Brazil IBGE Projection: Population: Residents: Male: Age 65 to 69 Years data was reported at 6,767,303.000 Person in 2060. This records an increase from the previous number of 6,757,874.000 Person for 2059. Brazil IBGE Projection: Population: Residents: Male: Age 65 to 69 Years data is updated yearly, averaging 5,036,045.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 6,827,971.000 Person in 2054 and a record low of 2,253,998.000 Person in 2010. Brazil IBGE Projection: Population: Residents: Male: Age 65 to 69 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GAB043: Population: Projection: by Region and Age: Male (Discontinued).

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Quarterly: average based on 3 monthly data points

Sampling procedure

Sample size:

Abstract This article discusses the classification of census tracts, according to their urban or rural situation, proposed by the Brazilian Institute of Geography and Statistics (IBGE). The contributions that this classification brings to urban planning are explored, and the limits and possibilities of the use of these data to map the contemporary urban expansion and to structure the territory beyond the urban-rural divide are emphasized. Users of census information should know the method used by the IBGE to construct cartographic databases utilized during collection. In addition, users should reflect on how IBGE data can be employed in the construction of public policies, highlighting the relevance of the collected information and stressing the role of municipalities in initiatives to develop improved databases in order to obtain better results in the next census.

Brezilya Coğrafya istatistik Enstitüsü ya da kısaca IBGE Portekizce Instituto Brasileiro de Geografia e Estatística in B

Geographic coverage

National

Kind of data

Census/enumeration data [cen]

Sampling procedure

10136022 Individuals 2652356 Households

Mode of data collection

Face-to-face

Brazil IBGE Projection: Population: Residents: Female: Age 30 to 34 Years data was reported at 6,600,995.000 Person in 2060. This records a decrease from the previous number of 6,668,550.000 Person for 2059. Brazil IBGE Projection: Population: Residents: Female: Age 30 to 34 Years data is updated yearly, averaging 7,692,298.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 8,761,261.000 Person in 2017 and a record low of 6,600,995.000 Person in 2060. Brazil IBGE Projection: Population: Residents: Female: Age 30 to 34 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GAB044: Population: Projection: by Region and Age: Female (Discontinued).

Brazil IBGE Projection: Population: Residents: Male: Age 50 to 54 Years data was reported at 6,946,162.000 Person in 2060. This records a decrease from the previous number of 7,032,873.000 Person for 2059. Brazil IBGE Projection: Population: Residents: Male: Age 50 to 54 Years data is updated yearly, averaging 7,270,417.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 8,056,849.000 Person in 2049 and a record low of 4,900,002.000 Person in 2010. Brazil IBGE Projection: Population: Residents: Male: Age 50 to 54 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GAB043: Population: Projection: by Region and Age: Male (Discontinued).

Arquivos extraídos do site do IBGE (referentes ao ano de 2015). Fonte: ftp://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2015/Brasil/BR/

The files were downloaded from the IBGE (Brazilian Institute of Geography and Statistics) website, and the maps are from 2015. Source (in Portuguese): ftp://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2015/Brasil/BR/

Brazil IBGE Projection: Population: Residents: Female: Age 5 to 9 Years data was reported at 5,478,538.000 Person in 2060. This records a decrease from the previous number of 5,519,183.000 Person for 2059. Brazil IBGE Projection: Population: Residents: Female: Age 5 to 9 Years data is updated yearly, averaging 6,646,363.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 7,770,558.000 Person in 2010 and a record low of 5,478,538.000 Person in 2060. Brazil IBGE Projection: Population: Residents: Female: Age 5 to 9 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GAB044: Population: Projection: by Region and Age: Female (Discontinued).

Geographic coverage

National

Kind of data

Census/enumeration data [cen]

Sampling procedure

5870469 Individuals 1343377 Households

Mode of data collection

Face-to-face

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No (dwellings in original sample are interpreted as households in IPUMS) - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Households: Structurally independent living quarters, consisting of one or more rooms with a private entrance. - Group quarters: Group living together under relations of administrative subordination.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Instituto Brasileiro de Geografia e Estatística

SAMPLE UNIT: Household (called "dwelling" in original sample)

SAMPLE FRACTION: 6.0% (approx.)

SAMPLE SIZE (person records): 10,136,022

Mode of data collection

Face-to-face [f2f]

Response rate

COVERAGE: No official estimates, UNDERCOUNT: No official estimates

Abstract: The study aimed to compare two concepts of rurality, one proposed by the Brazilian Institute of Geography and Statistics (IBGE) and the other by the World Bank, to determine which is better fitted to the territory’s reality, besides analyzing the infant mortality profile of rural municipalities (counties) in the state of Paraíba, Brazil, according to the best criterion for rurality. This was an observational epidemiological study conducted in the state of Paraíba. The method for analyzing rural/urban typologies was based on data mining, using the Apriori algorithm of association. Infant mortality was analyzed with descriptive statistics. The data were obtained from the Mortality Information System of the Brazilian Ministry of Health, from 2007 to 2016, and municipal indicators were from IBGE. The World Bank definition of rurality showed kappa = 0.337, compared to the IBGE definition, with kappa = 0.616. Among the 223 municipalities that were analyzed, the World Bank classified 130 (65.66%) correctly, and the IBGE 183 (82.06%). The predominant epidemiological profile of infant mortality in rural municipalities in Paraiba state was male gender (57.4%), brown skin color (61.1%), age from 0 to 7 days (52.4%), low birthweight (44%), and gestational age less than 37 weeks (43.2%). Underlying cause of death was classified as avoidable death via interventions by the Brazilian Unified National Health System (65.2%). The urban/rural typology adopted by the IBGE was better than the World Bank at classifying the municipalities in Paraiba state. This classification allowed studying the infant mortality profile in rural municipalities, which was similar to the overall profile, except for maternal schooling.

Dataset name: asppl_dataset_v2.csv

Version: 2.0

Dataset period: 06/07/2018 - 01/14/2022

Dataset Characteristics: Multivalued

Number of Instances: 8118

Number of Attributes: 9

Missing Values: Yes

Area(s): Health and education

Sources:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Occupational Classification (CBO) (Brasil, 2022b);

• National Registry of Health Establishments (CNES) (Brasil, 2022c);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the asppl_dataset_v2.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).

Table 1: Description of AVASUS dataset features.

Attributes	Description	datatype	Value
gender	Gender of the course participant.	Categorical.	Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)
course_progress	Percentage of completion of the course.	Numerical.	Range from 0 to 100.
course_evaluation	A score given to the course by the participant.	Numerical.	0, 1, 2, 3, 4, 5 or NaN.
evaluation_commentary	Comment made by the participant about the course.	Categorical.	Free text or NaN.
region	Brazilian region in which the participant resides.	Categorical.	Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South).
CNES	The CNES code refers to the health establishment where the participant works.	Numerical.	CNES Code or NaN.
health_care_level	Identification of the health care network level for which the course participant works.	Categorical.	“ATENCAO PRIMARIA”, “MEDIA COMPLEXIDADE”, “ALTA COMPLEXIDADE”, and their possible combinations. (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")
year_enrollment	Year in which the course participant registered.	Numerical.	Year (YYYY).
CBO	Participant occupation.	Categorical.	Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)

Dataset name: prison_syphilis_and_population_brazil.csv

Dataset period: 2017 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 13

Missing Values: No

Source:

• National Penitentiary Department (DEPEN) (Brasil, 2022d);

Description: The data contained in the prison_syphilis_and_population_brazil.csv dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.

Table 2: Description of DEPEN dataset Features.

Attributes	Description	datatype	Value
Region	Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.	Categorical.	Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.
syphilis_2017	Number of syphilis cases in the prison system in 2017.	Numerical.	Number of syphilis cases.
syphilis_rate_2017	Normalized rate of syphilis cases in 2017.	Numerical.	Syphilis case rate.
syphilis_2018	Number of syphilis cases in the prison system in 2018.	Numerical.	Number of syphilis cases.
syphilis_rate_2018	Normalized rate of syphilis cases in 2018.	Numerical.	Syphilis case rate.
syphilis_2019	Number of syphilis cases in the prison system in 2019.	Numerical.	Number of syphilis cases.
syphilis_rate_2019	Normalized rate of syphilis cases in 2019.	Numerical.	Syphilis case rate.
syphilis_2020	Number of syphilis cases in the prison system in 2020.	Numerical.	Number of syphilis cases.
syphilis_rate_2020	Normalized rate of syphilis cases in 2020.	Numerical.	Syphilis case rate.
pop_2017	Prison population in 2017.	Numerical.	Population number.
pop_2018	Prison population in 2018.	Numerical.	Population number.
pop_2019	Prison population in 2019.	Numerical.	Population number.
pop_2020	Prison population in 2020.	Numerical.	Population number.

Dataset name: students_cumulative_sum.csv

Dataset period: 2018 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 7

Missing Values: No

Source:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the students_cumulative_sum.csv dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.

Table 3: Description of Students dataset Features.

Dataset name: asppl-dataset.csv

Version: 1.0

Dataset period: 06/07/2018- 05/25/2021

Dataset Characteristics: Multivalued

Number of Instances: 4861

Number of Attributes: 33

Missing Values: Yes

Area(s): Health and education

Sources:

• Primary: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];

• Secondary:

  1. Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];

  2. National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and

  3. Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].

Description: The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.

README

Dataset name: avasus_dataset.csv

Version: 1.0

Dataset period: July 24, 2018 - February 22, 2024

Dataset Characteristics: Multivalued

Number of Instances: 1533

Number of Attributes: 5

Missing Values: Yes

Area(s): Health and education

Sources:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2024a);
• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2024d).

Description: The "avasus_dataset.csv" dataset (see Table 1) originates from participants of the "Training Course on Underlying Cause-of-Death Coding – ICD-10". The course was available on the Brazilian National Health System - AVASUS (Brasil, 2024a). This dataset provides elementary data to analyze the course's scope and participant profiles.

Note: The dataset's content is provided in Brazilian Portuguese (pt-br), originating from native speakers.

Table 1: Description of AVASUS dataset features.

Dataset name: cbo_dataset.csv

Version: 1.0

Dataset period: July 24, 2018 - February 22, 2024

Dataset Characteristics: Multivalued

Number of Instances: 1135

Number of Attributes: 6

Missing Values: Yes

Area(s): Health and education

Sources:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2024a);
• Brazilian Occupational Classification (CBO) (Brasil, 2024b);
• National Registry of Health Establishments (CNES) (Brasil, 2024c);
• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2024d).

Description: The "cbo_dataset.csv" dataset (see Table 2) originates from participants of the "Training Course on Underlying Cause-of-Death Coding – ICD-10". The course was available on the Brazilian National Health System - AVASUS (Brasil, 2024a). This dataset provides elementary data to analyze the course's scope and the participants' professional profiles.

Note: The dataset's content is provided in Brazilian Portuguese (pt-br), originating from native speakers.

Table 1: Description of AVASUS dataset features.

REFERENCES

Brasil (2024a). AVASUS - Virtual Learning Environment of the Brazilian Health System. Available from: https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php. Accessed Jul 8, 2024.

Brasil (2024b). CBO - classificação brasileira de ocupações. Available from: https://cbo.mte.gov.br/cbosite/pages/home.jsf. Accessed Oct 23, 2024.

Brasil (2024c). CNES - cadastro nacional de estabelecimentos de saúde. Available from: https://cnes.datasus.gov.br/. Accessed Oct 23, 2024.

Brasil (2024d). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica. Accessed Jun 19, 2024.

ARTICLE:

Data Report: Online Learning Module of the ``Training Course on Underlying Cause-of-Death Coding – ICD-10'' of the Virtual Learning Environment of the Brazilian Health System

AUTHORS:

Aldiney J. Doreto^1,2, António M. Teixeira³, Janaina L. R. S. Valentim^1,4, João P. Q. Santos^1,4, Talita K. de B. Pinto¹, Yluska M. M. B. Mendes⁵, Aline P. Dias¹, Karla M. D. Coutinho^1,6, Ednara N. Gonçalves¹, Andréa S. Pinheiro¹, Felipe Fernandes¹, Natalia A. N. Batista^1,7, Karilany D. Coutinho^1,8, and Ricardo A. M. Valentim^1,8

¹Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil

²University of Minho (UMinho)/Open University of Portugal (UAb), Lisbon, Portugal

³Department of Education and Distance Learning, Open University of Portugal (UAb), Lisbon, Portugal

⁴Advanced Nucleus for Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil

⁵Secretariat of Health Surveillance and Environment of the Brazilian Ministry of Health, Brasilia, Federal District, Brazil

⁶Health Sciences Graduate Program, Federal University of Rio Grande do Norte, Natal, Brazil

⁷Social Sustainability and Development Graduate Program, Open University of Portugal, Lisbon, Portugal

⁸Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, RN, Brazil

Abstract

No intuito de aprimorar o sistema das estatísticas socioeconômicas, o IBGE, em convênio com o Banco Mundial, realizou um projeto piloto de pesquisa multitemática para atender a necessidade de informações que (a) qualifiquem e indiquem os determinantes do bem-estar social de diferentes grupos sociais e (b) permitam identificar os efeitos de políticas governamentais nas condições de vida domiciliar.

O objetivo da pesquisa é fornecer informações adequadas para planejamento, acompanhamento e análises de políticas econômicas e programas sociais em relação ao seus impactos nas condições de vida domiciliar, em especial nas das populações mais carentes.

Substantivamente, a pesquisa proporciona um panorama do bem-estar dos moradores dos domicílios e possibilita o estudo de seus determinantes. Partindo da premissa que quantificar e situar um problema não é suficiente, a pesquisa busca explicações que permitam indicar soluções. Por exemplo, o conhecimento de quantos pobres existem, como e onde moram e o que fazem é apenas uma parte da investigação. Para se produzirem informações que possam subsidiar soluções mais efetivas, é necessário um levantamento detalhado sobre as causas e conseqüências da pobreza. O mesmo princípio se aplica a outras áreas do bem-estar social.

Desta forma, o questionário da pesquisa é planejado para fornecer um conjunto de informações integradas com o objetivo de: - medir a distribuição do bem-estar e o nível de pobreza, principalmente, em áreas onde predominam a agricultura de subsistência, a economia informal e o emprego sazonal; - descrever os padrões de acesso e utilização de serviços públicos - educação, saúde, saneamento básico, etc.;
- compreender como os moradores dos domicílios reagem às condições econômicas e aos impactos de medidas governamentais; e - permitir análises complexas das relações entre os vários aspectos do bem-estar social, como o impacto da saúde no emprego, o padrão de gastos nos níveis nutricionais dos moradores, etc.

A pesquisa, no entanto, não trata os vários temas investigados com a mesma profundidade que as informações levantadas em pesquisas tópicas. Ao mesmo tempo, por ter uma amostra pequena, a precisão dos resultados é menor do que aqueles das pesquisas tópicas. Mas, pela sua abrangência temática, a pesquisa permite um bom resumo multidimensional do bem-estar e o estudo das interações entre os vários fatores.

Geographic coverage

Northeast Region, Southeast Region

Kind of data

Sample survey data [ssd]

Sampling procedure

O PLANEJAMENTO DA AMOSTRA

O desenho amostral da PPV - Pesquisa sobre Padrões de Vida - foi discutido com os técnicos do Banco Mundial e a dimensão da amostra foi fixada em função do orçamento disponível para a realização da pesquisa.

Como pesquisa piloto optou-se por sua realização apenas nas Regiões Nordeste e Sudeste do País, considerando 10 estratos geográficos, a saber: Região Metropolitana de Fortaleza, Região Metropolitana de Recife, Região Metropolitana de Salvador, restante da área urbana do Nordeste, restante da área rural do Nordeste, Região Metropolitana de Belo Horizonte, Região Metropolitana do Rio de Janeiro, Região Metropolitana de São Paulo, restante da área urbana do Sudeste e restante da área rural do Sudeste.

Tal como em outras pesquisas domiciliares realizadas pelo IBGE, optou-se por um desenho com dois estágios de seleção, com estratificação das unidades primárias e seleção proporcional a uma medida de tamanho e seleção aleatória das unidades de segundo estágio. A unidade primária é o setor da base geográfica do Censo Demográfico de 1991 e a unidade de segundo estágio é o domicílio.

O tamanho da amostra para cada estrato geográfico foi fixado em 480 domicílios. Em cada estrato geográfico foi fixado em 60 o número de setores a serem selecionados e 8 domicílios em cada setor, com exceção para os estratos que correspondem ao restante da área rural de cada Região onde fixou-se em 30 o número de setores e em 16 o número de domicílios a serem selecionados por setor, em função da dificuldade de acesso a esses setores, o que implicaria em aumento de custo.

O tamanho da amostra fixado foi defendido pelos técnicos do Banco Mundial em função da experiência nos outros países onde a pesquisa foi ou está sendo conduzida, pela necessidade de produzir informações com a maior rapidez possível e por julgar que o objetivo da pesquisa não é produzir tabulações com cruzamentos de variáveis, tal como ocorre com as informações da Pesquisa Nacional por Amostra de Domicílios - PNAD, mas o de fornecer indicadores de tendência ou de variação em níveis bastante agregados.

A definição dos estratos estatísticos

Conforme descrito anteriormente, o setor é a unidade primária de amostragem, o domicílio é a unidade secundária e unidade de investigação. A estratificação das unidades primárias de amostragem foi definida em duas etapas: a primeira, considerando a divisão geográfica de interesse, que resultou na definição de 10 estratos geográficos; para cada um dos estratos geográficos, a segunda estratificação foi definida por critérios estatísticos, considerando as informações sobre a renda média mensal do chefe do domicílio, variável que foi investigada no Censo Demográfico de 1991 para todos os domicílios.

A alocação da amostra nos estratos de renda

Vale lembrar que o tamanho final da amostra de domicílios foi fixada em função do custo, mais especificamente dos recursos financeiros disponíveis. Em conseqüência, o tamanho da amostra de setores e o número de domicílios a serem selecionados por setor também foram fixados, a saber: - 60 setores e 8 domicílios por setor, nos estratos geográficos urbanos e regiões metropolitanas (estratos geográficos 1,2,3,4,6,7,8 e 9); - 30 setores e 16 domicílios por setor, nos estratos geográficos rurais (estratos geográficos 5 e 10).

Antes da alocação nos estratos de renda, a amostra total nos 10 estratos geográficos ficou com 540 setores e 4.800 domicílios. Foi utilizada a alocação proporcional, com base no número de domicílios particulares permanentes ocupados, obtidos pelo Censo 91.

Vale lembrar quem, durante o procedimento de alocação, os valores resultantes foram arredondados para o maior inteiro e em um único estrato, após o arredondamento, o valor resultante 1 foi alterado para 2 a fim de permitir o cálculo de variâncias. Como pode ser observado, em função da variabilidade da fração amostral, a amostra resultante não é a autoponderada.

A SELEÇÃO DA AMOSTRA

A seleção da amostra de setores

Para a seleção da amostra de setores, segundo o desenho adotado, qual seja, amostra estratificada com probabilidade proporcional ao tamanho, foi utilizado um programa em linguagem SAS, utilizando a macro de seleção PPTCOM (ver Silva (1989), que foi adaptada para considerar automaticamente os estratos geográficos e estratos de renda definidos. A medida de tamanho adotada foi o número de domicílios em cada setor, conforme definição de hi P mais adiante.

Após a seleção dos setores, foi realizada uma comparação desses setores com os setores pertencentes às amostras da PNAD - Pesquisa Nacional por Amostra de Domicílios, da PME - Pesquisa Mensal de Emprego e da amostra selecionada para a POF 96/96 - Pesquisa de Orçamentos Familiares. Como o esquema de seleção das amostras dessas pesquisas é o mesmo, qual seja, seleção com probabilidade proporcional ao tamanho, era de se esperar que houvesse coincidências de setores selecionados para duas ou mais pesquisas. Foram avaliados os procedimentos adotados nessas outras pesquisas para contornar o problema de setores (ou domicílios) serem investigados em mais de um pesquisa no mesmo período. Nenhuma das soluções adotadas em outras pesquisas foi considerada satisfatória, tendo sido decidido substituir os setores coincidentes com os de outras pesquisas, além daqueles que foram selecionados mais de uma vez na própria PPV, uma vez que a seleção foi com reposição.

Em função dessa decisão, foi selecionada uma segunda amostra, usando os mesmos procedimentos adotados quando da seleção da primeira amostra. Dessa segunda amostra foram extraídos todos os setores coincidentes com os das outras três pesquisas, todos os setores coincidentes com os selecionados na primeira amostra e, também, aqueles selecionados mais de uma vez nessa segunda amostra da PPV. Os setores restantes foram analisados comparativamente àqueles a serem substituídos e, para a substituição propriamente dita, foram consideradas algumas variáveis de controle, a saber: estrato geográfico, estrato de renda, situação (urbana ou rural) e tipo de setor (normal ou de favela). Além disso, foi considerado o valor da probabilidade de seleção. Isto significa que um setor substituto tem as mesmas características nas variáveis de controle e tem uma probabilidade de seleção aproximadamente igual à de um setor qualquer dentre os que foram substituídos. Ao todo, foram substituídos 78 setores.

A operação de listagem e a seleção de domicílios

A operação de listagem de setores tem por objetivo construir um cadastro, o mais atualizado possível, dos domicílios existentes nos setores selecionados para a amostra, a fim de permitir a seleção dos domicílios a serem investigados. Em função disso, a operação de listagem foi realizada em quatro etapas, cada uma abrangendo os setores de um trimestre da pesquisa.

Uma vez terminada a listagem dos setores, foi realizada a seleção dos domicílios, que, como definido anteriormente, foi feita com eqüiprobabilidade, considerando os tamanhos de

Abstract

The Census of Agriculture investigates information on agricultural establishments and agricultural activities developed inside them, including characteristics of the producers and establishments, economy and employment in the rural area, livestock, cropping and agribusiness. Its data collection unit is every production unit dedicated, either entirely or partially, to agricultural, forest or aquaculture activities, subordinated to a single administration – producer or administrator –, regardless of its size, legal nature or location, aiming at producing either for living or sales.

The first Census of Agriculture dates back to 1920, and it was conducted as part of the General Census. It did not take place in the 1930s due to reasons of political and institutional nature. From 1940 onward, the survey was decennial up to 1970 and quinquennial later on, taking place in the beginning of the years ending in 1 and 6 and relating to the years ending in 0 and 5. In the 1995-1996 Census of Agriculture, the information was related to the crop year (August 1995 to July 1996). In the 2006 Census of Agriculture, the reference for the data returned to be the calendar year. The 2006 edition was characterized both by the technological innovation introduced in the field operation, in which the paper questionnaire was replaced by the electronic questionnaire developed in Personal Digital Assistants - PDAs and by the methodological refinement, particularly concerning the redesign of its contents and incorporation of new concepts. That edition also implemented the National Address List for Statistical Purposes - Cnefe, which gathers the detailed description of the addresses of housing units and agricultural establishments, geographic coordinates of every housing unit and establishment (agricultural, religious, education, health and other) in the rural area, bringing subsidies for the planning of future IBGE surveys. The 2017 Census of Agriculture returned to reference the crop year – October 2016 to September 2017 –, though in a different period than that adopted in the 1995-1996 Census of Agriculture. New technologies were introduced in the 2017 survey to control the data collection, like: previous address list, use of satellite images in the PDAs to better locate the enumerator in relation to the terrain, and use of coordinates of the address and location where the questionnaire is open, which allowed a better coverage and assessment of the work.

The survey provides information on the total agricultural establishments; total area of those establishments; characteristics of the producers; characteristics of the establishments (use of electricity, agricultural practices, use of fertilization, use of agrotoxins, use of organic farming, land use, existence of water resources, existence of warehouses and silos, existence of tractors, machinery and agricultural implements, and vehicles, among other aspects); employed personnel; financial transactions; livestock (inventories and animal production); aquaculture and forestry (silviculture, forestry, floriculture, horticulture, permanent crops, temporary crops and rural agribusiness).

The periodicity of the survey is quinquennial, though the surveys in 1990, 1995, 2000 and 2005, 2010 and 2015 were not carried out due to budget restrictions from the government; the 1990 Census of Agriculture did not take place; the 1995 survey was carried out in 1996 together with the Population Counting; the 2000 survey did not take place; that of 2005 was carried out in 2007, together with the Population Counting once again; that of 2010 did not take place and that of 2015 was carried out in 2017. Its geographic coverage is national, with results disclosed for Brazil, Major Regions, Federation Units, Mesoregions, Microregions and Municipalities. The results of the 2006 Census of Agriculture, which has the calendar year as the reference period, are not strictly comparable with those from the 1995-1996 Census of Agriculture and 2017 Census of Agriculture, whose reference period is the crop year in both cases.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as any production unit dedicated wholly or partially to agricultural, forestry and aquaculture activities, subject to a single management, with the objective of producing for sale or subsistence, regardless of size, legal form (own, partnership, lease, etc.) or location (rural or urban). The agricultural holdings were classified according to the legal status of the producer as: individual holder, condominium, consortium or partnership; cooperative; incorporated or limited liability company; public utility institutions (church, NGO, hospital), or government.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Frame The 2000 Population and Housing Census and the cartographic documentation constituted the source of the AC 2006 frame. No list frames were available in digital media with georeferenced addresses of the holdings. Census coverage was ensured on the basis of the canvassing of the EAs by enumerators.

(b) Complete and/or sample enumeration methods The AC 2006 was a complete enumeration operation of all agricultural holdings in the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

An electronic questionnaire was used for data collection on:

Total agricultural establishments Total area of agricultural establishments Total area of crops Area of pastures Area of woodlands Total tractors Implements Machinery and vehicles Characteristics of the establishment and of the producer Total staff employed Total cattle, buffallo, goats, Sheep, pigs, poultry (chickens, fowls, chickens and chicks) Other birds (ducks, geese, teals, turkeys, quails, ostriches, partridges, pheasants and others) Plant production

The AC 2006 covered all 16 items recommended by FAO under the WCA 2010.

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING The entire data collection and supervision software was developed in house by IBGE, using the Visual Studio platform in the Microsoft Operations Manager 2005 environment and Microsoft SQL Server 2000, with the assistance of Microsoft Brazil consulting. In addition, the GEOPAD application was installed to view, navigate and view maps and use GPS guidance. Updated versions of the software were installed automatically as soon as census enumerators connected the PDAs to the central server to transmit the data collected. Once internally validated by the device, the data were immediately transmitted to the database at the IBGE state unit. The previous AC (1996) served as the basis for defining the parameter values for the electronic editing process.

(b) CENSUS DATA QUALITY Automatic validation was incorporated into PDAs. Previously programmed skip patterns and real-time edits, performed during enumeration, ensured faster and more reliable interviews. In addition, the Bluetooth® technology incorporated into the PDAs allowed for direct data transmission to IBGE's central mainframe by each of enumerators on a weekly basis.

Data appraisal

The preliminary census results were published in 2007. The final results were released in 2009 through a printed volume and CD-ROMs. The census results were disseminated at the national and subnational scope (country, state and municipality) and are available online at IBGE's website.

The database "Census tracts variables data" contains fifteen variables associated with urban inequality. The database "Data_PSL_S-III" contains thirty four variables associated with urban inequality. The data are from the city of Maringá, Paraná, Brazil. The primary source is the Brazilian Institute of Geography and Statistics (IBGE).

Abstract Household surveys are one of the primary methodologies used in population-based studies. This narrative review of the literature aims to gather and describe the leading national and international household surveys of relevance. In Brazil, the historical role played by the Brazilian Institute of Geography and Statistics (IBGE) in conducting the most relevant research in the production of social data stands out. The Medical-Health Care Survey (AMS) and the National Household Sample Survey (PNAD), with the serial publication of Health Supplements, are the country’s primary sources of health information. In 2013, in partnership with the Ministry of Health, IBGE launched the National Health Survey (PNS), the most significant household health survey ever conducted in Brazil. The PNS-2019 received a major thematic and sampling expansion and, for the first time, applied the Primary Care Assessment Tool to assess PHC services in all 27 Brazilian states.

Consumer Price Index monthly for Brazil since 1981. All data is provided by IBGE (The Brazilian Institute of Geography and Statistics) and downloaded via SIDRA.