Mohammad Alarifi, Timothy Patrick, A. Jabour, Min Wu, Jake Luo
{"title":"Health Consumer Social Economic Factors and Health Conditions as Predictor for Health Literacy in Radiology Domain","authors":"Mohammad Alarifi, Timothy Patrick, A. Jabour, Min Wu, Jake Luo","doi":"10.1166/jmihi.2021.3864","DOIUrl":null,"url":null,"abstract":"Patient literacy of radiology is imperative for patient engagement in care and management of their own health. Little is known about the factors that could predict patient literacy of radiology reports, testing, or treatment. This study aims to identify the most important factors of\n health consumer social economic and health conditions as a predictor of health literacy in the radiology domain. The study recruited 616 participants using Amazon.com’s Mechanical Turk (MTURK) and presented\n these participants with our questionnaire. We measured the level of participants’ radiology awareness, social factors, and health status. Descriptive statics including Chi-Square and linear regression models were used to test if the factors could predict radiology literacy. The area\n under the receiver–operator curve was calculated to determine the prediction accuracy of the regression models. linear regression indicated that 15 of the 19 social-economic factors and health conditions were significantly associated with radiology literacy (P < .05). On the\n other hand, only 12 of the 19 factors were significant by using Pearson Chi-Square (P < .05). Stepwise linear regression analysis demonstrated the r squared linear of 9 out of 12 common factors. These factors are the level of education, smoking, radiology experience, insurance status,\n white race, employment status, disability status, gender, and income at 0.209. These nine factors had a good ability to predict radiology literacy (area under the receiver operator curve of 0.677 [95%CI 0.549; 0.804, P = 0.013]). Social economic factors and health conditions can be\n used to successfully predict radiology literacy. We were able to successfully identify the predictive factors that have a high association with the radiology literacy by comparing social factors and health status versus radiology awareness.","PeriodicalId":393031,"journal":{"name":"J. Medical Imaging Health Informatics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Medical Imaging Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1166/jmihi.2021.3864","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Patient literacy of radiology is imperative for patient engagement in care and management of their own health. Little is known about the factors that could predict patient literacy of radiology reports, testing, or treatment. This study aims to identify the most important factors of
health consumer social economic and health conditions as a predictor of health literacy in the radiology domain. The study recruited 616 participants using Amazon.com’s Mechanical Turk (MTURK) and presented
these participants with our questionnaire. We measured the level of participants’ radiology awareness, social factors, and health status. Descriptive statics including Chi-Square and linear regression models were used to test if the factors could predict radiology literacy. The area
under the receiver–operator curve was calculated to determine the prediction accuracy of the regression models. linear regression indicated that 15 of the 19 social-economic factors and health conditions were significantly associated with radiology literacy (P < .05). On the
other hand, only 12 of the 19 factors were significant by using Pearson Chi-Square (P < .05). Stepwise linear regression analysis demonstrated the r squared linear of 9 out of 12 common factors. These factors are the level of education, smoking, radiology experience, insurance status,
white race, employment status, disability status, gender, and income at 0.209. These nine factors had a good ability to predict radiology literacy (area under the receiver operator curve of 0.677 [95%CI 0.549; 0.804, P = 0.013]). Social economic factors and health conditions can be
used to successfully predict radiology literacy. We were able to successfully identify the predictive factors that have a high association with the radiology literacy by comparing social factors and health status versus radiology awareness.