COVID‐19: Generate and apply local modelled Transmission and Morbidity effects to provide an estimate of the variation in overall Relative Healthcare Resource Impact at General Practice Granularity

Introduction Acute Respiratory Syndrome Coronavirus‐2 (SARS‐CoV‐2) is the name given to the 2019 novel coronavirus. COVID‐19 is the name given to the disease associated with the virus. SARS‐CoV‐2 is a new strain of coronavirus not been previously identified in humans. Methods Two key factors, case incidence and case morbidity were analysed for England. When taken together they give an estimate of relative demand on healthcare utilisation. To analyse case incidence, the latest values for indicators that could be associated with infection transmission rates were collected from the Office of National Statistics (ONS) and Quality Outcome Framework (QOF) sources. These included population density, %age>16, at fulltime work/education, %age over 60, %BME ethnicity, social deprivation as IMD2019, location as latitude/longitude, and patient engagement as %self‐confident in their own long‐term condition management. Average case morbidity was calculated. To provide a comparative measure of overall healthcare resource impact, individual GP practice impact scores were compared against the median practice. Results The case incidence regression is a dynamic situation but it currently shows that Urban, %Working and age>60 were the strongest determinants of case incidence. The local population comorbidity remains unchanged. The range of relative healthcare impact was wide with 80% of practices falling at 20%‐250% of the national median. Once practice population numbers were included we found that the top 33% of GP practices supporting 45% of the patient population would require 68% of COVID‐19 healthcare resources. The model provides useful information about the relative impact of Covid‐19 on healthcare workload at GP practice granularity in all parts of England. Conclusion Covid‐19 is impacting on the utilisation of health/social care resources across the world. This model provides a way of predicting relative local levels of disease burden based on defined criteria, thereby providing a method for targeting limited care resources to optimise national/regional/local responses to the COVID‐19 outbreak.