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 i

SUBMITTER: Rowlands G 

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

REPOSITORIES: biostudies-literature