Bed demand and occupancy within the Brazilian National Health System for the most common types of cancer in Brazil, from 2018 to 2021: a cross-sectional study.
Mariana Araujo Neves Lima, Daniel Antunes Maciel Villela
{"title":"Bed demand and occupancy within the Brazilian National Health System for the most common types of cancer in Brazil, from 2018 to 2021: a cross-sectional study.","authors":"Mariana Araujo Neves Lima, Daniel Antunes Maciel Villela","doi":"10.1590/S2237-96222024v33e20231172.en","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To analyze bed demand and occupancy within the Brazilian National Health System (Sistema Único de Saúde - SUS) for the main types of cancer in Brazil, from 2018 to 2021.</p><p><strong>Methods: </strong>This was a descriptive cross-sectional study, using data from the Hospital Information System. Queuing theory model was used for calculating average admission rate, average hospitalization rate, probability of overload, and average number of people in the queue.</p><p><strong>Results: </strong>The Southeast and South regions showed the highest average hospitalization rates, while the North region showed the lowest rates. The Southeast region presented a high probability of surgical bed overload, especially in the states of São Paulo (99.0%), Minas Gerais (97.0%) and Rio de Janeiro (97.0%). São Paulo state showed an overload above 95.0% in all types of beds analyzed.</p><p><strong>Conclusion: </strong>There was a high probability of oncology bed occupancy within the Brazilian National Health System, especially surgical and medical beds, and regional disparities in bed overload.</p><p><strong>Main results: </strong>The study found a high demand for hospital admissions to oncological bed in the Southeast region and a high probability of system overload in the states of the Southeast and Northeast regions of Brazil, thus highlighting the inequities in access to healthcare services in the country.</p><p><strong>Implications for services: </strong>This study presents a methodology for the improved allocation of resources and management of surgical and medical bed flows in areas with the highest bed overload and regions with low service availability.</p><p><strong>Perspectives: </strong>It is necessary to promote public policies that ensure the equitable supply of beds for oncological treatment within the SUS, especially in states with bed overload and healthcare service gaps.</p>","PeriodicalId":51473,"journal":{"name":"Epidemiologia e Servicos de Saude","volume":"33 ","pages":"e20231172"},"PeriodicalIF":2.5000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11348741/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiologia e Servicos de Saude","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1590/S2237-96222024v33e20231172.en","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"Multidisciplinary","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To analyze bed demand and occupancy within the Brazilian National Health System (Sistema Único de Saúde - SUS) for the main types of cancer in Brazil, from 2018 to 2021.
Methods: This was a descriptive cross-sectional study, using data from the Hospital Information System. Queuing theory model was used for calculating average admission rate, average hospitalization rate, probability of overload, and average number of people in the queue.
Results: The Southeast and South regions showed the highest average hospitalization rates, while the North region showed the lowest rates. The Southeast region presented a high probability of surgical bed overload, especially in the states of São Paulo (99.0%), Minas Gerais (97.0%) and Rio de Janeiro (97.0%). São Paulo state showed an overload above 95.0% in all types of beds analyzed.
Conclusion: There was a high probability of oncology bed occupancy within the Brazilian National Health System, especially surgical and medical beds, and regional disparities in bed overload.
Main results: The study found a high demand for hospital admissions to oncological bed in the Southeast region and a high probability of system overload in the states of the Southeast and Northeast regions of Brazil, thus highlighting the inequities in access to healthcare services in the country.
Implications for services: This study presents a methodology for the improved allocation of resources and management of surgical and medical bed flows in areas with the highest bed overload and regions with low service availability.
Perspectives: It is necessary to promote public policies that ensure the equitable supply of beds for oncological treatment within the SUS, especially in states with bed overload and healthcare service gaps.