Long COVID spans multiple healthcare challenges, particularly delivering sustainable, high-quality care for multimorbidity and long-term conditions (LTCs). The recently published WHO clinical case definition of post COVID-19 condition helps in optimizing recognition and care of persons suffering long COVID syndrome. This ‘living’ definition of long COVID considered common symptoms e.g., fatigue, shortness of breath, cognitive dysfunction and others having an impact on everyday functioning. A significant proportion of long COVID patients are likely to experience physical and mental health problems. Hence it is important to assess the risks of developing long COVID symptoms among patients with pre-existing LTCs and mental ill health conditions. Such scientific inquiry will help inform long COVID case management strategies for patients with pre-existing LTCs and mental health conditions. It is also important to safeguard the development of LTCs and mental health outcomes among long COVID patients, as these may share important two-way linkages. It is hoped that the study would help identify those at the highest risks for long COVID, suffering from pre-existing long term physical and mental comorbidities.
Long COVID research is helping to refine treatment strategies around the world. We aim to understand risks for developing long COVID conditions specifically among patients who suffer pre-existing long term conditions (LTCs) (e.g., Coronary Heart Disease, COPD, Diabetes, mast-cell activation syndrome, inflammatory condition/autoimmune disease, rheumatic arthritis) and mental health conditions (e.g., depression and anxiety) using NHS records. We will: (i) develop and validate EHR phenotypes for each LTC and share on the HDR UK- CALIBER portal; (ii) estimate the age-, sex- and LTCs and mental health conditions- specific background (pre-long COVID) risks of developing long COVID and deaths, (iii) estimate risks for developing new LTCs and/mental health conditions among long COVID patients.
Using CPRD GOLD and Aurum primary care electronic health records (to determine COVID cases, characteristics and LTCs) linked with HES-APC (to determine COVID cases and LTCs), ONS (to determine all-cause and cause-specific mortality) and COVID datasets (to determine COVID cases), we will study risks of long COVID among people with pre-existing LTCs and mental health conditions.
Our initial study population will be individuals with a positive COVID diagnosis, from which we will assess risk of long COVID and risk factors involved using multivariable logistic regression models.
Among individuals with long COVID we will assess risks of mortality and development of further LTC’s using multivariable Cox regression models.
As part of the STIMULATE-ICP project, we will work with patients, public, researchers, clinicians and policymakers to provide information on long COVID integrated care pathways. This study will help plan and implement LTC care across conditions, and identify the background risks for developing other LTCs / mental health conditions. We will work with policy makers to develop care pathways for managing dual risks of long COVID and other LTCs/ mental health conditions, with benefits for LTCs beyond the COVID-19 pandemic.
• Long COVID symptom clusters
• All-cause and cause-specific mortality of patients with long COVID
• Newly developed LTCs among long COVID patients
• Newly developed mental health conditions among long COVID patients
• Healthcare resource use for patients with long COVID
Amitava Banerjee - Chief Investigator - University College London ( UCL )
Laura Pasea - Corresponding Applicant - University College London ( UCL )
Arturo Gonzalez-Izquierdo - Collaborator - University College London ( UCL )
Christina Van der Feltz-Cornelis - Collaborator - University of York
Han-I Wang - Collaborator - University of York
Mehrdad Alizadeh Mizani - Collaborator - University College London ( UCL )
Mohamed Mohamed - Collaborator - University College London ( UCL )
Muhammad (Ashkan) Dashtban - Collaborator - University College London ( UCL )
Muhammad Qummer ul Arfeen - Collaborator - University College London ( UCL )
Yi Mu - Collaborator - University College London ( UCL )
Donna Clutterbuck - Collaborator - University College London ( UCL )
Nisreen Alwan - Collaborator - University College London ( UCL )
CHESS (Hospitalisation in England Surveillance System);HES Admitted Patient Care;ICNARC (COVID-19 Intensive Care National Audit and Research Centre);ONS Death Registration Data;Patient Level Index of Multiple Deprivation;SGSS (Second Generation Surveillance System);COVID-19 Linkages