Socioeconomic determinants

Brazilian deprivation index (IBP)

Description

It provides deprivation measures for each Brazilian municipality based on the 2010 Brazilian census data. It is used to evaluate health inequalities across the country. The 2010 Brazilian Census social and economic estimatimations are the basis for calculating the deprivation measure, available at [1] and [2].

The IBP index combines three factors:

1. the percentage of families with income per capita below half of the minimum wage;

2. the percentage of illiterate people older than 7 years old;

3. the percentage of people without adequate access to drinkable water, sewage, garbage collection, bathroom, or shower;

A complete documentation about the construction of the index is presented in [2]. The original data source and visualization can be found in [1] and [2].

Data access information

The data and documentation are available at the University of Glasgow [2]. The data and this report are distributed under the Creative Commons Share-Alike license (CC BY-SA 4.0) and can be freely used by researchers, policymakers, or members of the public.

Methods of data collection

The data can be download in [1] or [2]. The database describe the deprivation index for each municipality in 2010. No other update are available in the institutions that created it.

Data-specific information

The IBP for Brazilian municipalities dataset has a total of 15 columns (variables) and shows a total of 5566 registries (rows) and a size of 734 KB.

Limitations of IBP dataset

The IBP dataset includes only 3 measures of deprivation excluding others like employment, crime, health, education, and access to public services. It is also exclusively related to the year 2010 making comparison along time impracticable. The different population and ethnic groups (eg, indigenous peoples, quilombolas, riverside populations) are also not considered and there are some biases inherent to rural areas.

Variable list for IBP database

Original field name	Field label	Type	Category	Description
ip_cd_d	Localization code, in this case it is the same as the city.	Number	Uncategorized	Localization code, in this case it is the same as the city.
ip_cd_m	City code	Number	Uncategorized	city code
ip_nm_f	State name	String	Uncategorized	State name
ip_nm_r	Region name	String	Uncategorized	Region name
ip_cd_f	State acronym	String	Uncategorized	State acronym
ip_vl_f	State code	Number	Uncategorized	State code
ip_vl_p	Population according to IBGE (Brazilian Institute of Geography and Statistics) 2010 census	Number	Uncategorized	Population according to IBGE 2010 census
ip_nm_m	city name	String	Uncategorized	City name
ip_vl_n	Value of the deprivation index	Number	Uncategorized	Value of the deprivation index
ip_dcl_	Decile of the deprivation index	Number	Uncategorized	Decile of the deprivation index
ip_qntl_n	Quintile of the deprivation index	Number	Uncategorized	Quintile of the deprivation index
ip_prcnt_r	Percentage of people with per capita income below 1/2 minimum wage	Number	Uncategorized	Percentage of people with per capita income below 1/2 minimum wage
ip_prcnt_d	Percentage of illiterate people over 7 years old	Number	Uncategorized	Percentage of illiterate people over 7 years old.
ip_prcnt_m	Percentage of population in inappropriate homes.	Number	Uncategorized	Percentage of population in inappropriate homes.
ip_cd_c	empty/unknown

To facilitate visualisation, we have also provided a data explorer that allows users to view the first rows the dataset along with metadata, including column descriptions, variable type, and variable harmonisation where applicable. This also allows broader re-use of this dataset, particularly since the original descriptors and data dictionaries are usually only available in Portuguese. Please, see https://pamepi.rondonia.fiocruz.br/en/ibp_en.html.

Note

the Platform for analytical models in epidemiology - PAMEpi offers support in the documentation and collection of this database. More details in [3], [4] and [5].

References

[1] (1,2,3)

CIDACS. (2020, 09 01). IBP. Retrieved October 07, 2022, from https://cidacs.bahia.fiocruz.br/ibp/painel/.

[2] (1,2,3,4,5)

Allik, M., Ramos, D., Agranonik, M., Pinto Júnior, E.P., Ichihara, M.Y., Barreto, M.L., Leyland, A.H. and Dundas, R., 2020. Developing a small-area deprivation measure for Brazil. See https://researchdata.gla.ac.uk/980/.

[3]

Platform for analytical models in epidemiology - PAMEpi. https://pamepi.rondonia.fiocruz.br/en/index_en.html. Accessed: February 25, 2022.

[4]

GitHub directory - PAMepi/PAMepi-scripts-datalake: v1.0.0 (v1.0.0). Zenodo. . https://doi.org/10.5281/zenodo.6384641. Accessed: February 25, 2022.

[5]

da Silva, N.B., Valencia, L.I.O., Ferreira, A., Pereira, F.A., de Oliveira, G.L., Oliveira, P.F., Rodrigues, M.S., Ramos, P.I. and Oliveira, J.F., 2022. Brazilian COVID-19 data streaming. arXiv preprint arXiv:2205.05032.