Besides population age structure, health and other demographic factors can contribute to understanding the COVID-19 burden

Nepomuceno, Marília R.; Acosta, Enrique; Alburez-Gutiérrez, Diego; Aburto, José Manuel; Gagnon, Alain; Turra, Cássio M.

doi:10.1073/pnas.2008760117

Cited by 115 publications

(118 citation statements)

References 8 publications

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“…Disparities in age-specific case-fatality rates across countries may result from differences in age-specific prevalence of comorbidities, which exacerbate the risk of death from COVID-19 considerably [1,19] or differences in quality or saturation levels of the healthcare system, among other potential factors [20]. The trend over time in the Italian CFR is an example where changes in age-specific case-fatality rates are driving trends, instead of changes in the case age distribution.…”

Section: Discussionmentioning

confidence: 99%

Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality

et al. 2020

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The population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries at any given time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy, Germany, Spain, and New York City over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late June 2020, CFRs varied from 2.2% in South Korea to 14.0% in Italy. The age-structure of detected cases often explains more than two-thirds of crosscountry variation in the CFR. In Italy, the CFR increased from 4.2% to 14.0% between March 9 and June 30, 2020, and more than 90% of the change was due to increasing agespecific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well-designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases.

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Section: Discussionmentioning

confidence: 99%

Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality

et al. 2020

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“…In a particular old state, Parana, our results demonstrate that, with the evolution of the pandemic and small take up to the measures of social isolation by the population, the age distribution of deaths and cases has rejuvenated over time. As a future research agenda, besides the age-structure, we will incorporate the structure of morbidity in the state, as claimed by several researchers as being an important feature for comparisons (Nepomuceno et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

The changing age-structure of Coronavirus SARS-CoV-2 deaths and cases a case study of Paraná, Brazil

Guimarães¹,

Nepomuceno²,

Nasr³

et al. 2020

Preprint

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The goal of this paper is to explore the demographic evolution of the Coronavirus SARS-CoV-2 deaths and cases in the state of Paraná, Brazil. We focus on changes in the age-pattern of cases and deaths attributed to the COVID-19. Paraná is an interesting case of study in Brazil due to several aspects. First, it is one of the most developed states of the country. Second, the population growth rate is rapidly approaching zero growth, with an observed average growth rate of 0.78 percent per year in the decade 2010/2020, and of 0.28 percent per year in the period 2030/2040. Third, Parana has an older population age-structure than that of the whole country. Finally, although the state government pushed for earlier Non-Pharmaceutical Interventions to control the pandemic, data shows that they only took effect in the very late stages. Taken together, we claim that these aspects created a very particular setting, in which changes over time in the age-structure of the deaths attributed to COVID-19 could be observed: in the beginning of the pandemic, the age-structure of the deaths was concentrated among the elderly. As the pandemic unfolds, deaths were spreading over younger ages. Finally, we speculate on mechanisms behind the changes in the age structure of the COVID-19 deaths in Paraná.

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“…They could be variables related to age structure and prevalence of comorbidities associated with COVID-19 mortality. For example, age structure explained part of the between-country differences in COVID-19 mortality and case-fatality rates [4,20]; median prevalence of the five conditions most frequently associated with severe COVID-19 in USA allowed to identify the areas at highest risk for COVID-19 death [21]; age-specific prevalence of comorbidities explained the differences in mortality between Nigeria, Brazil and Italy [22]. Economic and healthcare associated variables are other aggregated data potentially useful to predict COVID-19 severity and spread [68][69][70], as well as inequalities within the general population [71].…”

Section: Determinantmentioning

confidence: 99%

“…The majority of them considered variables that reflected at population level the risk factors for COVID-19 severity at an individual level, such as population ageing, prevalence of diseases associated with COVID-19 death and severity, healthcare system capacity to face the public health emergency, etc. [4,[20][21][22]. The assessment of COVID-19 mortality determinants could benefit from similarities between this and other respiratory infectious diseases, particularly influenza, as these diseases share several characteristics.…”

Section: Introductionmentioning

confidence: 99%

Ecologic association between influenza and COVID-19 mortality rates in European countries

Petti¹,

Cowling

2020

Epidemiol. Infect.

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Ecologic studies investigating COVID-19 mortality determinants, used to make predictions and design public health control measures, generally focused on population-based variable counterparts of individual-based risk factors. Influenza is not causally associated with COVID-19, but shares population-based determinants, such as similar incidence/mortality trends, transmission patterns, efficacy of non-pharmaceutical interventions, comorbidities and underdiagnosis. We investigated the ecologic association between influenza mortality rates and COVID-19 mortality rates in the European context. We considered the 3-year average influenza (2014–2016) and COVID-19 (31 May 2020) crude mortality rates in 34 countries using EUROSTAT and ECDC databases and performed correlation and regression analyses. The two variables – log transformed, showed significant Spearman's correlation ρ = 0.439 (P = 0.01), and regression coefficients, b = 0.743 (95% confidence interval, 0.272–1.214; R2 = 0.244; P = 0.003), b = 0.472 (95% confidence interval, 0.067–0.878; R2 = 0.549; P = 0.02), unadjusted and adjusted for confounders (population size and cardiovascular disease mortality), respectively. Common significant determinants of both COVID-19 and influenza mortality rates were life expectancy, influenza vaccination in the elderly (direct associations), number of hospital beds per population unit and crude cardiovascular disease mortality rate (inverse associations). This analysis suggests that influenza mortality rates were independently associated with COVID-19 mortality rates in Europe, with implications for public health preparedness, and implies preliminary undetected SARS-CoV-2 spread in Europe.

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Besides population age structure, health and other demographic factors can contribute to understanding the COVID-19 burden

Cited by 115 publications

References 8 publications

Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality

Monitoring trends and differences in COVID-19 case-fatality rates using decomposition methods: Contributions of age structure and age-specific fatality

The changing age-structure of Coronavirus SARS-CoV-2 deaths and cases a case study of Paraná, Brazil

Ecologic association between influenza and COVID-19 mortality rates in European countries

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