to treat health conditions which may be associated with inflammatory disease.

Population Differences

Differences in the treatment of multiple long-term conditions are well documented. For example, people of different ethnicity can respond differently to medicines, and clinical trials often exclude the older population. We will use data from Electronic Health Records to assess inequalities in the diagnosis and treatment of multiple long-term conditions, looking at characteristics such as age, sex, ethnicity, and social circumstances.

Electronic Health Records may not have enough information on all patient characteristics which we are interested in. We will conduct in-depth interviews with patients to:

  • understand patients’ experiences, perceptions, and priorities
  • explore real-world differences in the treatment of multiple long-term conditions, the meaning of these differences for individuals, and public perceptions of inequality in healthcare
  • uncover different methods of recording healthcare data which may be useful in our investigation

We will also conduct follow-up interviews to ensure patients’ real-world experiences and perceptions can influence how we interpret the findings from our research.