Desenvolvimento e validação de um índice de vulnerabilidade social aplicado a políticas públicas do SUS

Drachler, Maria de Lourdes; Lobato, Marcos Antônio de Oliveira; Lermen, José Inácio; Fagundes, Sandra Maria Sales; Ferla, Alcindo Antônio; Drachler, Carlos Wietzke; Teixeira, Luciana Barcellos; Leite, José Carlos de Carvalho

doi:10.1590/1413-81232014199.12012013

Cited by 13 publications

(14 citation statements)

References 11 publications

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“…Differentiated research fronts indicate the recognition that urban inequality is made of different dimensions. In this context, researchers have used composite indicators not only to represent urban inequality [4,5], but also to represent its different dimensions. For example, it is possible to indicate socioeconomic [6,27,28], neighborhood [7,29], and household inequalities [8,30], among others [31,32].…”

Section: Urban Inequalitymentioning

confidence: 99%

“…Thus, it is disregarded, for example, that the socioeconomic condition of families influences their housing conditions [3]. This limitation means that intra-urban inequality composite indicators constructed from methods based on aggregation and weighting (e.g., see [4,5]) do not capture the effects among the underlying dimensions of the inequality. Such methods do not allow for consideration of the influence that indicators have on each other, such as socioeconomic [6], neighborhood [7], and household [8] inequalities.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

et al. 2020

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Composite indicators are almost always determined by methods that aggregate a reasonable number of manifest variables that can be weighted—or not—as new synthesis variables. A problem arises when these aggregations and weightings do not capture the possible effects that the various underlying dimensions of the phenomenon have on each other, and consequently distort the assessment of intra-urban inequality. In this paper, we explore the direct and indirect effects that the different underlying dimensions of intra-urban inequality have on indicators that represent this phenomenon. Structural equation modeling was used to build a composite indicator that captures the direct and indirect effects of the underlying dimensions of intra-urban inequality. From this modeling that combines confirmatory factor analysis with a system of simultaneous equations, the intra-urban inequality of the urban conurbation of Maringá–Sarandi–Paiçandu, Brazil was measured. The model comprises first- and second-order structures. The first-order structure is composed of non-observed variables that represent three underlying dimensions of intra-urban inequality. The second-order structure is the intra-urban inequality composite indicator that synthesizes the non-observed variables of the first-order structure. The model aims at demonstrating how to perform a theorized measurement of urban inequality so that it makes it possible to identify which dimensions most influence the others, as well as which dimensions are more relevant to this purpose.

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Section: Urban Inequalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

et al. 2020

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“…The IVS-5 measures the position of the municipality in terms of the number of standard deviations above or below the state average of social vulnerability. Thus, the municipalities were placed in order of increasing vulnerability, considering that negative values indicate a lower proportion of the population in a condition of social vulnerability than the state average (0.00) and positive values indicate a higher proportion of social vulnerability (11) . In this study, the municipalities were grouped into four vulnerability levels: <-0.50; -0.50 to 0.00; 0.01 to 1.00; >1.00.…”

Section: Methodsmentioning

confidence: 99%

Desigualdades socioeconômicas e regionais na cobertura de exames citopatológicos do colo do útero

Manica

Drachler

Teixeira

et al. 2016

Rev. Gaúcha Enferm.

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Objectives To identify socioeconomic and regional inequalities of pap smear coverage in the state of Rio Grande do Sul. Methods An ecological study based on data of the 2011-2012 national health information system to estimate the annual coverage of pap smears for the overall female population of the state and for women without private health insurance. We estimated annual pap smear coverage according to the Municipal Social Vulnerability Index and health macro-regions and regions of the state. Results The percentage of women without private health insurance ranged from 38.1% to 94.2% in the health regions. Pap smear coverage was 17.3% for the overall female population and 23.8% for women without private health insurance. Pap smear coverage was higher in more socially vulnerable municipalities and regions with a higher percentage of women with private health insurance. Conclusions The prevalence of private health insurance should be considered in studies that address the coverage of the Brazilian Unified Health System (SUS).

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“…O argumento dedutivo não contemplou plenamente as necessidades para a seleção de indicadores que se adéquem às necessidades de análise da situação de determinantes sociais da saúde de um país em desenvolvimento com características marcantes, como o Brasil, e que tivessem também a facilidade de operação logística. O argumento normativo foi proposto a partir da técnica Delphi de consenso de especialistas (DRACHLER et al, 2014;GUILLARD-GONÇALVES et al, 2014;JANNUZZI, 2005;HAGENLOCHER, 2014;LYNN, 1986;SCHUMANN;MOURA, 2015;TALLMAN, 2016).…”

Section: Métodosunclassified

Construção E Aplicação De Um Índice De Vulnerabilidade Em Saúde

Peixoto

Duque

Oliveira

et al. 2020

ICSA

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Objetivo: Construir e aplicar um Índice de Vulnerabilidade em Saúde. Métodos: Estudo metodológico de construção e validação de um indicador sintético de vulnerabilidade em saúde com dados provenientes do Censo Demográfico brasileiro. A validade dos indicadores foi examinada mediante escores numéricos atribuídos por avaliadores externos, utilizando-se a técnica delphi de consenso. Foi realizada a agregação dos indicadores a partir da Análise de Componentes Principais (ACP). Por fim, foram construídos mapas na cidade de Aracaju, Sergipe, Brasil para a identificação das áreas de vulnerabilidade. A autocorrelação espacial foi avaliada através do Índice de Moran Global e do Índice de Associação Espacial Local. Resultados: Através do indicador de vulnerabilidade em saúde foi possível realizar o diagnóstico e monitoramento das condições de vulnerabilidade a que as pessoas estão expostas, pois demonstrou que é capaz de identificar as desigualdades sociais diante da heterogeneidade das áreas analisadas. Conclusão: A aplicação do Índice de Vulnerabilidade em Saúde no município analisado mostrou-se válida demonstrando a capacidade de aproximação com a realidade, sendo uma ferramenta útil para planejamento e avaliação das políticas de saúde no território e em outras regiões brasileiras.

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Desenvolvimento e validação de um índice de vulnerabilidade social aplicado a políticas públicas do SUS

Cited by 13 publications

References 11 publications

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

Measuring Intra-Urban Inequality with Structural Equation Modeling: A Theory-Grounded Indicator

Desigualdades socioeconômicas e regionais na cobertura de exames citopatológicos do colo do útero

Construção E Aplicação De Um Índice De Vulnerabilidade Em Saúde

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