|PROJECT 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 (EMERGENCY MEDICAL SERVICE CALL CONDITION CLUSTER STUDY: EMSC3)|
|Funding body||School of Geography Seed Grant Funding 2019/2020 awarded to Dr Harriet Moore|
|Overarching aim||To investigate the epidemiology of 999 ambulance attendances for ambulatory care sensitive conditions in East Midlands, UK and Ontario, Canada, by identifying spatial clusters of single and multiple common ambulance acute service-sensitive conditions (diabetes, asthma, mental health, COPD, angina and epilepsy), using call condition records from the East Midlands Ambulance Service NHS Trust, and equivalent services in Ontario involving state-of-the-art spatial analysis (with SatScan software). We aim to identify clusters of higher/lower than expected occurrence of acute conditions in relation to socioeconomic factors, such as deprivation, environmental factors, such as access to healthy assets and hazards, and built environment factors, such as social housing. In the current climate of COVID-19 pandemic, this analysis will begin with a focus on acute respiratory conditions, particularly focussing on people with underlying or multiple comorbidities (e.g. chronic heart, lung and kidney disease or diabetes) and calls for suspected COVID-19. The study is on the UK Health Research Authority list of approved COVID-19 research.|
1. To identify unique single and co-occurring spatial clusters of most commonly occurring NHS ambulatory conditions for younger and older populations using EMAS data for acute presentations in the UK, and equivalent emergency service data for Ontario, Canada.
To conduct a comparative analysis of Emergency Medical Systems (ambulance) in the UK (East Midlands) and Canada (Ontario), including:
2. To identify spatial clusters of higher/lower than expected occurrence of single and multiple acute conditions including calls for suspected COVID-19 with consideration of socioeconomic and environmental factors.
3. To explore underlying mechanisms and factors that influence the spatial dynamics of acute presentation in younger (<18) and older (18+) people.
4. To elucidate pathways and mechanisms that explain the observed single and multimorbidity patterns by using the Social-Ecological Framework to synthesize multi-scale data, including big spatial data (e.g., EMS, deprivation and AHAHI).
5. To explore factors linked with transportation rates in children (<18) and adults (18+) for acute (initially respiratory) presentations in people with underlying or multiple at-risk factors for suspected SARS-COV2 infection (e.g. chronic heart, lung and kidney disease or diabetes).
|Methods||Spatial cluster analysis.|
|Outputs||Peer reviewed publications and conference presentations and recommendations for EMS service delivery.|
|Impact||We aim to create impact by using the findings to inform future Emergency Medical Services provision and design.|