Distribution of health literacy strengths and weaknesses across socio-demographic groups: a cross-sectional survey using the Health Literacy Questionnaire (HLQ).

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Abstract

Recent advances in the measurement of health literacy allow description of a broad range of personal and social dimensions of the concept. Identifying differences in patterns of health literacy between population sub-groups will increase understanding of how health literacy contributes to health inequities and inform intervention development. The aim of this study was to use a multi-dimensional measurement tool to describe the health literacy of adults in urban and rural Victoria, Australia.

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Bayesian structural equation modeling: a more flexible representation of substantive theory.

Bengt Muthén, Tihomir Asparouhov (2012)

This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximum-likelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford's (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.

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The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ)

Richard Osborne, Roy Batterham, Gerald Elsworth … (2013)

Background Health literacy has become an increasingly important concept in public health. We sought to develop a comprehensive measure of health literacy capable of diagnosing health literacy needs across individuals and organisations by utilizing perspectives from the general population, patients, practitioners and policymakers. Methods Using a validity-driven approach we undertook grounded consultations (workshops and interviews) to identify broad conceptually distinct domains. Questionnaire items were developed directly from the consultation data following a strict process aiming to capture the full range of experiences of people currently engaged in healthcare through to people in the general population. Psychometric analyses included confirmatory factor analysis (CFA) and item response theory. Cognitive interviews were used to ensure questions were understood as intended. Items were initially tested in a calibration sample from community health, home care and hospital settings (N=634) and then in a replication sample (N=405) comprising recent emergency department attendees. Results Initially 91 items were generated across 6 scales with agree/disagree response options and 5 scales with difficulty in undertaking tasks response options. Cognitive testing revealed that most items were well understood and only some minor re-wording was required. Psychometric testing of the calibration sample identified 34 poorly performing or conceptually redundant items and they were removed resulting in 10 scales. These were then tested in a replication sample and refined to yield 9 final scales comprising 44 items. A 9-factor CFA model was fitted to these items with no cross-loadings or correlated residuals allowed. Given the very restricted nature of the model, the fit was quite satisfactory: χ 2 WLSMV(866 d.f.) = 2927, p<0.000, CFI = 0.936, TLI = 0.930, RMSEA = 0.076, and WRMR = 1.698. Final scales included: Feeling understood and supported by healthcare providers; Having sufficient information to manage my health; Actively managing my health; Social support for health; Appraisal of health information; Ability to actively engage with healthcare providers; Navigating the healthcare system; Ability to find good health information; and Understand health information well enough to know what to do. Conclusions The HLQ covers 9 conceptually distinct areas of health literacy to assess the needs and challenges of a wide range of people and organisations. Given the validity-driven approach, the HLQ is likely to be useful in surveys, intervention evaluation, and studies of the needs and capabilities of individuals.

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Literacy and health outcomes: a systematic review of the literature.

Darren A DeWalt, Nancy D Berkman, Stacey Lynn Sheridan … (2004)

To review the relationship between literacy and health outcomes. We searched MEDLINE, Cumulative Index to Nursing and Allied Health (CINAHL), Educational Resources Information Center (ERIC), Public Affairs Information Service (PAIS), Industrial and Labor Relations Review (ILLR), PsychInfo, and Ageline from 1980 to 2003. We included observational studies that reported original data, measured literacy with any valid instrument, and measured one or more health outcomes. Two abstractors reviewed each study for inclusion and resolved disagreements by discussion. One reviewer abstracted data from each article into an evidence table; the second reviewer checked each entry. The whole study team reconciled disagreements about information in evidence tables. Both data extractors independently completed an 11-item quality scale for each article; scores were averaged to give a final measure of article quality. We reviewed 3,015 titles and abstracts and pulled 684 articles for full review; 73 articles met inclusion criteria and, of those, 44 addressed the questions of this report. Patients with low literacy had poorer health outcomes, including knowledge, intermediate disease markers, measures of morbidity, general health status, and use of health resources. Patients with low literacy were generally 1.5 to 3 times more likely to experience a given poor outcome. The average quality of the articles was fair to good. Most studies were cross-sectional in design; many failed to address adequately confounding and the use of multiple comparisons. Low literacy is associated with several adverse health outcomes. Future research, using more rigorous methods, will better define these relationships and guide developers of new interventions.

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Journal

Journal ID (iso-abbrev): BMC Public Health

Title: BMC public health

Publisher: Springer Science and Business Media LLC

ISSN (Electronic): 1471-2458

ISSN (Print): 1471-2458

Publication date (Electronic): Jul 21 2015

Volume: 15

Affiliations

[1 ] Public Health Innovation, Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia. alison.beauchamp@deakin.edu.au.

[2 ] Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. alison.beauchamp@deakin.edu.au.

[3 ] Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. rachelle.buchbinder@monash.edu.

[4 ] Monash Department of Clinical Epidemiology, Cabrini Hospital, Malvern, VIC, Australia. rachelle.buchbinder@monash.edu.

[5 ] Public Health Innovation, Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia. sarity.dodson@deakin.edu.au.

[6 ] Public Health Innovation, Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia. roy.batterham@deakin.edu.au.

[7 ] Public Health Innovation, Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia. gerald.elsworth@deakin.edu.au.

[8 ] Public Health Innovation, Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia. crystal.mcphee@deakin.edu.au.

[9 ] School of Nursing, Monash University, Melbourne, Australia. louise.sparkes@deakin.edu.au.

[10 ] Public Health Innovation, Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia. melanie.hawkins@deakin.edu.au.

[11 ] Public Health Innovation, Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Melbourne, VIC, 3125, Australia. richard.osborne@deakin.edu.au.

Article

Publisher Item ID: 10.1186/s12889-015-2056-z

DOI: 10.1186/s12889-015-2056-z

PMC ID: 4508810

PubMed ID: 26194350

SO-VID: 6305364a-4733-4970-b129-fb12a041acf8

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Distribution of health literacy strengths and weaknesses across socio-demographic groups: a cross-sectional survey using the Health Literacy Questionnaire (HLQ).

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Most cited references 68

Bayesian structural equation modeling: a more flexible representation of substantive theory.

The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ)

Literacy and health outcomes: a systematic review of the literature.

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