The assessment of all-cause and cause-specific excess mortality is a key intelligence function of the public health surveillance. It serves as an early detection and warning system of underlying conditions impacting mortality, and as a mean to corroborate and quantify how an emerging condition is affecting mortality in the population.
In the context of COVID-19 pandemic, accurately comparing how this condition is affecting mortality in different countries is important to evaluate policies and strategic actions taken by governments, the health care response, and public health interventions. Data that are unaffected by differences in how countries are testing for the virus or coverage and ability to detect cases, and access to health care including treatments and medicines, the human resource capacity, among others are highly desirable. Although valuable, the reported counts of deaths due to COVID-19 have limitations as they are influenced by variations across countries in testing coverage and often exclude persons dying in the community.
The analysis of all-cause excess mortality by age, sex, and other dimensions of interest would provide an additional and highly comparable public health measure. This measure is obtained by comparing the current observed weekly mortality to the expected (baseline) deaths based on the historic mortality data. This approach sidesteps the limitations and challenges of consistently counting deaths from a given cause.
Quantify and assess the all-cause and cause-specific excess mortality by age, sex and other dimensions in countries of the Americas in 2020. Methodological approaches for measuring and assessing the excess of mortality are illustrated with a case study for Ecuador, a country with publicly available data required for the analysis. Implications of the methodological approaches and findings for public health will be discussed.
Registered death data were extracted from the Instituto Nacional de Estadísticas y Censos (INEC), Ecuador [INEC 2020] (available online: Databases of general deaths and historic datasets; Dirección General de Registro Civil, Identificación t Cedulación, Estadísticas del Registro Civil, Cifras defunciones.
Nominal death data (individual level) from 2013 to 2018 were cleaned, variables of interest harmonized and finally integrated into a tidy dataset. The resulting dataset includes the following variables: country, date of death, age group (5-year brackets with an open ended age group at 85+), sex, underlying cause of death (ICD-10, 4-character subcategories), province of residence, province of occurrence, canton of residence, canton of occurrence, place of death (health facility, at home, with or without medical attentions received before dead), ethnicity, and area of residence.
An data visualization was created to facilitate data exploration and dissemination.
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