ANÁLISE ESPACIAL DOS CUSTOS COM INTERNAÇÕES POR CAUSAS SENSÍVEIS À ATENÇÃO BÁSICA NO RIO GRANDE DO SUL (2010-2022)<sup> </sup>

Objective: To analyze the spatial distribution and cost of hospitalizations due to Primary Care Sensitive Conditions (PCSC) in Rio Grande do Sul, between 2010 and 2022. Methods: This was an observational, ecological, and retrospective study. The cost analysis was conducted using the macrocosting method, and spatial analysis was performed using the Local Indicator of Spatial Association (LISA). Additionally, Simple Correspondence Analysis (SCA) was applied to correlate the international disease codes (ICD) of the PCSCs with the regional health departments. Results: The spatial analysis identified a significant number of municipalities with high PCSC rates per 1,000 inhabitants surrounded by other localities with similarly high rates (high-high), as well as low-low clusters, particularly in the years 2010 and 2020, indicating structural differences across regions regarding Primary Care. The SCA revealed not only the remarkable drop in the R22 Pampa health region associated with grouped ICD codes, but also highlighted changes in the state’s health landscape following the onset of the COVID-19 pandemic. Consequently, there was increased visibility of municipalities with pulmonary and respiratory diseases. Conclusion: Despite the decline in PCSC rates, most health regions showed an annual variation in positive death rates, suggesting that more complex cases have become more prominent.

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Journal
Hygeia - Revista Brasileira de Geografia Médica e da Saúde
Published
2025-10-02
DOI
https://doi.org/10.14393/hygeia2176856
Primary Topic
Academic Research in Diverse Fields
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article
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article

ANÁLISE ESPACIAL DOS CUSTOS COM INTERNAÇÕES POR CAUSAS SENSÍVEIS À ATENÇÃO BÁSICA NO RIO GRANDE DO SUL (2010-2022)

Reisoli Bender Filho, Tatiane Costa da Costa, Fabrício Varoni de Oliveira
Hygeia - Revista Brasileira de Geografia Médica e da Saúde
Academic Research in Diverse Fields
article

ANÁLISE ESPACIAL DOS CUSTOS COM INTERNAÇÕES POR CAUSAS SENSÍVEIS À ATENÇÃO BÁSICA NO RIO GRANDE DO SUL (2010-2022)

Reisoli Bender Filho, Tatiane Costa da Costa, Fabrício Varoni de Oliveira
article en

Abstract

Objective: To analyze the spatial distribution and cost of hospitalizations due to Primary Care Sensitive Conditions (PCSC) in Rio Grande do Sul, between 2010 and 2022. Methods: This was an observational, ecological, and retrospective study. The cost analysis was conducted using the macrocosting method, and spatial analysis was performed using the Local Indicator of Spatial Association (LISA). Additionally, Simple Correspondence Analysis (SCA) was applied to correlate the international disease codes (ICD) of the PCSCs with the regional health departments. Results: The spatial analysis identified a significant number of municipalities with high PCSC rates per 1,000 inhabitants surrounded by other localities with similarly high rates (high-high), as well as low-low clusters, particularly in the years 2010 and 2020, indicating structural differences across regions regarding Primary Care. The SCA revealed not only the remarkable drop in the R22 Pampa health region associated with grouped ICD codes, but also highlighted changes in the state’s health landscape following the onset of the COVID-19 pandemic. Consequently, there was increased visibility of municipalities with pulmonary and respiratory diseases. Conclusion: Despite the decline in PCSC rates, most health regions showed an annual variation in positive death rates, suggesting that more complex cases have become more prominent.

Hygeia - Revista Brasileira de Geografia Médica e da SaúdeVol. 21
Universidade Federal de Pelotas (BR), Universidade Federal de Santa Maria (BR)
Openalex Percentile: Top 69%
Academic Research in Diverse Fields
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ANÁLISE ESPACIAL DOS CUSTOS COM INTERNAÇÕES POR CAUSAS SENSÍVEIS À ATENÇÃO BÁSICA NO RIO GRANDE DO SUL (2010-2022)<sup> </sup> — Reisoli Bender Filho, Tatiane Costa da Costa, et al. · Hygeia - Revista Brasileira de Geografia Médica e da Saúde (2025) | TGRS Research Map | TGRS