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This repository provides the R code and the data for the paper Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: what can we learn from it?

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Age reporting for the oldest old in the Brazilian COVID–19 vaccination database: what can we learn from it?

License: MIT License: CC BY 4.0

This repository provides the reproducible R code and the data for the paper Age reporting for the oldest old in the Brazilian COVID–19 vaccination database: what can we learn from it?

Proportion of individuals aged 90 and older (90+/80+) by the proportion of centenarians (100+/80+) and life expectancy at age 50 (e(50)). Men and women. Brazil and its regions (vaccination records) and Sweden.


Citation

Turra, Cássio M., Fernando Fernandes, Júlia Almeida Calazans, and Marília R. Nepomuceno. 2023. “Age Reporting in the Brazilian COVID–19 Vaccination Database: What Can We Learn from It?” Demographic Research 48(28): 829–848. https://doi.org/10.4054/DemRes.2023.48.28.


Abstract

Background: Age misreporting affects population estimates at older ages. In Brazil, every citizen must be registered and show an identity document to vaccinate against COVID–19. This requirement to present proof of age provides a unique opportunity for measuring the oldest-old population using novel administrative data.

Objectives: To offer critically assessed estimates of the Brazilian population aged 80 and older based on data from the vaccination registration system (VRS). To uncover discrepancies between the number of vaccinated oldest-old people and the projections used to estimate target populations for COVID–19 vaccination.

Methods: We calculate data quality indicators based on data from the VRS – namely, 100+/80+ and 90+/80+ population proportions, sex ratios, and the Myers blended index – and compare them to those based on data on target populations from Brazilian censuses and demographic projections, and from Sweden – a country with high-quality data. We also estimate vaccination coverage ratios using population projections adjusted to excess deaths as the denominators.

Results: Requiring documentation reduces age heaping, age exaggeration, and sex ratios marginally. However, it cannot solve the problem of the misreporting of birth dates due to the absence of long–standing birth registration systems in Brazil, particularly in the northern and central regions. In addition, we find a mismatch between the projected populations and numbers of vaccinated people across regions.

Conclusions: Despite improvements in data quality in Brazil, we are still not confident about the accuracy of age reporting among the oldest old in the less advantaged Brazilian regions. The postponement of the 2020 census reduced the ability of authorities to define the target populations for vaccinations against COVID–19 and other diseases.

Contributions: This is the first study to compare population estimates for the oldest old in administrative data and census data in Brazil. Age misreporting resulted in discrepancies that may have compromised the efficacy of the COVID–19 vaccination campaign.


Reproducibility

We use population data for Brazil from the Brazilian Ministry of Health’s (MoH) open microdata registers for the national COVID-19 vaccination campaign (Brasil 2021); population projections from the Brazilian Institute of Geography and Statistics (IBGE 2018); population projections from the United Nations (United Nations 2019); and Brazilian censuses from 1980 to 2010 (IPUMS) (Center 2020). We use population data for Sweden from the Human Mortality Database (HMD) (Human Mortality Database 2021). We also use estimates for the distributions of excess deaths in Brazil, including the proportions by age groups, sex, and region of residence, and the absolute numbers by epidemiological week and region of residence from the Conselho Nacional de Secretários de Saúde (CONASS (Conselho Nacional de Secretários de Saúde) 2021).

The IBGE, UN, IPUMS, HMD and CONASS data files are in the data-raw sub-directory.

The data file with the Brazilian MoH vaccination records has 387,750,333 observations and around 190 GB. A compressed 40 GB version is available at DOI and should be downloaded and copied into the data-raw sub-directory.

The R code script files are in the r-scripts sub-directory.

The script to read, clean, prepare, tabulate and analyze data is 1-covid-19-datasus-vaccine.R and will write to the data-treated sub-directory.

The script to reproduce the figures is 2-covid-19-datasus-vaccine-plots.R and will write to the output sub-directory.

Data References

Brasil. 2021. “Campanha Nacional de Vacinação contra Covid-19 - Registros de Vacinação COVID-19 - Open Data.” openDataSUS. https://opendatasus.saude.gov.br/dataset/covid-19-vacinacao/.

Center, Minnesota Population. 2020. “Integrated Public Use Microdata Series, International: Version 7.3.” https://international.ipums.org; Minneapolis, MN: IPUMS. https://doi.org/10.18128/D020.V7.3.

CONASS (Conselho Nacional de Secretários de Saúde). 2021. “Painel de Análise Do Excesso de Mortalidade Por Causas Naturais No Brasil.” https://www.conass.org.br/indicadores-de-obitos-por-causas-naturais/.

Human Mortality Database. 2021. University of California, Berkeley (USA) and Max Planck Institute for Demographic Research (Germany).

IBGE. 2018. Projeções Da População: Brasil e Unidades Da Federação - Revisão 2018. Second. Séries Relatórios Metodológicos, volume 40. Rio de Janeiro: IBGE, Coordenação de População e Indicadores Sociais.

United Nations. 2019. World Population Prospects 2019: Online Edition. New York: United Nations, Department of Economic and Social Affairs, Population Division.


Questions

For any problems or questions, please open an issue or start a discussion.


Authors

Cássio M Turra, Demography Department, Cedeplar, Universidade Federal de Minas Gerais, Brazil @CassioMTurra

Fernando Fernandes, Demography Department, Cedeplar, Universidade Federal de Minas Gerais, Brazil @demographyandme

Júlia A. Calazans, Demography Department, Cedeplar, Universidade Federal de Minas Gerais, Brazil @_JuliaCalazans_

Marília R. Nepomuceno, Max–Planck–Institut für Demografische Forschung, Rostock, Germany @MariliaNepo


License

The data used and presented is licensed under CC BY 4.0. The code to read, format, analyze and display that data is licensed under The MIT license.

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This repository provides the R code and the data for the paper Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: what can we learn from it?

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