Understanding is still developing about risk factors for COVID-19 infection or mortality. This is especially true with respect to identifying spatial risk factors and therefore identifying which geographic areas have populations who are at greatest risk of acquiring severe disease. This is a secondary analysis of patient records in a confined area of eastern England, covering persons who tested positive for SARS-CoV-2 through end May 2020, including dates of death and residence area. For each residence area (local super output area), we obtained data on air quality, deprivation levels, care home bed capacity, age distribution, rurality, access to employment centres and population density. We considered these covariates as risk factors for excess cases and excess deaths in the 28 days after confirmation of positive covid status relative to the overall case load and death recorded for the study area as a whole. We used the conditional autoregressive Besag-York-Mollie model to investigate the spatial dependency of cases and deaths allowing for a Poisson error structure. Structural equation models were also applied to clarify relationships between predictors and outcomes. Excess case counts or excess deaths were both predicted by the percentage of population age 65 years, care home bed capacity and less rurality: older population and more urban areas saw excess cases. Greater deprivation did not correlate with excess case counts but was significantly linked to higher mortality rates after infection. Neither excess cases nor excess deaths were predicted by population density, travel time to local employment centres or air quality indicators. Only 66% of mortality could be explained by locally high case counts. The results show a clear link between greater deprivation and higher COVID-19 mortality that is separate from wider community prevalence and other spatial risk factors.