ABSTRACT
The global impact of COVID-19 has challenged health systems across the world. This situation highlighted the need to develop policies based on scientific evidence to prepare the health systems and mitigate the pandemic. In this scenario, governments were urged to predict the impact of the measures they were implementing, how they related to the population's behavior, and the capacity of health systems to respond to the pandemic. The overarching aim of this research was to develop a customizable and open-source tool to predict the impact of the expansion of COVID-19 on the level of preparedness of the health systems of different Latin American and the Caribbean countries, with two main objectives. Firstly, to estimate the transmission dynamics of COVID-19 and the preparedness and response capacity of health systems in those countries, based on different scenarios and public policies implemented to control, mitigate, or suppress the spread of the epidemic. Secondly, to facilitate policy makers' decisions by allowing the model to adjust its parameters according to the specific pandemic trajectory and policy context. How many infections and deaths are estimated per day?; When are the peaks of cases and deaths expected, according to the different scenarios?; Which occupancy rate will ICU services have along the epidemiological curve?; When is the optimal time increase restrictions in order to prevent saturation of ICU beds?, are some of the key questions that the model can respond, and is publicly accessible through the following link: http://shinyapps.iecs.org.ar/modelo-covid19/. This open-access and open code tool is based on a SEIR model (Susceptible, Exposed, Infected and Recovered). Using a deterministic epidemiological model, it allows to frame potential scenarios for long periods, providing valuable information on the dynamics of transmission and how it could impact on health systems through multiple customized configurations adapted to specific characteristics of each country.
ABSTRACT
Para determinar el nivel y las diferencias de mortalidad entre los departamentos de Argentina entre 2009 y 2011, aplicamos y comparamos estimaciones de esperanza de vida al nacer en la región pampeana. Usamos tres métodos de suavizado: un enfoque bayesiano, un método de tablas de vida relacional y un enfoque indirecto de los niveles de mortalidad, en los departamentos de la región. Los resultados permiten ordenar los departamentos de las provincias de acuerdo con su esperanza de vida (con intervalos de confianza estadísticos) y calcular indicadores de desigualdad intraprovincial. Encontramos que la provincia de Buenos Aires fue la de mayor dispersión medida por el rango y el coeficiente de variación. El artículo contribuye a la discusión y aplicaciones de modelos alternativos para estimar la mortalidad local y proporciona la posibilidad de analizar los patrones espaciales para comprender los resultados demográficos individuales relacionados con características geográficas y socioeconómicas asociadas.
Para determinar o nível e as diferenças de mortalidade entre os departamentos da Argentina para o período de 2009 a 2011, aplicamos e comparamos as estimativas de expectativa de vida ao nascer na região pampeana. Usamos três métodos de suavização: uma abordagem bayesiana, uma abordagem de tabela de vida relacional e uma abordagem indireta para os níveis de mortalidade, em 218 departamentos da região. Os resultados permitem ordenar os departamentos das províncias de acordo com a sua esperança de vida (com intervalos de confiança estatísticos) e calcular indicadores de desigualdade intraprovincial. Constatamos que a província de Buenos Aires foi a que apresentou maior dispersão medida pela amplitude e coeficiente de variação. O artigo contribui para a discussão e aplicação de modelos alternativos para estimar a mortalidade em nível local e oferece a possibilidade de analisar os padrões espaciais essenciais para a compreensão dos resultados demográficos individuais relacionados às características geográficas e socioeconômicas associadas, bem como uma ferramenta para orientar planos de desenvolvimento e alocação de recursos.
To determine the level and differences in mortality among departments of Argentina between 2009 and 2011, we applied and compared estimates of life expectancy at birth in the Pampean Region. We used three smoothing methods: a Bayesian approach, a relational life table approach, and an indirect approach to mortality levels, in 218 departments of the region. The results allow ordering the departments of the provinces according to life expectancy (with statistical confidence intervals) and calculating indicators of intraprovincial inequality. We found that the province of Buenos Aires was the one with the highest dispersion measured by the range and the coefficient of variation. The article contributes to the discussion and application of alternative models to estimate mortality at the local level and provides the possibility of analyzing spatial patterns essential to understanding the individual demographic outcomes related to associated geographic and socioeconomic characteristics, as well as a tool to guide development plans and allocate resources.
Subject(s)
Humans , Infant, Newborn , Life Expectancy , Mortality , Bayes Theorem , Life Expectancy at Birth , Argentina , Demography , Life Tables , Indicators and ReagentsABSTRACT
INTRODUCCIÓN: a medida que se desplaza la mortalidad hacia edades avanzadas y predomina la morbilidad por enfermedades crónicas no transmisibles, la esperanza de vida al nacer resulta insuficiente para estimar el estado de salud de las poblaciones, cuyos integrantes pueden padecer durante períodos prolongados las secuelas de enfermedades discapacitantes. En estos escenarios, la esperanza de vida libre de limitaciones permanentes (EVLLP) y con limitacionespermanentes (EVCLP) constituyen indicadores recomendados paraevaluar las condiciones de salud. OBJETIVOS: Estimar la EVLLP porenfermedades físicas o cognitivas para Argentina en 2010. MÉTODOS: Se utilizó el método Sullivan para calcular ambos indicadores. La información básica se obtuvo del Censo Nacional de Población, Hogares y Viviendas de 2010. Se realizaron cálculos de EVLLP y EVCLP al nacer y por edades para la población argentina en 2010. También se elaboró un indicador de impacto de las limitaciones sobre la esperanza de vida para valorar las diferencias halladas. RESULTADOS: Se conocieron las diferencias provinciales en el perfil de salud-discapacidad según sexoy el impacto de la discapacidad en el curso de vida completo segúnprovincias y sexo. CONCLUSIONES: Se espera que las mujeres vivan más años libres de limitaciones, pero con mayor carga potencial de limitaciones permanentes. La Ciudad Autónoma de Buenos Aires es la jurisdicción con mayor EVLLP y con menor carga potencial de discapacidad, mientras que la provincia con mayor impacto dediscapacidad es Jujuy.
INTRODUCTION: as mortality shifts to extreme ages, and morbidity from chronic non-communicable diseasespredominates, life expectancy at birth is not enough to assess the overall health status of populations, whose members may suffer the consequences for long periods of disabling diseases. In these scenarios, indicators like life expectancy free of disabilities (LEFD) and with disabilities (LEWD) arerecommended to evaluate population health conditions. OBJECTIVES: To estimate the LEFD due to physical or cognitive diseases for Argentina in 2010. METHODS: Sullivan methodwas used to calculate both indicators. The basic information was obtained from the 2010 Census of Argentina. LEFD and LEWD at birth and specific ages for the Argentine populationwere calculated. An indicator about impact of the disability on life expectancy was developed to assess the differences found RESULTS:. The study revealed provincial differencesrelated to health and disease profile by sex as well as the impact of the disability on the whole life course by provinces and sex.. CONCLUSIONS: Women are expected to live free of limitations longer than men, but with a higher load of permanent disabilities. Buenos Aires City is the jurisdictionwith the highest life expectancy free of disability and with the lowest potential load, while the province with the greatest impact of disability is Jujuy.