| Texto completo | |
| Autor(es): |
Barrozo, Ligia Vizeu
[1, 2, 3]
;
Fornaciali, Michel
[4]
;
Saldiva de Andre, Carmen Diva
[5]
;
Zimeo Morais, Guilherme Augusto
[4]
;
Mansur, Giselle
[1, 3]
;
Cabral-Miranda, William
[1, 2]
;
de Miranda, Marina Jorge
[6]
;
Sato, Joao Ricardo
[4, 7]
;
Amaro Junior, Edson
[4]
Número total de Autores: 9
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Fac Filosofia Letras & Ciencias Humanas, Dept Geog, Sao Paulo, SP - Brazil
[2] Univ Sao Paulo, Inst Estudos Avancdos, Sao Paulo, SP - Brazil
[3] Programa Apoio Desenvolvimento Inst SUS PROADI SU, Sao Paulo - Brazil
[4] Hosp Israelita Albert Einstein, Big Data Analyt, Sao Paulo, SP - Brazil
[5] Univ Sao Paulo, Inst Matemat & Estat, Dept Estat, Sao Paulo - Brazil
[6] Minist Saude MS, Secretaria Vigilancia Saude SVS, Dept Anal Saude & Vigilancia Doencas Transmissive, Brasilia, DF - Brazil
[7] Univ Fed ABC, Ctr Matemat Computacao & Cognicao, Sao Paulo, SP - Brazil
Número total de Afiliações: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PLoS One; v. 15, n. 4 APR 29 2020. |
| Citações Web of Science: | 0 |
| Resumo | |
The individual's socioeconomic conditions are the most relevant to predict the quality of someone's health. However, such information is not usually found in medical records, making studies in the area difficult. Therefore, it is common to use composite indices that characterize a region socioeconomically, such as the Human Development Index (HDI). The main advantage of the HDI is its understanding and adoption on a global scale. However, its applicability is limited for health studies since its longevity dimension presents mathematical redundancy in regression models. Here we introduce the GeoSES, a composite index that summarizes the main dimensions of the Brazilian socioeconomic context for research purposes. We created the index from the 2010 Brazilian Census, whose variables selection was guided by theoretical references for health studies. The proposed index incorporates seven socioeconomic dimensions: education, mobility, poverty, wealth, income, segregation, and deprivation of resources and services. We developed the GeoSES using Principal Component Analysis and evaluated its construct, content, and applicability. GeoSES is defined at three scales: national (GeoSES-BR), Federative Unit (GeoSES-FU), and intra-municipal (GeoSES-IM). GeoSES-BR dimensions showed a good association with HDI-M (correlation above 0.85). The model with the poverty dimension best explained the relative risk of avoidable cause mortality in Brazil. In the intra-municipal scale, the model with GeoSES-IM was the one that best explained the relative risk of mortality from circulatory system diseases. By applying spatial regressions, we demonstrated that GeoSES shows significant explanatory potential in the studied scales, being a compelling complement for future researches in public health. (AU) | |
| Processo FAPESP: | 13/21728-2 - Uso de modernas técnicas de autópsia na investigação de doenças humanas (MODAU) |
| Beneficiário: | Paulo Hilário Nascimento Saldiva |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |