We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. Six models were used to estimate expected mortality final estimates of expected mortality were based on an ensemble of these models. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality.

In addition, we obtained excess mortality data for 12 states in India. The Lancet Regional Health – Western PacificĪll-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 20, and for up to 11 year previously.The Lancet Regional Health – Southeast Asia.The Lancet Gastroenterology & Hepatology.

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