Emergency Medical Service Call Condition Cluster Study (EMSC3) [COVID-19]
Research type
Research Study
Full title
Identifying and explaining clusters of acute physical and mental health conditions in the East Midlands of the UK and Ontario using ambulance call condition data: SatScan analysis and evaluation of health care system effectiveness
IRAS ID
264573
Contact name
Niroshan Siriwardena
Contact email
Sponsor organisation
University of Lincoln
Duration of Study in the UK
3 years, 0 months, 0 days
Research summary
The study involves research limited to secondary use of anonymised routine data previously collected in the course of normal care (without an intention to use it for research at the time of collection) which is therefore generally excluded from REC review, provided that the patients or service users are not identifiable to the research team in carrying out the research, which is the case. \n\nThe number of people with multiple illnesses, known as multimorbidity, is rising in the UK. Health care systems are struggling to provide effective care for people with multiple illnesses, particularly when these include both physical and mental health illnesses. Most research focuses on single illnesses. Little is understood about why people experience multiple illnesses simultaneously. \n\nThe research explores the causes of child and adult multimorbidity in the East Midlands of the UK, initially focusing on respiratory conditions. Regions across the East Midlands experience the full range of socio-economic, ethnicity, education, and environmental conditions that characterize the UK more broadly, making this an ideal area for the research.\n\nThe research involves using records of ambulance attendance after 999 calls from the East Midlands Ambulance Service (EMAS) to identify ‘clusters’ (a large number of cases in one spatial location) of cases where multiple types of emergency conditions occur. Ambulance calls reflect very severe conditions that reduce quality of life. Clusters of multiple occurring conditions suggest areas where people experience multiple illnesses. We will use spatial analysis software to identify clusters.\n
REC name
N/A
REC reference
N/A