Vienna Institute of Demography, VID/OEAW Demographic aspects of the COVID-19 pandemic and its consequences, 2020.

The changing age-structure of Coronavirus SARS-CoV-2 deaths and cases:

a case study of Paraná, Brazil

Raquel Guimaraes, Marília Nepomuceno, Acácia Nasr, Junior Garcia, Maria Goretti, Nestor Junior & Carlos Alberto Preto

Background• Demographic aspects of Paraná state in Brazil:

• Older population age-structure – ↓ fertility rate and ↑ life expectancy

• Paraná is one of the most developed states of the country.

• The Human Development Index (HDI): 0.749.

• Current situation of the pandemic in state:

• First case of Covid-19: March 12, 2020.

• As of July 27, 2020: 68,000 cases and 1,703 deaths → death coefficient of 0.05 (Brazil: 42 per 100,000 inhabitants) and a fatality rate of 2.5 percent (Brazil: 3.6 percent).

• Limited testing (Mills 2020; Paixão et al. 2020).

Background28 5

0 42 64 12

3 195 30

2 379

633

848

1,52

0

1,44

1

1,80

5

1,70

5

1,74

3

1,86

1

1,78

5

1,85

3

1,71

5

1,79

4

1,59

2

1,49

6

1,48

7

1,37

2

1,05

1

1,07

8

1,14

0

942 984

1,24

4

2,10

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0

500

1,000

1,500

2,000

2,500

17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

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Semana Epidemiológica

Average daily COVID19 cases in the State of Paraná, according to epidemiological week. Last updated: 21 nov 2020.

Elaboração: equipe do Nesde Fonte: SESA/PR Projeto de Pesquisa SEI UFPR 23075 023370/2020 07

Background

• Principal measures regarding the prevention of COVID-19 adopted by the Government of the State of Paraná:

Research question

• Given these demographic features of Paraná state in Brazil, and the availability of

administrative microdata by gender, age and sex for the confirmed COVID-19 cases and

deaths, our goal is to explore the demographic evolution of the Coronavirus SARS-CoV-2

deaths and cases in the state.

Hypothesis

• Loose restrictions of social isolation, ↑ levels of inequality compared with more developed

populations (Oliveira et al., 2020) → Younger adults likely contribute to community

transmission of COVID-19 younger age-structure of the deaths and cases.

Relevance

• Given the role of asymptomatic and presymptomatic transmission strict adherence to

community mitigation strategies and personal preventive behaviors by younger adults is

needed to help reduce their risk for infection and subsequent transmission of SARS-CoV-2

to persons at higher risk for severe illness.

Methods

Data

• We use administrative records from the Paraná Health Secretariat.

Design• We monitor COVID-19 cases and confirmed COVID-19 deaths by single age, sex

and municipality of residence.

• The municipalities are classified in low, medium, high as per the percentage of

elderly in the population, and low, medium, high as per the number of

confirmed COVID-19 cases (cumulative) in the period of reference.

Results

• COVID-19 cases and deaths in the state of Paraná endorse the rejuvenation of the age-structure when the pandemic unfolds in the State.

• Cases are more concentrated among individuals in the economically active age groups(prime ages), unlike the beginning of the pandemic.

• The initial age-sex distribution of COVID-19 deaths was more concentrated among the50-60s and above → As the pandemic unfolds, there is a clear rejuvenation of the age

structure of deaths.

ResultsRelative age-distribution by sex of COVID-19 cases and deaths according to the temporal

evolution of the disease

ISO weeks 11-15, 2020 ISO weeks 11-25, 2020

ResultsRelative age-distribution by sex of COVID-19 cases and deaths according to the temporal

evolution of the disease

ISO weeks 11-30, 2020

68.50

68.18

68.29

68.00

68.10

68.20

68.30

68.40

68.50

68.60

ISO Weeks 11-15 ISO Weeks 11-25 ISO Weeks 11-30

Average age - COVID-19 Deaths

47.15

41.27

40.41

36.00

38.00

40.00

42.00

44.00

46.00

48.00

ISO Weeks 11-15 ISO Weeks 11-25 ISO Weeks 11-30

Average age - Cases Confirmed

Results

• Regarding the heterogeneity in space on the demographic aging process at the local level

and the relationship with the age-distribution of cases:

• The number of low-low municipalities decreases (blue) → Out of 109 municipalities

with this condition, only 45 persist until week 30.

• The Rejuvenation process can be seen from the medium-low municipalities in week 11-

15 (green).

ResultsRegional distribution of the confirmed COVID-19 cases, according to the percentage of elderly in the

municipality and temporal evolution of the pandemic

ISO weeks 11-15, 2020 ISO weeks 11-25, 2020

ResultsRegional distribution of the confirmed COVID-19 cases, according to the percentage of elderly in the

municipality and temporal evolution of the pandemic

ISO weeks 11-30, 2020

Results

• Regarding the heterogeneity in space on the demographic aging process at the local level

and the relationship with the age-distribution of deaths:

• The number of municipalities that turn out to have a higher number of deaths (red)

increases systematically over the weeks (from 5, to 47, and then to 59 localities).

• The number of municipalities with the lower percentage of elderly and lower number

of registered deaths (low-low, blue) decreases.

ResultsRegional distribution of the number of COVID-19 deaths, according to the percentage of elderly in the

municipality and temporal evolution of the pandemic

ISO weeks 11-15, 2020 ISO weeks 11-25, 2020

ResultsRegional distribution of the number of COVID-19 deaths, according to the percentage of elderly in the

municipality and temporal evolution of the pandemic

ISO weeks 11-30, 2020

Conclusions

• 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).

ReferencesMills, M., 2020. Demographic Science COVID-19. https://doi.org/10.17605/OSF.IO/SE6WY

Nepomuceno, M.R., Acosta, E., Alburez-Gutierrez, D., Aburto, J.M., Gagnon, A., Turra, C.M., 2020. Besides population age structure, health and other demographic factors can contribute to understanding the COVID-19 burden. Proc. Natl. Acad. Sci. 117, 13881–13883. https://doi.org/10.1073/pnas.2008760117.

Oliveira, Gisliany Lillian Alves, Luciana Conceição Lima, Ivanovitch Silva, Marcel da Câmara Ribeiro-Dantas, KayoHenrique Monteiro, and Patricia Takako Endo. 2020. ‘Medidas de Distanciamento Social e Mobilidade Na América Do Sul Durante a Pandemia Por COVID-19: Condições Necessárias e Suficientes?’ ArXiv:2006.04985 [Cs].

Paixão, B., Baroni, L., Salles, R., Escobar, L., de Sousa, C., Pedroso, M., Saldanha, R., Coutinho, R., Porto, F., Ogasawara, E., 2020. Estimation of COVID-19 under-reporting in Brazilian States through SARI. ArXiv200612759 Cs Stat.

State of Paraná, Health Secretariat, 2020. Notifica COVID-19/SESA, July 27, 2020.

https://doi.org/10.17605/OSF.IO/SE6WYhttps://doi.org/10.1073/pnas.2008760117

Thank you for attention!raquel.guimaraes@ufpr.br

Número do slide 1BackgroundBackgroundBackgroundResearch questionHypothesisRelevanceMethodsResultsResultsResultsNúmero do slide 12ResultsResultsResultsResultsResultsResultsConclusionsReferencesThank you for attention!