Epidemiologia · Published 2026-07-28 · DOI 10.3390/epidemiologia7040105
Diego I. Álvarez-López, Elizabeth Ferreira-Guerrero, Lina S. Palacio-Mejía, Amado D. Quezada-Sánchez, Jorge Laureano-Eugenio, Sergio Trujillo-López, Mariann Duarte-Badillo, Gerardo Álvarez-Hernández
<b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.
Abstract from DOAJ. Public domain (CC0 1.0).
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Álvarez-López, D., Ferreira-Guerrero, E., Palacio-Mejía, L., et al. (2026). Excess Mortality During the COVID-19 Pandemic in Sonora, Mexico, 2015–2022: A Time-Series Analysis of Cause-Specific Mortality. Epidemiologia. https://doi.org/10.3390/epidemiologia7040105