M. Domingues, P. Wachholz, Christiano Barbosa da Silva, Lidiane Charbel Souza Peres, Paula Ferreira Chacon, P. C. Bezerra, S. Lohmann, V. Moreira, Y. Duarte, K. Giacomin
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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.","PeriodicalId":52782,"journal":{"name":"Geriatrics Gerontology and Aging","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Methodological description of the mapping of Brazilian long-term care facilities for older adults\",\"authors\":\"M. Domingues, P. Wachholz, Christiano Barbosa da Silva, Lidiane Charbel Souza Peres, Paula Ferreira Chacon, P. C. Bezerra, S. Lohmann, V. Moreira, Y. Duarte, K. 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引用次数: 2
摘要
目的:描述在巴西建立长期护理设施(LCTFs)数据库所采用的方法学方法。方法:这项探索性研究在2020年8月至2021年7月期间进行了12个月,主要基于可公开获取的数据。首先,采用2019年的统一社会救助系统(Sistema Único de Assistência Social [SUAS])数据库作为主要信息来源。此外,还咨询了公共机构和管理人员,并邀请他们分享其数据库,而研究人员和私营实体则通过提供电子表格进行合作。数据被组织在巴西每个州的电子表格中。不适合老年人(60岁及以上)的ltcf被排除在外。当发现重叠时,排除重复数据。结果:这份简短的报告描述了绘制巴西ltcf现状所采用的方法。尽管有一些警告,但这项研究在全国范围内对这些服务的识别、表征和监测方面取得了重要进展。在2019年的SUAS普查中,总共发现了5769个设施。除去不照顾60岁以上老人的设施,这一数字减少到2381个。对来自多个数据源的信息进行整合和过滤后,在全国范围内确定了7029个长期信托基金。结论:建立一个可靠的数据库对制定国家长期护理政策至关重要。通过包括多个来源,本次调查的范围比以前的所有工作都要广泛,并构成了该国前所未有的合作经验,包括有可能成为巴西LTC部门的第一个国家数据集。
Methodological description of the mapping of Brazilian long-term care facilities for older adults
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.