Excess Mortality in Massachusetts During the Delta and Omicron Waves of COVID-19

Abstract

The COVID-19 pandemic has produced excess deaths, the number of all-cause fatalities exceeding the expected number in any period. 1,2 Given reports that the Omicron variant may confer less risk than prior variants, we compared excess mortality in Massachusetts, a highly vaccinated state, during the Delta and initial Omicron periods. 3 Methods | We applied autoregressive integrated moving average (ARIMA) models to US Census populations (2014-2019) and seasonal ARIMA (sARIMA) models to Massachusetts Department of Health all-cause mortality statistics (from January 5, 2015, through February 8, 2020) to account for prepandemic age and mortality trends and to project the age-stratified (0-17, 18-49, 50-64, and 65 years) weekly population and the weekly number of expected deaths in Massachusetts during the pandemic period (February 9, 2020, through February 20, 2022), focusing on the Delta (June 28, 2021, through December 5, 2021), the Delta-Omicron transition (December 6-26, 2021), and Omicron COVID-19 periods (December 27, 2021, through February 20, 2022). Period barriers were determined by variant dominance in regional wastewater. 4 The population of Massachusetts is approximately 6.9 million individuals. We corrected expected deaths for the decreased population owing to cumulative pandemicassociated excess deaths (eAppendix in the Supplement). Population covariates were used to calculate 95% CIs for expected deaths. Excess mortality for each period was defined as the difference between the observed deaths and point estimate for sARIMA-determined expected deaths. Incident rate ratios were calculated to compare the Omicron and Delta periods. According to the Massachusetts Department of Health, deaths recorded from 2020 to 2022 were considered provisional. Excess mortality for individuals aged 0 to 17 years was determined and included in the overall analysis, but not presented separately because the death rates were considered too small to be reliable.