Health Literacy and Sustainable Healthcare: Machine Learning Modelling of Health Literacy Disparities in Australia (Preprint)

2020 ◽  
Author(s):  
Meng Ji
Keyword(s):  
Machine Learning ◽  
Health Literacy ◽  
Health Information ◽  
Health Management ◽  
Health Condition ◽  
Contributing Factors ◽  
Management Ability ◽  
Domain 3 ◽  
Information Domain

BACKGROUND Health literacy is a key issue in sustainable healthcare support to reduce health inequality and disparity. In multicultural societies with large and changing migration populations, there is a pressing need to understand the disparity of health literacy among diverse, complex population segments. This study offers much-needed insights into the correlation and interaction among various underlying dimensions of health literacy among diverse populations in Australia. This is based on the 2018 Health Literacy Survey (HLS) conducted by the Australian Bureau of Statistics (ABS) with 5,790 fully responding Australian adults aged above 15. OBJECTIVE Using machine learning to identify major contributing factors (especially, specific value ranges of key health literacy domains) to health literacy disparities in Australia. METHODS Statistical machine learning models (XGBoost Tree) were used to identify and measure the disparity of health literacy between Australian populations characterised by demographic, educational and socio-economic attributes: age, sex, country of birth, main language spoken at home, labour force status, equivalised income of household (EIH), family composition of household, level of highest educational attainment, disability status, Australian states and territories, remoteness and index of relative socio-economic disadvantage (SED). RESULTS Our analysis found that among the nine domains of the 2018 Australian HLS, there were distinct patterns of disparities in health literacy among Australians. Populations which reported higher scores of self-health management ability (SHMA) (Domain 3: 3.08-3.22) were Australians aged under 35 or above 55, having Year 12 or above educational attainment, English-speaking, married with/without children, female, in the top two EIH quintiles, in the lowest two SED quintiles, having no disability or restrictive long-term health condition, and living in the states of Queensland, Victoria, Western Australia, South Australia, Northern Territory. Populations which reported lower scores of SHMA (Domain 3: 2.99-3.08) were Australians aged between 35 and 55; having Year 11 or below education; speaking languages other than English at home; living alone or single parents with dependent children, male, in the bottom three EIH quintiles, in the highest three SED quintiles; having profound or severe core activity limitation, or other disability or restrictive long-term health condition, and living in the Australian states of New South Wales, Tasmania, and the Australian Capital Territory. CONCLUSIONS Our study identified major contributing factors (especially, specific value ranges of key health literacy domains) to health literacy disparities in Australia. These include education (Year 10/11 or below), disability (profound/severe disability), household income (lowest quintiles), the relative SED index (highest quintiles), gender (male Australians), age (aged 35-55 years), main home language (other than English), geographical location (major cities, inner, outer regional, remote Australia). Higher value ranges of these variables are strongly associated with higher scores of key health literacy domains such as access to healthcare support (Domain 1), access to sufficient health information (Domain 2), ability to appraise health information (Domain 5), ability to find good health information (Domain 8) and ability to understand health information well to know how to apply the health information (Domain 9). Higher scores on these domains in turn can have real impact on the overall self-health management ability (Domain 3). CLINICALTRIAL n/a

Health literacy among mothers of under 3 years old children at the Hanoi Medical University Clinic: A latent class analysis

2021 ◽  
Vol 30 (9) ◽  
pp. 51-58
Author(s):  
Nguyen Thi Huong Thao ◽  
Pham Quang Thai ◽  
Do Thi Thanh Toan ◽  
Dinh Thai Son ◽  
Luu Ngoc Hoat ◽  
...  
Keyword(s):  
Health Literacy ◽  
Health Information ◽  
Latent Class ◽  
Service Providers ◽  
Health Management ◽  
Factors Affecting ◽  
Limited Health Literacy ◽  
Education Levels ◽  
Literacy Scores ◽  
Medical University

Health literacy refers to the degree to which people can access and understand health information, as well as communicate their health needs to service providers. The scale has been standardized and divided into 3 groups: Health care, prevention of disease, health promotion. Children under 3 years have immature immunological system, which can affect their development in the future. However, the health management, diseases treatment, and diseases prevention of children younger than 3 years of age depend signifcantly on the health literacy of their mothers. This study aims to describe the health literacy of mothers who have children under 3 years and some factors affecting their health literacy. Data were collected on 389 mothers of children younger than 3 years who take their children to the vaccination clinics at Hanoi Medical University and latent analysis was conducted to identify class of health literacy within the sample. Three health literacy classes were identifed. The lowest mean health literacy index was within the disease prevention dimension, where the largest number of respondents showed limited health literacy. Three distinct health literacy level were identifed and termed low (n = 35.9%), moderate (n = 243, 62.5%) and high health literacy (n = 111, 28.5%). We found that higher scores of Health Literacy Scores (HLS) closely correlated with higher educational levels, the job of mothers, the age of children and the frequency of searching for health information using the internet. There were signifcant better overall scores in HLS among parents with higher education levels (university degree or higher with more than under high school graduated).

scholarly journals Health literacy levels of British adults: a cross-sectional survey using two domains of the Health Literacy Questionnaire (HLQ)

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rebecca M. Simpson ◽  
Emma Knowles ◽  
Alicia O’Cathain
Keyword(s):  
Health Care ◽  
Health Literacy ◽  
Health Information ◽  
Health Care Providers ◽  
Health Condition ◽  
Population Characteristics ◽  
Care Providers ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Health Literacy Questionnaire

Abstract Background A person’s health literacy determines whether they are able to make appropriate health decisions and are able to follow treatment instructions. This is important because low health literacy is associated with mortality and extra costs to the healthcare system. Our aim was to describe the health literacy levels of British adults using a nationally representative population survey, and show how health literacy levels vary by population characteristics. Methods A population based cross-sectional survey including questions from two domains from the Health Literacy Questionnaire™: 1) Understanding health information well enough to know what to do, and 2) Ability to actively engage with health care providers. Both domains are made up of 5 Likert style questions with 5 levels ranging from ‘cannot do or always difficult’ (1) to ‘always easy’ (5). The survey was conducted by NatCen in Britain (2018) as part of the annual British Social Attitudes survey. We used weighted descriptive analyses and regression to explore the relationship between population characteristics and health literacy. Weighted analyses were used to ensure the sample was representative of the British population. Results A total of 2309 responded to the questionnaire. The mean score for ‘understanding information’ was 3.98 (95% CI: 3.94, 4.02) and for ‘ability to engage’ was 3.83 (95% CI: 3.80, 3.87), where 5 is the highest score. 19.4% had some level of difficulty reading and understanding written health information, and 23.2% discussing health concerns with health care providers. The adjusted logistic regression for ‘understanding information’ showed that those with lower health literacy were more likely to be in the most socially deprived quintile (OR 2.500 95% CI: 1.180, 5.296), have a limiting health condition or disability (OR 4.326 95% CI: 2.494, 7.704), and have no educational qualifications (OR 7.588 95% CI: 3.305, 17.422). This was similar for the ‘ability to engage’ domain. Conclusions This study described the distribution of health literacy levels for the British population in 2018. Interventions to improve health literacy will best be targeted at those with lower levels of education, those living in the most deprived areas, and those with a limiting health condition or disability.

The association between health information attitudes and skills in patients with chronic disease in China

2016 ◽  
Vol 23 (4) ◽  
pp. 59-69 ◽  
Author(s):  
Fuzhi Wang ◽  
Aijing Luo ◽  
Dan Luo ◽  
Dehua Hu ◽  
Wenzhao Xie ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Health Information ◽  
Health Management ◽  
Hunan Province ◽  
Bivariate Correlation ◽  
Chronic Disease Patients ◽  
Physical Examinations ◽  
Social Demographic ◽  
The Relationship

Objectives: To assess and explore the relationship between the health information (HI)-related attitudes and skills of patients with chronic disease in China. Methods: A questionnaire was developed to measure the participants’ HI-related attitudes and skills. The study included all participants ( N = 1671) undergoing routine physical examinations at the Health Management Centre, Third Xiangya Hospital of Central South University, Changsha, Hunan province, from September to November 2013. The Kruskal–Wallis test was used to assess the impacts of social demographic factors and chronic disease conditions on the patients’ HI-related attitudes and skills. Multiple linear regression and bivariate correlation analyses were adopted to explain the relationship between attitudes and skills. Results: The chronic disease patients clearly know that HI was valuable for their health, but their general HI-related skills were inadequate, particularly for elderly and undereducated patients. Additionally, the participants’ HI attitudes positively correlated with their HI-related skills ( r = 0.47, p < 0.001). Because the attitudes ascended by grade (i.e. negative, moderate, and active), the HI-related evaluation, expression and comprehension, and seeking skills categories increased by 11%, 5.3%, and 8.4%, respectively. Conclusions: Although the chronic disease patients held explicit and active attitudes towards HI, their skills were unsatisfactory. Attitudes and skills, however, present a positive relationship. These results suggest that training in HI-related skills should be the main goal of health literacy promotion in patients who suffer from long-term chronic diseases, particularly elderly and undereducated patients. However, cultivating an active attitude towards HI is important to improve HI-related skills.

scholarly journals Predicting Health Information Suitability for Children Using Machine-Learning Assisted Selection of Semantic Features (Preprint)

2021 ◽  
Author(s):  
Wenxiu Xie ◽  
Meng Ji ◽  
Yanmeng Liu ◽  
Tianyong Hao ◽  
Chi-Yin Chow
Keyword(s):  
Machine Learning ◽  
Health Literacy ◽  
Health Information ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Online Health Information ◽  
Semantic Features ◽  
Young Readers ◽  
Selection Of ◽  
Automatic Feature Selection

BACKGROUND Suitability of health resources for specific readerships represents a critical yet underexplored area of research in health informatics, despite its importance in health literacy and health education. High relevance of health information can improve the suitability and readability of online health educational resources for young readers. It has an important role in developing the health literacy of children with increasing exposure to online health information. Existing research on health resource evaluation is limited to the analysis of the morphological and syntactic complexity. Besides, empirical instruments do not exist to evaluate the suitability of online health information for children. OBJECTIVE We aimed to develop algorithms to predict suitability of online health information for this understudied user group, using a small number of semantic features to provide accurate and convenient tools for automatic prediction of the suitability of online health information for children. METHODS Combining machine learning and linguistic insights, we identified semantic features to predict the suitability of online health information for children, as an emerging and large readership on online health information. The selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using Ridge Classifier, support vector machine, extreme gradient boost, followed by revision by linguists, education experts based on effective health information design. We compared algorithms using the automatically selected features (19) and linguistically enhanced features (20), using the initial features (115) as the baseline. RESULTS Using 5-fold cross-validation, comparing with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P =0.0206, 95% CI: -0.016, 0.1929); mean specificity (P = 0.0205, 95% CI: -0.016, 0.199); mean AUC (P =0.017, 95% CI: -0.007, 0.140); mean Macro F1 (P =0.0061, 95% CI: 0.016, 0.167). The statistically improved performance of the final model (20 features) stands in contrast with the statistically insignificant changes between the original feature set (115) and the automatically selected features (19): mean sensitivity (P =0.134, 95% CI: -0.1699, 0.0681), mean specificity (P = 0.1001, 95% CI: -0.1389, 0.4017); mean AUC (P =0.0082, 95% CI: 0.0059, 0.1126), and mean macro F1 (P = 0.9796, 95% CI: -0.0555, 0.0548). This demonstrates the importance and effectiveness of combing automatic feature selection and expert-based linguistic revision to develop most effective machine learning algorithms from high-dimensional datasets. CONCLUSIONS Our study developed machine learning algorithms for evaluating health information suitability for children, an important readership who is having increasing reliance on online health information for developing their health literacy. User-adaptive automatic assessment of online health contents holds much promise for distant and remote health education among young readers. Our study leveraged the precision, adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.

scholarly journals Predicting Health Educational Material Understandability using Machine Learning Algorithms (Preprint)

2021 ◽  
Author(s):  
Meng Ji ◽  
Yanmeng Liu ◽  
Mengdan Zhao ◽  
Ziqing Lyu ◽  
Boren Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cost Effectiveness ◽  
Health Literacy ◽  
Health Education ◽  
Health Information ◽  
Learning Algorithms ◽  
Medical Knowledge ◽  
Machine Learning Algorithms ◽  
Health Resource ◽  
Education Resources

BACKGROUND Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. OBJECTIVE This paper fills a critical gap in current patient-oriented health resource development, which requires reliable, accurate evaluation instruments to increase the efficiency, cost-effectiveness of health education resource evaluation. We aim to translate internationally endorsed clinical guidelines, Patient Education Materials Assessment Tool (PEMAT) to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities. METHODS Based on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, C5 decision tree for automated health information understandability evaluation. The five machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the five models. RESULTS It was found that information evidentness, relevance to educational purposes and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (IELT test score mean 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). The results challenged traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials. CONCLUSIONS Machine learning algorithms were developed to predict health information understandability for international college students aged 25-30. 13 natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance and logic is critical.

scholarly journals The development of health literacy in patients with a long-term health condition: the health literacy pathway model

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Michelle Edwards ◽  
Fiona Wood ◽  
Myfanwy Davies ◽  
Adrian Edwards
Keyword(s):  
Health Literacy ◽  
Health Condition ◽  
Pathway Model

scholarly journals Individuals with Chronic Conditions Want More Guidance from Health Professionals in Finding Quality Online Health Sources

2016 ◽  
Vol 11 (2) ◽  
pp. 186
Author(s):  
Cari Merkley
Keyword(s):  
Health Literacy ◽  
Health Information ◽  
Health Care Providers ◽  
Health Professionals ◽  
Chronic Health Condition ◽  
Health Condition ◽  
Care Providers ◽  
Chronic Health ◽  
Media Channels

Objective – To explore how and when individuals with chronic health conditions seek out health information online, and the challenges they encounter when doing so. Design – Qualitative study employing thematic analysis. Setting – Urban Western Australia. Subjects – 17 men and women between 19 and 85 years of age with at least 1 chronic health condition. Methods – Participants were recruited in late 2013 at nine local pharmacies, through local radio, media channels, and a university's social media channels. Participants were adult English speakers who had looked for information on their chronic health condition(s) using the Internet. Semi-structured face-to-face interviews were conducted with each participant, audio recorded, and transcribed. The transcripts were coded in QSR Nvivo using two different processes – an initial data-driven inductive approach to coding, followed by a theory driven analysis of the data. Main Results – Three major themes emerged: trust, patient activation, and relevance. Many of the participants expressed trust both in health professionals and in the efficacy of search engines like Google. However, there was uncertainty about the quality of some of the health information sources found. Searching for information online was seen by some participants as a way to feel more empowered about their condition(s) and treatment, but they reported frustration in finding information that was relevant to their specific condition(s) given the volume of information available. Low health literacy emerged in participant interviews as an intrinsic barrier to effective online searches for health information, along with low patient motivation and lack of time. The many extrinsic barriers identified included difficulty determining the quality of information found, the accessibility of the information (e.g., journal paywalls), and poor relationships with health care providers. Conclusion – Individuals look for online health information to help manage their chronic illnesses, but their ability to do so is influenced by their levels of health literacy and other external barriers to effective online navigation. Consumers may prefer to receive recommendations from health professionals for high quality health websites rather than training in how to navigate and identify these resources themselves.

scholarly journals Health Literacy and Atrial Fibrillation: Relevance and Future Directions for Patient-centred Care

2017 ◽  
Vol 12 (1) ◽  
pp. 52 ◽  
Author(s):  
Konstantinos N Aronis ◽  
Brittany Edgar ◽  
Wendy Lin ◽  
Maria Auxiliadora Parreiras Martins ◽  
Michael K Paasche-Orlow ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Making ◽  
Health Literacy ◽  
Health Information ◽  
Stroke Prevention ◽  
Clinical Importance ◽  
Shared Decision ◽  
Future Directions ◽  
Symptom Monitoring

Atrial fibrillation (AF) is a common cardiac arrhythmia with significant clinical outcomes, and is associated with high medical and social costs. AF is complicated for patients because of its specialised terminology, long-term adherence, symptom monitoring, referral to specialty care, array of potential interventions and potential for adversity. Health literacy is a frequently under-recognised, yet fundamental, component towards successful care in AF. Health literacy is defined as the capacity to obtain, process and understand health information, and has had markedly limited study in AF. However, health literacy could contribute to how patients interpret symptoms, navigate care, and participate in treatment evaluation and decision-making. This review aims to summarise the clinical importance and essential relevance of health literacy in AF. We focus here on central aspects of AF care that are most related to self-care, including understanding the symptoms of AF, shared decision-making, adherence and anticoagulation for stroke prevention. We discuss opportunities to enhance AF care based on findings from the literature on health literacy, and identify important gaps. Our overall objective is to articulate the importance and relevance of integrating health literacy in the care of individuals with AF.

Exploring Internet and Media-mediated Health Information within the Context of Personal Health Management

2013 ◽  
Author(s):  
Shelagh K. Genuis
Keyword(s):  
Health Information ◽  
Information Literacy ◽  
Information Seeking ◽  
Health Management ◽  
The Internet ◽  
Personal Health ◽  
Étude Qualitative ◽  
Informal Information ◽  
Etude Qualitative

This qualitative paper explores how health information mediated by the internet and media is used and made valuable within the life of consumers managing non-crisis health challenges, and how informal information seeking and gathering influences self-positioning within patient-clinician relationships. Findings have implications for health information literacy and collaborative, patient-centred care.Cette étude qualitative explore comment l’information sur la santé relayée par Internet et les médias est utilisée et rendue utile dans le contexte de consommateurs gérant des problèmes médicaux non urgents, et comment la recherche et la collecte d’information informelles influencent l’auto-positionnement dans la relation patient clinicien. Les résultats ont des applications dans la maîtrise de l’information médicale et les soins collaboratifs centrés sur le patient.

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