A mismatch between population health literacy and the complexity of health information: an observational study

Rowlands, Gillian; Protheroe, Joanne; Winkley, John; Richardson, Marty; Seed, Paul; Rudd, Rima E.

doi:10.3399/bjgp15x685285

Cited by 201 publications

(188 citation statements)

References 9 publications

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“…Just as the readability of health information needs to match the literacy skills of its users (Rowlands, et al, 2015), so does the readability of health information and services online. In England, around 11 million people lack basic digital literacy, with around 7 million having never used the internet (ONS, 2016).…”

Section: Introductionmentioning

confidence: 99%

The digital divide: Examining socio-demographic factors associated with health literacy, access and use of internet to seek health information

2017

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This paper aims to examine the socio-demographic characteristics associated with access and use of internet for health-related purposes and its relationship with health literacy. Data were drawn from a health literacy survey (N=1046) and analysed using logistic regression. Results show a strong association between health literacy, internet access and use. Socio-demographic characteristics particularly age, education, income, perceived health and social isolation also predict internet access. Thus, in addition to widening access, the movement towards digitisation of health information and services should also consider digital skills development to enable people to utilise digital technology more effectively, especially among traditionally hard-to-reach communities.Health information and services are becoming more accessible online. It has been argued that making health information and services available online can improve patient experience by enhancing shared decision making by promoting informed choice (Gann & Grant, 2013). Internet use has also been associated with health promoting behaviours (Xavier, et al, 2013), better mental health (Forsman & Nordmyr, 2015) and improved financial decision making (James, et al, 2013).Although it has its merits, concerns have been raised on how the proliferation of internet-based health information and services could reinforce existing social inequities in health (McAuley, 2014). While the internet has been used by patients to gather information, gain support and to make sense of one's health condition (Ziebland, 2004), those who are in the greatest need of health information are least likely to have access to new technologies (Aydın, Kaya, & Turan, 2015). Previous research from North America and Europe reflect a 'digital divide', whereby socio-economic and demographic factors such as age, income, education and health status were able to predict people's likelihood to access and use the internet to seek health information (Kontos, et al., 2014). In Great Britain, while the internet was accessed either every day or almost every day by 78% of adults (39.3 million) in 2015, only about 49% used it to look for health-related information (ONS, 2016). Barriers to access and internet use included financial restrictions (i.e., equipment and internet access costs are too high), medical and disabilityrelated constraints (i.e., the technology is not easily accessible for some patients), and digital complexity (i.e., accessing and navigating the internet is too complex) (Connolly & Crosby, 2014).While efforts are being made to widen the reach and accessibility of internet technology, the general population still needs to keep up with the growing amount of health-related information and services online. Just as the readability of health information needs to match the literacy skills of its users (Rowlands, et al., 2015), so does the readability of health information and services online. In England, around 11 million people lack basic digital literacy, with around 7 million having ...

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Section: Introductionmentioning

confidence: 99%

The digital divide: Examining socio-demographic factors associated with health literacy, access and use of internet to seek health information

2017

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“…All detract from patients' capacities to comply with ED follow-up instructions. Health materials are written at levels of complexity that poorly match population health literacy levels, with approximately half of all sampled printed materials generated in health care settings written at a level of complexity beyond the skills of the patients for whom they were intended [13]. A recent systematic review found that at least 40% of ED patients possess health literacy at or below the eighth-grade level.…”

Section: Introductionmentioning

confidence: 97%

Seventy-two-hour antibiotic retrieval from the ED: a randomized controlled trial of discharge instructional modality

Olives

Patel

Thompson

et al. 2016

The American Journal of Emergency Medicine

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“…Low health literacy affects a significant proportion of English adults; 6 in 10 (61%) adults aged between 16 and 65 years of age in England lack the literacy and numeracy skills to fully understand common health-related information [2]. …”

Section: Introductionmentioning

confidence: 99%

Developing and Applying Geographical Synthetic Estimates of Health Literacy in GP Clinical Systems

Rowlands

Whitney

Moon

2018

IJERPH

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Background: Low health literacy is associated with poorer health. Research has shown that predictive models of health literacy can be developed; however, key variables may be missing from systems where predictive models might be applied, such as health service data. This paper describes an approach to developing predictive health literacy models using variables common to both “source” health literacy data and “target” systems such as health services. Methods: A multilevel synthetic estimation was undertaken on a national (England) dataset containing health literacy, socio-demographic data and geographical (Lower Super Output Area: LSOA) indicators. Predictive models, using variables commonly present in health service data, were produced. An algorithm was written to pilot the calculations in a Family Physician Clinical System in one inner-city area. The minimum data required were age, sex and ethnicity; other missing data were imputed using model values. Results: There are 32,845 LSOAs in England, with a population aged 16 to 65 years of 34,329,091. The mean proportion of the national population below the health literacy threshold in LSOAs was 61.87% (SD 12.26). The algorithm was run on the 275,706 adult working-age people in Lambeth, South London. The algorithm could be calculated for 228,610 people (82.92%). When compared with people for whom there were sufficient data to calculate the risk score, people with insufficient data were more likely to be older, male, and living in a deprived area, although the strength of these associations was weak. Conclusions: Logistic regression using key socio-demographic data and area of residence can produce predictive models to calculate individual- and area-level risk of low health literacy, but requires high levels of ethnicity recording. While the models produced will be specific to the settings in which they are developed, it is likely that the method can be applied wherever relevant health literacy data are available. Further work is required to assess the feasibility, accuracy and acceptability of the method. If feasible, accurate and acceptable, this method could identify people requiring additional resources and support in areas such as medical practice.

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A mismatch between population health literacy and the complexity of health information: an observational study

Cited by 201 publications

References 9 publications

The digital divide: Examining socio-demographic factors associated with health literacy, access and use of internet to seek health information

The digital divide: Examining socio-demographic factors associated with health literacy, access and use of internet to seek health information

Seventy-two-hour antibiotic retrieval from the ED: a randomized controlled trial of discharge instructional modality

Developing and Applying Geographical Synthetic Estimates of Health Literacy in GP Clinical Systems

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