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Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores
Development of methods for population screening is necessary to improve the efficiency of secondary prevention of diseases. Until now, a common cutoff has been used for all people in the data set. However, if big data for health information can be used to modify individual cutoffs according to backg...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586984/ https://www.ncbi.nlm.nih.gov/pubmed/36271231 http://dx.doi.org/10.1038/s41598-022-22266-1 |
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author | Matsuyama, Takashi Narita, Akira Takanashi, Masaki Kogure, Mana Sato, Shuichi Nakamura, Tomohiro Nakane, Hideo Ogishima, Soichi Nagami, Fuji Nakaya, Naoki Tanno, Kozo Imaeda, Takao Hozawa, Atsushi |
author_facet | Matsuyama, Takashi Narita, Akira Takanashi, Masaki Kogure, Mana Sato, Shuichi Nakamura, Tomohiro Nakane, Hideo Ogishima, Soichi Nagami, Fuji Nakaya, Naoki Tanno, Kozo Imaeda, Takao Hozawa, Atsushi |
author_sort | Matsuyama, Takashi |
collection | PubMed |
description | Development of methods for population screening is necessary to improve the efficiency of secondary prevention of diseases. Until now, a common cutoff has been used for all people in the data set. However, if big data for health information can be used to modify individual cutoffs according to background factors, it may avoid wasting medical resources. Here we show that the estimated prevalence of the Center for Epidemiologic Studies Depression Scale positivity can be visualized by a heatmap using background factors from epidemiological big data and scores from the Athens Insomnia Scale. We also show that cutoffs based on the estimated prevalence can be used to decrease the number of people screened without decreasing the number of prevalent cases detected. Since this method can be applied to the screening of different outcomes, we believe our work can contribute to the development of efficient screening methods for various diseases. |
format | Online Article Text |
id | pubmed-9586984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95869842022-10-23 Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores Matsuyama, Takashi Narita, Akira Takanashi, Masaki Kogure, Mana Sato, Shuichi Nakamura, Tomohiro Nakane, Hideo Ogishima, Soichi Nagami, Fuji Nakaya, Naoki Tanno, Kozo Imaeda, Takao Hozawa, Atsushi Sci Rep Article Development of methods for population screening is necessary to improve the efficiency of secondary prevention of diseases. Until now, a common cutoff has been used for all people in the data set. However, if big data for health information can be used to modify individual cutoffs according to background factors, it may avoid wasting medical resources. Here we show that the estimated prevalence of the Center for Epidemiologic Studies Depression Scale positivity can be visualized by a heatmap using background factors from epidemiological big data and scores from the Athens Insomnia Scale. We also show that cutoffs based on the estimated prevalence can be used to decrease the number of people screened without decreasing the number of prevalent cases detected. Since this method can be applied to the screening of different outcomes, we believe our work can contribute to the development of efficient screening methods for various diseases. Nature Publishing Group UK 2022-10-21 /pmc/articles/PMC9586984/ /pubmed/36271231 http://dx.doi.org/10.1038/s41598-022-22266-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Matsuyama, Takashi Narita, Akira Takanashi, Masaki Kogure, Mana Sato, Shuichi Nakamura, Tomohiro Nakane, Hideo Ogishima, Soichi Nagami, Fuji Nakaya, Naoki Tanno, Kozo Imaeda, Takao Hozawa, Atsushi Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores |
title | Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores |
title_full | Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores |
title_fullStr | Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores |
title_full_unstemmed | Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores |
title_short | Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores |
title_sort | visualization of estimated prevalence of ces-d positivity accounting for background factors and ais scores |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586984/ https://www.ncbi.nlm.nih.gov/pubmed/36271231 http://dx.doi.org/10.1038/s41598-022-22266-1 |
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