Jéssica Souza, Cristiano Boccolini, Lais Baroni, Kele Belloze, Eduardo Bezerra, Marcel Pedroso, Ronaldo Fernandes Santos Alves, Eduardo Ogasawara
{"title":"Evaluation of statistical process control charts for infant mortality monitoring in Brazilian cities with different population sizes.","authors":"Jéssica Souza, Cristiano Boccolini, Lais Baroni, Kele Belloze, Eduardo Bezerra, Marcel Pedroso, Ronaldo Fernandes Santos Alves, Eduardo Ogasawara","doi":"10.1186/s13104-024-06943-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>The control chart is a classic statistical technique in epidemiology for identifying trends, patterns, or alerts. One meaningful use is monitoring and tracking Infant Mortality Rates, which is a priority both domestically and for the World Health Organization, as it reflects the effectiveness of public policies and the progress of nations. This study aims to evaluate the applicability and performance of this technique in Brazilian cities with different population sizes using infant mortality data.</p><p><strong>Results: </strong>In this article, we evaluate the effectiveness of the statistical process control chart in the context of Brazilian cities. We present three categories of city groups, divided based on population size and classified according to the quality of the analyses when subjected to the control method: consistent, interpretable, and inconsistent. In cities with a large population, the data in these contexts show a lower noise level and reliable results. However, in intermediate and small-sized cities, the technique becomes limited in detecting deviations from expected behaviors, resulting in reduced reliability of the generated patterns and alerts.</p>","PeriodicalId":9234,"journal":{"name":"BMC Research Notes","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11462655/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Research Notes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s13104-024-06943-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: The control chart is a classic statistical technique in epidemiology for identifying trends, patterns, or alerts. One meaningful use is monitoring and tracking Infant Mortality Rates, which is a priority both domestically and for the World Health Organization, as it reflects the effectiveness of public policies and the progress of nations. This study aims to evaluate the applicability and performance of this technique in Brazilian cities with different population sizes using infant mortality data.
Results: In this article, we evaluate the effectiveness of the statistical process control chart in the context of Brazilian cities. We present three categories of city groups, divided based on population size and classified according to the quality of the analyses when subjected to the control method: consistent, interpretable, and inconsistent. In cities with a large population, the data in these contexts show a lower noise level and reliable results. However, in intermediate and small-sized cities, the technique becomes limited in detecting deviations from expected behaviors, resulting in reduced reliability of the generated patterns and alerts.
BMC Research NotesBiochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
3.60
自引率
0.00%
发文量
363
审稿时长
15 weeks
期刊介绍:
BMC Research Notes publishes scientifically valid research outputs that cannot be considered as full research or methodology articles. We support the research community across all scientific and clinical disciplines by providing an open access forum for sharing data and useful information; this includes, but is not limited to, updates to previous work, additions to established methods, short publications, null results, research proposals and data management plans.