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Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China

BACKGROUND: It is difficult to accurately assess the health literacy(HL) level of Mongolians by using Chinese conventional HL questionnaire, due to their particularity in language, culture and living environment. Therefore, it is very important to design an exclusive HL questionnaire for them. In ad...

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Autores principales: Hong, Yan, Zhang, Xiaoda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637321/
https://www.ncbi.nlm.nih.gov/pubmed/36335364
http://dx.doi.org/10.1186/s12889-022-14392-2
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author Hong, Yan
Zhang, Xiaoda
author_facet Hong, Yan
Zhang, Xiaoda
author_sort Hong, Yan
collection PubMed
description BACKGROUND: It is difficult to accurately assess the health literacy(HL) level of Mongolians by using Chinese conventional HL questionnaire, due to their particularity in language, culture and living environment. Therefore, it is very important to design an exclusive HL questionnaire for them. In addition, the existing statistical models cannot meet the requirement of HL assessment with high precision, so it is necessary to study a new HL assessment model. METHODS: A HL questionnaire with 68 questions is designed by combing the HLS-EU-Q47and the characteristics of Mongolians in China. 742 Mongolians aged 18 to 87 in Inner Mongolia of China answered the questionnaire. A data set with 742 samples is constructed, where each sample has 68 features and 1 target. Based on it, the XGB and LGBM regression models are respectively constructed to assess the HL levels of respondents, and their evaluation effects are compared. The impact of each question on the HL level is quantitatively analyzed by using the feature-importance function in LGBM model to verify the effectiveness of the questionnaire and to find the key factors for affecting HL. RESULTS: The HL questionnaire has the high reliability, which is reflected by the high internal consistency (Cronbach’s coefficient=0.807) and test-retest reliability (Mutual Information Score= 0.803). The validity of the HL questionnaire is obtained by solving KMO and Bartlett Spherical Test Chi-square Value, which are 0.765 and 2486 ([Formula: see text] ), respectively. [Formula: see text] index and the absolute error obtained by using the HL assessment model based on LGBM are 0.98347 and 11, which are better than ones by applying the model based-XGB, respectively. The quantitative analysis results show that all 68 questions have influence on HL level, but their degree are different. The first three factors are age, salary level, the judgment ability for the HL information in media, respectively. The HL level distribution of the respondents was 66.71[Formula: see text] excellent, 25.74[Formula: see text] good and 7.54[Formula: see text] poor, respectively. CONCLUSIONS: The presented HL questionnaire with 68 questions and LGBM regression model can obtain the HL level assessment results with high precision for Mongolians in China. The impact of each question in the questionnaire on the final assessment results can be quantified by using the feature-importance function in LGBM model, which is better than the existing qualitative analysis methods.
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spelling pubmed-96373212022-11-07 Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China Hong, Yan Zhang, Xiaoda BMC Public Health Research BACKGROUND: It is difficult to accurately assess the health literacy(HL) level of Mongolians by using Chinese conventional HL questionnaire, due to their particularity in language, culture and living environment. Therefore, it is very important to design an exclusive HL questionnaire for them. In addition, the existing statistical models cannot meet the requirement of HL assessment with high precision, so it is necessary to study a new HL assessment model. METHODS: A HL questionnaire with 68 questions is designed by combing the HLS-EU-Q47and the characteristics of Mongolians in China. 742 Mongolians aged 18 to 87 in Inner Mongolia of China answered the questionnaire. A data set with 742 samples is constructed, where each sample has 68 features and 1 target. Based on it, the XGB and LGBM regression models are respectively constructed to assess the HL levels of respondents, and their evaluation effects are compared. The impact of each question on the HL level is quantitatively analyzed by using the feature-importance function in LGBM model to verify the effectiveness of the questionnaire and to find the key factors for affecting HL. RESULTS: The HL questionnaire has the high reliability, which is reflected by the high internal consistency (Cronbach’s coefficient=0.807) and test-retest reliability (Mutual Information Score= 0.803). The validity of the HL questionnaire is obtained by solving KMO and Bartlett Spherical Test Chi-square Value, which are 0.765 and 2486 ([Formula: see text] ), respectively. [Formula: see text] index and the absolute error obtained by using the HL assessment model based on LGBM are 0.98347 and 11, which are better than ones by applying the model based-XGB, respectively. The quantitative analysis results show that all 68 questions have influence on HL level, but their degree are different. The first three factors are age, salary level, the judgment ability for the HL information in media, respectively. The HL level distribution of the respondents was 66.71[Formula: see text] excellent, 25.74[Formula: see text] good and 7.54[Formula: see text] poor, respectively. CONCLUSIONS: The presented HL questionnaire with 68 questions and LGBM regression model can obtain the HL level assessment results with high precision for Mongolians in China. The impact of each question in the questionnaire on the final assessment results can be quantified by using the feature-importance function in LGBM model, which is better than the existing qualitative analysis methods. BioMed Central 2022-11-05 /pmc/articles/PMC9637321/ /pubmed/36335364 http://dx.doi.org/10.1186/s12889-022-14392-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hong, Yan
Zhang, Xiaoda
Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China
title Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China
title_full Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China
title_fullStr Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China
title_full_unstemmed Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China
title_short Questionnaire and LGBM Model for Assessing Health Literacy levels of Mongolians in China
title_sort questionnaire and lgbm model for assessing health literacy levels of mongolians in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637321/
https://www.ncbi.nlm.nih.gov/pubmed/36335364
http://dx.doi.org/10.1186/s12889-022-14392-2
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