Abstract

Background/Objectives: Excess mortality (EM) is a key indicator for assessing the population-level impact of large-scale health crises, particularly when cause-of-death ascertainment is incomplete or delayed. However, EM estimates are sensitive to methodological decisions, highlighting the need for operationally feasible approaches suitable for routine public health surveillance. Methods: We conducted time series analyses of routinely collected mortality data from a subnational setting in northern Mexico. Deaths recorded between 2015 and 2022 were grouped into 28 cause-of-death categories. Expected deaths during the COVID-19 pandemic period (March 2020–July 2022) were estimated using negative binomial regression models fitted to pre-pandemic data (2015–2019), incorporating a linear trend and monthly indicators to account for long-term trends and seasonality. Newey–West standard errors were used to address serial correlation and heteroskedasticity. EM was defined as the difference between observed and expected deaths. Results: An estimated 14,482 excess deaths were observed during the pandemic period, corresponding to a 30.9% increase relative to expected mortality. Time-series models identified four distinct peaks of excess mortality coinciding with major pandemic waves. Although COVID-19 accounted for most excess deaths, among non-COVID causes, only ischemic heart disease and diabetes showed statistically significant excess mortality among major non-communicable diseases after baseline adjustment. However, additional categories—including non-transport-related accidents, ill-defined causes, and other endocrine, metabolic, hematological, and immunological diseases—also exhibited statistically significant excess mortality. For several causes, increases in crude mortality did not translate into statistically significant excess mortality. Conclusions: Negative binomial time series regression provides an implementable framework for estimating EM, underscoring the importance of expected mortality estimation for understanding population-level mortality dynamics during health emergencies.

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