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

ABSTRACT: 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.

SUBMITTER: Rowlands G

PROVIDER: S-EPMC6121561 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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

Rowlands Gill G Whitney David D Moon Graham G

International journal of environmental research and public health 20180810 8

<i>Background</i>: 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. <i>Methods</i>: A multilevel synthetic ...[more]

PMID: 30103375

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Project description:ObjectiveTo explore characteristics associated with, and prevalence of, low health literacy in patients recruited to investigate the role of depression in patients on General Practice (GP) Coronary Heart Disease (CHD) registers (the Up-Beat UK study).DesignCross-sectional cohort. The health literacy measure was the Rapid Estimate of Health Literacy in Medicine (REALM). Univariable analyses identified characteristics associated with low health literacy and compared health service use between health literacy statuses. Those variables where there was a statistically significant/borderline significant difference between health literacy statuses were entered into a multivariable model.Setting16 General Practices in South London, UK.ParticipantsInclusion: patients >18 years, registered with a GP and on a GP CHD register. Exclusion: patients temporarily registered.Primary outcome measureREALM.ResultsOf the 803 Up-Beat cohort participants, 687 (85.55%) completed the REALM of whom 106 (15.43%) had low health literacy. Twenty-eight participants could not be included in the multivariable analysis due to missing predictor variable data, leaving a sample of 659. The variables remaining in the final model were age, gender, ethnicity, Indices of Multiple Deprivation score, years of education, employment; body mass index and alcohol intake, and anxiety scores (Hospital Anxiety and Depression Scale). Univariable analysis also showed that people with low health literacy may have more, and longer, practice nurse consultations than people with adequate health literacy.ConclusionsThere is a disadvantaged group of people on GP CHD registers with low health literacy. The multivariable model showed that patients with low health literacy have significantly higher anxiety levels than people with adequate health literacy. In addition, the univariable analyses show that such patients have more, and longer, consultations with practice nurses. We will collect 4-year longitudinal cohort data to explore the impact of health literacy in people on GP CHD registers and the impact of health literacy on health service use.

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

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

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