A comprehensive integrated dataset on Brazilian health facilities: from 2005 to 2021
BMC Research Notes volume 16, Article number: 151 (2023)
The National Registry of Healthcare Facilities is a system with the registry of every healthcare facility in Brazil with information on the capacity building and healthcare workforce regarding its public or private nature. Despite being publicly available, it can only be accessed in separated disjoint tables, with different primary units of analysis. The objective is to offer an interoperable dataset containing monthly data from 2005 to 2021 with information on healthcare facilities, including their physical and human resources, services and teams, enriched with municipal information.
Database with historical data and geographic information for each health facility in Brazil. It is composed by 5 distinct tables, organized according to combinations of time, space, and types of resources, services and teams. This database opens up a range of possibilities for research topics, from case studies in a single health facility and period, analysis of a group of health facilities with characteristics of interest, to a broader study using the entire dataset and aggregated data by municipality. Furthermore, the fact that there is a row for each health facility/month/year facilitates the integration with other datasets from the Brazilian healthcare system. In addition to being a potential object of study in the health area, the dataset is also convenient in data science, especially for studies focused on time series.
Approximately half the world’s population lacks access to healthcare services, medicines, and vaccination . Brazil includes universal health coverage in its Constitution, which is operated through the Unified National Health System (“Sistema Único de Saúde” in Portuguese) , aligned with the target 3.8 of the Sustainable Development Goals . The National Registry of Healthcare Facilities (“Cadastro Nacional de Estabelecimentos de Saúde”; or CNES, in Portuguese) is a system with the registry of every healthcare facility in Brazil with information on the capacity building and healthcare workforce regarding its public or private nature . The CNES must be fed monthly by each healthcare unit, providing updated knowledge about the existing healthcare network to assist in health planning and effective and efficient management. Public consultations can be made on the CNES system (http://cnes.datasus.gov.br/), but the outputs will be provided in separated tables for each system dimension: healthcare facility’ general information, physical resources, human resources, services, and health teams. Every dataset dimensions has between 49,989,883 and 55,355,133 registers. Moreover, the number of attributes varies between 451 and 849.
Our objective is to provide a machine-readable and interoperable dataset on the CNES dimensions. This dataset will support studies that describe the Brazilian healthcare situation, that employ healthcare dimensions as confounders in ecological designs, or studies on the impact of public policies on health outcomes. Data from CNES are currently only available through tabular sets or via FTP and in a disaggregated way. The presented datasets make them available to the community, in a standardized way and following the FAIR principles. They provide unified access to the datasets and enrichments with geospatial information, which allows analysis in total granularity (accessing Brazilian health facilities), and spatial and temporal aggregated data.
The datasets created contain the different dimensions of the National Registry of Health Facilities (CNES), which are: physical resources (beds and equipments), human resources (health professionals), services and health teams. The dataset and its related files are available for download, as shown in Table 1. Each of these datasets results from a construction method with the following steps: (i) data extraction, (ii) data integration, (iii) data mapping, (iv) reshaping data, and (v) data enrichment. The criteria adopted for data management were based on detailed studies of the dataset, and the support of specialists in the field.Footnote 1
The datasets were extracted from DATASUS, and each one represents a different dimension of health facilities, with records of physical resources, such as types of existing beds and amounts of equipments existing and in use in the health facilities, as well as the health services offered, numbers of professionals and types of health teams. For this, data on health facilities (ST), complementary data from the CNES (DC), and data from each dimension were extracted. In addition, data extraction was performed over time (months and years) and in geographic space (federative units), with the observation of health units.
The datasets developed are the result of integrating the three types of databases previously extracted: complementary data, health facilities, and dimension, aggregated by competence date (months and years) and federative unit in Brazil.
The data mapping step aims to create variables where, for each numeric value of a source variable, a categorical definition value is created.
To reshape data, the values in indexes/columns are organized, creating new datasets. From there, the treatment of invalid values is performed.
Finally, the dataset was enriched with information about the municipalities, states, and regions obtained from the DATASUS FTP server.Footnote 2 The attributes resulting from this process are the coordinates, area, regional health codes, and other Boolean attributes, indicating if the municipality is the state’s capital or border, for example. The data dictionaryFootnote 3 presents a full description. All of these steps are explained in details and can be reproduced for the CNES Teams dataset, as an example, with the Jupyter Notebook file available at https://doi.org/10.7303/syn34388051.1.
The constructed datasets provide information about the CNES in a machine-readable, ready-to-use format. They allow evaluating and understanding the spatial distribution and temporal evolution of Brazil’s private and/or public health infrastructure. Furthermore, they contribute to the aggregation of ecological studies, with the municipality as the primary unit of analysis, helping in studies that seek to observe particular public health aspects such as hospitalization for diarrhea or pneumonia, vaccine coverage, and preventable infant mortality, for example .
As the feeding of this data is the responsibility of the health facilities themselves, errors and inconsistencies in the registration of data may occur, as well as the discontinuity of the same. Thus, despite the data being monthly, its quality is not guaranteed on a monthly basis.
When using these data, consider that the first years were periods of transition/adaptation of the system, as well as the correct use and registration of health facilities in the CNES, so inconsistencies and duplicities may occur, mainly in the first 3 years.
There are variables which go into disuse, therefore their feeding are discontinued.
The year 2005 is included in the dataset from the eighth month onwards, and as it represents the beginning of the CNES system, its data are not fully complete in the first months.
Availability of data and materials
The CNES datasets corresponding to beds , equipments , professionals , services , and teams ; the CNES data dictionaries related to beds , equipments , professionals , services , and teams , as well as the CNES ETL process report  and an descriptive example of the CNES teams ETL process  is freely and openly available on the Synapse repository at https://doi.org/10.7303/syn26343275. Anyone can browse the content on the Synapse website, but you must register for an account using your email address to download the files and datasets. Please see Table 1 for details and links to the data.
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Dataset CNES Beds. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32209177.1.
Dataset CNES Equipments. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32208612.1.
Dataset CNES Professionals. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32210090.1.
Dataset CNES Services. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32211006.1.
Dataset CNES Teams. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32207908.1.
Dictionary CNES Beds. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32167629.1.
Dictionary CNES Equipments. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32167630.1.
Dictionary CNES Professionals. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32167631.1.
Dictionary CNES Services. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32167632.1.
Dictionary CNES Teams. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn32167633.1.
Report of the CNES ETL Process. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS. 2022. https://doi.org/10.7303/syn34388050.2.
Example of the CNES Teams ETL Process. National Registry of Healthcare Facilities of Brazil (CNES) gathered together by PCDaS 2022. https://doi.org/10.7303/syn34388051.1.
Fernandes Santos Alves R, de Moraes Mello Boccolini P, Baroni LR, de Almeida Relvas-Brandt L, de Abreu Junqueira Gritz R, Siqueira Boccolini C. Brazilian spatial, demographic, and socioeconomic data from 1996 to 2020. BMC Res Notes. 2022;15(1):1–4.
The authors thank CNPq, CAPES, and FAPERJ for partially sponsoring this research.
This work was supported, in whole or part, by the Bill & Melinda Gates Foundation [Grant ID INV-027961] and National Council for Scientific and Technological Development [Grants 445877/2020-0 and 445882/2020-4]. Under the grant conditions of the Bill & Melinda Gates Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.
Ethics approval and consent to participate
We used data from open sources. The Brazilian Institute of Geography and Statistics, the Institute for Applied Economic Research, and the Ministry of Health of Brazil are committed to respecting the ethical precepts and ensuring data privacy and security. The Brazilian legislation exempts the use of public and anonymized secondary data from ethical approval.
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Salles, R., de Paula Dornellas Ribeiro, V., Gritz, R. et al. A comprehensive integrated dataset on Brazilian health facilities: from 2005 to 2021. BMC Res Notes 16, 151 (2023). https://doi.org/10.1186/s13104-023-06422-y
- Health facilities
- Data integration
- Healthcare system