Aggregation of mortality data by place and cause mask underlying trends in COVID-19 excess mortality

Difficulties ascertaining the true burden of infection and mortality from SARS-CoV-2 hindered disease surveillance and healthcare resource allocation throughout the COVID-19 pandemic.

Although all-cause excess mortality estimates have long been deployed to avoid the challenges of case detection when testing availability is variable, aggregation of mortality by cause of death and spatial unit may mask underlying patterns and hide underlying associations with social factors. Furthermore, some non-infectious causes of death may have decreased during the pandemic, resulting in all-cause excess mortality capturing a net pandemic effect rather than the direct burden of Covid-19.

Using mortality data in the state of Michigan from January 2015 – November 2019, we calculated the expected mortality for December 2019 – November 2022 using negative binomial regression. Comparing these estimates to the observed mortality, we calculated the burden of excess mortality during the winter 2019 influenza season and COVID-19 pandemic, as divided into 7 distinct periods based on dominant viral strain and pharmaceutical and non-pharmaceutical interventions implemented. All analyses were performed with the data aggregated by cause of death and to the county level and repeated at both the county and census tract level disaggregated into two crude cause of death categories: acute respiratory infections (ARIs) and all other causes (non-acute respiratory infections, non-ARIs). We examined spatial heterogeneity of excess mortality by cause using the Theil index to determine if variation in excess mortality was driven by differences between or within counties.

The excess mortality rate from ARI peaked in the first phase of the pandemic, with a ratio of 10.7 observed to expected deaths (95% CrI: 10.3 – 11.1). However, the all- cause excess mortality rate rose only to 1.20 excess deaths per expected (95% CrI: 1.17 – 1.22) and the non- ARI excess mortality rate was unchanged. Comparing ARI excess mortality rates calculated at the county vs. census tract level demonstrated large and unpredictable variation, with county level estimates ranging from 0.42 to 1.2 times the tract level estimates. However, county level estimates of non-ARI and all- cause excess mortality ratios were consistently closer to tract level estimates. Within county variation of ARI mortality decreased during the pandemic and slowly returned to reference levels, suggesting spatial patterning of ARI mortality became more similar across census tracts as rates rose statewide. Social vulnerability was associated with an increased ARI excess mortality rate ratio at the census tract level during the first pandemic phase (EMRR: 1.1, 95% CrI: 1.08 - 1.13), but this effect was attenuated when aggregated to all-cause mortality or to the county level.

Our findings indicate that using county-level all-cause excess mortality as a proxy for ARI excess mortality obscured the burden of ARI death, particularly during the most acute phases of the COVID-19 pandemic. Relying on aggregated metric of all-cause mortality limits the power to detect large shifts in cause-specific mortality, and that limitation can be mitigated by even using coarse categories such as ARI vs. non-ARI deaths. Similarly, finer scale spatial units allow for the detection of local trends necessary to identify associated social factors.