Detalhes bibliográficos
| Ano de defesa: |
2021 |
| Autor(a) principal: |
Silva, Regina Nunes da |
| Orientador(a): |
Silva, Francilene Amaral da |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Não Informado pela instituição
|
| Programa de Pós-Graduação: |
Pós-Graduação em Ciências da Saúde
|
| Departamento: |
Não Informado pela instituição
|
| País: |
Não Informado pela instituição
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://ri.ufs.br/jspui/handle/riufs/16845
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Resumo: |
The removal of a limb is one of the oldest therapeutic practices in medicine which means the total or partial removal of an extremity, which is usually performed surgically. The incidence of lower limb amputation (AMI) in the world ranges from 2.8 to 43.9/105 inhabitants/year, while in Brazil there is an incidence of 13.9/105 inhabitants/year. This study aims to analyze the epidemiological profile and spatial autocorrelation of limb amputations in Alagoas. It is an epidemiological, ecological study using secondary data. The database for the period 2007 to 2018 was accessed through the Hospital Information System of the Unified Health System (SIH/SUS), using the TAB program for Windows (TabWin) developed by the SUS it Department (Datasus). The study was carried out with secondary data and already published in Datasus. The municipalities were considered in the study because there are records of admissions that had amputation as an outcome – main diagnoses ICD-10. The twelve-year historical series – 2007 to 2018 of hospitalizations that resulted in amputation was constructed. Descriptive analysis, association measures and respective 95% confidence intervals were performed. The units of analysis were the municipalities and the amputation rate, including cases of limb amputation registered between 2007-2018. The incidence rate was smoothed by the local Bayesian method to correct for random fluctuations in populations or small numbers of occurrences through a reestimation that considers that there is autocorrelation between rates in neighboring areas and the Moran index was used to identify spatial autocorrelations through values ranging from -1 to 1 in TerraView software and thematic maps edited in QGis software. Spearman correlation and simple regression model compared rates with regional socioeconomic indicators. A total of 636 cases of amputation resulting from DM were analyzed, with 39 deaths. There was an increase in amputations from 2014 to 2016; the mean age was 64 years of age with a range from one to 98 years. The lower limb amputation rate averaged 61%. Males present with a relative risk 37% higher risk of amputation due to diabetes than females and 101% of deaths. There is an 80% higher relative risk of death from amputation in the elderly than in non-elderly people. In the spatial analysis, 9,345 amputation records were reported in the state. The Bayesian estimate smoothed areas and discriminated heterogeneity with more municipalities with high rates and areas of epidemiological transition. Moran's spatial analysis showed evidence of positive spatial autocorrelation. The univariate regression analysis showed the amputation incidence rate related to indicators that reflect the population's vulnerable life situation, the unemployment rate (11.4%), per capita income (8.5%) and the male illiteracy rate (12.9%) percentages of explanation. Actions by multiprofessional health teams in education, prevention and treatment programs fostering improved care interventions to prevent lower limb injuries can favor DM control by reducing amputations and deaths, as it is subject to monitoring, care and control that can be performed by primary health care. The spatial distribution of amputations showed a non-homogeneous distribution pattern with agglomerations in the agreste and eastern regions of Alagoas. They are the ones that most portray the scenario of inequalities in income distribution, education conditions and difficulty in accessing health services. Understanding the geographic growth of CNCDs reveals them as a critical point in public health. |
