Methodological description of the mapping of Brazilian long-term care facilities for older adults
Geriatrics, Gerontology and Aging
Methodological description of the mapping of Brazilian long-term care facilities for older adults
Autor Correspondente: Karla Giacomin | [email protected]
Palavras-chave: aged; homes for the aged; nursing homes; residential facilities; Brazil.
Resumos Cadastrados
Resumo Inglês:
OBJECTIVE: To describe the methodological approach adopted to build a database of long-term care facilities (LCTFs) in Brazil.
METHODS: This exploratory research was conducted for 12 months, between August 2020 and July 2021, based on primarily publicly accessible data. First, the Unified Social Assistance System (Sistema Único de Assistência Social [SUAS]) database from 2019 was adopted as the primary source of information. In addition, public agencies and managers were consulted and invited to share their databases, while researchers and private entities collaborated by making their spreadsheets available. Data were organized in spreadsheets for each Brazilian state. LTCFs not catering to older adults (aged 60 years and over) were excluded. Duplicate data were excluded when overlaps were identified.
RESULTS: This brief communication describes the methodology adopted for mapping the current status of Brazilian LTCFs. Despite its caveats, this study represents an important advance in the identification, characterization, and monitoring of these services nationwide. A total of 5769 facilities were found in the 2019 SUAS census. After excluding facilities not caring for residents aged 60 years or over, this number decreased to 2381 LTCFs. The consolidation and filtering of information from multiple data sources led to the identification of 7029 LTCFs throughout the country.
CONCLUSION: Building a solid database was paramount to devising a national policy on long-term care. By including multiple sources, the scope of this survey was wider than all previous efforts and constituted an unprecedented collaborative experience in the country, including the potential to become the first national dataset for the Brazilian LTC sector.
