Institute of Health and Society, Newcastle University, Newcastle-upon-Tyne NE2 4BN, UK.
Division of Health and Social Care Research, King's College London, London WC2R 2LS, UK.
Int J Environ Res Public Health. 2018 Aug 10;15(8):1709. doi: 10.3390/ijerph15081709.
: 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. : 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. : 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. : 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.
: 健康素养水平较低与健康状况较差有关。研究表明，可以开发健康素养的预测模型；然而，在可能应用预测模型的系统（如卫生服务数据）中，关键变量可能缺失。本文描述了一种使用健康素养数据和卫生服务等“目标”系统中常见的变量来开发预测健康素养模型的方法。: 对包含健康素养、社会人口统计学数据和地理（下层超级输出区：LSOA）指标的全国（英国）数据集进行了多层次综合估计。使用健康服务数据中常见的变量生成了预测模型。编写了一个算法来在一个内城地区的家庭医生临床系统中进行试点计算。所需的最小数据是年龄、性别和种族；其他缺失数据使用模型值进行估算。: 英国有 32845 个 LSOA，16 至 65 岁的人口为 34329091 人。在 LSOA 中，低于健康素养门槛的全国人口比例平均值为 61.87%（标准差 12.26%）。该算法在伦敦南部兰贝斯的 275706 名成年工作年龄人群中运行。该算法可以为 228610 人（82.92%）进行计算。与有足够数据计算风险评分的人相比，数据不足的人更有可能年龄较大、男性和居住在贫困地区，尽管这些关联的强度很弱。: 使用关键社会人口统计学数据和居住区域进行逻辑回归可以生成预测模型来计算个人和区域层面的低健康素养风险，但需要高水平的种族记录。虽然所生成的模型将特定于其开发的环境，但该方法可能适用于任何有相关健康素养数据的地方。需要进一步的工作来评估该方法的可行性、准确性和可接受性。如果可行、准确且可接受，该方法可以识别出需要在医疗实践等领域获得额外资源和支持的人群。
