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Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan

OBJECTIVE: Health literacy (HL) is the degree of individuals’ capacity to access, understand, appraise and apply health information and services required to make appropriate health decisions. This study aimed to establish a predictive algorithm for identifying community-dwelling older adults with a...

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Autores principales: Hou, Wen-Hsuan, Kuo, Ken N, Chen, Mu-Jean, Chang, Yao-Mao, Tsai, Han-Wei, Chan, Ding-Cheng, Su, Chien-Tien, Han, Der-Sheng, Shen, Hsiu-Nien, Li, Chung-Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627398/
https://www.ncbi.nlm.nih.gov/pubmed/34824102
http://dx.doi.org/10.1136/bmjopen-2020-045411
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author Hou, Wen-Hsuan
Kuo, Ken N
Chen, Mu-Jean
Chang, Yao-Mao
Tsai, Han-Wei
Chan, Ding-Cheng
Su, Chien-Tien
Han, Der-Sheng
Shen, Hsiu-Nien
Li, Chung-Yi
author_facet Hou, Wen-Hsuan
Kuo, Ken N
Chen, Mu-Jean
Chang, Yao-Mao
Tsai, Han-Wei
Chan, Ding-Cheng
Su, Chien-Tien
Han, Der-Sheng
Shen, Hsiu-Nien
Li, Chung-Yi
author_sort Hou, Wen-Hsuan
collection PubMed
description OBJECTIVE: Health literacy (HL) is the degree of individuals’ capacity to access, understand, appraise and apply health information and services required to make appropriate health decisions. This study aimed to establish a predictive algorithm for identifying community-dwelling older adults with a high risk of limited HL. DESIGN: A cross-sectional study. SETTING: Four communities in northern, central and southern Taiwan. PARTICIPANTS: A total of 648 older adults were included. Moreover, 85% of the core data set was used to generate the prediction model for the scoring algorithm, and 15% was used to test the fitness of the model. PRIMARY AND SECONDARY OUTCOME MEASURES: Pearson’s χ(2) test and multiple logistic regression were used to identify the significant factors associated with the HL level. An optimal cut-off point for the scoring algorithm was identified on the basis of the maximum sensitivity and specificity. RESULTS: A total of 350 (54.6%) patients were classified as having limited HL. We identified 24 variables that could significantly differentiate between sufficient and limited HL. Eight factors that could significantly predict limited HL were identified as follows: a socioenvironmental determinant (ie, dominant spoken dialect), a health service use factor (ie, having family doctors), a health cost factor (ie, self-paid vaccination), a heath behaviour factor (ie, searching online health information), two health outcomes (ie, difficulty in performing activities of daily living and requiring assistance while visiting doctors), a participation factor (ie, attending health classes) and an empowerment factor (ie, self-management during illness). The scoring algorithm yielded an area under the curve of 0.71, and an optimal cut-off value of 5 represented moderate sensitivity (62.0%) and satisfactory specificity (76.2%). CONCLUSION: This simple scoring algorithm can efficiently and effectively identify community-dwelling older adults with a high risk of limited HL.
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spelling pubmed-86273982021-12-10 Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan Hou, Wen-Hsuan Kuo, Ken N Chen, Mu-Jean Chang, Yao-Mao Tsai, Han-Wei Chan, Ding-Cheng Su, Chien-Tien Han, Der-Sheng Shen, Hsiu-Nien Li, Chung-Yi BMJ Open Public Health OBJECTIVE: Health literacy (HL) is the degree of individuals’ capacity to access, understand, appraise and apply health information and services required to make appropriate health decisions. This study aimed to establish a predictive algorithm for identifying community-dwelling older adults with a high risk of limited HL. DESIGN: A cross-sectional study. SETTING: Four communities in northern, central and southern Taiwan. PARTICIPANTS: A total of 648 older adults were included. Moreover, 85% of the core data set was used to generate the prediction model for the scoring algorithm, and 15% was used to test the fitness of the model. PRIMARY AND SECONDARY OUTCOME MEASURES: Pearson’s χ(2) test and multiple logistic regression were used to identify the significant factors associated with the HL level. An optimal cut-off point for the scoring algorithm was identified on the basis of the maximum sensitivity and specificity. RESULTS: A total of 350 (54.6%) patients were classified as having limited HL. We identified 24 variables that could significantly differentiate between sufficient and limited HL. Eight factors that could significantly predict limited HL were identified as follows: a socioenvironmental determinant (ie, dominant spoken dialect), a health service use factor (ie, having family doctors), a health cost factor (ie, self-paid vaccination), a heath behaviour factor (ie, searching online health information), two health outcomes (ie, difficulty in performing activities of daily living and requiring assistance while visiting doctors), a participation factor (ie, attending health classes) and an empowerment factor (ie, self-management during illness). The scoring algorithm yielded an area under the curve of 0.71, and an optimal cut-off value of 5 represented moderate sensitivity (62.0%) and satisfactory specificity (76.2%). CONCLUSION: This simple scoring algorithm can efficiently and effectively identify community-dwelling older adults with a high risk of limited HL. BMJ Publishing Group 2021-11-25 /pmc/articles/PMC8627398/ /pubmed/34824102 http://dx.doi.org/10.1136/bmjopen-2020-045411 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Public Health
Hou, Wen-Hsuan
Kuo, Ken N
Chen, Mu-Jean
Chang, Yao-Mao
Tsai, Han-Wei
Chan, Ding-Cheng
Su, Chien-Tien
Han, Der-Sheng
Shen, Hsiu-Nien
Li, Chung-Yi
Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan
title Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan
title_full Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan
title_fullStr Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan
title_full_unstemmed Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan
title_short Simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in Taiwan
title_sort simple scoring algorithm to identify community-dwelling older adults with limited health literacy: a cross-sectional study in taiwan
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627398/
https://www.ncbi.nlm.nih.gov/pubmed/34824102
http://dx.doi.org/10.1136/bmjopen-2020-045411
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