This dashboard draws together data from a range of sources to help public and community sector partners understand levels of vulnerability in neighbourhoods across Essex. It has been prepared to help support and inform the partnership response to the current Coronavirus crisis.

Mirroring guidance from government, the dashboard presents data on the age of the local populations and on the prevalence of certain health conditions at the neighbourhood (LSOA) level. These measures reflect the risk characteristics associated with disproportionate COVID-19 mortality. The mortality ratio of COVID-19 is significantly higher in the older age population (>65) and poses a real risk to those who have underlying health conditions or co-morbidities.

The dashboard also presents a wider ‘vulnerability’ index. This index combines around fifty indicators reflecting both the health of local populations and the wider (non-health related) characteristics that might leave households vulnerable through the crisis. 

Finally, the dashboard presents a ‘cluster analysis’ grouping neighbourhoods across Essex into a management set of categories based on local health outcomes, local demographics and the wider vulnerability of households.

We hope that the dashboard and the analyses it presents will support partners to take action at the strategic and operational level to help tackle the Coronavirus outbreak.

The dashboard has been prepared by Essex County Council’s Research and Citizen Insight Team. If you have any questions or queries about this dashboard please contact [email protected]

We’ll be publishing more research and insight that enables us to better understand residents’ wellbeing and needs and further supports us to inform our response.

Technical notes

Datasets examined have been chosen because of their small-area capability, informing on LSOA (Lower Super Output Areas) or neighbourhoods with an average population of 1500 people per LSOA. There are 872 LSOA neighbourhoods in Essex.

These datasets include demographic ONS populations in quinary age bands and single years with an emphasis on population over the age of 65. Moreover, we estimated certain disease prevalence conditions at LSOA geography using GP registered patient populations catchments and GP QOF prevalence.

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