Applying Relational Models to the Graduation of Disability Schedules

Abstract

Age-specific rates of particular disability types are important for planning purposes and are a valuable input to estimates of populations with different disabilities. However, survey estimates of schedules of disability rates for the constituent countries of the UK display evidence of sampling variability and sub-national disability schedules are either extremely unreliable as a result of small sample sizes or are unavailable for reasons of disclosure protection. This paper develops and evaluates a method to smooth sampling variability in national schedules of disability using a technique that has applicability to sub-national estimation of age-specific disability rates. Relational models accurately quantify the relationship between national schedules of limiting long term illness (LLTI) (census) and particular disability types (Health Survey for England) using 2 or 3 parameters depending on disability type. These parameters are used to adjust LLTI schedule to represent different disability schedules smoothing sampling fluctuations. The specification of the relational model depends upon the complexity of the age-specific relationship between LLTI and a particular disability type. For some disability types a simple Brass relational model (2 parameters) provides a good fit but for others a modified version of the Ewbank relational model (3 parameters) is required.

The Sensitivity of the Healthy Life Years Indicator: Approaches for Dealing with Age-Specific Prevalence Data

“…In addition, health is a stock variable sensitive to past experience (Barendregt et al 1997;Brouard/Robine 1992;Murray et al 2002) and with a complex interaction between health and mortality, making it diffi cult to accurately model age-specifi c health prevalence (Riffe et al 2016(Riffe et al , 2017. In this regard, it has been shown that some relational models provide a good fi t for modelling some types of disabilities by age (Marshall et al 2013), while alternative summary measures that incorporate the mortality history of cohorts and therefore combine health and mortality information have also been proposed (Sauerberg et al 2020). However, these methods are more important to capture the relationship between age-specifi c prevalence of disability and various disability types rather than to harmonise and extrapolate prevalence at older ages.…”