Discussion
fracture, Fracture, injury and decline, mild disability, disability
EVENTS
We report the predictive utility of a short self-report postal screener suitable for administration to older people in the primary care setting. The postal fall risk screener was simple to administer but had limited accuracy in identifying those at higher risk of falling. According to accepted standards of interpretation, accuracy for single falls was poor, and repeat falls was acceptable/fair. We also examined whether addition of easy to collect sociodemographic and health-related variables could improve prediction. Additional variables improved all fall models to fair accuracy. This gives the possibility of extending current UK and international guidance for opportunistic screening for falls to a more population-based and systematic approach. The postal screener had poorer prediction of fracture risk. Fracture risk was only partially predicted by fall risk, but the addition of age, gender and BMI could improve performance of the short tool.Our PreFIT postal screener incorporated risk factors previously identified within the USA WHAS [Our short one-page screener had better prediction of those at greater risk of falling, people falling repeatedly over one year. This is perhaps unsurprising, but reassuringly, this is the group of fallers who are most likely to experience injury and decline in function [A thorough, high-quality systematic review completed by Gade and colleagues identified 72 different prognostic models in 30 studies predicting falls in community-dwelling older adults (including the WHAS development cohort), although only three models had been externally validated, using data not used in original model development [Our analytical approach adheres to TRIPOD recommendations for development and validation of prediction models [It is important for clinicians to understand the populations in whom models are developed and validated, bearing in mind any differences between source and test population characteristics. Item selection for our postal screener was informed by the WHAS dataset from another setting, undertaken in a female-only US sample of older women with mild disability. PreFIT included both sexes without restriction by upper age, functional or cognitive ability. Our large sample size of almost 10,000 older people from rural and urban settings across England, with an age range spanning thirty years, from 70 to 101 years, included older people with morbidity and disability. We used this comprehensive trial dataset to develop and validate our prognostic risk models using a split-sample validation approach (type IIa), as outlined by TRIPOD [We carefully considered our testing approach given demands to improve methodological quality and reporting standards of prognostic risk models. A systematic review of 120 prediction models found that the median number of predictors included in statistical models was six, median sample size 795 and median number of outcome events was 106 [Factors affecting sensitivity and specificity include prevalence and length of observation [The PreFIT screener also compared well to other established clinical prediction tools in other areas of medicine. Higaonna [