Spatial Risk Factors for Pillar 1 COVID‐19 Excess Cases and Mortality in Rural Eastern England, UK
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Abstract
Understanding is still developing about spatial risk factors for COVID‐19 infection or mortality. 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. We obtained residence area data on air quality, deprivation levels, care home bed capacity, age distribution, rurality, access to employment centers, 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 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 centers, or air quality indicators. Only 66% of mortality was explained by locally high case counts. Higher deprivation clearly linked to higher COVID‐19 mortality separate from wider community prevalence and other spatial risk factors.
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SciScore for 10.1101/2020.12.03.20239681: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: The findings relate very much to the types of cases that are found under the Pillar 1 testing framework. These are positive swabs found by testing health professionals (often found through surveillance rather than symptomatic presentation) and patients with urgent medical need. A more conventional sampling framework could …
SciScore for 10.1101/2020.12.03.20239681: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: The findings relate very much to the types of cases that are found under the Pillar 1 testing framework. These are positive swabs found by testing health professionals (often found through surveillance rather than symptomatic presentation) and patients with urgent medical need. A more conventional sampling framework could include all symptomatic cases (including those without urgent medical needs). Some such cases were found concurrently in May 2020 under Pillar 2 testing protocols. Possible demographic differences between Pillar 1 and Pillar 2 cases in the county of Norfolk alone are described in the data shown as Appendix 5. Pillar 2 and Pillar 1 patients were not very different from each other in simple demographic traits (age distribution and sex). We have only incomplete information about the occupations of COVID+ patients in our dataset; occupational risk may well have been more important than anything to do with residential origin for individual case status. However, most cases were above age 60 while most deceased were older than the statutory pension age (67 years currently). Occupational exposure is especially unlikely to be relevant to the mortality outcome. We have tried to be transparent about the covariate specifications. We do not believe that different thresholds (such as considering population age 70 or older) would change the broad conclusions. Lack of variation in ethnic profile was both helpful and a drawback in the analysis. We cannot use dat...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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